Selection of initial seeds greatly affects the quality of the clusters and in
k-means type algorithms. Most of the seed selection methods result different
results in different independent runs. We propose a single, optimal, outlier
insensitive seed selection algorithm for k-means type algorithms as extension
to k-means++. The experimental results on synthetic, real and on microarray
data sets demonstrated that effectiveness of the new algorithm in producing the
clustering results

A framework for adaptive and non-adaptive statistical compressive sensing is
developed, where a statistical model replaces the standard sparsity model of
classical compressive sensing. We propose within this framework optimal
task-specific sensing protocols specifically and jointly designed for
classification and reconstruction. A two-step adaptive sensing paradigm is
developed, where online sensing is applied to detect the signal class in the
first step, followed by a reconstruction step adapted to the detected class and
the observed samples.

In this study we investigate the fast image filtering algorithm based on
Intro sort algorithm and fast noise reduction of infrared images. Main feature
of the proposed approach is that no prior knowledge of noise required. It is
developed based on Stefan- Boltzmann law and the Fourier law. We also
investigate the fast noise reduction approach that has advantage of less
computation load. In addition, it can retain edges, details, text information
even if the size of the window increases.

We present an algorithm using transformation groups and their irreducible
representations to generate an orthogonal basis for a signal in the vector
space of the signal. It is shown that multiresolution analysis can be done with
amplitudes using a transformation group. G-lets is thus not a single transform,
but a group of linear transformations related by group theory. The algorithm
also specifies that a multiresolution and multiscale analysis for each
resolution is possible in terms of frequencies.

This paper presents a novel reaction-diffusion (RD) method for implicit
active contours, which is completely free of the costly re-initialization
procedure in level set evolution (LSE). A diffusion term is introduced into
LSE, resulting in a RD-LSE equation, to which a piecewise constant solution can
be derived. In order to have a stable numerical solution of the RD based LSE,
we propose a two-step splitting method (TSSM) to iteratively solve the RD-LSE
equation: first iterating the LSE equation, and then solving the diffusion
equation.

A scattering transform defines a signal representation which is invariant to
translations and Lipschitz continuous relatively to deformations. It is
implemented with a non-linear convolution network that iterates over wavelet
and modulus operators. Lipschitz continuity locally linearizes deformations.
Complex classes of signals and textures can be modeled with low-dimensional
affine spaces, computed with a PCA in the scattering domain. Classification is
performed with a penalized model selection.

Biometric technologies are the foundation of personal identification systems.
It provides an identification based on a unique feature possessed by the
individual. This paper provides a walkthrough for image acquisition,
segmentation, normalization, feature extraction and matching based on the Human
Iris imaging. A Canny Edge Detection scheme and a Circular Hough Transform, is
used to detect the iris boundaries in the eye's digital image. The extracted
IRIS region was normalized by using Image Registration technique.

3D motion tracking is a critical task in many computer vision applications.
Existing 3D motion tracking techniques require either a great amount of
knowledge on the target object or specific hardware. These requirements
discourage the wide spread of commercial applications based on 3D motion
tracking. 3D motion tracking systems that require no knowledge on the target
object and run on a single low-budget camera require estimations of the object
projection features (namely, area and position).

The recent technological progress in acquisition, modeling and processing of
3D data leads to the proliferation of a large number of 3D objects databases.
Consequently, the techniques used for content based 3D retrieval has become
necessary. In this paper, we introduce a new method for 3D objects recognition
and retrieval by using a set of binary images CLI (Characteristic level
images). We propose a 3D indexing and search approach based on the similarity
between characteristic level images using Hu moments for it indexing.

This report concerns the use of techniques for sparse signal representation
and sparse error correction for automatic face recognition. Much of the recent
interest in these techniques comes from the paper "Robust Face Recognition via
Sparse Representation" by Wright et al. (2009), which showed how, under certain
technical conditions, one could cast the face recognition problem as one of
seeking a sparse representation of a given input face image in terms of a
"dictionary" of training images and images of individual pixels.

Recognition systems are commonly designed to authenticate users at the access
control levels of a system. A number of voice recognition methods have been
developed using a pitch estimation process which are very vulnerable in low
Signal to Noise Ratio (SNR) environments thus, these programs fail to provide
the desired level of accuracy and robustness. Also, most text independent
speaker recognition programs are incapable of coping with unauthorized attempts
to gain access by tampering with the samples or reference database.

We revisit the additive model learning literature and adapt a penalized
spline formulation due to Eilers and Marx, to train additive classifiers
efficiently. We also propose two new embeddings based two classes of orthogonal
basis with orthogonal derivatives, which can also be used to efficiently learn
additive classifiers. This paper follows the popular theme in the current
literature where kernel SVMs are learned much more efficiently using a
approximate embedding and linear machine.

A fundamental operation in many vision tasks, including motion understanding,
stereopsis, visual odometry, or invariant recognition, is establishing
correspondences between images or between images and data from other
modalities. We present an analysis of the role that multiplicative interactions
play in learning such correspondences, and we show how learning and inferring
relationships between images can be viewed as detecting rotations in the
eigenspaces shared among a set of orthogonal matrices.

Most image labeling problems such as segmentation and image reconstruction
are fundamentally ill-posed and suffer from ambiguities and noise. Higher order
image priors encode high level structural dependencies between pixels and are
key to overcoming these problems. However, these priors in general lead to
computationally intractable models. This paper addresses the problem of
discovering compact representations of higher order priors which allow
efficient inference.

Object parsing and segmentation from point clouds are challenging tasks
because the relevant data is available only as thin structures along object
boundaries or other object features and is corrupted by large amounts of noise.
One way to handle this kind of data is by employing shape models that can
accurately follow the object boundaries.

We introduce a novel tracking technique which uses dynamic confidence-based
fusion of two different information sources for robust and efficient tracking
of visual objects. Mean-shift tracking is a popular and well known method used
in object tracking problems. Originally, the algorithm uses a similarity
measure which is optimized by shifting a search area to the center of a
generated weight image to track objects. Recent improvements on the original
mean-shift algorithm involves using a classifier that differentiates the object
from its surroundings.

We study linear models under heavy-tailed priors from a probabilistic
viewpoint. Instead of computing a single sparse most probable (MAP) solution as
in standard compressed sensing, the focus in the Bayesian framework shifts
towards capturing the full posterior distribution on the latent variables,
which allows quantifying the estimation uncertainty and learning model
parameters using maximum likelihood. The exact posterior distribution under the
sparse linear model is intractable and we concentrate on a number of
alternative variational Bayesian techniques to approximate it.

A new approach in iris recognition based on Circular Fuzzy Iris Segmentation
(CFIS) and Gabor Analytic Iris Texture Binary Encoder (GAITBE) is proposed and
tested here. CFIS procedure is designed to guarantee that similar iris segments
will be obtained for similar eye images, despite the fact that the degree of
occlusion may vary from one image to another. Its result is a circular iris
ring (concentric with the pupil) which approximates the actual iris. GAITBE
proves better encoding of statistical independence between the iris codes
extracted from different irides using Hilbert Transform.

We analyze and improve low rank representation (LRR), the state-of-the-art
algorithm for subspace segmentation of data. We prove that for the noiseless
case, the optimization model of LRR has a unique solution, which is the shape
interaction matrix (SIM) of the data matrix. So in essence LRR is equivalent to
factorization methods. We also prove that the minimum value of the optimization
model of LRR is equal to the rank of the data matrix. For the noisy case, we
show that LRR can be approximated as a factorization method that combines noise
removal by column sparse robust PCA.

We propose a traffic congestion estimation system based on unsupervised
on-line learning algorithm. The system does not rely on background extraction
or motion detection. It extracts local features inside detection regions of
variable size which are drawn on lanes in advance. The extracted features are
then clustered into two classes using K-means and Gaussian Mixture Models(GMM).
A Bayes classifier is used to detect vehicles according to the previous cluster
information which keeps updated whenever system is running by on-line EM
algorithm.

An algorithm for pose and motion estimation using corresponding features in
images and a digital terrain map is proposed. Using a Digital Terrain (or
Digital Elevation) Map (DTM/DEM) as a global reference enables recovering the
absolute position and orientation of the camera. In order to do this, the DTM
is used to formulate a constraint between corresponding features in two
consecutive frames. The utilization of data is shown to improve the robustness
and accuracy of the inertial navigation algorithm.

This paper demonstrates two different fusion techniques at two different
levels of a human face recognition process. The first one is called data fusion
at lower level and the second one is the decision fusion towards the end of the
recognition process. At first a data fusion is applied on visual and
corresponding thermal images to generate fused image. Data fusion is
implemented in the wavelet domain after decomposing the images through
Daubechies wavelet coefficients (db2). During the data fusion maximum of
approximate and other three details coefficients are merged together.

This papers introduces a new family of iris encoders which use 2-dimensional
Haar Wavelet Transform for noise attenuation, and Hilbert Transform to encode
the iris texture. In order to prove the usefulness of the newly proposed iris
encoding approach, the recognition results obtained by using these new encoders
are compared to those obtained using the classical Log- Gabor iris encoder.
Twelve tests involving single/multienrollment and conducted on Bath Iris Image
Database are presented here.

It is well-known that spatial averaging can be realized (in space or
frequency domain) using algorithms whose complexity does not depend on the size
or shape of the filter. These fast algorithms are generally referred to as
constant-time or O(1) algorithms in the image processing literature. Along with
the spatial filter, the edge-preserving bilateral filter [bilateralFilter]
involves an additional range kernel. This is used to restrict the averaging to
those neighborhood pixels whose intensity are similar or close to that of the
pixel of interest.

Structural pattern recognition describes and classifies data based on the
relationships of features and parts. Topological invariants, like the Euler
number, characterize the structure of objects of any dimension. Cohomology can
provide more refined algebraic invariants to a topological space than does
homology. It assigns `quantities' to the chains used in homology to
characterize holes of any dimension. Graph pyramids can be used to describe
subdivisions of the same object at multiple levels of detail.

Statistical dependencies among wavelet coefficients are commonly represented
by graphical models such as hidden Markov trees(HMTs). However, in linear
inverse problems such as deconvolution, tomography, and compressed sensing, the
presence of a sensing or observation matrix produces a linear mixing of the
simple Markovian dependency structure. This leads to reconstruction problems
that are non-convex optimizations. Past work has dealt with this issue by
resorting to greedy or suboptimal iterative reconstruction methods.

Template matching is one of the most prevalent pattern recognition methods
worldwide. It has found uses in most visual concept detection fields. In this
work, we investigate methods for improving template matching by adjusting the
weights of different regions of the template. We compare several weight maps
and test the methods using the FERET face test set in the context of human eye
detection.

A new method is proposed to get image features' geometric information. Using
Gaussian as an input signal, a theoretical optimal solution to calculate
feature's affine shape is proposed. Based on analytic result of a feature
model, the method is different from conventional iterative approaches. From the
model, feature's parameters such as position, orientation, background
luminance, contrast, area and aspect ratio can be extracted. Tested with
synthesized and benchmark data, the method achieves or outperforms existing
approaches in term of accuracy, speed and stability.

Discontinuity preserving smoothing is a fundamentally important procedure
that is useful in a wide variety of image processing contexts. It is directly
useful for noise reduction, and frequently used as an intermediate step in
higher level algorithms. For example, it can be particularly useful in edge
detection and segmentation. Three well known algorithms for discontinuity
preserving smoothing are nonlinear anisotropic diffusion, bilateral filtering,
and mean shift filtering.

The most common primary brain tumors are gliomas, evolving from the cerebral
supportive cells. For clinical follow-up, the evaluation of the preoperative
tumor volume is essential. Volumetric assessment of tumor volume with manual
segmentation of its outlines is a time-consuming process that can be overcome
with the help of computerized segmentation methods. In this contribution, two
methods for World Health Organization (WHO) grade IV glioma segmentation in the
human brain are compared using magnetic resonance imaging (MRI) patient data
from the clinical routine.

Diffusion Tensor Imaging (DTI) provides the possibility of estimating the
location and course of eloquent structures in the human brain. Knowledge about
this is of high importance for preoperative planning of neurosurgical
interventions and for intraoperative guidance by neuronavigation in order to
minimize postoperative neurological deficits. Therefore, the segmentation of
these structures as closed, three-dimensional object is necessary.

A novel framework of compressed sensing, namely statistical compressed
sensing (SCS), that aims at efficiently sampling a collection of signals that
follow a statistical distribution, and achieving accurate reconstruction on
average, is introduced.

In this paper, we explore the use of the diffusion geometry framework for the
fusion of geometric and photometric information in local and global shape
descriptors. Our construction is based on the definition of a diffusion process
on the shape manifold embedded into a high-dimensional space where the
embedding coordinates represent the photometric information. Experimental
results show that such data fusion is useful in coping with different
challenges of shape analysis where pure geometric and pure photometric methods
fail.

The past decade has seen the growing popularity of Bag of Features (BoF)
approaches to many computer vision tasks, including image classification, video
search, robot localization, and texture recognition. Part of the appeal is
simplicity. BoF methods are based on orderless collections of quantized local
image descriptors; they discard spatial information and are therefore
conceptually and computationally simpler than many alternative methods.

English Character Recognition (CR) has been extensively studied in the last
half century and progressed to a level, sufficient to produce technology driven
applications. But same is not the case for Indian languages which are
complicated in terms of structure and computations. Rapidly growing
computational power may enable the implementation of Indic CR methodologies.
Digital document processing is gaining popularity for application to office and
library automation, bank and postal services, publishing houses and
communication technology.

This chapter presents a framework for detecting fake regions by using various
methods including watermarking technique and blind approaches. In particular,
we describe current categories on blind approaches which can be divided into
five: pixel-based techniques, format-based techniques, camera-based techniques,
physically-based techniques and geometric-based techniques. Then we take a
second look on the geometric-based techniques and further categorize them in
detail. In the following section, the state-of-the-art methods involved in the
geometric technique are elaborated.

Rank-based analysis is a basic approach for many real world applications.
Recently, with the developments of compressive sensing, an interesting problem
was proposed to recover a lowrank matrix from sparse noise. In this paper, we
will address this problem and propose a low rank matrix recovery algorithm
based on sparsity tacking. The core of the proposed Sparsity Tracking
Recovery(STR) is a heuristic kernel, which is introduced to penalize the noise
distribution. With the heuristic method, the sparse entries in the noise matrix
can be accurately tracked and discouraged to be zero.

The goal of this paper is the development of a novel approach for the problem
of Noise Removal, based on the theory of Reproducing Kernels Hilbert Spaces
(RKHS). The problem is cast as an optimization task in a RKHS, by taking
advantage of the celebrated semiparametric Representer Theorem. Examples verify
that in the presence of gaussian noise the proposed method performs relatively
well compared to wavelet based technics and outperforms them significantly in
the presence of impulse or mixed noise.

In this paper a fuzzy clustering model for fuzzy data with outliers is
proposed. The model is based on Wasserstein distance between interval valued
data which is generalized to fuzzy data. In addition, Keller's approach is used
to identify outliers and reduce their influences. We have also defined a
transformation to change our distance to the Euclidean distance. With the help
of this approach, the problem of fuzzy clustering of fuzzy data is reduced to
fuzzy clustering of crisp data.

Classification is one of the most important tasks of machine learning.
Although the most well studied model is the two-class problem, in many
scenarios there is the opportunity to label critical items for manual revision,
instead of trying to automatically classify every item. In this paper we adapt
a paradigm initially proposed for the classification of ordinal data to address
the classification problem with reject option.

The problem of identifying the 3D pose of a known object from a given 2D
image has important applications in Computer Vision ranging from robotic vision
to image analysis. Our proposed method of registering a 3D model of a known
object on a given 2D photo of the object has numerous advantages over existing
methods: It does neither require prior training nor learning, nor knowledge of
the camera parameters, nor explicit point correspondences or matching features
between image and model.

Color quantization is an important operation with numerous applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, a fast color quantization
method based on k-means is presented.

Adaptive sparse coding methods learn a possibly overcomplete set of basis
functions, such that natural image patches can be reconstructed by linearly
combining a small subset of these bases. The applicability of these methods to
visual object recognition tasks has been limited because of the prohibitive
cost of the optimization algorithms required to compute the sparse
representation. In this work we propose a simple and efficient algorithm to
learn basis functions.

An inverse iterative algorithm for microwave imaging based on moment method
solution is presented here. The iterative scheme has been developed on
constrained optimization technique and is certain to converge. Different mesh
size for the model has been used here to overcome the Inverse Crime. The
synthetic data at the receivers is contaminated with different percentage of
noise. The ill-posedness of the problem is solved by Levenberg-Marquardt
method. The algorithm is applied to synthetic data and the reconstructed image
is then further enhanced through the Image enhancement technique

There is an abundant literature on face detection due to its important role
in many vision applications. Since Viola and Jones proposed the first real-time
AdaBoost based face detector, Haar-like features have been adopted as the
method of choice for frontal face detection. In this work, we show that simple
features other than Haar-like features can also be applied for training an
effective face detector.

This paper proposes a framework for modeling instantaneous changes natural
scenes in real time using Lagrangian Particle Framework and a fluid-particle
grid approach. This research can be divided into 3 distinct sections: the first
one discusses a multi-camera rig that can measure ego-motion accurately up to
88%, how this device becomes the backbone of our framework, and some
improvements devised to optimize a know framework for depth maps and 3d
structure estimation from a single still image called make3d.

This paper deals with an improvement of vertex based nonlinear diffusion for
mesh denoising. This method directly filters the position of the vertices using
Laplace, reduced centered Gaussian and Rayleigh probability density functions
as diffusivities. The use of these PDFs improves the performance of a
vertex-based diffusion method which are adapted to the underlying mesh
structure. We also compare the proposed method to other mesh denoising methods
such as Laplacian flow, mean, median, min and the adaptive MMSE filtering. To
evaluate these methods of filtering, we use two error metrics.

Image hashing is the process of associating a short vector of bits to an
image. The resulting summaries are useful in many applications including image
indexing, image authentication and pattern recognition. These hashes need to be
invariant under transformations of the image that result in similar visual
content, but should drastically differ for conceptually distinct contents. This
paper proposes an image hashing method that is invariant under rotation,
scaling and translation of the image.

In this paper, Deterministic Cellular Automata (DCA) based video shot
classification and retrieval is proposed. The deterministic 2D Cellular
automata model captures the human facial expressions, both spontaneous and
posed. The determinism stems from the fact that the facial muscle actions are
standardized by the encodings of Facial Action Coding System (FACS) and Action
Units (AUs). Based on these encodings, we generate the set of evolutionary
update rules of the DCA for each facial expression.

Human ovarian reserve is defined by the population of nongrowing follicles
(NGFs) in the ovary. Direct estimation of ovarian reserve involves the
identification of NGFs in prepared ovarian tissue. Previous studies involving
human tissue have used hematoxylin and eosin (HE) stain, with NGF populations
estimated by human examination either of tissue under a microscope, or of
images taken of this tissue. In this study we replaced HE with proliferating
cell nuclear antigen (PCNA), and automated the identification and enumeration
of NGFs that appear in the resulting microscopic images.

The importance of manifolds and Riemannian geometry in mathematics is
spreading to applied fields in which the need to model non-linear structure has
spurred wide-spread interest in geometry. The transfer of interest has created
demand for methods for computing classical constructs of geometry on manifolds
occurring in practical applications. This paper develops initial value problems
for the computation of the differential of the exponential map and Jacobi
fields on parametrically and implicitly represented manifolds.

In this paper we present an efficient computer aided mass classification
method in digitized mammograms using Artificial Neural Network (ANN), which
performs benign-malignant classification on region of interest (ROI) that
contains mass. One of the major mammographic characteristics for mass
classification is texture. ANN exploits this important factor to classify the
mass into benign or malignant. The statistical textural features used in
characterizing the masses are mean, standard deviation, entropy, skewness,
kurtosis and uniformity.

VERSA provides a general-purpose framework for defining and recognizing
events in live or recorded surveillance video streams. The approach for event
recognition in VERSA is using a declarative logic language to define the
spatial and temporal relationships that characterize a given event or activity.
Doing so requires the definition of certain fundamental spatial and temporal
relationships and a high-level syntax for specifying frame templates and query
parameters.

We propose a new approach for constructing a 3D representation from a 2D
wireframe drawing. A drawing is simply a parallel projection of a 3D object
onto a 2D surface; humans are able to recreate mental 3D models from 2D
representations very easily, yet the process is very difficult to emulate
computationally. We hypothesize that our ability to perform this construction
relies on the angles in the 2D scene, among other geometric properties. Being
able to reproduce this reconstruction process automatically would allow for
efficient and robust 3D sketch interfaces.

Nonlinear bilateral filters (BF) deliver a fine blend of computational
simplicity and blur-free denoising. However, little is known about their
nature, noise-suppressing properties, and optimal choices of filter parameters.
Our study is meant to fill this gap-explaining the underlying mechanism of
bilateral filtering and providing the methodology for optimal filter selection.
Practical application to CT image denoising is discussed to illustrate our
results.

In this paper we present a simple novel approach to tackle the challenges of
scaling and rotation of face images in face recognition. The proposed approach
registers the training and testing visual face images by log-polar
transformation, which is capable to handle complicacies introduced by scaling
and rotation. Log-polar images are projected into eigenspace and finally
classified using an improved multi-layer perceptron. In the experiments we have
used ORL face database and Object Tracking and Classification Beyond Visible
Spectrum (OTCBVS) database for visual face images.

Here an efficient fusion technique for automatic face recognition has been
presented. Fusion of visual and thermal images has been done to take the
advantages of thermal images as well as visual images. By employing fusion a
new image can be obtained, which provides the most detailed, reliable, and
discriminating information. In this method fused images are generated using
visual and thermal face images in the first step. In the second step, fused
images are projected into eigenspace and finally classified using a radial
basis function neural network.

Artificial neural networks have already shown their success in face
recognition and similar complex pattern recognition tasks. However, a major
disadvantage of the technique is that it is extremely slow during training for
larger classes and hence not suitable for real-time complex problems such as
pattern recognition. This is an attempt to develop a parallel framework for the
training algorithm of a perceptron. In this paper, two general architectures
for a Multilayer Perceptron (MLP) have been demonstrated.

In this paper fusion of visual and thermal images in wavelet transformed
domain has been presented. Here, Daubechies wavelet transform, called as D2,
coefficients from visual and corresponding coefficients computed in the same
manner from thermal images are combined to get fused coefficients. After
decomposition up to fifth level (Level 5) fusion of coefficients is done.
Inverse Daubechies wavelet transform of those coefficients gives us fused face
images.

This paper aims at determining the characteristics of a face image by
extracting its components. The FASY (FAce SYnthesis) System is a Face Database
Retrieval and new Face generation System that is under development. One of its
main features is the generation of the requested face when it is not found in
the existing database, which allows a continuous growing of the database also.
To generate the new face image, we need to store the face components in the
database. So we have designed a new technique to extract the face components by
a sophisticated method.

This paper presents a novel type-2 Fuzzy logic System to define the Shape of
a facial component with the crisp output. This work is the part of our main
research effort to design a system (called FASY) which offers a novel face
construction approach based on the textual description and also extracts and
analyzes the facial components from a face image by an efficient technique. The
Fuzzy model, designed in this paper, takes crisp value of width and height of a
facial component and produces the crisp value of Shape for different facial
components.

A novel, generic scheme for off-line handwritten English alphabets character
images is proposed. The advantage of the technique is that it can be applied in
a generic manner to different applications and is expected to perform better in
uncertain and noisy environments. The recognition scheme is using a multilayer
perceptron(MLP) neural networks. The system was trained and tested on a
database of 300 samples of handwritten characters. For improved generalization
and to avoid overtraining, the whole available dataset has been divided into
two subsets: training set and test set.

The paper presents a two stage classification approach for handwritten
devanagari characters The first stage is using structural properties like
shirorekha, spine in character and second stage exploits some intersection
features of characters which are fed to a feedforward neural network. Simple
histogram based method does not work for finding shirorekha, vertical bar
(Spine) in handwritten devnagari characters.

In this paper a scheme for offline Handwritten Devnagari Character
Recognition is proposed, which uses different feature extraction methodologies
and recognition algorithms. The proposed system assumes no constraints in
writing style or size. First the character is preprocessed and features namely
: Chain code histogram and moment invariant features are extracted and fed to
Multilayer Perceptrons as a preliminary recognition step. Finally the results
of both MLP's are combined using weighted majority scheme.

Classification methods based on learning from examples have been widely
applied to character recognition from the 1990s and have brought forth
significant improvements of recognition accuracies. This class of methods
includes statistical methods, artificial neural networks, support vector
machines (SVM), multiple classifier combination, etc.

Minimization of boundary curvature is a classic regularization technique for
image segmentation in the presence of noisy image data. Techniques for
minimizing curvature have historically been derived from descent methods which
could be trapped in a local minimum and therefore required a good
initialization. Recently, combinatorial optimization techniques have been
applied to the optimization of curvature which provide a solution that achieves
nearly a global optimum. However, when applied to image segmentation these
methods required a meaningful data term.

A general framework for solving image inverse problems is introduced in this
paper. The approach is based on Gaussian mixture models, estimated via a
computationally efficient MAP-EM algorithm. A dual mathematical interpretation
of the proposed framework with structured sparse estimation is described, which
shows that the resulting piecewise linear estimate stabilizes the estimation
when compared to traditional sparse inverse problem techniques. This
interpretation also suggests an effective dictionary motivated initialization
for the MAP-EM algorithm.

The physiological and behavioral trait is employed to develop biometric
authentication systems. The proposed work deals with the authentication of iris
and signature based on minimum variance criteria. The iris patterns are
preprocessed based on area of the connected components. The segmented image
used for authentication consists of the region with large variations in the
gray level values. The image region is split into quadtree components. The
components with minimum variance are determined from the training samples. Hu
moments are applied on the components.

Maps are used to describe far-off places . It is an aid for navigation and
military strategies. Mapping of the lands are important and the mapping work is
based on (i). Natural resource management & development (ii). Information
technology ,(iii). Environmental development ,(iv). Facility management and
(v). e-governance.

The use of OCR in postal services is not yet universal and there are still
many countries that process mail sorting manually. Automated Arabic/Indian
numeral Optical Character Recognition (OCR) systems for Postal services are
being used in some countries, but still there are errors during the mail
sorting process, thus causing a reduction in efficiency. The need to
investigate fast and efficient recognition algorithms/systems is important so
as to correctly read the postal codes from mail addresses and to eliminate any
errors during the mail sorting stage.

This paper presents an efficient human recognition system based on vein
pattern from the palma dorsa. A new absorption based technique has been
proposed to collect good quality images with the help of a low cost camera and
light source. The system automatically detects the region of interest from the
image and does the necessary preprocessing to extract features. A Euclidean
Distance based matching technique has been used for making the decision.

A logical approach to object recognition on image is proposed. The main idea
of the approach is to perform the object recognition as a logical inference on
a set of rules describing an object shape.

Capacity, Robustness, & Perceptual quality of watermark data are very
important issues to be considered. A lot of research is going on to increase
these parameters for watermarking of the digital images, as there is always a
tradeoff among them. . In this paper an efficient watermarking algorithm to
improve payload and robustness without affecting perceptual quality of image
data based on DWT is discussed.

In this paper we present a Bayesian image zooming/super-resolution algorithm
based on a patch based representation. We work on a patch based model with
overlap and employ a Locally Linear Embedding (LLE) based approach as our data
fidelity term in the Bayesian inference. The image prior imposes continuity
constraints across the overlapping patches. We apply an error back-projection
technique, with an approximate cross bilateral filter. The problem of nearest
neighbor search is handled by a variant of the locality sensitive hashing (LSH)
scheme.

In this paper, a novel representation for facial expressions in
two-dimensional image sequences is presented. We apply a variation of
two-dimensional heteroscedastic linear discriminant analysis (2DHLDA)
algorithm, as an efficient dimensionality reduction technique, to Gabor
representation of the input sequence. 2DHLDA is an extension of the
two-dimensional linear discriminant analysis (2DLDA) approach and removes the
equal within-class covariance. By applying 2DHLDA in two directions, we
eliminate the correlations between both image columns and image rows.

Medical image segmentation demands an efficient and robust segmentation
algorithm against noise. The conventional fuzzy c-means algorithm is an
efficient clustering algorithm that is used in medical image segmentation. But
FCM is highly vulnerable to noise since it uses only intensity values for
clustering the images. This paper aims to develop a novel and efficient fuzzy
spatial c-means clustering algorithm which is robust to noise. The proposed
clustering algorithm uses fuzzy spatial information to calculate membership
value. The input image is clustered using proposed ISFCM algorithm.

This paper presents a feature level fusion approach which uses the improved
K-medoids clustering algorithm and isomorphic graph for face and palmprint
biometrics. Partitioning around medoids (PAM) algorithm is used to partition
the set of n invariant feature points of the face and palmprint images into k
clusters. By partitioning the face and palmprint images with scale invariant
features SIFT points, a number of clusters is formed on both the images. Then
on each cluster, an isomorphic graph is drawn.

Restoration of digital images from their degraded measurements has always
been a problem of great theoretical and practical importance in numerous
applications of imaging sciences. A specific solution to the problem of image
restoration is generally determined by the nature of degradation phenomenon as
well as by the statistical properties of measurement noises. The present study
is concerned with the case in which the images of interest are corrupted by
convolutional blurs and Poisson noises.

In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is
proposed which is an extension to the original 2DPCA. We state that the
covariance matrix of 2DPCA is equivalent to the average of the main diagonal of
the covariance matrix of PCA. This implies that 2DPCA eliminates some
covariance information that can be useful for recognition. E2DPCA instead of
just using the main diagonal considers a radius of r diagonals around it and
expands the averaging so as to include the covariance information within those
diagonals. The parameter r unifies PCA and 2DPCA.

Facial Action Coding System consists of 44 action units (AUs) and more than
7000 combinations. Hidden Markov models (HMMs) classifier has been used
successfully to recognize facial action units (AUs) and expressions due to its
ability to deal with AU dynamics. However, a separate HMM is necessary for each
single AU and each AU combination. Since combinations of AU numbering in
thousands, a more efficient method will be needed. In this paper an accurate
real-time sequence-based system for representation and recognition of facial
AUs is presented.

Recent studies in the field of human vision science suggest that the human
responses to the stimuli on a visual display are non-deterministic. People may
attend to different locations on the same visual input at the same time. Based
on this knowledge, we propose a new stochastic model of visual attention by
introducing a dynamic Bayesian network to predict the likelihood of where
humans typically focus on a video scene. The proposed model is composed of a
dynamic Bayesian network with 4 layers.

Integrated Traffic Management Systems (ITMS) are now implemented in different
cities in India to primarily address the concerns of road-safety and security.
An automated Red Light Violation Detection System (RLVDS) is an integral part
of the ITMS. In our present work we have designed and developed a complete
system for generating the list of all stop-line violating vehicle images
automatically from video snapshots of road-side surveillance cameras.

In the present work, we have used Tesseract 2.01 open source Optical
Character Recognition (OCR) Engine under Apache License 2.0 for recognition of
handwriting samples of lower case Roman script. Handwritten isolated and
free-flow text samples were collected from multiple users. Tesseract is trained
to recognize user-specific handwriting samples of both the categories of
document pages. On a single user model, the system is trained with 1844
isolated handwritten characters and the performance is tested on 1133
characters, taken form the test set.

Objective of the current work is to develop an Optical Character Recognition
(OCR) engine for information Just In Time (iJIT) system that can be used for
recognition of handwritten textual annotations of lower case Roman script.
Tesseract open source OCR engine under Apache License 2.0 is used to develop
user-specific handwriting recognition models, viz., the language sets, for the
said system, where each user is identified by a unique identification tag
associated with the digital pen.

The objective of the paper is to recognize handwritten samples of Roman
numerals using Tesseract open source Optical Character Recognition (OCR)
engine. Tesseract is trained with data samples of different persons to generate
one user-independent language model, representing the handwritten Roman
digit-set. The system is trained with 1226 digit samples collected form the
different users.

The Coherence Length Diagram and the related maps have been shown to
represent a useful tool for image analysis. Setting threshold parameters is one
of the most important issues when dealing with such applications, as they
affect both the computability, which is outlined by the support map, and the
appearance of the coherence length diagram itself and of defect maps.

Fast evolution of Internet technologies has led to an explosive growth of
video data available in the public domain and created unprecedented challenges
in the analysis, organization, management, and control of such content. The
problems encountered in video analysis such as identifying a video in a large
database (e.g. detecting pirated content in YouTube), putting together video
fragments, finding similarities and common ancestry between different versions
of a video, have analogous counterpart problems in genetic research and
analysis of DNA and protein sequences.

High-throughput screening (HTS) using model organisms is a promising method
to identify a small number of genes or drugs potentially relevant to human
biology or disease. In HTS experiments, robots and computers do a significant
portion of the experimental work. However, one remaining major bottleneck is
the manual analysis of experimental results, which is commonly in the form of
microscopy images. This manual inspection is labor intensive, slow and
subjective.

Principle objective of Image enhancement is to process an image so that
result is more suitable than original image for specific application. Digital
image enhancement techniques provide a multitude of choices for improving the
visual quality of images. Appropriate choice of such techniques is greatly
influenced by the imaging modality, task at hand and viewing conditions. This
paper will provide an overview of underlying concepts, along with algorithms
commonly used for image enhancement.

Among the many ways to model signals, a recent approach that draws
considerable attention is sparse representation modeling. In this model, the
signal is assumed to be generated as a random linear combination of a few atoms
from a pre-specified dictionary. In this work we analyze two Bayesian denoising
algorithms -- the Maximum-Aposteriori Probability (MAP) and the
Minimum-Mean-Squared-Error (MMSE) estimators, under the assumption that the
dictionary is unitary.

Text binarisation process classifies individual pixels as text or background
in the textual images. Binarization is necessary to bridge the gap between
localization and recognition by OCR. This paper presents Sliding window method
to binarise text from textual images with textured background. Suitable
preprocessing techniques are applied first to increase the contrast of the
image and blur the background noises due to textured background. Then Edges are
detected by iterative thresholding.

The efficient realization of linear space-variant (non-convolution) filters
is a challenging computational problem in image processing. In this paper, we
demonstrate that it is possible to filter an image with a Gaussian-like
elliptic window of varying size, elongation and orientation using a fixed
number of computations per pixel. The associated algorithm, which is based on a
family of smooth compactly supported piecewise polynomials, the
radially-uniform box splines, is realized using pre-integration and local
finite-differences.

This report presents properties of the Discrete Pulse Transform on
multi-dimensional arrays introduced by the authors two or so years ago. The
main result given here in Lemma 2.1 is also formulated in a paper to appear in
IEEE Transactions on Image Processing. However, the proof, being too technical,
was omitted there and hence it appears in full in this publication.

Business card images are of multiple natures as these often contain graphics,
pictures and texts of various fonts and sizes both in background and
foreground. So, the conventional binarization techniques designed for document
images can not be directly applied on mobile devices. In this paper, we have
presented a fast binarization technique for camera captured business card
images. A card image is split into small blocks. Some of these blocks are
classified as part of the background based on intensity variance.

Rendering techniques based on a random grid can be improved by adapting
brushstrokes to the shape of different areas of the original picture. In this
paper, the concept of Coherence Length Diagram is applied to determine the
adaptive brushstrokes, in order to simulate an impressionist painting. Some
examples are provided to instance the proposed algorithm.

A novel approach for recognition of handwritten compound Bangla characters,
along with the Basic characters of Bangla alphabet, is presented here. Compared
to English like Roman script, one of the major stumbling blocks in Optical
Character Recognition (OCR) of handwritten Bangla script is the large number of
complex shaped character classes of Bangla alphabet. In addition to 50 basic
character classes, there are nearly 160 complex shaped compound character
classes in Bangla alphabet.

Estimating intrinsic dimensionality of data is a classic problem in pattern
recognition and statistics. Principal Component Analysis (PCA) is a powerful
tool in discovering dimensionality of data sets with a linear structure; it,
however, becomes ineffective when data have a nonlinear structure. In this
paper, we propose a new PCA-based method to estimate intrinsic dimension of
data with nonlinear structures.

This paper attempts to undertake the study of three types of noise such as
Salt and Pepper (SPN), Random variation Impulse Noise (RVIN), Speckle (SPKN).
Different noise densities have been removed between 10% to 60% by using five
types of filters as Mean Filter (MF), Adaptive Wiener Filter (AWF), Gaussian
Filter (GF), Standard Median Filter (SMF) and Adaptive Median Filter (AMF). The
same is applied to the Saturn remote sensing image and they are compared with
one another. The comparative study is conducted with the help of Mean Square
Errors (MSE) and Peak-Signal to Noise Ratio (PSNR).

Acquisition-to-acquisition signal intensity variations (non-standardness) are
inherent in MR images. Standardization is a post processing method for
correcting inter-subject intensity variations through transforming all images
from the given image gray scale into a standard gray scale wherein similar
intensities achieve similar tissue meanings. The lack of a standard image
intensity scale in MRI leads to many difficulties in tissue characterizability,
image display, and analysis, including image segmentation.

This paper proposes an efficient technique for partitioning large biometric
database during identification. In this technique feature vector which
comprises of global and local descriptors extracted from offline signature are
used by fuzzy clustering technique to partition the database. As biometric
features posses no natural order of sorting, thus it is difficult to index them
alphabetically or numerically. Hence, some supervised criteria is required to
partition the search space.

Ear biometric is considered as one of the most reliable and invariant
biometrics characteristics in line with iris and fingerprint characteristics.
In many cases, ear biometrics can be compared with face biometrics regarding
many physiological and texture characteristics. In this paper, a robust and
efficient ear recognition system is presented, which uses Scale Invariant
Feature Transform (SIFT) as feature descriptor for structural representation of
ear images.

This paper uses Support Vector Machines (SVM) to fuse multiple classifiers
for an offline signature system. From the signature images, global and local
features are extracted and the signatures are verified with the help of
Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers.
SVM is used to fuse matching scores of these matchers.

Intensive research has been done on optical character recognition ocr and a
large number of articles have been published on this topic during the last few
decades. Many commercial OCR systems are now available in the market, but most
of these systems work for Roman, Chinese, Japanese and Arabic characters. There
are no sufficient number of works on Indian language character recognition
especially Kannada script among 12 major scripts in India. This paper presents
a review of existing work on printed Kannada script and their results.

Segmenting a MRI images into homogeneous texture regions representing
disparate tissue types is often a useful preprocessing step in the
computer-assisted detection of breast cancer. That is why we proposed new
algorithm to detect cancer in mammogram breast cancer images. In this paper we
proposed segmentation using vector quantization technique. Here we used Linde
Buzo-Gray algorithm (LBG) for segmentation of MRI images. Initially a codebook
of size 128 was generated for MRI images. These code vectors were further
clustered in 8 clusters using same LBG algorithm.

Automated tracking of animal movement allows analyses that would not
otherwise be possible by providing great quantities of data. The additional
capability of tracking in realtime - with minimal latency - opens up the
experimental possibility of manipulating sensory feedback, thus allowing
detailed explorations of the neural basis for control of behavior. Here we
describe a new system capable of tracking the position and body orientation of
animals such as flies and birds. The system operates with less than 40 msec
latency and can track multiple animals simultaneously.

The main focus of image mining in the proposed method is concerned with the
classification of brain tumor in the CT scan brain images. The major steps
involved in the system are: pre-processing, feature extraction, association
rule mining and hybrid classifier. The pre-processing step has been done using
the median filtering process and edge features have been extracted using canny
edge detection technique. The two image mining approaches with a hybrid manner
have been proposed in this paper.

As the Internet help us cross cultural border by providing different
information, plagiarism issue is bound to arise. As a result, plagiarism
detection becomes more demanding in overcoming this issue. Different plagiarism
detection tools have been developed based on various detection techniques.
Nowadays, fingerprint matching technique plays an important role in those
detection tools. However, in handling some large content articles, there are
some weaknesses in fingerprint matching technique especially in space and time
consumption issue.

This paper reports a method allowing for the determination of the shape of
deformed fiber-like objects. Compared to existing methods, it provides
analytical results including the local slope and curvature which are of first
importance, for instance, in beam mechanics. The presented VIC (Virtual Image
Correlation) method consists in looking for the best correlation between the
image of the fiber-like object and a virtual beam image, using an algorithm
close to the Digital Image Correlation method developed in experimental solid
mechanics.

The need of sign language is increasing radically especially to hearing
impaired community. Only few research groups try to automatically recognize
sign language from video, colored gloves and etc. Their approach requires a
valid segmentation of the data that is used for training and of the data that
is used to be recognized. Recognition of a sign language image sequence is
challenging because of the variety of hand shapes and hand motions.

This paper has been withdrawn by the author ali pourmohammad.

The k-nearest neighbors (k-NN) classification rule has proven extremely
successful in countless many computer vision applications. For example, image
categorization often relies on uniform voting among the nearest prototypes in
the space of descriptors. In spite of its good properties, the classic k-NN
rule suffers from high variance when dealing with sparse prototype datasets in
high dimensions. A few techniques have been proposed to improve k-NN
classification, which rely on either deforming the nearest neighborhood
relationship or modifying the input space.

Vector quantization(VQ) is a lossy data compression technique from signal
processing for which simple competitive learning is one standard method to
quantize patterns from the input space. Extending competitive learning VQ to
the domain of graphs results in competitive learning for quantizing input
graphs. In this contribution, we propose an accelerated version of competitive
learning graph quantization (GQ) without trading computational time against
solution quality. For this, we lift graphs locally to vectors in order to avoid
unnecessary calculations of intractable graph distances.

Finding the three-dimensional representation of all or a part of a scene from
a single two dimensional image is a challenging task. In this paper we propose
a method for identifying the pose and location of objects with circular
protrusions in three dimensions from a single image and a 3d representation or
model of the object of interest. To do this, we present a method for
identifying ellipses and their properties quickly and reliably with a novel
technique that exploits intensity differences between objects and a geometric
technique for matching an ellipse in 2d to a circle in 3d.

In this paper, a new efficient feature extraction method based on the
adaptive threshold of wavelet package coefficients is presented. This paper
especially deals with the assessment of autonomic nervous system using the
background variation of the signal Heart Rate Variability HRV extracted from
the wavelet package coefficients. The application of a wavelet package
transform allows us to obtain a time-frequency representation of the signal,
which provides better insight in the frequency distribution of the signal with
time.

Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject.

Spatial Independent Component Analysis (ICA) is an increasingly used
data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
data. To date, it has been used to extract meaningful patterns without prior
information. However, ICA is not robust to mild data variation and remains a
parameter-sensitive algorithm. The validity of the extracted patterns is hard
to establish, as well as the significance of differences between patterns
extracted from different groups of subjects.

Naturally, with the mounting application of biometric systems, there arises a
difficulty in storing and handling those acquired biometric data. Fingerprint
recognition has been recognized as one of the most mature and established
technique among all the biometrics systems. In recent times, with fingerprint
recognition receiving increasingly more attention the amount of fingerprints
collected has been constantly creating enormous problems in storage and
transmission.