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 

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A Basic Digital Watermarking transformation Domain

Algorithm

in

Discrete

Cosine

A Comparison between a Neural Network and a SVM and Zernike Moments Based Blob Recognition Modules

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

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A Messy Watermarking for Medical Image Authentication

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A More Secure Steganography Method in Spatial Domain

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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


IEEE 2012-13 

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A Basic Digital Watermarking transformation Domain

Algorithm

in

Discrete

Cosine

A Comparison between a Neural Network and a SVM and Zernike Moments Based Blob Recognition Modules

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

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A Messy Watermarking for Medical Image Authentication

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A More Secure Steganography Method in Spatial Domain

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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


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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 Enhancement

Transform-Based

Satellite

Image

Resolution

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


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Image based Secret Communication using Double Compression

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Image Fusion Method Based on NSCT and Robustness Analysis

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Image Preprocessing Methods in Face Recognition

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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

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Motion and Feature Based Person Tracking In S urveillance Videos

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Multiregion Image Segmentation by Parametric Kernel Graph Cuts

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

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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 Steganography

System

Powered

By

Biometric

Security

Using


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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 Com pression The License Plate Recognition System Based on Fuzzy Theory and BP Neural Network

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Wave(Let) Decide Choosy Pixel Embedding for stego

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Wavelet Enhanced Fusion Algorithm for Multisensor Images

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Transform Domain Progressive Image Decoding

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Desaturation of Digital Camera Images using chroma correlation

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Face Recognition using Gabor Filters and Local Binary Patterns

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Constant-brightness-plane based histogram equalization for color images

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Image Contrast enhancement using histogram specification

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Human Iris localization using modified ellipse fitting

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Image object segmentation and Region based Gamma mapping


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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

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Combining Remote Sensing Satellite Images using Wavelet Planes

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Color Image restoration from high concentration impulse noise

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Lighting variation correction in Human Face Databases using Global and Local Face Features Illumination invariant Human domain magnitude correction

face

recognition

using

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Robotic Scene Analysis based image enhancement

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Binary data hiding based Biometric Authentication System

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transform

A highly secure steganographic scheme for medical and military images Image noise removal from random valued salt and pepper noise using directional filtering


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A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features

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Intelligent Compression of Medical Images with Texture Information

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Satellite Image Enhancement using Image Modulation Function

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Randomization and Integer mapping based Lossless Watermarking of Images Selective blurring of Image Application to Film making

content

using

Gaussian

Model

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Object Removal and Filling of Missing region in Images

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Digital Camera Image Enhancement using Alternating Projections

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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 Microaneurysms Detection

Vessel

Segmentation

Methods

for

Image Retrieval from database using color quantization Background Detection and Image Enhancement of poorly Lighted images Medical Retinal blood vessel detection measurements for eye disease detection

using

gradient

angle


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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 algorithm and Color Space Conversion Moving Object Segmentation Frequency representation

in

video

using

sequences

K-Means

using

Time-

Genetic Algorithm based Image Noise Removal Exact Image Enhancement and Histogram processing using Wavelet Coefficients

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Lossless Color-Space Conversion of Images

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Image Quantization for segmentation using Partitioning Pixel Values

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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


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A closed-form approximation of the exact unbiased inverse of the Anscombe variance-stabilizing transformation Mixture of Gaussians-based Pattern Image Sequences

Background

Subtraction

for

Bayer-

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 TIMEFREQUENCY 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

Numero Uno TechnologieS 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 opensource 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

Numero Uno TechnologieS 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 Image Processing, IEEE Transactions on

ABSTRACT We presented an exact unbiased inverse of the Anscombe variancestabilizing transformation and showed that when applied to Poisson image denoising, the combination of variance stabilization and state-ofthe-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 THREELEG VSC AND A TRANSFORMER AS THREE-PHASE FOUR-WIRE DSTATCOM Industry Applications, IEEE Transactions on

ABSTRACT

Numero Uno TechnologieS 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 ACTUATORBASED 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

Numero Uno TechnologieS 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 GRIDCONNECTED 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.

Numero Uno TechnologieS 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

Numero Uno TechnologieS 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 Image Processing, IEEE Transactions on

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 Image Processing, IEEE Transactions on

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

Numero Uno TechnologieS 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 Image Processing, IEEE Transactions on

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

Numero Uno TechnologieS 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 SUPERRESOLUTION AND ENHANCEMENT Image Processing, IEEE Transactions on

ABSTRACT

Numero Uno TechnologieS 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 Image Processing, IEEE Transactions on

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

Numero Uno TechnologieS 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

Numero Uno TechnologieS 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

Numero Uno TechnologieS 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.

Numero Uno TechnologieS 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 noreference 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.

Numero Uno TechnologieS 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:

Numero Uno TechnologieS 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 an d 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.

Numero Uno TechnologieS 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.

Numero Uno TechnologieS 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 extr action 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 part icularly 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

Numero Uno TechnologieS 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.

Numero Uno TechnologieS 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:

Final Year Matlab Project List With Abstract 2012

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