This document discusses texturing in XNA game development. It explains that textures are images applied to surfaces to make objects look more realistic. The document covers UV coordinates, vertex types for textures, loading and applying textures, texture tiling, transparency, and billboarding. It provides code examples for creating and drawing a textured quad and handling transparency using alpha blending.
Volume rendering 3D volume data (medical CT scans) in Unity3D.
Covering the following topics:
- Raymarching
- Maximum Intensity Projection
- Direct Volume Rendering with compositing
- Isosurface rendering
- Transfer functions
- 2D Transfer Functions
- Slice rendering
Source code here: https://github.com/mlavik1/UnityVolumeRendering
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Faire de la reconnaissance d'images avec le Deep Learning - Cristina & Pierre...Jedha Bootcamp
Reconnaissance de visages sur vos photos Facebook, détection de maladies via imagerie médicale, les applications de la reconnaissance d'images grâce à l'intelligence artificielle offrent de vastes possibilités. Lors de cet événement, Cristina & Pierre - Machine Learning Engineers chez Photobox - vous feront une démonstration des outils de reconnaissance d'images via ces algorithmes de Deep Learning.
This document summarizes the terrain rendering techniques used in the MMO action real-time strategy game Kingdom Under Fire II. It discusses using geometry clipmaps to render large terrains with high detail while maintaining performance. It also describes using texture clipmaps to allow high resolution texturing across large areas. Layer blending is used to combine texture tiles, with improvements like using indexed layers and filtering in the pixel shader. Storage and streaming techniques keep terrain data efficiently organized and loaded as needed.
11.optimal nonlocal means algorithm for denoising ultrasound imageAlexander Decker
The document presents a new algorithm for denoising ultrasound images called the optimal nonlocal means algorithm. It calculates the mean distance of all pixel neighborhoods in the image, rather than totaling all neighborhood distances as in the original nonlocal means algorithm. The proposed algorithm exhibits better performance in noise removal, visual quality of restored images, and mean square error compared to the original algorithm, as evidenced by experiments on phantom and normal ultrasound images. Numerical measurements of SNR, RMSE, and PSNR support that calculating nonlocal means with mean neighborhood distances provides a better method for image denoising.
The document proposes a hybrid method called Wavelet Embedded Anisotropic Diffusion (WEAD) for image denoising. WEAD is a two-stage filter that first applies anisotropic diffusion to reduce noise, followed by wavelet-based Bayesian shrinkage. This reduces the convergence time of anisotropic diffusion, allowing the image to be denoised with less blurring compared to anisotropic diffusion or wavelet methods alone. Experimental results on various images demonstrate that WEAD achieves better denoising performance than anisotropic diffusion or Bayesian shrinkage methods, as measured by higher PSNR and SSIM scores and fewer required iterations.
Pierre Bénard Ph.D. defense, 2011/07/07Pierre Bénard
This document discusses techniques for temporally coherent stylization of 3D animations based on textures. It presents two new region stylization methods: Dynamic Solid Textures, which provide accurate 3D motion and infinite zoom while maintaining temporal coherence; and NPR Gabor Noise, a dynamic noise primitive that allows for coherent stylization through a smooth level-of-detail mechanism. It also evaluates different stylization techniques through a perceptual user study, finding that object space methods produce more coherent motion and temporal continuity. Finally, it discusses line stylization and mapping policies for brush strokes.
Volume rendering 3D volume data (medical CT scans) in Unity3D.
Covering the following topics:
- Raymarching
- Maximum Intensity Projection
- Direct Volume Rendering with compositing
- Isosurface rendering
- Transfer functions
- 2D Transfer Functions
- Slice rendering
Source code here: https://github.com/mlavik1/UnityVolumeRendering
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Faire de la reconnaissance d'images avec le Deep Learning - Cristina & Pierre...Jedha Bootcamp
Reconnaissance de visages sur vos photos Facebook, détection de maladies via imagerie médicale, les applications de la reconnaissance d'images grâce à l'intelligence artificielle offrent de vastes possibilités. Lors de cet événement, Cristina & Pierre - Machine Learning Engineers chez Photobox - vous feront une démonstration des outils de reconnaissance d'images via ces algorithmes de Deep Learning.
This document summarizes the terrain rendering techniques used in the MMO action real-time strategy game Kingdom Under Fire II. It discusses using geometry clipmaps to render large terrains with high detail while maintaining performance. It also describes using texture clipmaps to allow high resolution texturing across large areas. Layer blending is used to combine texture tiles, with improvements like using indexed layers and filtering in the pixel shader. Storage and streaming techniques keep terrain data efficiently organized and loaded as needed.
11.optimal nonlocal means algorithm for denoising ultrasound imageAlexander Decker
The document presents a new algorithm for denoising ultrasound images called the optimal nonlocal means algorithm. It calculates the mean distance of all pixel neighborhoods in the image, rather than totaling all neighborhood distances as in the original nonlocal means algorithm. The proposed algorithm exhibits better performance in noise removal, visual quality of restored images, and mean square error compared to the original algorithm, as evidenced by experiments on phantom and normal ultrasound images. Numerical measurements of SNR, RMSE, and PSNR support that calculating nonlocal means with mean neighborhood distances provides a better method for image denoising.
The document proposes a hybrid method called Wavelet Embedded Anisotropic Diffusion (WEAD) for image denoising. WEAD is a two-stage filter that first applies anisotropic diffusion to reduce noise, followed by wavelet-based Bayesian shrinkage. This reduces the convergence time of anisotropic diffusion, allowing the image to be denoised with less blurring compared to anisotropic diffusion or wavelet methods alone. Experimental results on various images demonstrate that WEAD achieves better denoising performance than anisotropic diffusion or Bayesian shrinkage methods, as measured by higher PSNR and SSIM scores and fewer required iterations.
Pierre Bénard Ph.D. defense, 2011/07/07Pierre Bénard
This document discusses techniques for temporally coherent stylization of 3D animations based on textures. It presents two new region stylization methods: Dynamic Solid Textures, which provide accurate 3D motion and infinite zoom while maintaining temporal coherence; and NPR Gabor Noise, a dynamic noise primitive that allows for coherent stylization through a smooth level-of-detail mechanism. It also evaluates different stylization techniques through a perceptual user study, finding that object space methods produce more coherent motion and temporal continuity. Finally, it discusses line stylization and mapping policies for brush strokes.
This document discusses and compares different thresholding techniques for image denoising using wavelet transforms. It introduces the concept of image denoising using wavelet transforms, which involves applying a forward wavelet transform, estimating clean coefficients using thresholding, and applying the inverse transform. It then describes several common thresholding methods - hard, soft, universal, improved, Bayes shrink, and neigh shrink. Simulation results on test images corrupted with additive white Gaussian noise show that the proposed improved thresholding technique achieves lower MSE and higher PSNR than the universal hard thresholding method, demonstrating better noise removal performance while preserving image details.
Super Resolution in Digital Image processingRamrao Desai
Super-resolution aims to enhance image resolution by exploiting multiple low-resolution images. Key techniques include Bayesian methods using priors, Wiener filtering, Markov random fields, and learned models from example images. Super-resolution involves modeling blurring, sampling, and aliasing effects, and using techniques like deconvolution and example-based learning to recover high-frequency details beyond the Nyquist limit. It requires accurate motion estimation and modeling of the imaging process to combine information from multiple low-resolution images.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
Digital image processing short quesstion answersAteeq Zada
This document discusses several techniques for 2D spatial image filtering and background subtraction in digital image processing. It covers linear filtering, Gaussian filtering, frame differencing, running averages, and mixtures of Gaussian models. The key techniques are linear filtering using a kernel or mask, Gaussian filtering to smooth images, and running averages or mixtures of Gaussians to model the background pixels over time while adapting to changes in illumination, motion, or scene geometry.
This document discusses image degradation and restoration. It describes how images can become degraded through imperfect imaging systems, transmission channels, atmospheric conditions, and motion. It then discusses several methods for restoring degraded images, including inverse filtering, Wiener filtering, and Kalman filtering. Specific techniques are presented for restoring images corrupted with impulse noise or blurring, including using differences from the median and convolution models. The document concludes by describing simulations of image restoration techniques in MATLAB.
(1) The document presents a new method for denoising images called texture enhanced image denoising (TEID) that aims to preserve fine texture structures while removing noise.
(2) The TEID method develops a gradient histogram preservation (GHP) algorithm to ensure the gradient histogram of the denoised image is close to the estimated gradient histogram of the original image.
(3) An iterative histogram specification algorithm is proposed to solve the GHP-based image denoising model, which alternates between updating the image given the transform function and updating the transform function given the image to match the reference gradient histogram.
This document discusses digital image processing concepts including:
- Image acquisition and representation, including sampling and quantization of images. CCD arrays are commonly used in digital cameras to capture images as arrays of pixels.
- A simple image formation model where the intensity of a pixel is a function of illumination and reflectance at that point. Typical ranges of illumination and reflectance are provided.
- Image interpolation techniques like nearest neighbor, bilinear, and bicubic interpolation which are used to increase or decrease the number of pixels in a digital image. Examples of applying these techniques are shown.
- Basic relationships between pixels including adjacency, paths, regions, boundaries, and distance measures like Euclidean, city block, and
This presentation contains concepts of different image restoration and reconstruction techniques used nowadays in the field of digital image processing. Slides are prepared from Gonzalez book and Pratt book.
Labview with dwt for denoising the blurred biometric imagesijcsa
In this paper, biometric blurred image (fingerprint) denoising are presented and investigated by using
LabVIEW applications , It is blurred and corrupted with Gaussian noise. This work is proposed
algorithm that has used a discrete wavelet transform (DWT) to divide the image into two parts, this will
be increasing the manipulation speed of biometric images that are of the big sizes. This work has included
two tasks ; the first designs the LabVIEW system to calculate and present the approximation coefficients,
by which the image's blur factor reduced to minimum value according to the proposed algorithm. The
second task removes the image's noise by calculated the regression coefficients according to Bayesian-
Shrinkage estimation method.
This document provides an introduction to digital image processing. It discusses key topics like image representation as matrices, image digitization which involves sampling and quantization, and the basic steps in digital image processing such as image acquisition, preprocessing, segmentation, feature extraction, recognition and interpretation. Importance of image processing is highlighted for applications like remote sensing, machine vision, and medical imaging. Common techniques like noise filtering, contrast enhancement, compression and their importance are also summarized.
3 intensity transformations and spatial filtering slidesBHAGYAPRASADBUGGE
This document discusses basics of intensity transformations and spatial filtering of digital images. It covers the following key points:
- Intensity transformations map input pixel intensities to output intensities using an operator T. Common transformations include log, power-law, and piecewise-linear functions.
- Spatial filters operate on neighborhoods of pixels. Linear filters perform averaging or correlation while non-linear filters use ordering like median.
- Basic filters include smoothing to reduce noise, sharpening to enhance edges using Laplacian or unsharp masking, and gradient for edge detection.
- Fuzzy set theory can be applied to intensity transformations by defining membership functions for concepts like dark/bright. It can also be used for spatial filtering by defining
Fractal Image Compression By Range Block ClassificationIRJET Journal
This document proposes a method for fractal image compression using range block classification and particle swarm optimization. It begins with an abstract that describes fractal image compression as a lossy technique that partitions images into range and domain blocks, with each range block searching domain blocks for the best match using PSO. The document then provides more details on PSO, describes implementing fractal image compression with PSO by having particles represent domain block locations and fitness measure matching between blocks, and shows experimental results compressing test images with the method. The goal is to improve compression ratio and decompressed image quality over traditional techniques by using PSO for block matching.
Tensorflow London 13: Zbigniew Wojna 'Deep Learning for Big Scale 2D Imagery'Seldon
Speaker: Zbigniew Wojna, Deep Learning Researcher and founder of TensorFlight.Inc
Title: Architectures for big scale 2D imagery
Abstract: Zbigniew will present research he conducted during his Ph.D. at University College London and in collaboration with Google. His primary interest lays in the development of neural architectures for 2D imagery problems in big scale. He will present the recently published analysis of different upsampling methods in the decoder part of visual architectures, together with last week ongoing extension for GANs. Will discuss attention mechanism for text recognition and review for what kind of application it can be useful (automatically updating Google Maps based on Google Street View imagery). He will explain the idea behind inception and change in Inception-v3 to have it the best single model on ImageNet 2015 and how does it compare to Resnet architecture which was published 2 weeks after. Together with inception, will present his winning submission to MS COCO 2016 detection challenge and the extensive analysis of different models and backbone architectures inside. At the end will shortly review UCL effort working with 4096x4096 images at The Digital Mammography DREAM Challenge for breast cancer recognition, where they achieved 9th among 1375 teams worldwide and 2nd place in the community phase.
Bio: Zbigniew Wojna is deep learning researcher and founder of TensorFlight Inc. company providing instant remote commercial property inspection (for risk factors for reinsurance enterprises) based on satellite and street view type imagery. Zbigniew is currently in the final stage of his Ph.D. (already with more than 1000 citations) at the University College London under the supervision of Professor Iasonas Kokkinos and professor John Shawe-Taylor. His primary interest lies in finding and solving research problems around 2D machine vision applications usually in big scale. Zbigniew in his Ph.D. career spent most of the time working across different groups in DeepMind, Google Research, and Facebook Research. It includes DeepMind Health Team, Deep Learning Team for Google Maps in collaboration with Google Brain, Machine Perception with Kevin Murphy, Weak Localization Team with Vittorio Ferrari and Facebook AI Research Lab in Paris. His company TensorFlight Inc. was featured as top 2 AI startups among few hundreds by InnovatorsRace50 and closed seed funding last year.
Thanks to all TensorFlow London meetup organisers and supporters:
Seldon.io
Altoros
Rewired
Google Developers
Rise London
SinGAN - Learning a Generative Model from a Single Natural ImageJishnu P
SinGAN is a generative adversarial network (GAN) that can learn the distribution of a single natural image and generate new realistic samples from that image distribution. Unlike other GANs that require large datasets, SinGAN only needs a single image for training. It uses a multi-scale architecture with multiple generators and discriminators at different scales. SinGAN was shown to generate high quality samples for tasks like super resolution, image editing, and animation from a single image. It also has some failure cases like generating unrealistic samples at the boundaries.
Digital image processing img smoothningVinay Gupta
The document discusses image smoothing and sharpening techniques in digital image processing. It begins by defining what a digital image is and the goals of digital image processing. Then it discusses various applications of digital image processing like image enhancement, medical visualization, and human-computer interfaces. Key techniques covered include image smoothing using spatial filters to average pixel values in a neighborhood and image sharpening using spatial filters based on spatial differentiation to highlight edges. Examples of the Hubble space telescope and facial recognition are also mentioned.
[PDF] Automatic Image Co-segmentation Using Geometric Mean Saliency (Top 10% ...Koteswar Rao Jerripothula
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
The document summarizes a method for single-view 3D scene reconstruction using machine learning and optimization. It discusses previous work that labels image regions with geometric classes, but has limitations like labeling superpixels instead of pixels. The proposed method addresses these by labeling each pixel individually, assuming coherence between nearby labels, and prohibiting unlikely configurations. It formulates the problem as a global energy optimization and introduces order-preserving moves that allow labeling more pixels simultaneously, achieving better results than standard expansion moves.
The document summarizes a lecture on texture mapping in computer graphics. It discusses topics like texture mapping fundamentals, texture coordinates, texture filtering including mipmapping and anisotropic filtering, wrap modes, cube maps, and texture formats. It also provides examples of texture mapping in games and an overview of the texture sampling process in the graphics pipeline.
This document discusses and compares different thresholding techniques for image denoising using wavelet transforms. It introduces the concept of image denoising using wavelet transforms, which involves applying a forward wavelet transform, estimating clean coefficients using thresholding, and applying the inverse transform. It then describes several common thresholding methods - hard, soft, universal, improved, Bayes shrink, and neigh shrink. Simulation results on test images corrupted with additive white Gaussian noise show that the proposed improved thresholding technique achieves lower MSE and higher PSNR than the universal hard thresholding method, demonstrating better noise removal performance while preserving image details.
Super Resolution in Digital Image processingRamrao Desai
Super-resolution aims to enhance image resolution by exploiting multiple low-resolution images. Key techniques include Bayesian methods using priors, Wiener filtering, Markov random fields, and learned models from example images. Super-resolution involves modeling blurring, sampling, and aliasing effects, and using techniques like deconvolution and example-based learning to recover high-frequency details beyond the Nyquist limit. It requires accurate motion estimation and modeling of the imaging process to combine information from multiple low-resolution images.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
Digital image processing short quesstion answersAteeq Zada
This document discusses several techniques for 2D spatial image filtering and background subtraction in digital image processing. It covers linear filtering, Gaussian filtering, frame differencing, running averages, and mixtures of Gaussian models. The key techniques are linear filtering using a kernel or mask, Gaussian filtering to smooth images, and running averages or mixtures of Gaussians to model the background pixels over time while adapting to changes in illumination, motion, or scene geometry.
This document discusses image degradation and restoration. It describes how images can become degraded through imperfect imaging systems, transmission channels, atmospheric conditions, and motion. It then discusses several methods for restoring degraded images, including inverse filtering, Wiener filtering, and Kalman filtering. Specific techniques are presented for restoring images corrupted with impulse noise or blurring, including using differences from the median and convolution models. The document concludes by describing simulations of image restoration techniques in MATLAB.
(1) The document presents a new method for denoising images called texture enhanced image denoising (TEID) that aims to preserve fine texture structures while removing noise.
(2) The TEID method develops a gradient histogram preservation (GHP) algorithm to ensure the gradient histogram of the denoised image is close to the estimated gradient histogram of the original image.
(3) An iterative histogram specification algorithm is proposed to solve the GHP-based image denoising model, which alternates between updating the image given the transform function and updating the transform function given the image to match the reference gradient histogram.
This document discusses digital image processing concepts including:
- Image acquisition and representation, including sampling and quantization of images. CCD arrays are commonly used in digital cameras to capture images as arrays of pixels.
- A simple image formation model where the intensity of a pixel is a function of illumination and reflectance at that point. Typical ranges of illumination and reflectance are provided.
- Image interpolation techniques like nearest neighbor, bilinear, and bicubic interpolation which are used to increase or decrease the number of pixels in a digital image. Examples of applying these techniques are shown.
- Basic relationships between pixels including adjacency, paths, regions, boundaries, and distance measures like Euclidean, city block, and
This presentation contains concepts of different image restoration and reconstruction techniques used nowadays in the field of digital image processing. Slides are prepared from Gonzalez book and Pratt book.
Labview with dwt for denoising the blurred biometric imagesijcsa
In this paper, biometric blurred image (fingerprint) denoising are presented and investigated by using
LabVIEW applications , It is blurred and corrupted with Gaussian noise. This work is proposed
algorithm that has used a discrete wavelet transform (DWT) to divide the image into two parts, this will
be increasing the manipulation speed of biometric images that are of the big sizes. This work has included
two tasks ; the first designs the LabVIEW system to calculate and present the approximation coefficients,
by which the image's blur factor reduced to minimum value according to the proposed algorithm. The
second task removes the image's noise by calculated the regression coefficients according to Bayesian-
Shrinkage estimation method.
This document provides an introduction to digital image processing. It discusses key topics like image representation as matrices, image digitization which involves sampling and quantization, and the basic steps in digital image processing such as image acquisition, preprocessing, segmentation, feature extraction, recognition and interpretation. Importance of image processing is highlighted for applications like remote sensing, machine vision, and medical imaging. Common techniques like noise filtering, contrast enhancement, compression and their importance are also summarized.
3 intensity transformations and spatial filtering slidesBHAGYAPRASADBUGGE
This document discusses basics of intensity transformations and spatial filtering of digital images. It covers the following key points:
- Intensity transformations map input pixel intensities to output intensities using an operator T. Common transformations include log, power-law, and piecewise-linear functions.
- Spatial filters operate on neighborhoods of pixels. Linear filters perform averaging or correlation while non-linear filters use ordering like median.
- Basic filters include smoothing to reduce noise, sharpening to enhance edges using Laplacian or unsharp masking, and gradient for edge detection.
- Fuzzy set theory can be applied to intensity transformations by defining membership functions for concepts like dark/bright. It can also be used for spatial filtering by defining
Fractal Image Compression By Range Block ClassificationIRJET Journal
This document proposes a method for fractal image compression using range block classification and particle swarm optimization. It begins with an abstract that describes fractal image compression as a lossy technique that partitions images into range and domain blocks, with each range block searching domain blocks for the best match using PSO. The document then provides more details on PSO, describes implementing fractal image compression with PSO by having particles represent domain block locations and fitness measure matching between blocks, and shows experimental results compressing test images with the method. The goal is to improve compression ratio and decompressed image quality over traditional techniques by using PSO for block matching.
Tensorflow London 13: Zbigniew Wojna 'Deep Learning for Big Scale 2D Imagery'Seldon
Speaker: Zbigniew Wojna, Deep Learning Researcher and founder of TensorFlight.Inc
Title: Architectures for big scale 2D imagery
Abstract: Zbigniew will present research he conducted during his Ph.D. at University College London and in collaboration with Google. His primary interest lays in the development of neural architectures for 2D imagery problems in big scale. He will present the recently published analysis of different upsampling methods in the decoder part of visual architectures, together with last week ongoing extension for GANs. Will discuss attention mechanism for text recognition and review for what kind of application it can be useful (automatically updating Google Maps based on Google Street View imagery). He will explain the idea behind inception and change in Inception-v3 to have it the best single model on ImageNet 2015 and how does it compare to Resnet architecture which was published 2 weeks after. Together with inception, will present his winning submission to MS COCO 2016 detection challenge and the extensive analysis of different models and backbone architectures inside. At the end will shortly review UCL effort working with 4096x4096 images at The Digital Mammography DREAM Challenge for breast cancer recognition, where they achieved 9th among 1375 teams worldwide and 2nd place in the community phase.
Bio: Zbigniew Wojna is deep learning researcher and founder of TensorFlight Inc. company providing instant remote commercial property inspection (for risk factors for reinsurance enterprises) based on satellite and street view type imagery. Zbigniew is currently in the final stage of his Ph.D. (already with more than 1000 citations) at the University College London under the supervision of Professor Iasonas Kokkinos and professor John Shawe-Taylor. His primary interest lies in finding and solving research problems around 2D machine vision applications usually in big scale. Zbigniew in his Ph.D. career spent most of the time working across different groups in DeepMind, Google Research, and Facebook Research. It includes DeepMind Health Team, Deep Learning Team for Google Maps in collaboration with Google Brain, Machine Perception with Kevin Murphy, Weak Localization Team with Vittorio Ferrari and Facebook AI Research Lab in Paris. His company TensorFlight Inc. was featured as top 2 AI startups among few hundreds by InnovatorsRace50 and closed seed funding last year.
Thanks to all TensorFlow London meetup organisers and supporters:
Seldon.io
Altoros
Rewired
Google Developers
Rise London
SinGAN - Learning a Generative Model from a Single Natural ImageJishnu P
SinGAN is a generative adversarial network (GAN) that can learn the distribution of a single natural image and generate new realistic samples from that image distribution. Unlike other GANs that require large datasets, SinGAN only needs a single image for training. It uses a multi-scale architecture with multiple generators and discriminators at different scales. SinGAN was shown to generate high quality samples for tasks like super resolution, image editing, and animation from a single image. It also has some failure cases like generating unrealistic samples at the boundaries.
Digital image processing img smoothningVinay Gupta
The document discusses image smoothing and sharpening techniques in digital image processing. It begins by defining what a digital image is and the goals of digital image processing. Then it discusses various applications of digital image processing like image enhancement, medical visualization, and human-computer interfaces. Key techniques covered include image smoothing using spatial filters to average pixel values in a neighborhood and image sharpening using spatial filters based on spatial differentiation to highlight edges. Examples of the Hubble space telescope and facial recognition are also mentioned.
[PDF] Automatic Image Co-segmentation Using Geometric Mean Saliency (Top 10% ...Koteswar Rao Jerripothula
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
The document summarizes a method for single-view 3D scene reconstruction using machine learning and optimization. It discusses previous work that labels image regions with geometric classes, but has limitations like labeling superpixels instead of pixels. The proposed method addresses these by labeling each pixel individually, assuming coherence between nearby labels, and prohibiting unlikely configurations. It formulates the problem as a global energy optimization and introduces order-preserving moves that allow labeling more pixels simultaneously, achieving better results than standard expansion moves.
The document summarizes a lecture on texture mapping in computer graphics. It discusses topics like texture mapping fundamentals, texture coordinates, texture filtering including mipmapping and anisotropic filtering, wrap modes, cube maps, and texture formats. It also provides examples of texture mapping in games and an overview of the texture sampling process in the graphics pipeline.
This document discusses color and texture mapping in OpenGL. It explains that glColor sets the color state and colors are linearly interpolated along vertices. It then defines different OpenGL texture types including 1D, 2D, 3D, cube map, and array textures. It describes how glTexImage2D creates a texture from image data and sets the texture state. Finally, it briefly mentions texture filtering, wrapping, mipmapping, and providing example code.
Texture synthesis aims to produce new texture samples from an example that are similar but not repetitive. It analyzes the example using a CNN to compute gram matrices representing the texture at different layers, then synthesizes new textures by passing noise through the CNN and minimizing differences from the example's gram matrices. Style transfer extends this to merge the texture of one image onto the content of another by matching gram matrices between layers to transfer style while preserving content. It has been shown that style and content are separable in CNN representations. Style transfer can be viewed as a type of domain adaptation between content and style domains.
This is the version of my 3D math talk that I used at CocoaConf Atlanta. This version includes the graphic representations of the different steps in implementing the shader.
The document describes an algorithm called Extreme DXT Compression for compressing textures into DXT1 and DXT5 formats. It uses SSE2 and SSSE3 instructions for high performance and produces quality comparable to the Real-Time DXT Compression algorithm but with roughly 300% better performance. The algorithm tightly packs data, processes two 4x4 blocks at once, and minimizes comparisons, jumps and loops to optimize for processors like the Core 2 Duo.
Generating super resolution images using transformersNEERAJ BAGHEL
The document summarizes a research paper on using transformers for the task of natural language processing. Some key points:
- Transformers use attention mechanisms to draw global dependencies between input and output without regard to sequence length, addressing limitations of RNNs and CNNs for NLP tasks.
- The proposed transformer architecture contains self-attention layers in the encoder and decoder, as well as an attention mechanism between the encoder and decoder.
- The transformer uses scaled dot-product attention and multi-head attention. Self-attention allows relating different positions of a single sequence to compute representations.
- Other components include feedforward layers and positional encoding to inject information about the relative or absolute positions of the tokens in the sequence
Realtime Per Face Texture Mapping (PTEX)basisspace
This presentation shows the original method for implementing Per-Face Texture Mapping (PTEX) in real-time on commodity hardware. PTEX is used throughout the film industry to handle texture seams robustly while simultaneously easing artist workflow.
Texture mapping is a graphic design process where a 2D texture map is wrapped around a 3D object to give it a surface texture. It accounts for the object's 3D position. Avatar used texture mapping extensively to create its virtual world. Textures played a key role in developing rich, varied character and environmental assets. Texture mapping techniques can also be applied to photography by layering texture photos over object photos, using layer masks and blending modes like Overlay. Warping and liquifying textures allows reshaping them to match the object. Students are assigned to take photos of textures and objects, then create texture maps by overlaying textures onto objects.
Texture mapping is a graphic design process that involves wrapping a 2D texture map around a 3D object to give it a surface texture. This technique is commonly used in 3D graphics and film visual effects. It involves correctly positioning the texture to account for the object's 3D geometry. Avatar extensively used texture mapping to create its virtual worlds and characters. Photographers can also use texture mapping by layering texture images over photos and adjusting settings like blending modes, hue, warp tools to enhance images. Students are assigned to find textures and photos to practice applying different textures as overlays and manipulating layers.
The document discusses OpenGL texturing. It describes how textures are loaded and applied to geometry. Textures are loaded using LoadTexture, which reads in texture data from a file. Textures are then enabled and bound. Texture parameters like filtering and wrapping modes are also set. Texture coordinates are assigned to vertices to map portions of the texture onto the geometry. When finished, textures can be cleaned up by deleting them to free memory.
Benoit fouletier guillaume martin unity day- modern 2 d techniques-gce2014Mary Chan
Using lessons learned from working on AAA 2D games, a 4-strong indie team set out to create a complete pipeline for creating modern 2D games with an organic feel and a high level of polish... on indie-scale resources.
The tools and techniques developed to reach that ambitious goal will be presented, from the innovative animation system, the terrain, vegetation and level art system, to the effective but powerful rendering model, and more.
Intended audience & prerequisites: Anyone working on a 2D game: programmers, animators, level designers, level artists.
The talk will be of particular interest to teams using Unity, but rather than being purely technical, the talk will outline principles that can be applied in any engine.
Session takeaway: I believe 2D has a great future ahead of her, and that we can do much more with it. I intend to demonstrate how to improve the production pipeline, and invest in tools to become more technical the way 3D does, while retaining the unique advantages of 2D.
Game Credits:
Rayman Origins (Ubisoft Montpellier)
Rayman Legends (Ubisoft Montpellier)
Tetrobot and Co. (Swing Swing Submarine)
Seasons After Fall [working title] (Swing Swing Submarine)
Texture mapping is a process that maps a 2D texture image onto a 3D object's surface. This allows the 3D object to take on the visual characteristics of the 2D texture. The document discusses key aspects of texture mapping like how textures are represented as arrays of texels, how texture coordinates are assigned to map textures onto object surfaces, and techniques like mipmapping, filtering and wrapping that are used to render textures properly at different distances and orientations. OpenGL functions like glTexImage2D and glTexCoord are used to specify textures and texture coordinates for 3D rendering with texture mapping.
This document summarizes the technology behind the rendering of various effects in the game Shadow Warrior, including:
1. Skinned decals were implemented using a geometry-based approach to allow decals to stably cover animating character meshes. The decals are generated asynchronously using adjacency information and skinning matrices.
2. A foliage system was created to allow large open levels with instanced vegetation that uses LoD and is easy to author. Vegetation is planted procedurally based on spawn meshes and stored in multi-resolution grids.
3. Dynamic water rendering was implemented with multiple LoD levels, distortion based on wave parameters, and filtering to prevent aliasing based on vertex frequency limits. Waves are
An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image. Image Texture gives us information about the spatial arrangement of color or intensities in an image or selected region of an image. This presentation consists of its types, uses, methods and approaches.
Improved Alpha-Tested Magnification for Vector Textures and Special Effectsナム-Nam Nguyễn
This document presents a technique for improving the rendering of vector textures at high magnifications using distance fields. A distance field is generated from a high-resolution image and stored in a low-resolution texture. This allows the texture to be rendered using alpha testing on all hardware, producing crisp edges. Programmable shaders can apply effects like soft edges, outlines, and drop shadows by manipulating the distance field. The technique was integrated into the Source game engine to improve text and UI rendering with minimal performance impact.
The document discusses texturing in OpenGL. It explains that textures are used to add visual detail to 3D graphics by mapping images onto surfaces. The key steps for using a texture are: 1) load the texture, 2) map it onto a polygon, 3) draw the polygon. Additional topics covered include texture coordinates, filtering modes, wrapping modes, blending textures for transparency effects, and using TGA images with an alpha channel for transparency. Code examples are provided for basic texture mapping and blending textures to achieve transparency.
Ultra Fast, Cross Genre, Procedural Content Generation in Games [Master Thesis]Mohammad Shaker
In my MSc. thesis, I have re-tackled the problem of procedurally generating content for physics-based games I have previously investigated in my BSc. graduation thesis. This time around I propose two novel methods: the first is projection based for faster generation of physics-based games content. The other, The Progressive Generation, is a generic, wide-range, across genre, customisable with playability check method all bundled in a fast progressive approach. This new method is applied on two completely different games: NEXT And Cut the Rope.
Short, Matters, Love - Passioneers Event 2015Mohammad Shaker
Short, Matters, Love is a presentation I prepared for freshmen students at the Faculty of Information Technology in Damascus, Syria organised by Passioneers - 2015
This document discusses Unity3D and game development. It provides an overview of Unity3D and other game engines like Unreal Engine, comparing their features and costs. Examples are given of popular games made with each engine. The document also lists several games the author has made using Unity3D and provides some additional resources and references.
The document discusses various topics related to mobile application design including cloud interaction, Android touch and gesture interaction, UI element sizing, screen sizes, changing orientation, retaining objects during configuration changes, multi-device targeting, and wearables. It provides examples and guidelines for designing applications that can adapt to different devices and configurations.
The document discusses principles of interaction design, color theory, and game design. It covers topics like primary and secondary colors, color harmonies, using color to attract attention and set mood, the importance of white space and negative space in design, and how games like Journey, Fez, Luftrausers, Monument Valley, Ori and the Blind Forest, and Limbo effectively use techniques like the rule of thirds, establishing a sense of goal, and game feel.
This document discusses various topics related to typography including letter shapes like the letter "T", how words for concepts like water have evolved across languages, symbols for ideas like fish, and different writing styles such as styles that would be impossible to write. It examines typography from multiple perspectives like shapes, language evolution, symbols, and stylization.
Interaction Design L04 - Materialise and CouplingMohammad Shaker
This document discusses various aspects of coupling and interaction design in mobile applications. It addresses good and bad examples of coupling on Android and iOS, such as how apps are switched between. It also discusses using accurate text to represent backend processes, and using faster progress bars to reduce cognitive load on users. Visualizations are suggested to improve progress bars.
The document discusses various options for storing data in an Android application including SharedPreferences for simple key-value pairs, internal storage for private files, external storage for public files, SQLite databases for structured data, network connections for storing data on a web server, and ContentProviders for sharing data between applications. It provides details on using SharedPreferences, internal SQLite databases stored in the application's files, and ContentProviders for sharing Contacts data with other apps.
The document discusses various interaction design concepts in Android including toasts, notifications, threads, broadcast receivers, and alarms. It provides code examples for creating toasts, setting notification priorities, and scheduling alarms to fire at boot or at specific times using the AlarmManager. Broadcast receivers can be used to set alarms during device boot by listening for the BOOT_COMPLETED intent filter and implementing the onReceive callback.
This document provides an overview of various mobile development technologies and frameworks including Cloud, iOS, Android, iPad Pro, Xcode, Model-View-Controller (MVC), C, Objective-C, Foundation data types, functions calls, Swift, iOS Dev Center, coordinate systems, Windows Phone, .NET support, MVVM, binding, WebClient, and navigation. It also mentions tools like Expression Blend and frameworks like jQuery Mobile, PhoneGap, Sencha Touch, and Xamarin.
This document discusses various topics related to mobile app design including user experience (UX), user interface (UI), interaction design, user constraints like limited data/battery and screen size, and using context like location to improve the user experience. It provides examples of a pizza ordering app and making ATM machines smarter. It also covers design patterns and principles like focusing on user needs and testing designs through feedback.
This document discusses principles of visual organization and responsive grid systems for web design. It mentions laws of proximity, similarity, common fate, continuity, closure, and symmetry which help organize visual elements. It also discusses column-based and ratio-based grid systems as well as responsive grid systems that adapt to different screen widths, citing examples from Pinterest, Bootstrap, and the website www.mohammadshaker.com which demonstrates responsive design.
This document provides an overview comparison of key aspects of mobile app development for iOS and Android platforms. It discusses differences in app store policies, pricing, monetization options like ads and in-app purchases, development tools including engines like Unity and Unreal, and the publishing process. Key points mentioned include Android apps averaging over 2.5x the price of similar iOS apps, Apple's restrictive app review policies, the 70/30 revenue split in Google Play Store, and tools for user testing and publishing on both platforms. It also shares stats on the revenue and success of specific apps like Monument Valley.
The document discusses various ways to implement cloud functionality in Android applications using services like Parse and Android Backup. It provides code examples for backing up app data to the cloud using Android Backup, setting up a backend using Parse, pushing notifications with Parse, and performing analytics tracking with Parse.
This document discusses several topics related to developing Android apps including:
1. Adding markers to maps by setting an onMapClickListener and adding a MarkerOptions to the clicked location.
2. Signing into apps with Google accounts using the Google Identity API.
3. Following Material Design guidelines for visual style and user interfaces.
4. Maintaining multiple APK versions and using OpenGL ES for games.
This document discusses various techniques for styling Android applications including adding styles, overriding styles, using themes, custom backgrounds, nine-patch images, and animations. It provides links to tutorials and documentation on animating views with zoom animations and other motion effects.
This document provides information about various Android development topics including:
- ListAdapters and mapping models to UI using an MVVM-like pattern
- Creating custom lists
- Starting a new activity using an Intent and passing data between activities
- Understanding the Android activity lifecycle and methods like onPause() and onResume()
- Handling configuration changes that recreate the activity
- Working with permissions
The document discusses common patterns for working with lists, launching new screens, and handling activity state changes. It also provides code examples for starting a new activity, passing data between activities, and handling the activity lifecycle callbacks.
This document provides an overview of various topics related to mobile application development including cloud computing, interaction design, Android, iOS, web technologies like HTML5 and JavaScript, programming languages like Java and Objective-C, frameworks, gaming, user experience design, and more. It discusses tools for Android development and covers basics of creating an Android app like setting up the IDE, creating the UI, adding interactivity, debugging, and referencing documentation.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
2. that appears in a video game needs to be textured; this includes everything from plants
to people. If things aren’t textured well, your game just won’t look right.
4. Texturing
• What’s it?!
Textures are images applied to surfaces that are created using primitive objects
XNA is Perfect at Texturing
textures can be colored, filtered, blended,
and transformed at run time!
5. Texturing
• What’s it?!
Textures are images applied to surfaces that are created using primitive objects
textures can be colored, filtered, blended,
and transformed at run time!
XNA is Perfect at Texturing
XNA supports:
.bmp, .dds, .dib, .hdr, .jpg, .pfm, .png, .ppm, and .tga image formats for textures
6. Texturing
• UV Coordinates
– 2D World
• a texture is a two-dimensional object that is mapped onto a 2D polygon
– 3D World
• a texture is a two-dimensional object that is mapped onto a 3D polygon
10. Texturing
• VertexPositionColorTexture
– This format allows you to apply image textures to your primitive shapes, and you can even
shade your images with color.
– For example, with this vertex type you could draw a rectangle with an image texture and then
you could show it again with a different shade of color!
VertexPositionColorTexture vertex = new
VertexPositionColorTexture(Vector3 position, Color color, Vector2 uv);
11. Texturing
• VertexPositionNormalTexture
– This format allows you to add textures to your primitive objects. The normal data enables
lighting for this textured format.
VertexPositionNormalTexture vertex = new
VertexPositionNormalTexture(Vector3 position, Vector3 normal, Vector2 uv);
12. Texturing
• VertexPositionTexture
– This format only permits storage of position and texture data.
– It may be useful if you don’t need lighting and were concerned about saving space or
improving performance for large amounts of vertices.
VertexPositionTexture vertex = new
VertexPositionTexture(Vector3 position, Vector2 uv);
17. Texturing
• TRANSPARENT TEXTURES
An alpha channel can be used to “mask” all pixels of a specific color in an image. Alpha
data is stored in the last color byte of a pixel after the red, green, and blue bytes.
When alpha blending is enabled in your XNA code and the alpha channel is active,
transparency is achieved for the pixels where the alpha setting is set to 0.
New “Alpha” Channel!
18. Texturing
• TRANSPARENT TEXTURES
An alpha channel can be used to “mask” all pixels of a specific color in an image. Alpha
data is stored in the last color byte of a pixel after the red, green, and blue bytes.
When alpha blending is enabled in your XNA code and the alpha channel is active,
transparency is achieved for the pixels where the alpha setting is set to 0.
New “Alpha” Channel!
52. Texturing
• TEXTURE TILING
Using a small image to cover a large surface makes tiling a useful way
to increase the performance of your textures and decrease the size of
your image files.
53. Texture Tiling
• In Load Content
// Right Top
verts[0] = new VertexPositionTexture(
new Vector3(-1, 1, 0), new Vector2(10, 0));
// Left Top
verts[1] = new VertexPositionTexture(
new Vector3(1, 1, 0), new Vector2(1, 0));
// Right Bottom
verts[2] = new VertexPositionTexture(
new Vector3(-1, -1, 0), new Vector2(10, 10));
// Left Bottom
verts[3] = new VertexPositionTexture(
new Vector3(1, -1, 0), new Vector2(1, 10));
61. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
62. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
63. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
64. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
65. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
69. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
rotationY = Matrix.CreateRotationY(GetViewerAngle());
70. Billboarding
float GetViewerAngle()
{
// use camera look direction to get
// rotation angle about Y
float x = cam.view.X - cam.position.X;
float z = cam.view.Z - cam.position.Z;
return (float)Math.Atan2(x, z) + MathHelper.Pi;
}
rotationY = Matrix.CreateRotationY(GetViewerAngle());