IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2013-2014 FOR M.Tech/ B.E/B.Tech

VenSoft Technologies www.ieeedeveloperslabs.in Email:vensoftprojects@yahoo.com Contact: 9448847874

IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2013-2014 FOR M.Tech/ B.E/B.Tech
1. A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and
Mutual Information
Abstract: Automatic image registration is a vital yet challenging task, particularly for remote
sensing images. A fully automatic registration approach which is accurate, robust, and fast is
required. For this
purpose, anovel coarse-to-fine scheme for automatic image registration is
proposed
in
this
paper.
This scheme
consists
of a preregistration
process
(coarse registration) and a fine-tuning process (fine registration).To begins with, the
preregistration process is implemented by the scale-invariant feature transform approach
equipped with a reliable outlier removal procedure. The coarse results provide a near-optimal
initial solution for the optimizer in the fine-tuning process. Next, the fine-tuning process is
implemented by the maximization of mutual information using a modified Marquardt–
Levenberg search strategy in a multi resolution framework. The proposed algorithm is
tested on various remote sensing optical and synthetic aperture radar images taken at different
situations (multispectral, multi sensor, and multi temporal) with the affine transformation
model. The experimental results demonstrate the accuracy, robustness, and efficiency of the
proposed algorithm.
Published in: Geo science and Remote Sensing, IEEE Transactions on (Volume: PP , Issue: 99 )

Index Terms— Image registration, mutual information (MI),outlier removal, scale-invariant
feature transform (SIFT).
2. Image Segmentation Using a Sparse Coding Model of Cortical Area V1
Abstract: Algorithms that encode images using a sparse set of basis functions have previously
been shown to explain aspects of the physiology of a primary visual cortex (V1), and have
been used for applications, such as image compression, restoration, and classification.
Here, a sparse coding algorithm, that has previously been used to account for the response
properties of orientation tuned cells in primary visual cortex, is applied to the
task of perceptually salient boundary detection. The proposed algorithm is currently limited
to using only intensity information at a single scale. However, it is shown to out-perform the
current state-of-the-art image segmentation method (Pb) when this method is also restricted to
using the same information.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 )

Index Terms— Image Segmentation; Edge detection; Neural Networks; Predictive Coding;
Sparse Coding; Primary Visual Cortex



3. How to SAIF-ly Boost Denoising Performance
Abstract: Spatial domain image filters (e.g., bilateral filter, non-local means, locally adaptive
regression kernel) have achieved great success in de noising. Their overall performance,
however, has not generally surpassed the leading transform domain-based filters (such as BM3D). One important reason is that spatial domain filters lack efficiency to adaptively fine tune
their de noising strength; something that is relatively easy to do in transform domain method
with shrinkage operators. In the pixel domain, the smoothing strength is usually controlled
globally by, for example, tuning a regularization parameter. In this paper, we propose spatially
adaptive iterative filtering (SAIF) a new strategy to control the de noising strength locally for
any spatial domain method. This approach is capable of filtering local image content iteratively
using the given base filter, and the type of iteration and the iteration number are automatically
optimized with respect to estimated risk (i.e., mean-squared error). In exploiting the estimated
local signal-to-noise-ratio, we also present a new risk estimator that is different from the oftenemployed SURE method, and exceeds its performance in many cases. Experiments illustrate
that our strategy can significantly relax the base algorithm's sensitivity to its tuning (smoothing)
parameters,
and
effectively boost the performance of
several
existing de
noising filters to generate state-of-the-art results under both simulated and practical
conditions.

Index Terms— Image de noising, spatial domain filter, risk estimator, SURE, pixel aggregation

4. Nonlocally Centralized Sparse Representation for Image Restoration
Abstract: Sparse representation models code an image patch as a linear combination of a few
atoms chosen out from an over-complete dictionary, and they have shown promising results in
various image restoration applications. However, due to the degradation of the
observed image (e.g., noisy, blurred, and/or down-sampled), the sparse representations by
conventional models may not be accurate enough for a faithful reconstruction of the
original image. To improve the performance of sparse representation-based image restoration,
in this paper the concept of sparse coding noise is introduced, and the goal of image
restoration turns to how to suppress the sparse coding noise. To this end, we exploit the image
nonlocal self-similarity to obtain good estimates of the sparse coding coefficients of the
original image, and then centralize the sparse coding coefficients of the observed image to
those estimates. The so-called non locally centralized sparse representation (NCSR) model is as
simple as the standard sparse representation model, while our extensive experiments on



various types of image restoration problems, including de noising, de blurring and superresolution, validate the generality and state-of-the-art performance of the proposed NCSR
algorithm.
Date of Publication: April 2013
Index Terms— Image restoration, nonlocal similarity, sparse representation.
5. Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling
Abstract: Sparse representation is proven to be a promising approach to image superresolution, where the low-resolution (LR) image is usually modeled as the down-sampled
version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the
Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart
without blurring, the super-resolution problem becomes an image interpolation problem. In
such cases, however, the conventional sparse representation models (SRM) become less
effective, because the data fidelity term fails to constrain the image local structures. In
natural images, fortunately, many nonlocal similar patches to a given patch could provide
nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal selfsimilarity
into
SRM
for image interpolation.
More
specifically,
a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in
SRM.
We
show
that
the
NARM-induced
sampling
matrix
is
less
coherent with the representation dictionary, and consequently makes SRM more effective for
image interpolation. Our extensive experimental results demonstrate that the proposed NARMbased image interpolation method can effectively reconstruct the edge structures and suppress
the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of
PSNR as well as perceptual quality metrics such as SSIM and FSIM.

Index Terms—Image interpolation, nonlocal autoregressive model, sparse representation,
super-resolution.
6. Acceleration of the Shiftable

Algorithm for Bilateral Filtering and Nonlocal Means

Abstract: A direct implementation of the bilateral filter requires O(σs2) operations per pixel,
where
σs is the(effective)
width of the spatial
kernel.
A
fast


implementation of the bilateral filter that required O(1) operations per pixel with respect to
σs was recently proposed. This was done by using trigonometric functions for the range
kernel of the bilateral filter, and by exploiting their so-called shift ability property. In particular,
a fast implementation of the Gaussian bilateral filter was realized by approximating the
Gaussian range kernel using raised cosines. Later, it was demonstrated that this idea could be
extended to a larger class of filters, including the popular non-local means filter. As already
observed, a flip side of this approach was that the run time depended on the width
σr of the range kernel. For an image with dynamic range [0,T], the run time scaled as O(T2/σr2)
with σr. This made it difficult to implement narrow range kernels, particularly for images with
large dynamic range. In this paper, we discuss this problem, and propose some simple steps to
accelerate the implementation, in general, and for small σr in particular. We provide some
experimental results to demonstrate the acceleration that is achieved using these
modifications.

Index Terms—Bilateral filter, non-local means, shiftability, constant-time algorithm, Gaussian
kernel, truncation, running maximum, max filter, recursive filter, O(1) complexity.
7. Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking
Abstract: Visual tracking usually requires an object appearance model that is robust to
changing illumination, pose, and other factors encountered in video. Many recent trackers
utilize appearance samples in previous frames to form the bases upon which the object
appearance model is built. This approach has the following limitations: 1) The bases are data
driven, so they can be easily corrupted, and 2) it is difficult to robustly update the bases in
challenging situations. In this paper, we construct an appearance model using the 3D discrete
cosine transform (3D-DCT). The 3D-DCT is based on a set of cosine basis functions which are
determined by the dimensions of the 3D signal and thus independent of the input video data. In
addition, the 3D-DCT can generate a compact energy spectrum whose high-frequency
coefficients are sparse if the appearance samples are similar. By discarding these highfrequency
coefficients,
we
simultaneously
obtain
a compact 3D-DCT-based
object representation and a signal reconstruction-based similarity measure (reflecting the
information loss from signal reconstruction). To efficiently update the object representation,
we propose an incremental 3D-DCTalgorithm which decomposes the 3D-DCT into successive
operations of the 2D discrete cosine transform (2D-DCT) and 1D discrete cosine transform (1DDCT) on the input video data. As a result, the incremental 3D-DCT algorithm only needs to



compute the 2D-DCT for newly added frames as well as the 1D-DCT along the third dimension,
which significantly reduces the computational complexity. Based on this incremental 3DDCT algorithm, we design a discriminative criterion to evaluate the likelihood of a test sample
belonging to the foreground object. We then embed the discriminative criterion into a particle
filtering framework for object state inference over time. Experimental results demonstrate the
effectiveness and robustness of the proposed tracker.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35 , Issue: 4 )

Index Terms—Visual tracking, appearance model, compact representation, discrete cosine
transform (DCT), incremental learning, template matching.
8 . Visual Saliency Based on Scale-Space Analysis in the Frequency Domain
Abstract: We address the issue of visual saliency from three perspectives. First, we
consider saliency detection as a frequency domain analysis problem. Second, we achieve this by
employing the concept of non saliency. Third, we simultaneously consider the detection of
salient regions of different size. The paper proposes a new bottom-up paradigm for
detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of
natural images. We show that the convolution of the image amplitude spectrum with a lowpass Gaussian kernel of an appropriate scale is equivalent to an image
saliency detector. The saliency map
is
obtained
by
reconstructing the 2D
signal
using the original phase and the amplitude spectrum, filtered at a scale selected by
minimizing saliency map
entropy.
A
Hypercomplex
Fourier
Transform
performs the analysis in the frequency domain. Using available databases, we demonstrate
experimentally that the proposed model can predict human fixation data. We also introduce a
new image database and use it to show that the saliency detector can highlight both small and
large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we
show that it is able to predict salient regions on which people focus their attention.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35
, Issue: 4 )

Index Terms—Visual attention, saliency, Hypercomplex Fourier Transform, eye-tracking, scale
space analysis.



9. Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude
Spectrum
Abstract: With the wide applications of saliency information in visual signal processing,
many saliency detection methods have been proposed. However, some key characteristics of
the human visual system (HVS) are still neglected in building these saliency detection models. In this
paper,
we
propose
a
new saliencydetection model based on the human visual sensitivity and the amplitude spectrum
of quaternion Fourier transform (QFT). We use the amplitude spectrum of QFT to represent the color,
intensity, and orientation distributions for image patches. The saliency value for each image patch is
calculated by not only the differences between the QFT amplitude spectrum of this patch and other
patches in the whole image, but also the visual impacts for these differences determined by
the human visual sensitivity.

The

experiment

results

show

that

the

proposed saliency detection model outperforms the state-of-the-art detection models. In addition,
we apply our proposed model in the application of image retargeting and achieve better performance
over the conventional algorithms.
Published in: Multimedia, IEEE Transactions on (Volume:14 , Issue: 1 )

Date of Publication: Feb. 2012
Index Terms—Amplitude spectrum, Fourier transform, human visual sensitivity, saliency
detection, visual attention.
10. Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face
Recognition
Abstract: Local-feature-based face recognition (FR) methods, such as Gabor features encoded
by local binary pattern, could achieve state-of-the-art FR results in large-scale face databases
such as FERET and FRGC. However, the time and space complexity of Gabor transformation are
too high for many practical FR applications. In this paper, we propose a new
and efficient local feature extraction scheme,
namely
monogenic binary coding (MBC),
for face representation
and recognition. Monogenic signal
representation
decomposes an original signal into three complementary components: amplitude, orientation,
and phase. We encode the monogenic variation in each local region and monogenic feature in
each pixel, and then calculate the statistical features (e.g., histogram) of the
extracted local features.
The local statistical features extracted
from
the
complementary monogenic components (i.e., amplitude, orientation, and phase) are then fused
for effective FR. It is shown that the proposed MBC scheme has significantly lower time and
space complexity than the Gabor-transformation-based local feature methods. The extensive



FR experiments on four large-scale databases demonstrated the effectiveness of MBC, whose
performance is competitive with and even better than state-of-the-artlocal-feature-based FR
methods.
Published in: Information Forensics and Security, IEEE Transactions on (Volume:7 , Issue:
6)
Biometrics Compendium, IEEE
Date of Publication: Dec. 2012
Index Terms—Face recognition, Gabor filtering, LBP, monogenic binary coding, monogenic
signal analysis.
11. Demosaicking of Noisy Bayer-Sampled Color Images With Least-Squares Luma-Chroma
Demultiplexing and Noise Level Estimation
Abstract: This paper adapts the least-squares luma-chroma de multiplexing (LSLCD) de
mosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for
the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in
each of the red, green, and blue color channels is then developed. Based on the
estimated noise parameters,
one of a
finite
set of
configurations
adapted
to
a
particular level of noise is selected to de mosaic the noisy data. The noise-adaptive de
mosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results
demonstrate state-of-the-art performance over a wide range of noise levels, with low computational
complexity. Many results with several algorithms, noise levels, and images are presented on our
companion web site along with software to allow reproduction of our results.

Date of Publication: Jan. 2013
Index Terms—color filter array, Bayer sampling, demosaicking, noise estimation, noise
reduction, noise model
12. Fuzzy Clustering with Modified MRF Energy Function for Change Detection in Synthetic
Aperture Radar Images
Abstract:
In this
paper,
we
put
forward
a
novel
approach for change detection in synthetic aperture radar (SAR)images.
The
approach
classifies changed and unchanged regions by fuzzy c-means (FCM) clustering with a novel
Markov random field (MRF) energy function. In order to reduce the effect of speckle noise, a



novel form of MRF energy function with an additional term is established to modify the
membership of each pixel. And the degree of modification is determined by the relationship of
the neighborhood pixels. The specific form of the additional term is contingent on different
situations, and is established ultimately by utilizing the least square method. Our contributions
lie in two aspects. Firstly, in order to reduce the effect of speckle noise, the proposed approach
focuses on modifying the membership instead of modifying the objective function. It is
computational simple in all the steps involved. Its objective function can just return to the
original form of FCM, which leads to its less time consumption than that of some recently
improved FCM algorithms obviously. Secondly, the proposed approach modifies the
membership of each pixel according to a novel form of MRF energy function through which the
neighbors of each pixel as well as their relationship are concerned with. Theoretical analysis
and experimental results on real SAR datasets show that the proposed approach can detect the
realchanges as well as mitigate the effect of speckle noises. Theoretical analysis and
experiments also demonstrate its low time complexity.
Published in: Fuzzy Systems, IEEE Transactions on (Volume: PP , Issue: 99 )

Date of Publication : 26 February 2013
Index Terms— Fuzzy clustering, image change detection, synthetic aperture radar, Markov
random field.
13. Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image
Segmentation
Abstract:
In
this
paper,
we
present
an
improved fuzzy C-means (FCM)
algorithm for image segmentation by
introducing
a
tradeoff
weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the
space distance of all neighboring pixels and their gray-level difference simultaneously. By using
this factor, the new algorithm can accurately estimate the damping extent of neighboring
pixels. In order to further enhance its robustness to noise and outliers, we introduce
a kernel distance measure to its objective function. The new algorithm adaptively determines
the kernel parameter by using a fast bandwidth selection rule based on the distance variance of
all
data
points
in
the
collection.
Furthermore,
the
tradeoff
weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental
results
on
synthetic and real images show
that
the
new
algorithm
is
effective andefficient, and is relatively independent of this type of noise.



Date of Publication: Feb. 2013
Index Terms—Fuzzy clustering, gray-level constraint, image segmentation, kernel metric,
spatial constraint.
14. Re initialization-Free Level Set Evolution via Reaction Diffusion
Abstract: This paper presents a novel reaction-diffusion (RD) method for implicit active
contours
that
is
completely free of
the
costly
re
initialization procedure
in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in an RD-LSE
equation, from which a piecewise constant solution can be derived. In order to obtain a stable
numerical solution from the RD-based LSE, we propose a two-step splitting method to
iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then solve
the diffusion equation. The second step regularizes the level set function obtained in the first
step to ensure stability, and thus the complex and costly re initialization procedure is
completely eliminated from LSE. By successfully applying diffusion to LSE, the RD-LSE model is
stable by means of the simple finite difference method, which is very easy to implement. The
proposed RD method can be generalized to solve the LSE for both variational level set method
and partial differential equation-based level set method. The RD-LSE method shows very good
performance on boundary anti leakage. The extensive and promising experimental results on
synthetic and real images validate the effectiveness of the proposed RD-LSE approach.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 1
Index Terms—Active contours, image segmentation, level set, partial differential equation
(PDE), reaction-diffusion, variational method.
15. Online Object Tracking With Sparse Prototypes
Abstract: Online object tracking is a challenging problem as it entails learning an effective
model to account for appearance change caused by intrinsic and extrinsic factors. In this paper,
we propose a novel online object tracking algorithm with sparse prototypes, which exploits
both classic principal component analysis (PCA) algorithms with recent sparse representation
schemes for learning effective appearance models. We introduce l1 regularization into the PCA
reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that
account explicitly for data and noise. For tracking, objects are represented by
the sparse prototypes learned online with update. In order to reduce tracking drift, we present
a method that takes occlusion and motion blur into account rather than simply includes image



observations for model update. Both qualitative and quantitative evaluations on challenging
image sequences demonstrate that the proposed tracking algorithm performs favorably against
several state-of-the-art methods.
Index Terms—Appearance model, _1 minimization, object tracking, principal component
analysis (PCA), sparse prototypes
16. Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption
Abstract:
Recently,
more
and
more
attention
is
paid
to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property
that the original cover can be losslessly recovered after embedded data is extracted while
protecting the image content's confidentiality. All previous methods embed data by reversibly
vacating room from the encrypted images, which may be subject to some errors on data
extraction
and/or image restoration. In this
paper,
we
propose
a
novel
method by reserving room before encryption with a traditional RDH algorithm, and thus it is
easy for the data hider to reversibly embed data in the encrypted image. The proposed method
can achieve real reversibility, that is, data extraction and image recovery are free of any error.
Experiments show that this novel method can embed more than 10 times as large payloads for
the same image quality as the previous methods, such as for PSNR=40 dB.
Published in: Information Forensics and Security, IEEE Transactions on (Volume:8 , Issue:
3)
Date of Publication: March 2013
Index Terms— Reversible data hiding, image encryption, privacy protection, histogram shift.
17. Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
Abstract: Recovering a large matrix from a small subset of its entries is a challenging problem
arising in many real applications, such as image inpainting and recommender systems. Many
existing approaches formulate this problem as a general low-rank matrix approximation
problem. Since the rank operator is non convex and discontinuous, most of the recent
theoretical studies use the nuclear norm as a convex relaxation. One major limitation of the
existing approaches based on nuclear norm minimization is that all the singular values are



simultaneously minimized, and thus the rank may not be well approximated in practice. In this
paper, we propose to achieve a better approximation to the rank
of matrix by truncatednuclear norm, which is given by the nuclear norm subtracted by the sum
of the largest few singular values. In addition, we develop a novel matrix completion algorithm
by minimizing the Truncated Nuclear Norm. We further develop three efficient iterative
procedures, TNNR-ADMM, TNNR-APGL, and TNNR-ADMMAP, to solve the optimization
problem. TNNR-ADMM utilizes the alternating direction method of multipliers (ADMM), while
TNNR-AGPL applies the accelerated proximal gradient line search method (APGL) for the final
optimization. For TNNR-ADMMAP, we make use of an adaptive penalty according to a novel
update rule for ADMM to achieve a faster convergence rate. Our empirical study shows
encouraging results of the proposed algorithms in comparison to the state-of-theart matrix completion algorithms on both synthetic and real visual datasets.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35
, Issue: 9 )
Date of Publication: Sept. 2013
Index Terms—Matrix completion, nuclear norm minimization, alternating direction method
of multipliers, accelerated proximal gradient Method
18. Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts
Abstract: In this paper, a forensic tool able to discriminate between original and forged regions
in an image captured by a digital camera is presented. We make the assumption that
the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to
the de mosaicking algorithm. The proposed method is based on a new feature measuring the
presence of de mosaicking artifacts at a local level, and on a new statistical model allowing to
derive the tampering probability of each 2 × 2image block without requiring to know a priori
the position of the forged region. Experimental results on different cameras equipped with
different de mosaicking algorithms demonstrate both the validity of the theoretical model and
the effectiveness of our scheme.
Published in: Information Forensics and Security, IEEE Transactions on (Volume:7 , Issue: 5 )
Date of Publication: Oct. 2012
Index Terms—Image forensics, CFA artifacts, digital camera demosaicing, tampering
probability map, forgery localization.


IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2013-2014 FOR M.Tech/ B.E/B.Tech

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IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2013-2014 FOR M.Tech/ B.E/B.Tech