final year matlab project list with abstract 2012

Numero Uno TechnologieS

FINAL YEAR PROJECTS &

IEEE PROJECTS 2012-13

MATLAB IEEE

PROJECT TITLES


IEEE 2012-13

A Basic Digital Watermarking Algorithm in Discrete Cosine

transformation Domain

A Comparison between a Neural Network and a SVM and Zernike

Moments Based Blob Recognition Modules

A Frequency Domain Multi-User Detector for TD-CDMA Systems

A Messy Watermarking for Medical Image Authentication

A More Secure Steganography Method in Spatial Domain

A New Digital Image Scrambling Encryption Algorithm Based on

Chaotic Sequence

A Novel Method for using Adaptive Array Antennas in Ds-Cdma

Mobile Radio Systems

A Novel Method of Image Steganography in DWT Domain

A Novel Robust Watermarking Algorithm Based On Two Levels DCT

and Two Levels SVD

A Novel Shape-based Diagnostic Approach for Early Diagnosis of

Lung Nodules

A Novel Trust Region Tracking Algorithm Based on Kernel Density

Estimation

A Simple and Fast Algorithm to Detect the Fovea Region in Fundus

Retinal Image


A Steganographic method based on Integer Wavelet Transform and

Genetic Algorithm

A Steganographic Method based on the JPEG Digital images

Adaptive Image Watermarking Algorithm Based on Biorthogonal

Wavelet Transform

An Advanced Motion Detection Algorithm with Video Quality Analysis

for Video Surveillance Systems

Boosting Color Feature Selection for Color Face Recognition

Boosting Text Extraction From Biomedical Images using Text Region

Detection

Color Extended Visual Cryptography Using Error Diffusion

Data Hiding in Motion Vectors of Compressed Video Based on Their

Associated Prediction Error

Discrete Wavelet Transform-Based Satellite Image Resolution

Enhancement

Efficient Relevance Feedback for Content-Based Image Retrieval by

Mining User Navigation Patterns

Encryption and Multiplexing of Fingerprints for Enhanced Security

Enhanced Assessment of the Wound-Healing Process by Accurate

Multiview Tissue Classification

General framework of the construction of biorthogonal wavelets

based on Bernstein bases

Gradient Pro?le Prior and Its Applications in Image Super-Resolution

and Enhancement


Image based Secret Communication using Double Compression

Image Fusion Method Based on NSCT and Robustness Analysis

Image Preprocessing Methods in Face Recognition

Image Segmentation Using Kernel Fuzzy C-Means Clustering on Level

Set Method on Noisy Images

Improved Red Blood Cell Counting in Thin Blood Smears

Integrity Preservation and Privacy Protection for Medical Images with

Histogram-Based Reversible Data Hiding

Key of Packaged Granary Grain Quantity Recognition — Grain Bags

Image Processing

Lung Cancer Detection by Using Artificial Neural Network and Fuzzy

Clustering Methods

Motion and Feature Based Person Tracking In Surveillance Videos

Multiregion Image Segmentation by Parametric Kernel Graph Cuts

Multi-resolution, multi-sensor image fusion general fusion framework

Neural Network based Handwritten Character Recognition system

without feature extraction

Neural Networks for the Detection and Localization of Breast Cancer

Number Plate Recognition for Use in Different Countries Using an

Improved Segmentation

Online Voting System Powered By Biometric Security Using

Steganography


Parametrisation construction frame of lifting scheme

Peak Power Analysis of MC-CDMA Employing Golay Complementary

Sequences

Reduced-Reference Image Quality Assessment Using Reorganized

DCT-Based Image Representation

Removal of High Density Salt and Pepper Noise Through Modi?ed

Decision Based Unsymmetric Trimmed Median Filter

Text Segmentation for MRC Document Compression

The License Plate Recognition System Based on Fuzzy Theory and BP

Neural Network

Wave(Let) Decide Choosy Pixel Embedding for stego

Wavelet Enhanced Fusion Algorithm for Multisensor Images

Transform Domain Progressive Image Decoding

Desaturation of Digital Camera Images using chroma correlation

Face Recognition using Gabor Filters and Local Binary Patterns

Constant-brightness-plane based histogram equalization for color

images

Image Contrast enhancement using histogram specification

Human Iris localization using modified ellipse fitting

Image object segmentation and Region based Gamma mapping


Support Vector Machine based retinal blood vessel detection and

classification for eye disease detection

Optic Disc detection using oriented line filter response for eye

disease detection

Image Segmentation and classification for Highway Traffic Symbol

recogntion

Wavelet domain Remote Sensing Satellite Image sharpening

Forest Detection and Enhancement of Remote Sensing Satellite

Images

Combining Remote Sensing Satellite Images using Wavelet Planes

Color Image restoration from high concentration impulse noise

Lighting variation correction in Human Face Databases using Global

and Local Face Features

Illumination invariant Human face recognition using transform

domain magnitude correction

Robotic Scene Analysis based image enhancement

Binary data hiding based Biometric Authentication System

A highly secure steganographic scheme for medical and military

images

Image noise removal from random valued salt and pepper noise

using directional filtering


A New Supervised Method for Blood Vessel Segmentation in Retinal

Images by Using Gray-Level and Moment Invariants-Based Features

Intelligent Compression of Medical Images with Texture Information

Satellite Image Enhancement using Image Modulation Function

Randomization and Integer mapping based Lossless Watermarking of

Images

Selective blurring of Image content using Gaussian Model -

Application to Film making

Object Removal and Filling of Missing region in Images

Digital Camera Image Enhancement using Alternating Projections

A Low-Cost VLSI Implementation for Efficient Removal of Impulse

Noise

Blood Vessel Segmentation in Angiograms using Fuzzy Inference

System and Mathematical Morphology

Comparative Study of Image Segmentation Techniques and Object

Matching using Segmentation

Evaluation of Retinal Vessel Segmentation Methods for

Microaneurysms Detection

Image Retrieval from database using color quantization

Background Detection and Image Enhancement of poorly Lighted

images

Medical Retinal blood vessel detection using gradient angle

measurements for eye disease detection


Transform Domain Color image enhancement using Discrete Cosine

Transform

Mean preserved Image Enhancment using Histogram Specification

Blood vessel orientation based Optic Disc detection in medical retinal

fundus images

Two-Stage Hierarchical Image Segmentation using K-Means

algorithm and Color Space Conversion

Moving Object Segmentation in video sequences using Time-

Frequency representation

Genetic Algorithm based Image Noise Removal

Exact Image Enhancement and Histogram processing using Wavelet

Coefficients

Lossless Color-Space Conversion of Images

Image Quantization for segmentation using Partitioning Pixel Values

Digital Image Processing Techniques for the Detection and Removal

of Cracks in Digitized Paintings

An SVD-based gray scale image quality measure for local and global

assessment

Enhancing Digital Cephalic Radiography With Mixture Models and

Local Gamma Correction


A closed-form approximation of the exact unbiased inverse of the

Anscombe variance-stabilizing transformation

Mixture of Gaussians-based Background Subtraction for Bayer-

Pattern Image Sequences

Removal of Artifacts from JPEG Compressed Document Images

Scalable Face Image Retrieval with Identity-Based Quantization and

Multi-Reference Re-ranking

Screening of Diabetic Retinopathy - Automatic Segmentation of Optic

Disc in Colour fundus Images

X-Ray Image Categorization and Retrieval Using Patch-based Visual

Words Representation


AN ALGORITHM FOR INTELLIGIBILITY PREDICTION OF TIME-

FREQUENCY WEIGHTED NOISY SPEECH Audio, Speech, and Language Processing, IEEE Transactions on

ABSTRACT

In the development process of noise-reduction algorithms, an objective

machine-driven intelligibility measure which shows high correlation with

speech intelligibility is of great interest. Besides reducing time and costs

compared to real listening experiments, an objective intelligibility measure

could also help provide answers on how to improve the intelligibility of noisy

unprocessed speech.

In this paper, a short-time objective intelligibility measure (STOI) is

presented, which shows high correlation with the intelligibility of noisy and

time–frequency weighted noisy speech (e.g., resulting from noise reduction)

of three different listening experiments.

In general, STOI showed better correlation with speech intelligibility

compared to five other reference objective intelligibility models. In contrast

to other conventional intelligibility models which tend to rely on global

statistics across entire sentences, STOI is based on shorter time segments

(386 ms).

Experiments indeed show that it is beneficial to take segment lengths of this

order into account. In addition, a free Matlab implementation is provided.


ADAPTIVE MULTISCALE COMPLEXITY ANALYSIS OF FETAL

HEART RATE Biomedical Engineering, IEEE Transactions on

ABSTRACT

Per partum fetal asphyxia is a major cause of neonatal morbidity and

mortality. Fetal heart rate monitoring plays an important role in early

detection of acidosis, an indicator for asphyxia.

This problem is addressed in this paper by introducing a novel complexity

analysis of fetal heart rate data, based on producing a collection of piecewise

linear approximations of varying dimensions from which a measure of

complexity is extracted.

This procedure specifically accounts for the highly non-stationary context of

labor by being adaptive and multiscale. Using a reference dataset, made of

real per partum fetal heart rate data, collected in situ and carefully

constituted by obstetricians, the behavior of the proposed approach is

analyzed and illustrated.

Its performance is evaluated in terms of the rate of correct acidosis

detection versus the rate of false detection, as well as how early the

detection is made. Computational cost is also discussed. The results are

shown to be extremely promising and further potential uses of the tool are

discussed


TISSUE-SPECIFIC COMPARTMENTAL ANALYSIS FOR DYNAMIC

CONTRAST-ENHANCED MR IMAGING OF COMPLEX TUMORS Medical Imaging, IEEE Transactions on

ABSTRACT

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)

provides a noninvasive method for evaluating tumor vasculature patterns

based on contrast accumulation and washout. However, due to limited

imaging resolution and tumor tissue heterogeneity, tracer concentrations at

many pixels often represent a mixture of more than one distinct

compartment.

This pixel-wise partial volume effect (PVE) would have profound impact on

the accuracy of pharmacokinetics studies using existing compartmental

modeling (CM) methods. We therefore propose a convex analysis of

mixtures (CAM) algorithm to explicitly mitigate PVE by expressing the

kinetics in each pixel as a nonnegative combination of underlying

compartments and subsequently identifying pure volume pixels at the

corners of the clustered pixel time series scatter plot simplex.

The algorithm is supported theoretically by a well-grounded mathematical

framework and practically by plug-in noise filtering and normalization

preprocessing. We demonstrate the principle and feasibility of the CAM-CM

approach on realistic synthetic data involving two functional tissue

compartments, and compare the accuracy of parameter estimates obtained

with and without PVE elimination using CAM or other relevant techniques.

Experimental results show that CAM-CM achieves a significant improvement

in the accuracy of kinetic parameter estimation.

We apply the algorithm to real DCE-MRI breast cancer data and observe

improved pharmacokinetics parameter estimation, separating tumor tissue


into regions with differential tracer kinetics on a pixel-by-pixel basis and

revealing biologically plausible tumor tissue heterogeneity patterns.

This method combines the advantages of multivariate clustering, convex

geometry analysis, and compartmental modeling approaches. The open-

source MATLAB software of CAM-CM is publicly available from the Web.

CELLULAR NEURAL NETWORKS, NAVIER-STOKES EQUATION AND

MICROARRAY IMAGE RECONSTRUCTION Image Processing, IEEE Transactions on

ABSTRACT

Despite the latest improvements in the microarray technology, many

developments are needed particularly in the image processing stage. Some

hardware implementations of microarray image processing have been

proposed and proved to be a promising alternative to the currently available

software systems. However, the main drawback is the unsuitable addressing

of the quantification of the gene spots which depend on many assumptions.

It is our aim in this paper to present a new Image Reconstruction algorithm

using Cellular Neural Network, which solves the Navier-Stokes equation. This

algorithm offers a robust method to estimate the background signal within

the gene spot region.

Quantitative comparisons are carried out, between our approach and some

available methods in terms of objective standpoint. It is shown that the

proposed algorithm gives highly accurate and realistic measurements in a

fully automated manner, and also, in a remarkably efficient time.


MEMORY-EFFICIENT ARCHITECTURE FOR HYSTERESIS

THRESHOLDING AND OBJECT FEATURE EXTRACTION

Image Processing, IEEE Transactions on

ABSTRACT

Hysteresis thresholding is a method that offers enhanced object detection. Due to

its recursive nature, it is time consuming and requires a lot of memory resources.

This makes it avoided in streaming processors with limited memory.

We propose two versions of a memory-efficient and fast architecture for hysteresis

thresholding: a high-accuracy pixel-based architecture and a faster block-based one

at the expense of some loss in the accuracy. Both designs couple thresholding with

connected component analysis and feature extraction in a single pass over the

image.

Unlike queue-based techniques, the proposed scheme treats candidate pixels

almost as foreground until objects complete; a decision is then made to keep or

discard these pixels. This allows processing on the fly, thus avoiding additional

passes for handling candidate pixels and extracting object features.

Moreover, labels are reused so only one row of compact labels is buffered. Both

architectures are implemented in MATLAB and VHDL. Simulation results on a set

of real and synthetic images show that the execution speed can attain an average


increase up to 24× for the pixel-based and 52× for the block-based when compared

to s

A CLOSED-FORM APPROXIMATION OF THE EXACT UNBIASED

INVERSE OF THE ANSCOMBE VARIANCE-STABILIZING

TRANSFORMATION


ABSTRACT

We presented an exact unbiased inverse of the Anscombe variance-

stabilizing transformation and showed that when applied to Poisson

image denoising, the combination of variance stabilization and state-of-

the-art Gaussian denoising algorithms is competitive with some of the

best Poisson denoising algorithms.

We also provided a Matlab implementation of our method, where the

exact unbiased inverse transformation appears in non-analytical form.


Here we propose a closed-form approximation of the exact unbiased

inverse, in order to facilitate the use of this inverse.

The proposed approximation produces results equivalent to those

obtained with the accurate (non-analytical) exact unbiased inverse, and

thus notably better than one would get with the asymptotically unbiased

inverse transformation, which is commonly used in applications.

IMPLEMENTATION OF NEURAL NETWORK CONTROLLED THREE-

LEG VSC AND A TRANSFORMER AS THREE-PHASE FOUR-WIRE

DSTATCOM

Industry Applications, IEEE Transactions on

ABSTRACT


In this paper, a neural-network (NN)-controlled distribution static compensator

(DSTATCOM) using a dSPACE processor is implemented for power quality

improvement in a three-phase four-wire distribution system.

A three-leg voltage-source-converter (VSC)-based DSTATCOM with a zig-zag

transformer is used for the compensation of reactive power for voltage regulation

or for power factor correction along with load balancing, elimination of harmonic

currents, and neutral current compensation at the point of common coupling.

The Adaline (adaptive linear element)-based NN is used to implement the control

scheme of the VSC. This technique gives similar performance as that of other

control techniques, but it is simple to implement and has a fast response and gives

nearly zero phase shift.

The zig-zag transformer is used for providing a path to the zero-sequence current

in a three-phase four-wire distribution system. This reduces the complexity and

also the cost of the DSTATCOM system.

The performance of the proposed DSTATCOM system is validated through

simulations using MATLAB software with its Simulink and Power System

Blockset toolboxes and hardware implementation.


POSTURE CONTROL OF ELECTROMECHANICAL ACTUATOR-

BASED THRUST VECTOR SYSTEM FOR AIRCRAFT ENGINE

Industrial Electronics, IEEE Transactions on

ABSTRACT

This paper deals with the dynamical modeling and posture control of the

electromechanical actuator (EMA)-based thrust vector control (TVC) system for

aircraft engine. Addressing the issues of the large inertia and low stiffness existed

in the TVC system driven by EMA, this paper established a 2-DOF mathematical

model to describe EMA dynamic characteristics.

In order to overcome the influence of the motion coupling of the TVC-EMA

existed in the pitching and yawing channels, we presented a kind of dual-channel

coordinated-control method which realizes the trust vector control for the swung

aircraft engine based on the inverse kinematics.

This control strategy uses the command Eulers angles transformation to solve the

desired actuator linear lengths, and tracks the desired lengths via the compound

control law composed of robust PID with the lead compensation and Bang-Bang

control in the two actuators.

The hybrid experimental simulation system based on dSPACE was set up, the

control parameters of the compound control methods were confirmed by off-line

simulation based on Matlab, and the load experiments of circular motion and step

response were implemented on the test system. The simulation and test results

show that the designed thrust vector controller can achieve the satisfactory control

performances.


MODELING, CONTROL AND MONITORING OF S3RS BASED

HYDROGEN COOLING SYSTEM IN THERMAL POWER PLANT

Industrial Electronics, IEEE Transactions on

ABSTRACT

The faster heat dissipation of generators in power plant call for hydrogen cooling,

and water is used as coolant to cool down the hot hydrogen which comes out from

the hydrogen cooling system (HCS) at generating end. Therefore, in large

generating plants the process of cooling and coolant becomes an integral part of the

Heat Exchangers. Hence, requirement of a reliable hydrogen cooling system is a

must. This paper presents development and implementation of supervisory control

and data acquisition (SCADA) based process control and monitoring system. A

novel method of Six Stage Standby Redundant Structured (S3RS) HCS is proposed

for the cooling of large generators in thermal power plant(s).

This proposed system is equally reliable for steam turbine based generating plants

and Integrated Gasification Combined Cycle (IGCC) plants. The entire process

control and monitoring, popularly known as human machine interface (HMI) of


HCS has been developed and simulated on RSViewSE, a real-time automation

platform by Rockwell Automation. And, the system reliability of the proposed

S3RS process model is implemented using MATLAB

POWER LOSS COMPARISON OF SINGLE- AND TWO-STAGE GRID-

CONNECTED PHOTOVOLTAIC SYSTEMS

Energy Conversion, IEEE Transactions on

ABSTRACT

This paper presents power loss comparison of single- and two-stage grid-connected

photovoltaic (PV) systems based on the loss factors of double line-frequency

voltage ripple (DLFVR), fast irradiance variation + DLFVR, fast dc load variation

+ DLFVR, limited operating voltage range + DLFVR, and overall loss factor

combination.


These loss factors will result in power deviation from the maximum power points.

In this paper, both single-stage and two-stage grid-connected PV systems are

considered. All of the effects on a two-stage system are insignificant due to an

additional maximum power point tracker, but the tracker will reduce the system

efficiency typically about 2.5%.

The power loss caused by these loss factors in a single-stage grid-connected PV

system is also around 2.5%; that is, a single-stage system has the merits of saving

components and reducing cost, and does not penalize overall system efficiency

under certain operating voltage ranges. Simulation results with the MATLAB

software package and experimental results have confirmed the analysis.

SIMPLE ANALYTICAL METHOD FOR DETERMINING PARAMETERS

OF DISCHARGING BATTERIES

Energy Conversion, IEEE Transactions on


ABSTRACT

This paper derives simple and explicit formulas for computing the parameters of

Thevenin's equivalent circuit model for a discharging battery. The general

Thevenin's equivalent circuit model has $n$ pairs of parallel resistors and

capacitors (nth-order model).

The main idea behind the new method is to transform the problem of solving a

system of high-order polynomial equations into one of solving several linear

equations and a single-variable $n$th-order polynomial equation, via some change

of variables. The computation can be implemented with a simple MATLAB code

less than half-page long.

Experimental and computational results are obtained for three types of batteries:

Li-polymer, lead--acid, and nickel metal hydride. For all the tested batteries, the

first-order models are not able to generate voltage responses that closely match the

measured responses, while second-order models can generate well-matched

responses. For some of the batteries, a third-order model can do a better job

matching the voltage responses.


BOOSTING COLOR FEATURE SELECTION FOR COLOR FACE

RECOGNITION


ABSTRACT

This paper introduces the new color face recognition (FR) method that makes

effective use of boosting learning as color-component feature selection framework.

The proposed boosting color-component feature selection framework is designed

for finding the best set of color-component features from various color spaces (or

models), aiming to achieve the best FR performance for a given FR task.

In addition, to facilitate the complementary effect of the selected color-component

features for the purpose of color FR, they are combined using the proposed

weighted feature fusion scheme.

The effectiveness of our color FR method has been successfully evaluated on the

following five public face databases (DBs): CMU-PIE, Color FERET,

XM2VTSDB, SCface, and FRGC 2.0.

Experimental results show that the results of the proposed method are impressively

better than the results of other state-of-the-art color FR methods over different FR

challenges including highly uncontrolled illumination, moderate pose variation,

and small resolution face images.


AUTOMATIC EXACT HISTOGRAM SPECIFICATION FOR CONTRAST

ENHANCEMENT AND VISUAL SYSTEM BASED QUANTITATIVE

EVALUATION


ABSTRACT

Histogram equalization, which aims at information maximization, is widely used in

different ways to perform contrast enhancement in images. In this paper, an

automatic exact histogram specification technique is proposed and used for global

and local contrast enhancement of images.

The desired histogram is obtained by first subjecting the image histogram to a

modification process and then by maximizing a measure that represents increase in

information and decrease in ambiguity. A new method of measuring image

contrast based upon local band-limited approach and center-surround retinal

receptive field model is also devised in this paper.

This method works at multiple scales (frequency bands) and combines the contrast

measures obtained at different scales using Lp-norm. In comparison to a few


existing methods, the effectiveness of the proposed automatic exact histogram

specification technique in enhancing contrasts of images is demonstrated through

qualitative analysis and the proposed image contrast measure based quantitative

analysis.

HIGH DYNAMIC RANGE IMAGE DISPLAY WITH HALO AND

CLIPPING PREVENTION


ABSTRACT

The dynamic range of an image is defined as the ratio between the highest and the

lowest luminance level. In a high dynamic range (HDR) image, this value exceeds

the capabilities of conventional display devices; as a consequence, dedicated

visualization techniques are required.

In particular, it is possible to process an HDR image in order to reduce its dynamic

range without producing a significant change in the visual sensation experienced


by the observer. In this paper, we propose a dynamic range reduction algorithm

that produces high-quality results with a low computational cost and a limited

number of parameters.

The algorithm belongs to the category of methods based upon the Retinex theory

of vision and was specifically designed in order to prevent the formation of

common artifacts, such as halos around the sharp edges and clipping of the

highlights, that often affect methods of this kind.

After a detailed analysis of the state of the art, we shall describe the method and

compare the results and performance with those of two techniques recently

proposed in the literature and one commercial software.

GRADIENT PROFILE PRIOR AND ITS APPLICATIONS IN IMAGE SUPER-

RESOLUTION AND ENHANCEMENT


ABSTRACT


In this paper, we propose a novel generic image prior-gradient profile prior, which

implies the prior knowledge of natural image gradients. In this prior, the image

gradients are represented by gradient profiles, which are 1-D profiles of gradient

magnitudes perpendicular to image structures.

We model the gradient profiles by a parametric gradient profile model. Using this

model, the prior knowledge of the gradient profiles are learned from a large

collection of natural images, which are called gradient profile prior.

Based on this prior, we propose a gradient field transformation to constrain the

gradient fields of the high resolution image and the enhanced image when

performing single image super-resolution and sharpness enhancement. With this

simple but very effective approach, we are able to produce state-of-the-art results.

The reconstructed high resolution images or the enhanced images are sharp while

have rare ringing or jaggy artifacts


EXPLORING DUPLICATED REGIONS IN NATURAL IMAGES


ABSTRACT

Duplication of image regions is a common method for manipulating original

images, using typical software like Adobe Photoshop, 3DS MAX, etc. In this

study, we propose a duplication detection approach that can adopt two robust

features based on discrete wavelet transform (DWT) and kernel principal

component analysis (KPCA). Both schemes provide excellent representations of

the image data for robust block matching.

Multiresolution wavelet coefficients and KPCA-based projected vectors

corresponding to image-blocks are arranged into a matrix for lexicographic sorting.

Sorted blocks are used for making a list of similar point-pairs and for computing

their offset frequencies. Duplicated regions are then segmented by an automatic

technique that refines the list of corresponding point-pairs and eliminates the

minimum offset-frequency threshold parameter in the usual detection method.

A new technique that extends the basic algorithm for detecting Flip and Rotation

types of forgeries is also proposed. This method uses global geometric

transformation and the labeling technique to indentify the mentioned forgeries.

Experiments with a good number of natural images show very promising results,

when compared with the conventional PCA-based approach. A quantitative

analysis indicate that the wavelet-based feature outperforms PCA- or KPCA-based

features in terms of average precision and recall in the noiseless, or uncompressed

domain, while KPCA-based feature obtains excellent performance in the additive

noise and lossy JPEG compression environments.


SCALABLE FACE IMAGE RETRIEVAL WITH IDENTITY-BASED

QUANTIZATION AND MULTI-REFERENCE RE-RANKING

ABSTRACT:

In this paper we aim to build a scalable face image retrieval system. For this

purpose, we develop a new scalable face representation using both local and global

features. In the indexing stage, we exploit special properties of faces to design new

component based local features, which are subsequently quantized into visual

words using a novel identity-based quantization scheme.

We also use a very small Hamming signature (40 bytes) to encode the

discriminative global feature for each face. In the retrieval stage, candidate images

are firstly retrieved from the inverted index of visual words.

We then use a new multi-reference distance to re-rank the candidate images using

the Hamming signature. On a one million face database, we show that our local

features and global Hamming signatures are complementary—the inverted index


based on local features provides candidate images with good recall, while the

multi-reference re-ranking with global Hamming signature leads to good precision.

As a result, our system is not only scalable but also outperforms the linear scan

retrieval system using the state-of the- art face recognition feature in term of the

quality.

ENHANCED ASSESSMENT OF THE WOUND-HEALING PROCESS BY

ACCURATE MULTIVIEW TISSUE CLASSIFICATION

ABSTRACT:

A pressure ulcer is a clinical pathology of localized damage to the skin and

underlying tissue caused by pressure, shear, or friction. Diagnosis, treatment, and

care of pressure ulcers are costly for health services.

Accurate wound evaluation is a critical task for optimizing the efficacy of

treatment and care. Clinicians usually evaluate each pressure ulcer by visual

inspection of the damaged tissues, which is an imprecise manner of assessing the


wound state. Current computer vision approaches do not offer a global solution to

this particular problem.

In this paper, a hybrid approach based on neural networks and Bayesian classifiers

is used in the design of a computational system for automatic tissue identification

in wound images. We focus here on tissue classification from color and texture

region descriptors computed after unsupervised segmentation. Due to perspective

distortions, uncontrolled lighting conditions and view points, wound assessments

vary significantly between patient examinations.

The experimental classification tests demonstrate that enhanced repeatability and

robustness are obtained and that metric assessment is achieved through real area

and volume measurements and wound outline extraction.

FACE RECOGNITION BY EXPLORING INFORMATION JOINTLY IN

SPACE, SCALE AND ORIENTATION


ABSTRACT:

Information jointly contained in image space, scale and orientation domains can

provide rich important clues not seen in either individual of these domains. The

position, spatial frequency and orientation selectivity properties are believed to

have an important role in visual perception.

This paper proposes a novel face representation and recognition approach by

exploring information jointly in image space, scale and orientation domains.

Specifically, the face image is first decomposed into different scale and orientation

responses by convolving multiscale and multiorientation Gabor filters.

Second, local binary pattern analysis is used to describe the neighboring

relationship not only in image space, but also in different scale and orientation

responses. This way, information from different domains is explored to give a

good face representation for recognition. Neural Networks provide significant

benefits in face recognition.

They are actively being used for such advantages as locating previously undetected

patterns, controlling devices based on feedback, and detecting characteristics in

face recognition. It improves the level of accuracy compared with existing face

recognition methods.


MIXTURE OF GAUSSIANS-BASED BACKGROUND

SUBTRACTION FOR BAYER-PATTERN IMAGE SEQUENCES

ABSTRACT:

This letter proposes a background subtraction method for Bayer-pattern image

sequences. The proposed method models the background in a Bayer-pattern

domain using a mixture of Gaussians (MoG) and classifies the foreground in an

interpolated red, green, and blue (RGB) domain.

This method can achieve almost the same accuracy as MoG using RGB color

images while maintaining computational resources (time and memory) similar to

MoG using grayscale images.

Experimental results show that the proposed method is a good solution to obtain

high accuracy and low resource requirements simultaneously.

This improvement is important for a low-level task like background subtraction

since its accuracy affects the performance of high-level tasks, and is preferable for

implementation in real-time embedded systems such as smart cameras.


NO-REFERENCE METRIC DESIGN WITH MACHINE LEARNING FOR

LOCAL VIDEO COMPRESSION ARTIFACT LEVEL

ABSTRACT

In decoded digital video, the local perceptual compression artifact level depends on

the global compression ratio and the local video content. In this paper, we show

how to build a highly relevant metric for video compression artifacts using

supervised learning.

To obtain the ground truth for training, we first build a reference metric for local

estimation of the artifact level, which is robust to scaling and sensitive to all types

of compression artifacts. Next, we design a large feature set and use AdaBoost to

create no-reference metrics trained with the output of the reference metric.

Two separate trained no-reference metrics, one for flat and one for detailed areas,

respectively, are necessary to cover all types of artifacts. The relevance of these

metrics is validated in a compression artifact reduction application, using objective

scores like PSNR and BIM, but also a subjective evaluation as proof.


We conclude that our created reference metric is an accurate local estimator of the

compression artifact level. We were able to copy the performance to two no-

reference metrics, based on a weighted mixture of low-level features.

A NOVEL 3-D COLOR HISTOGRAM EQUALIZATION METHOD WITH

UNIFORM 1-D GRAY SCALE HISTOGRAM

ABSTRACT:

The majority of color histogram equalization methods do not yield uniform

histogram in gray scale. After converting a color histogram equalized image into

gray scale, the contrast of the converted image is worse than that of an 1-D gray

scale histogram equalized image.


We propose a novel 3-D color histogram equalization method that produces

uniform distribution in gray scale histogram by defining a new cumulative

probability density function in 3-D color space.

Test results with natural and synthetic images are presented to compare and

analyze various color histogram equalization algorithms based upon 3-D color

histograms. We also present theoretical analysis for nonideal performance of

existing methods.

COLOR EXTENDED VISUAL CRYPTOGRAPHY USING ERROR

DIFFUSION

ABSTRACT:


Color visual cryptography (VC) encrypts a color secret message into color halftone

image shares. Previous methods in the literature show good results for black and

white or gray scale VC schemes, however, they are not sufficient to be applied

directly to color shares due to different color structures.

Some methods for color visual cryptography are not satisfactory in terms of

producing either meaningless shares or meaningful shares with low visual quality,

leading to suspicion of encryption.

This paper introduces the concept of visual information pixel (VIP)

synchronization and error diffusion to attain a color visual cryptography encryption

method that produces meaningful color shares with high visual quality.

VIP synchronization retains the positions of pixels carrying visual information of

original images throughout the color channels and error diffusion generates shares

pleasant to human eyes. Comparisons with previous approaches show the superior

performance of the new method.


A NEW SUPERVISED METHOD FOR BLOOD VESSEL

SEGMENTATION IN RETINAL IMAGES BY USING GRAY-LEVEL AND

MOMENT INVARIANTS-BASED FEATURES

ABSTRACT:

This paper presents a new supervised method for blood vessel detection in digital

retinal images. This method uses a neural network (NN) scheme for pixel

classification and computes a 7-D vector composed of gray-level and moment

invariants-based features for pixel representation.

The method was evaluated on the publicly available DRIVE and STARE

databases, widely used for this purpose, since they contain retinal images where

the vascular structure has been precisely marked by experts. Method performance

on both sets of test images is better than other existing solutions in literature.

The method proves especially accurate for vessel detection in STARE images. Its

application to this database (even when the NN was trained on the DRIVE

database) outperforms all analyzed segmentation approaches.

Its effectiveness and robustness with different image conditions, together with its

simplicity and fast implementation, make this blood vessel segmentation proposal

suitable for retinal image computer analyses such as automated screening for early

diabetic retinopathy detection.


USING A VISUAL DISCRIMINATION MODEL FOR THE DETECTION

OF COMPRESSION ARTIFACTS IN VIRTUAL PATHOLOGY IMAGES

ABSTRACT:

A major issue in telepathology is the extremely large and growing size of digitized

―virtual‖ slides, which can require several gigabytes of storage and cause

significant delays in data transmission for remote image interpretation and

interactive visualization by pathologists. Compression can reduce this massive

amount of virtual slide data, but reversible (lossless) methods limit data reduction

to less than 50%, while lossy compression can degrade image quality and

diagnostic accuracy.

―Visually lossless‖ compression offers the potential for using higher compression

levels without noticeable artifacts, but requires a rate-control strategy that adapts to

image content and loss visibility. We investigated the utility of a visual

discrimination model (VDM) and other distortion metrics for predicting JPEG

2000 bit rates corresponding to visually lossless compression of virtual slides for

breast biopsy specimens.


Threshold bit rates were determined experimentally with human observers for a

variety of tissue regions cropped from virtual slides. For test images compressed to

their visually lossless thresholds, just-noticeable difference (JND) metrics

computed by the VDM were nearly constant at the 95th percentile level or higher,

and were significantly less variable than peak signal-to-noise ratio (PSNR) and

structural similarity (SSIM) metrics.

Our results suggest that VDM metrics could be used to guide the compression of

virtual slides to achieve visually lossless compression while providing 5–12 times

the data reduction of reversible methods.

DETECTION OF ARCHITECTURAL DISTORTION IN PRIOR

MAMMOGRAMS

ABSTRACT:

We present methods for the detection of sites of architectural distortion in prior

mammograms of interval-cancer cases. We hypothesize that screening

mammograms obtained prior to the detection of cancer could contain subtle signs

of early stages of breast cancer, in particular, architectural distortion.


The methods are based upon Gabor filters, phase portrait analysis, a novel method

for the analysis of the angular spread of power, fractal analysis, Laws’ texture

energy measures derived from geometrically transformed regions of interest

(ROIs), and Haralick’s texture features. With Gabor filters and phase portrait

analysis, 4224 ROIs were automatically obtained from 106 prior mammograms of

56 interval-cancer cases, including 301 true-positive ROIs related to architectural

distortion, and from 52 mammograms of 13 normal cases.

For each ROI, the fractal dimension, the entropy of the angular spread of power, 10

Laws’ measures, and Haralick’s 14 features were computed. The areas under the

receiver operating characteristic curves obtained using the features selected by

stepwise logistic regression and the leave-one-ROI-out method are 0.76 with the

Bayesian classifier, 0.75 with Fisher linear discriminant analysis, and 0.78 with a

single-layer feed-forward neural network.

Free-response receiver operating characteristics indicated sensitivities of 0.80 and

0.90 at 5.8 and 8.1 false positives per image, respectively, with the Bayesian

classifier and the leave-one-image-out method.


A DIFFERENTIAL GEOMETRIC APPROACH TO AUTOMATED

SEGMENTATION OF HUMAN AIRWAY TREE

ABSTRACT:

Airway diseases are frequently associated with morphological changes that may

affect the physiology of the lungs. Accurate characterization of airways may be

useful for quantitatively assessing prognosis and for monitoring therapeutic

efficacy.

The information gained may also provide insight into the underlying mechanisms

of various lung diseases. We developed a computerized scheme to automatically

segment the 3-D human airway tree depicted on computed tomography (CT)

images.

The method takes advantage of both principal curvatures and principal directions

in differentiating airways from other tissues in geometric space. A ―puzzle game‖

procedure is used to identify false negative regions and reduce false positive

regions that do not meet the shape analysis criteria.

The negative impact of partial volume effects on small airway detection is partially

alleviated by repeating the developed differential geometric analysis on lung

anatomical structures modeled at multiple iso-values (thresholds).

In addition to having advantages, such as full automation, easy implementation and

relative insensitivity to image noise and/or artifacts, this scheme has virtually no

leakage issues and can be easily extended to the extraction or the segmentation of

other tubular type structures (e.g., vascular tree).

The performance of this scheme was assessed quantitatively using 75 chest CT

examinations acquired on 45 subjects with different slice thicknesses and using 20

publicly available test cases that were originally designed for evaluating the

performance of different airway tree segmentation algorithms.


A SUPERVISED FRAMEWORK FOR THE REGISTRATION

AND SEGMENTATION OF WHITE MATTER FIBER TRACTS

ABSTRACT:

A supervised framework is presented for the automatic registration and

segmentation of white matter (WM) tractographies extracted from brain DT-MRI.

The framework relies on the direct registration between the fibers, without

requiring any intensity-based registration as preprocessing.

An affine transform is recovered together with a set of segmented fibers. A

recently introduced probabilistic boosting tree classifier is used in a segmentation

refinement step to improve the precision of the target tract segmentation.

The proposed method compares favorably with a state-of-the-art intensity-based

algorithm for affine registration of DTI tractographies. Segmentation results for 12

major WM tracts are demonstrated.

Quantitative results are also provided for the segmentation of a particularly

difficult case, the optic radiation tract. An average precision of 80% and recall of

55% were obtained for the optimal configuration of the presented method.


CURVATURE INTERPOLATION METHOD FOR IMAGE ZOOMING

ABSTRACT:

We introduce a novel image zooming algorithm, called the curvature interpolation

method (CIM), which is partial- differential-equation (PDE)-based and easy to

implement. In order to minimize artifacts arising in image interpolation such as

image blur and the checkerboard effect, the CIM first evaluates the curvature of the

low-resolution image.

After interpolating the curvature to the high-resolution image domain, the CIM

constructs the high-resolution image by solving a linearized curvature equation,

incorporating the interpolated curvature as an explicit driving force.

It has been numerically verified that the new zooming method can produce clear

images of sharp edges which are already denoised and superior to those obtained


from linear methods and PDE-based methods of no curvature information. Various

results are given to prove effectiveness and reliability of the new method.

IMAGE RESOLUTION ENHANCEMENT BY USING DISCRETE AND

STATIONARY WAVELET DECOMPOSITION

ABSTRACT:

In this correspondence, the authors propose an image resolution enhancement

technique based on interpolation of the high frequency subband images obtained

by discrete wavelet transform (DWT) and the input image.

The edges are enhanced by introducing an intermediate stage by using stationary

wavelet transform (SWT). DWT is applied in order to decompose an input image

into different subbands. Then the high frequency subbands as well as the input

image are interpolated.


The estimated high frequency subbands are being modified by using high

frequency subband obtained through SWT. Then all these subbands are combined

to generate a new high resolution image by using inverse DWT (IDWT).

The quantitative and visual results are showing the superiority of the proposed

technique over the conventional and state-of-art image resolution enhancement

techniques.

TEXT SEGMENTATION FOR MRC DOCUMENT COMPRESSION

ABSTRACT:


The mixed raster content (MRC) standard (ITU-T T.44) specifies a framework for

document compression which can dramatically improve the compression/quality

tradeoff as compared to traditional lossy image compression algorithms.

The key to MRC compression is the separation of the document into foreground

and background layers, represented as a binary mask. Therefore, the resulting

quality and compression ratio of a MRC document encoder is highly dependent

upon the segmentation algorithm used to compute the binary mask.

In this paper, we propose a novel multiscale segmentation scheme for MRC

document encoding based upon the sequential application of two algorithms. The

first algorithm, cost optimized segmentation (COS), is a blockwise segmentation

algorithm formulated in a global cost optimization framework.

The second algorithm, connected component classification (CCC), refines the

initial segmentation by classifying feature vectors of connected components using

an Markov random field (MRF) model. The combined COS/CCC segmentation

algorithms are then incorporated into a multiscale framework in order to improve

the segmentation accuracy of text with varying size.

In comparisons to state-of-the-art commercial MRC products and selected

segmentation algorithms in the literature, we show that the new algorithm achieves

greater accuracy of text detection but with a lower false detection rate of nontext

features.

We also demonstrate that the proposed segmentation algorithm can improve the

quality of decoded documents while simultaneously lowering the bit rate.

final year matlab project list with abstract 2012

Documents

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blood vessel