Matlab image processing_2013_ieee

1. Robust Face Recognition for Uncontrolled Pose and Illumination Changes
Abstract — Face recognition has made significant advances in the last decade,
but

robust

commercial

applications

are

still

lacking.

Current

authentication/identification applications are limited to controlled settings, e.g.,
limited pose and illumination changes, with the user usually aware of being
screened and collaborating in the process. Among others, pose and illumination
changes are limited. To address challenges from looser restrictions, this paper
proposes a novel framework for real-world face recognition in uncontrolled
settings named Face Analysis for Commercial Entities (FACE). Its robustness
comes from normalization (“correction”) strategies to address pose and
illumination variations. In addition, two separate image quality indices
quantitatively assess pose and illumination changes for each biometric query,
before submitting it to the classifier. Samples with poor quality are possibly
discarded or undergo a manual classification or, when possible, trigger a new
capture. After such filter, template similarity for matching purposes is measured
using a localized version of the image correlation index. Finally, FACE adopts
reliability indices, which estimate the “acceptability” of the final identification
decision made by the classifier.
2. Reversible Watermarking Based on Invariant Image Classification and
Dynamic Histogram Shifting
Abstract — In this paper, we propose a new reversible watermarking scheme.
One first contribution is a histogram shifting modulation which adaptively takes
care of the local specificities of the image content. By applying it to the image
prediction-errors and by considering their immediate neighborhood, the scheme
we propose inserts data in textured areas where other methods fail to do so.
Furthermore, our scheme makes use of a classification process for identifying
parts of the image that can be watermarked with the most suited reversible
IGSLABS Technologies Pvt Ltd, www.makefinalyearproject.com, Email: nandu@igslabs.com, Phone: 9590544567

modulation. This classification is based on a reference image derived from the
image itself, a prediction of it, which has the property of being invariant to the
watermark insertion. In that way, the watermark embedder and extractor remain
synchronized for message extraction and image reconstruction.
3. Automatic Detection and Reconstruction of Building Radar Footprints
From Single VHR SAR Images
Abstract—The spaceborne synthetic aperture radar (SAR) systems CosmoSkyMed, TerraSAR-X, and TanDEM-X acquire imagery with very high spatial
resolution (VHR), supporting various important application scenarios, such as
damage assessment in urban areas after natural disasters. To ensure a reliable,
consistent, and fast extraction of the information from the complex SAR scenes,
automatic information extraction methods are essential. Focusing on the
analysis of urban areas, which is of prime interest of VHR SAR, in this paper,
we present a novel method for the automatic detection and 2-D reconstruction
of building radar footprints from VHR SAR scenes. Unlike most of the
literature methods, the proposed approach can be applied to single images. The
method is based on the extraction of a set of low-level features from the images
and on their composition to more structured primitives using a production
system. Then, the concept of semantic meaning of the primitives is introduced
and used for both the generation of building candidates and the radar footprint
reconstruction. The semantic meaning represents the probability that a primitive
belongs to a certain scattering class (e.g., double bounce, roof, facade) and has
been defined in order to compensate for the lack of detectable features in single
images. Indeed, it allows the selection of the most reliable primitives and
footprint hypotheses on the basis of fuzzy membership grades.


4. Interactive Segmentation for Change Detection in Multispectral RemoteSensing Images
Abstract—In this letter, we propose to solve the change detection (CD) problem
in multitemporal remote-sensing images using interactive segmentation
methods. The user needs to input markers related to change and no-change
classes in the difference image. Then, the pixels under these markers are used
by the support vector machine classifier to generate a spectral-change map. To
enhance further the result, we include the spatial contextual information in the
decision process using two different solutions based on Markov random field
and level-set methods.
5. Estimating Information from Image Colors: An Application to Digital
Cameras and Natural Scenes
Abstract—The colors present in an image of a scene provide information about
its constituent elements. But the amount of information depends on the imaging
conditions and on how information is calculated. This work had two aims. The
first was to derive explicitly estimators of the information available and the
information retrieved from the color values at each point in images of a scene
under different illuminations.
6. Airborne Vehicle Detection in Dense Urban Areas Using HoG Features
Abstract—Vehicle detection has been an important research field for years as
there are a lot of valuable applications, ranging from support of traffic planners
to real-time traffic management. Especially detection of cars in dense urban
areas is of interest due to the high traffic volume and the limited space. In city
areas many car-like objects (e.g., dormers) appear which might lead to
confusion. Additionally, the inaccuracy of road databases supporting the
extraction process has to be handled in a proper way. This paper describes an


integrated real-time processing chainwhich utilizes multiple occurrence of
objects in images.
7. Histology Image Retrieval in Optimized Multifeature Spaces
Abstract—Content-based histology image retrieval systems have shown great
potential in supporting decision making in clinical activities, teaching, and
biological research. In content-based image retrieval, feature combination plays
a key role. It aims at enhancing the descriptive power of visual features
corresponding to semantically meaningful queries. It is particularly valuable in
histology image analysis where intelligent mechanisms are needed for
interpreting varying tissue composition and architecture into histological
concepts. This paper presents an approach to automatically combine
heterogeneous visual features for histology image retrieval. The aim is to obtain
the most representative fusion model for a particular keyword that is associated
with multiple query images. The core of this approach is a multiobjective
learning method, which aims to understand an optimal visual-semantic
matching function by jointly considering the different preferences of the group
of query images. The task is posed as an optimization problem, and a
multiobjective optimization strategy is employed in order to handle potential
contradictions in the query images associated with the same keyword.
8. Automatic License Plate Recognition (ALPR)
Abstract—Automatic license plate recognition (ALPR) is the extraction of
vehicle license plate information from an image or a sequence of images. The
extracted information can be used with or without a database in many
applications, such as electronic payment systems (toll payment, parking fee
payment), and freeway and arterial monitoring systems for traffic surveillance.
The ALPR uses either a color, black and white, or infrared camera to take
images. The quality of the acquired images is a major factor in the success of

the ALPR. ALPR as a reallife application has to quickly and successfully
process license plates under different environmental conditions, such as indoors,
outdoors, day or night time. It should also be generalized to process license
plates from different nations, provinces, or states. These plates usually contain
different colors, are written in different languages, and use different fonts; some
plates may have a single color background and others have background images.
The license plates can be partially occluded by dirt, lighting, and towing
accessories on the car.

9. Context-Based Hierarchical Unequal Merging for SAR Image
Segmentation
Abstract—This paper presents an image segmentation method named Contextbased Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image
Segmentation (CHUMSIS), which uses superpixels as the operation units
instead of pixels. Based on the Gestalt laws, three rules that realize a new and
natural way to manage different kinds of features extracted from SAR images
are proposed to represent superpixel context. The rules are prior knowledge
from cognitive science and serve as top-down constraints to globally guide the
superpixel merging. The features, including brightness, texture, edges, and
spatial information, locally describe the superpixels of SAR images and are
bottom-up forces. While merging superpixels, a hierarchical unequalmerging
algorithm is designed, which includes two stages: 1) coarse merging stage and
2) fine merging stage. The merging algorithm unequally allocates computation
resources so as to spend less running time in the superpixels without ambiguity
and more running time in the superpixels with ambiguity.


10. Context-Dependent Logo Matching and Recognition
Abstract—We contribute, through this paper, to the design of a novel
variational framework able to match and recognize multiple instances of
multiple reference logos in image archives. Reference logos and test images are
seen as constellations of local features (interest points, regions, etc.) and
matched by minimizing an energy function mixing: 1) a fidelity term that
measures the quality of feature matching, 2) a neighborhood criterion that
captures feature co-occurrence/geometry, and 3) a regularization term that
controls the smoothness of the matching solution.
11. Human Detection in Images via Piecewise Linear Support Vector
Machines
Abstract — Human detection in images is challenged by the view and posture
variation problem. In this paper, we propose a piecewise linear support vector
machine (PL-SVM) method to tackle this problem. The motivation is to exploit
the piecewise discriminative function to construct a nonlinear classification
boundary that can discriminate multiview and multiposture human bodies from
the backgrounds in a high-dimensional feature space. A PL-SVM training is
designed as an iterative procedure of feature space division and linear SVM
training, aiming at the margin maximization of local linear SVMs. Each
piecewise SVM model is responsible for a subspace, corresponding to a human
cluster of a special view or posture. In the PL-SVM, a cascaded detector is
proposed with block orientation features and a histogram of oriented gradient
features. Extensive experiments show that compared with several recent SVM
methods, our method reaches the state of the art in both detection accuracy and
computational efficiency, and it performs best when dealing with low-resolution
human regions in clutter backgrounds.


12. Learning-based, automatic 2D-to-3D image and video conversion
Abstract — Despite a significant growth in the last few years, the availability of
3D content is still dwarfed by that of its 2D counterpart. In order to close this
gap, many 2D-to-3D image and video conversion methods have been proposed.
Methods involving human operators have been most successful but also timeconsuming and costly. Automatic methods, that typically make use of a
deterministic 3D scene model, have not yet achieved the same level of quality
for they rely on assumptions that are often violated in practice. In this paper, we
propose a new class of method that are based on the radically different approach
of learning the 2D-to-3D conversion from examples. We develop a method
based on globally estimating the entire depth map of a query image directly
from a repository of 3D images (image + depth pairs or stereopairs) using a
nearest-neighbor regression type idea. We demonstrate both the efficacy and the
computational efficiency of our methods on numerous 2D images and discuss
their drawbacks and benefits. Although far from perfect, our results demonstrate
that repositories of 3D content can be used for effective 2D-to-3D image
conversion. An extension to video is immediate by enforcing temporal
continuity of computed depth maps.
13. Automated Biometric Voice-Based Access Control in ATM
Abstract — An automatic teller machine requires a user to pass an identity test
before any transaction can be granted. The current method available for access
control in ATM is based on smartcard. Efforts were made to conduct an
interview with structured questions among the ATM users and the result
proofed that a lot of problems was associated with ATM smartcard for access
control. Among the problems are; it is very difficult to prevent another person
from attaining and using a legitimate persons card, also conventional smartcard
can be lost, duplicated, stolen or impersonated with accuracy. To address the

problems, the paper proposed the use of biometric voice-based access control
system in automatic teller machine. In the proposed system, access will be
authorized simply by means of an enroll user speaking into a microphone
attached to the automatic teller machine. There are 2 phases in implementation
of the proposed system: first training phase, second testing or operational phase.
14. Steganography using Genetic Algorithm along with Visual
Cryptography
Abstract— Image steganography is an emerging field of research for secure
data hiding and transmission over networks. The proposed system provides the
best approach for Least Significant Bit (LSB) based steganography using
Genetic Algorithm (GA) along with Visual Cryptography (VC). Original
message is converted into cipher text by using secret key and then hidden into
the LSB of original image. Genetic Algorithm and Visual Cryptography has
been used for enhancing the security. Genetic Algorithm is used to modify the
pixel location of stego image and the detection of this message is complex.
Visual Cryptography is used to encrypt the visual information. It is achieved by
breaking the image into two shares based on a threshold. The performance of
the proposed system is experimented by performing steganalysis and
conducting benchmarking test for analysing the parameters like Mean Squared
Error (MSE) and Peak Signal to Noise Ratio (PSNR). The main aim of this
paper is to design the enhanced secure algorithm which uses both
steganography using Genetic Algorithm and Visual Cryptography to ensure
improved security and reliability.
15. Human Skeleton Identification Methods to Reduce Uncomfortable


Light from a Digital Projector
Abstract-- When a speaker stands in front of a projector screen for a
presentation, the eyes will be hurt by the direct light from the digital projector.
This paper proposes a design to reduce the strong light by projecting a black
round mask on the speaker's head. The black round mask is superimposed to the
slide frame by the software of this design and the mask traces the speaker’s
head. The Webcam captures the images from the speaker with the projector
screen. The location of the speaker’s head is determined. This design efficiently
continues to trace the head location. The computer uses this head location and
superimposes a black round mask to reduce the uncomfortable feeling caused
by the strong light of the projector.
16. IMAGE STITCHING WITH COMBINED MOMENT INVARIANTS
AND SIFT FEATURES
Abstract - Image stitching is used to combine multiple photographic images
from camera network with overlapping field of view to produce panoramic
view. With image stitching, the view is enlarged and the amount of information
increases with the no. of images that are stitched. In the existing methods, the
whole images from the adjacent views are considered thus leads to increase in
both time and computational complexity. In this paper, an approach for image
stitching using invariant moments combined with SIFT features is presented to
reduce the time and computational complexity. It is observed that only a small
portion of the adjacent view images are overlapped. Hence, the proposed
method aims in detecting overlapping portion for extracting matching points.
The overlapping regions are determined using gradient based dominant edge
extraction and invariant moments. In the deduced region, the SIFT (Shift
Invariant Feature Transform) features are extracted to determine the matching
features. The registration is carried on with RANSAC (Random Sample

Consensus) algorithm and final output mosaic is obtained by warping the
images. The proposed approach results in reduced time and computational when
compared to existing methods.
17. Vertical-Edge-Based Car-License-Plate Detection Method
Abstract—This paper proposes a fast method for car-licenseplate detection
(CLPD) and presents three main contributions. The first contribution is that we
propose a fast vertical edge detection algorithm (VEDA) based on the contrast
between the grayscale values, which enhances the speed of the CLPD method.
After binarizing the input image using adaptive thresholding (AT), an
unwanted-line elimination algorithm (ULEA) is proposed to enhance the image,
and then, the VEDA is applied. The second contribution is that our proposed
CLPD method processes very-low-resolution images taken by a web camera.
After the vertical edges have been detected by the VEDA, the desired plate
details based on color information are highlighted. Then, the candidate region
based on statistical and logical operations will be extracted. Finally, an LP is
detected. The third contribution is that we compare the VEDA to the Sobel
operator in terms of accuracy, algorithm complexity, and processing time.


Bio-Medical Based Image Processing
18. Lossless medical image compression by IWT
Abstract - The proposed work is to compress the medical data without any
loss(i.e. lossless). Medical information is either in multidimensional or multiresolution form, this creates enormous amount of data. Retrieval, Efficient
storage, management and transmission of this voluminous data are highly
complex. This technique combines integer transforms and JPEGLS Prediction
to enhance the performance of lossless compression.
19. Analyzing Macular Edema In Diabetic Patients
Abstract— Diabetic macular edema (DME) is an advanced symptom of diabetic
retinopathy and can lead to irreversible vision loss. In this paper, a two-stage
methodology for the detection and classification of DME severity from color
fundus images is proposed. DME detection is carried out via a supervised
learning approach using the normal fundus images. A feature extraction
technique is introduced to capture the global characteristics of the fundus
images and discriminate the normal from DME images. Disease severity is
assessed using the neural networks.
20. Wavelet Based Image Fusion for Detection of Brain Tumor
Abstract— Brain tumor, is one of the major causes for the increase in mortality
among children and adults. Detecting the regions of brain is the major challenge
in tumor detection. In the field of medical image processing, multi sensor
images are widely being used as potential sources to detect brain tumor. In this
paper, a wavelet based image fusion algorithm is applied on the Magnetic
Resonance (MR) images and Computed Tomography (CT) images which are
used

as primary sources

to extract the

redundant and complementary

information in order to enhance the tumor detection in the resultant fused

image. The main features taken into account for detection of brain tumor
are location of tumor and size of the tumor, which is further optimized
through fusion of images using various wavelet transforms parameters. We
discuss

and

enforce

the

principle

of

evaluating and

comparing the

performance of the algorithm applied to the images with respect to various
wavelets type used for the wavelet analysis. The performance efficiency of the
algorithm is evaluated on the basis of PSNR values. The obtained results are
compared on the basis of PSNR with gradient vector field and big bang
optimization. The algorithms are analyzed in terms of performance with respect
to accuracy in estimation of tumor region and computational efficiency of the
algorithms.

Power Systems
21. Synchronous Detection and Digital control of Shunt Active Power Filter
in Power Quality Improvement
Abstract—Power Quality means to maintain purely sinusoidal current wave
form in phase with a purely sinusoidal voltage wave form. Power quality
improvement

using

traditional

compensation

methods

include

many

disadvantages like electromagnetic interference, possible resonance, fixed
compensation, bulkiness etc. So power system and power electronic engineers
need to develop adjustable and dynamic solutions using custom power devices.
These power conditioning equipments use static power electronic converters to
improve the power quality of distribution system customers. The devices
include Active Power Filter (APF), dynamic voltage restorer (DVR) and
Unified Power Quality Conditioner (UPQC). APF is a compensator used to
eliminate the disturbances in current. There are basically two types of APFs: the
shunt type and the series type. This paper examines the control of Shunt Active

Power Filter (SAPF) from two different aspects: Synchronous Detection
Method (SDM) and digital control based on instantaneous power theory (p-q
theory). Simulation results using MATLAB SIMULINK demonstrates the
application of these methods to the control of APF. Moreover, this work shows
that digital control provides better power quality improvement than SDM.


Matlab image processing_2013_ieee

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