SlideShare uma empresa Scribd logo

★Mean shift a_robust_approach_to_feature_space_analysis

•Transferir como PPT, PDF•
•
I
irisshicat
Tecnologia
1 de 46
Mean Shift A Robust Approach to Feature Space Analysis Kalyan Sunkavalli 04/29/2008 ES251R
An Example Feature Space
An Example Feature Space
An Example Feature Space Parametric Density Estimation?
Mean Shift ,[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region Slide Credit: Yaron Ukrainitz & Bernard Sarel
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Objective : Find the densest region
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Assumed Underlying PDF Estimate from data Data Samples Parametric Density Estimation The data points are sampled from an underlying PDF
Assumed Underlying PDF Data Samples Data point density Non-parametric Density Estimation PDF value
Assumed Underlying PDF Data Samples Non-parametric Density Estimation
Parzen Windows ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Kernels and Bandwidths ,[object Object],[object Object],(product of univariate kernels) (radially symmetric kernel)
Various Kernels Epanechnikov Normal Uniform
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Density Gradient Estimation Epanechnikov  Uniform Normal  Normal Modes of the probability density
Mean Shift KDE Mean Shift Mean Shift Algorithm ,[object Object],[object Object]
Mean Shift ,[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Properties of Mean Shift ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Properties of Mean Shift ,[object Object],[object Object],[object Object]
Mode detection using Mean Shift ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mode Finding on Real Data initialization detected mode tracks
Mean Shift Clustering
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Joint Spatial-Range Feature Space ,[object Object]
Discontinuity Preserving Smoothing
Discontinuity Preserving Smoothing
Discontinuity Preserving Smoothing
Discontinuity Preserving Smoothing
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering on Real Data
Image Segmentation
Image Segmentation
Image Segmentation
Image Segmentation
Image Segmentation
Acknowledgements ,[object Object],[object Object],[object Object]
Thank You

Recomendados

Mean shift and Hierarchical clustering
Mean shift and Hierarchical clustering Mean shift and Hierarchical clustering
Mean shift and Hierarchical clustering Yan Xu
 
Understandig PCA and LDA
Understandig PCA and LDAUnderstandig PCA and LDA
Understandig PCA and LDADr. Syed Hassan Amin
 
Genetic algorithm for hyperparameter tuning
Genetic algorithm for hyperparameter tuningGenetic algorithm for hyperparameter tuning
Genetic algorithm for hyperparameter tuningDr. Jyoti Obia
 
Deep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningDeep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningShubhmay Potdar
 
Multiclass classification of imbalanced data
Multiclass classification of imbalanced dataMulticlass classification of imbalanced data
Multiclass classification of imbalanced dataSaurabhWani6
 
Regularization in deep learning
Regularization in deep learningRegularization in deep learning
Regularization in deep learningKien Le
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
 
Machine Learning Clustering
Machine Learning ClusteringMachine Learning Clustering
Machine Learning ClusteringRupak Roy
 

Recomendados

Mean shift and Hierarchical clustering
Mean shift and Hierarchical clustering Mean shift and Hierarchical clustering
Mean shift and Hierarchical clustering Yan Xu
 
Understandig PCA and LDA
Understandig PCA and LDAUnderstandig PCA and LDA
Understandig PCA and LDADr. Syed Hassan Amin
 
Genetic algorithm for hyperparameter tuning
Genetic algorithm for hyperparameter tuningGenetic algorithm for hyperparameter tuning
Genetic algorithm for hyperparameter tuningDr. Jyoti Obia
 
Deep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningDeep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningShubhmay Potdar
 
Multiclass classification of imbalanced data
Multiclass classification of imbalanced dataMulticlass classification of imbalanced data
Multiclass classification of imbalanced dataSaurabhWani6
 
Regularization in deep learning
Regularization in deep learningRegularization in deep learning
Regularization in deep learningKien Le
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
 
Machine Learning Clustering
Machine Learning ClusteringMachine Learning Clustering
Machine Learning ClusteringRupak Roy
 
Fuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge DetectionFuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge DetectionDawn Raider Gupta
 
Support Vector Machine
Support Vector MachineSupport Vector Machine
Support Vector MachineShao-Chuan Wang
 
Clustering - K-Means, DBSCAN
Clustering - K-Means, DBSCANClustering - K-Means, DBSCAN
Clustering - K-Means, DBSCANMedicaps University
 
CNN Tutorial
CNN TutorialCNN Tutorial
CNN TutorialSungjoon Choi
 
Feedforward neural network
Feedforward neural networkFeedforward neural network
Feedforward neural networkSopheaktra YONG
 
Deep Learning - RNN and CNN
Deep Learning - RNN and CNNDeep Learning - RNN and CNN
Deep Learning - RNN and CNNPradnya Saval
 
What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?Kazuki Yoshida
 
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...Indraneel Pole
 
Computer Vision: Shape from Specularities and Motion
Computer Vision: Shape from Specularities and MotionComputer Vision: Shape from Specularities and Motion
Computer Vision: Shape from Specularities and MotionDamian T. Gordon
 
Support Vector Machines ( SVM )
Support Vector Machines ( SVM ) Support Vector Machines ( SVM )
Support Vector Machines ( SVM ) Mohammad Junaid Khan
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep LearningSebastian Ruder
 
Support vector machine
Support vector machineSupport vector machine
Support vector machineMusa Hawamdah
 
Types of clustering and different types of clustering algorithms
Types of clustering and different types of clustering algorithmsTypes of clustering and different types of clustering algorithms
Types of clustering and different types of clustering algorithmsPrashanth Guntal
 
Gradient descent method
Gradient descent methodGradient descent method
Gradient descent methodProf. Neeta Awasthy
 
Unit8: Uncertainty in AI
Unit8: Uncertainty in AIUnit8: Uncertainty in AI
Unit8: Uncertainty in AITekendra Nath Yogi
 
Bayesian Neural Networks
Bayesian Neural NetworksBayesian Neural Networks
Bayesian Neural NetworksNatan Katz
 
Methods of Optimization in Machine Learning
Methods of Optimization in Machine LearningMethods of Optimization in Machine Learning
Methods of Optimization in Machine LearningKnoldus Inc.
 
Artificial Neural Networks Lect5: Multi-Layer Perceptron & Backpropagation
Artificial Neural Networks Lect5: Multi-Layer Perceptron & BackpropagationArtificial Neural Networks Lect5: Multi-Layer Perceptron & Backpropagation
Artificial Neural Networks Lect5: Multi-Layer Perceptron & BackpropagationMohammed Bennamoun
 
Em Algorithm | Statistics
Em Algorithm | StatisticsEm Algorithm | Statistics
Em Algorithm | StatisticsTransweb Global Inc
 
Support Vector Machines- SVM
Support Vector Machines- SVMSupport Vector Machines- SVM
Support Vector Machines- SVMCarlo Carandang
 
Meanshift Tracking Presentation
Meanshift Tracking PresentationMeanshift Tracking Presentation
Meanshift Tracking Presentationsandtouch
 
A probabilistic model for recursive factorized image features ppt
A probabilistic model for recursive factorized image features pptA probabilistic model for recursive factorized image features ppt
A probabilistic model for recursive factorized image features pptirisshicat
 

Mais conteúdo relacionado

Mais procurados

Fuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge DetectionFuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge DetectionDawn Raider Gupta
 
Feedforward neural network
Feedforward neural networkFeedforward neural network
Feedforward neural networkSopheaktra YONG
 
Deep Learning - RNN and CNN
Deep Learning - RNN and CNNDeep Learning - RNN and CNN
Deep Learning - RNN and CNNPradnya Saval
 
What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?Kazuki Yoshida
 
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...Indraneel Pole
 
Computer Vision: Shape from Specularities and Motion
Computer Vision: Shape from Specularities and MotionComputer Vision: Shape from Specularities and Motion
Computer Vision: Shape from Specularities and MotionDamian T. Gordon
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep LearningSebastian Ruder
 
Support vector machine
Support vector machineSupport vector machine
Support vector machineMusa Hawamdah
 
Types of clustering and different types of clustering algorithms
Types of clustering and different types of clustering algorithmsTypes of clustering and different types of clustering algorithms
Types of clustering and different types of clustering algorithmsPrashanth Guntal
 
Bayesian Neural Networks
Bayesian Neural NetworksBayesian Neural Networks
Bayesian Neural NetworksNatan Katz
 
Methods of Optimization in Machine Learning
Methods of Optimization in Machine LearningMethods of Optimization in Machine Learning
Methods of Optimization in Machine LearningKnoldus Inc.
 
Artificial Neural Networks Lect5: Multi-Layer Perceptron & Backpropagation
Artificial Neural Networks Lect5: Multi-Layer Perceptron & BackpropagationArtificial Neural Networks Lect5: Multi-Layer Perceptron & Backpropagation
Artificial Neural Networks Lect5: Multi-Layer Perceptron & BackpropagationMohammed Bennamoun
 
Support Vector Machines- SVM
Support Vector Machines- SVMSupport Vector Machines- SVM
Support Vector Machines- SVMCarlo Carandang
 

Mais procurados (20)

Fuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge DetectionFuzzy Logic Based Edge Detection
Fuzzy Logic Based Edge Detection
 
Support Vector Machine
Support Vector MachineSupport Vector Machine
Support Vector Machine
 
Clustering - K-Means, DBSCAN
Clustering - K-Means, DBSCANClustering - K-Means, DBSCAN
Clustering - K-Means, DBSCAN
 
CNN Tutorial
CNN TutorialCNN Tutorial
CNN Tutorial
 
Feedforward neural network
Feedforward neural networkFeedforward neural network
Feedforward neural network
 
Deep Learning - RNN and CNN
Deep Learning - RNN and CNNDeep Learning - RNN and CNN
Deep Learning - RNN and CNN
 
What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?
 
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...
Restricted Boltzmann Machine - A comprehensive study with a focus on Deep Bel...
 
Computer Vision: Shape from Specularities and Motion
Computer Vision: Shape from Specularities and MotionComputer Vision: Shape from Specularities and Motion
Computer Vision: Shape from Specularities and Motion
 
Support Vector Machines ( SVM )
Support Vector Machines ( SVM ) Support Vector Machines ( SVM )
Support Vector Machines ( SVM )
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep Learning
 
Support vector machine
Support vector machineSupport vector machine
Support vector machine
 
Types of clustering and different types of clustering algorithms
Types of clustering and different types of clustering algorithmsTypes of clustering and different types of clustering algorithms
Types of clustering and different types of clustering algorithms
 
Gradient descent method
Gradient descent methodGradient descent method
Gradient descent method
 
Unit8: Uncertainty in AI
Unit8: Uncertainty in AIUnit8: Uncertainty in AI
Unit8: Uncertainty in AI
 
Bayesian Neural Networks
Bayesian Neural NetworksBayesian Neural Networks
Bayesian Neural Networks
 
Methods of Optimization in Machine Learning
Methods of Optimization in Machine LearningMethods of Optimization in Machine Learning
Methods of Optimization in Machine Learning
 
Artificial Neural Networks Lect5: Multi-Layer Perceptron & Backpropagation
Artificial Neural Networks Lect5: Multi-Layer Perceptron & BackpropagationArtificial Neural Networks Lect5: Multi-Layer Perceptron & Backpropagation
Artificial Neural Networks Lect5: Multi-Layer Perceptron & Backpropagation
 
Em Algorithm | Statistics
Em Algorithm | StatisticsEm Algorithm | Statistics
Em Algorithm | Statistics
 
Support Vector Machines- SVM
Support Vector Machines- SVMSupport Vector Machines- SVM
Support Vector Machines- SVM
 

Destaque

Meanshift Tracking Presentation
Meanshift Tracking PresentationMeanshift Tracking Presentation
Meanshift Tracking Presentationsandtouch
 
A probabilistic model for recursive factorized image features ppt
A probabilistic model for recursive factorized image features pptA probabilistic model for recursive factorized image features ppt
A probabilistic model for recursive factorized image features pptirisshicat
 
Color reduction using the combination of the kohonen self organized feature m...
Color reduction using the combination of the kohonen self organized feature m...Color reduction using the combination of the kohonen self organized feature m...
Color reduction using the combination of the kohonen self organized feature m...Konstantinos Zagoris
 
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...grssieee
 
Object tracking survey
Object tracking surveyObject tracking survey
Object tracking surveyRich Nguyen
 
Compressed air car technology
Compressed air car technologyCompressed air car technology
Compressed air car technologykeshav goyal
 
K means clustering
K means clusteringK means clustering
K means clusteringkeshav goyal
 
K mean-clustering algorithm
K mean-clustering algorithmK mean-clustering algorithm
K mean-clustering algorithmparry prabhu
 
A Review on Matching For Sketch Technique
A Review on Matching For Sketch TechniqueA Review on Matching For Sketch Technique
A Review on Matching For Sketch TechniqueIOSR Journals
 

Destaque (11)

Meanshift Tracking Presentation
Meanshift Tracking PresentationMeanshift Tracking Presentation
Meanshift Tracking Presentation
 
A probabilistic model for recursive factorized image features ppt
A probabilistic model for recursive factorized image features pptA probabilistic model for recursive factorized image features ppt
A probabilistic model for recursive factorized image features ppt
 
Color reduction using the combination of the kohonen self organized feature m...
Color reduction using the combination of the kohonen self organized feature m...Color reduction using the combination of the kohonen self organized feature m...
Color reduction using the combination of the kohonen self organized feature m...
 
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
 
Object tracking survey
Object tracking surveyObject tracking survey
Object tracking survey
 
Camshaft
CamshaftCamshaft
Camshaft
 
Compressed air car technology
Compressed air car technologyCompressed air car technology
Compressed air car technology
 
K means clustering
K means clusteringK means clustering
K means clustering
 
K mean-clustering algorithm
K mean-clustering algorithmK mean-clustering algorithm
K mean-clustering algorithm
 
Understanding feature-space
Understanding feature-spaceUnderstanding feature-space
Understanding feature-space
 
A Review on Matching For Sketch Technique
A Review on Matching For Sketch TechniqueA Review on Matching For Sketch Technique
A Review on Matching For Sketch Technique
 

Semelhante a ★Mean shift a_robust_approach_to_feature_space_analysis

P.maria sheeba 15 mco010
P.maria sheeba 15 mco010P.maria sheeba 15 mco010
P.maria sheeba 15 mco010W3Edify
 
Michal Erel's SIFT presentation
Michal Erel's SIFT presentationMichal Erel's SIFT presentation
Michal Erel's SIFT presentationwolf
 
Remotely sensed image segmentation using multiphase level set acm
Remotely sensed image segmentation using multiphase level set acmRemotely sensed image segmentation using multiphase level set acm
Remotely sensed image segmentation using multiphase level set acmKriti Bajpai
 
Importance of Mean Shift in Remote Sensing Segmentation
Importance of Mean Shift in Remote Sensing SegmentationImportance of Mean Shift in Remote Sensing Segmentation
Importance of Mean Shift in Remote Sensing SegmentationIOSR Journals
 
image-processing-husseina-ozigi-otaru.ppt
image-processing-husseina-ozigi-otaru.pptimage-processing-husseina-ozigi-otaru.ppt
image-processing-husseina-ozigi-otaru.pptRaviSharma65345
 
Ijarcet vol-2-issue-7-2273-2276
Ijarcet vol-2-issue-7-2273-2276Ijarcet vol-2-issue-7-2273-2276
Ijarcet vol-2-issue-7-2273-2276Editor IJARCET
 
Ijarcet vol-2-issue-7-2273-2276
Ijarcet vol-2-issue-7-2273-2276Ijarcet vol-2-issue-7-2273-2276
Ijarcet vol-2-issue-7-2273-2276Editor IJARCET
 
TOWARDS OPTIMALITY OF IMAGE SEGMENTATION PART- I
TOWARDS OPTIMALITY OF IMAGE SEGMENTATION PART- ITOWARDS OPTIMALITY OF IMAGE SEGMENTATION PART- I
TOWARDS OPTIMALITY OF IMAGE SEGMENTATION PART- IAnish Acharya
 
Super Resolution of Image
Super Resolution of ImageSuper Resolution of Image
Super Resolution of ImageSatheesh K
 
Paper review: Measuring the Intrinsic Dimension of Objective Landscapes.
Paper review: Measuring the Intrinsic Dimension of Objective Landscapes.Paper review: Measuring the Intrinsic Dimension of Objective Landscapes.
Paper review: Measuring the Intrinsic Dimension of Objective Landscapes.Wuhyun Rico Shin
 
Literature Survey on Interest Points based Watermarking
Literature Survey on Interest Points based WatermarkingLiterature Survey on Interest Points based Watermarking
Literature Survey on Interest Points based WatermarkingPriyatham Bollimpalli
 
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
 
Digital image processing
Digital image processingDigital image processing
Digital image processingABIRAMI M
 
06 image features
06 image features06 image features
06 image featuresankit_ppt
 
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)IJERD Editor
 
Data Science - Part IX - Support Vector Machine
Data Science - Part IX - Support Vector MachineData Science - Part IX - Support Vector Machine
Data Science - Part IX - Support Vector MachineDerek Kane
 

Semelhante a ★Mean shift a_robust_approach_to_feature_space_analysis (20)

P.maria sheeba 15 mco010
P.maria sheeba 15 mco010P.maria sheeba 15 mco010
P.maria sheeba 15 mco010
 
Michal Erel's SIFT presentation
Michal Erel's SIFT presentationMichal Erel's SIFT presentation
Michal Erel's SIFT presentation
 
Remotely sensed image segmentation using multiphase level set acm
Remotely sensed image segmentation using multiphase level set acmRemotely sensed image segmentation using multiphase level set acm
Remotely sensed image segmentation using multiphase level set acm
 
Importance of Mean Shift in Remote Sensing Segmentation
Importance of Mean Shift in Remote Sensing SegmentationImportance of Mean Shift in Remote Sensing Segmentation
Importance of Mean Shift in Remote Sensing Segmentation
 
Object tracking
Object trackingObject tracking
Object tracking
 
image-processing-husseina-ozigi-otaru.ppt
image-processing-husseina-ozigi-otaru.pptimage-processing-husseina-ozigi-otaru.ppt
image-processing-husseina-ozigi-otaru.ppt
 
Ijarcet vol-2-issue-7-2273-2276
Ijarcet vol-2-issue-7-2273-2276Ijarcet vol-2-issue-7-2273-2276
Ijarcet vol-2-issue-7-2273-2276
 
Ijarcet vol-2-issue-7-2273-2276
Ijarcet vol-2-issue-7-2273-2276Ijarcet vol-2-issue-7-2273-2276
Ijarcet vol-2-issue-7-2273-2276
 
TOWARDS OPTIMALITY OF IMAGE SEGMENTATION PART- I
TOWARDS OPTIMALITY OF IMAGE SEGMENTATION PART- ITOWARDS OPTIMALITY OF IMAGE SEGMENTATION PART- I
TOWARDS OPTIMALITY OF IMAGE SEGMENTATION PART- I
 
Super Resolution of Image
Super Resolution of ImageSuper Resolution of Image
Super Resolution of Image
 
Paper review: Measuring the Intrinsic Dimension of Objective Landscapes.
Paper review: Measuring the Intrinsic Dimension of Objective Landscapes.Paper review: Measuring the Intrinsic Dimension of Objective Landscapes.
Paper review: Measuring the Intrinsic Dimension of Objective Landscapes.
 
E1803053238
E1803053238E1803053238
E1803053238
 
Ijetr011917
Ijetr011917Ijetr011917
Ijetr011917
 
Literature Survey on Interest Points based Watermarking
Literature Survey on Interest Points based WatermarkingLiterature Survey on Interest Points based Watermarking
Literature Survey on Interest Points based Watermarking
 
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
 
Digital image processing
Digital image processingDigital image processing
Digital image processing
 
06 image features
06 image features06 image features
06 image features
 
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
 
Deep Learning.pptx
Deep Learning.pptxDeep Learning.pptx
Deep Learning.pptx
 
Data Science - Part IX - Support Vector Machine
Data Science - Part IX - Support Vector MachineData Science - Part IX - Support Vector Machine
Data Science - Part IX - Support Vector Machine
 

Mais de irisshicat

Object segmentation by alignment of poselet activations to image contours
Object segmentation by alignment of poselet activations to image contoursObject segmentation by alignment of poselet activations to image contours
Object segmentation by alignment of poselet activations to image contoursirisshicat
 
Biased normalized cuts
Biased normalized cutsBiased normalized cuts
Biased normalized cutsirisshicat
 
A probabilistic model for recursive factorized image features
A probabilistic model for recursive factorized image featuresA probabilistic model for recursive factorized image features
A probabilistic model for recursive factorized image featuresirisshicat
 
Pami meanshift
Pami meanshiftPami meanshift
Pami meanshiftirisshicat
 
Shape matching and object recognition using shape context belongie pami02
Shape matching and object recognition using shape context belongie pami02Shape matching and object recognition using shape context belongie pami02
Shape matching and object recognition using shape context belongie pami02irisshicat
 
. Color and texture-based image segmentation using the expectation-maximizat...
. Color and texture-based image segmentation using the expectation-maximizat.... Color and texture-based image segmentation using the expectation-maximizat...
. Color and texture-based image segmentation using the expectation-maximizat...irisshicat
 
Shape matching and object recognition using shape contexts
Shape matching and object recognition using shape contextsShape matching and object recognition using shape contexts
Shape matching and object recognition using shape contextsirisshicat
 

Mais de irisshicat (7)

Object segmentation by alignment of poselet activations to image contours
Object segmentation by alignment of poselet activations to image contoursObject segmentation by alignment of poselet activations to image contours
Object segmentation by alignment of poselet activations to image contours
 
Biased normalized cuts
Biased normalized cutsBiased normalized cuts
Biased normalized cuts
 
A probabilistic model for recursive factorized image features
A probabilistic model for recursive factorized image featuresA probabilistic model for recursive factorized image features
A probabilistic model for recursive factorized image features
 
Pami meanshift
Pami meanshiftPami meanshift
Pami meanshift
 
Shape matching and object recognition using shape context belongie pami02
Shape matching and object recognition using shape context belongie pami02Shape matching and object recognition using shape context belongie pami02
Shape matching and object recognition using shape context belongie pami02
 
. Color and texture-based image segmentation using the expectation-maximizat...
. Color and texture-based image segmentation using the expectation-maximizat.... Color and texture-based image segmentation using the expectation-maximizat...
. Color and texture-based image segmentation using the expectation-maximizat...
 
Shape matching and object recognition using shape contexts
Shape matching and object recognition using shape contextsShape matching and object recognition using shape contexts
Shape matching and object recognition using shape contexts
 

Último

TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...marcuskenyatta275
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...panagenda
 
Microsoft CSP Briefing Pre-Engagement - Questionnaire
Microsoft CSP Briefing Pre-Engagement - QuestionnaireMicrosoft CSP Briefing Pre-Engagement - Questionnaire
Microsoft CSP Briefing Pre-Engagement - QuestionnaireExakis Nelite
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...CzechDreamin
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe中 央社
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfFIDO Alliance
 
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsPLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsStefano
 
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaPowerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaCzechDreamin
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyUXDXConf
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceSamy Fodil
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfFIDO Alliance
 
ERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctBrainSell Technologies
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon
 
ECS 2024 Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty SecureECS 2024 Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty SecureFemke de Vroome
 
WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024Lorenzo Miniero
 
The Metaverse: Are We There Yet?
The Metaverse: Are We There Yet?The Metaverse: Are We There Yet?
The Metaverse: Are We There Yet?Mark Billinghurst
 
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPTiSEO AI
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...FIDO Alliance
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftshyamraj55
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfFIDO Alliance
 

Último (20)

TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
 
Microsoft CSP Briefing Pre-Engagement - Questionnaire
Microsoft CSP Briefing Pre-Engagement - QuestionnaireMicrosoft CSP Briefing Pre-Engagement - Questionnaire
Microsoft CSP Briefing Pre-Engagement - Questionnaire
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
 
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsPLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. Startups
 
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaPowerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara Laskowska
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM Performance
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
ERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage Intacct
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdf
 
ECS 2024 Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty SecureECS 2024 Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty Secure
 
WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024
 
The Metaverse: Are We There Yet?
The Metaverse: Are We There Yet?The Metaverse: Are We There Yet?
The Metaverse: Are We There Yet?
 
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoft
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
 

★Mean shift a_robust_approach_to_feature_space_analysis