Final Year Matlab Project List With Abstract 2012
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Transcript of Final Year Matlab Project List With Abstract 2012
Numero Uno TechnologieS
FINAL YEAR PROJECTS &
IEEE PROJECTS 2012-13
MATLAB IEEE
PROJECT TITLES
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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
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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 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
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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
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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 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
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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
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
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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
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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 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
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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
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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.
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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
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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
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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.
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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
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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 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.
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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
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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.
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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.
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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
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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.
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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
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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.
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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.
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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
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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
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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
Image Processing, IEEE Transactions on
ABSTRACT
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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
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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.
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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
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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
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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
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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.
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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.
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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.
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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.
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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:
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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:
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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.