Abstract: Now a days, a significant part of commercial and governmental organisations like museums, cultural organizations, libraries, commercial enterprises, etc. invest intensively in new technologies for image digitization, digital libraries, image archiving and retrieval. Hence image authorization, authentication and security has become prime need. In this paper, we present a semi-fragile watermarking scheme for color images. The method converts the host image into YIQ color space followed by application of orthogonal dual domains of DCT and DWT transforms. The DCT helps to separate relevant from irrelevant image content to generate silent image features. DWT has excellent spatial localisation to help aid in spatial tamper characterisation. Thus image adaptive watermark is generated based of image features which allows the sharp detection of microscopic changes to locate modifications in the image. Further, the scheme utilises the multipurpose watermark consisting of soft authenticator watermark and chrominance watermark. Which has been proved fragile to some predefined processing like intentinal fabrication of the image or forgery and robust to other incidental attacks caused in the communication channel.
Abstract: Image clustering is a process of grouping images
based on their similarity. The image clustering usually uses the color
component, texture, edge, shape, or mixture of two components, etc.
This research aims to explore image clustering using color
composition. In order to complete this image clustering, three main
components should be considered, which are color space, image
representation (feature extraction), and clustering method itself. We
aim to explore which composition of these factors will produce the
best clustering results by combining various techniques from the
three components. The color spaces use RGB, HSV, and L*a*b*
method. The image representations use Histogram and Gaussian
Mixture Model (GMM), whereas the clustering methods use KMeans
and Agglomerative Hierarchical Clustering algorithm. The
results of the experiment show that GMM representation is better
combined with RGB and L*a*b* color space, whereas Histogram is
better combined with HSV. The experiments also show that K-Means
is better than Agglomerative Hierarchical for images clustering.
Abstract: The ability to recognize humans and their activities by computer vision is a very important task, with many potential application. Study of human motion analysis is related to several research areas of computer vision such as the motion capture, detection, tracking and segmentation of people. In this paper, we describe a segmentation method for extracting human body contour in modified HLS color space. To estimate a background, the modified HLS color space is proposed, and the background features are estimated by using the HLS color components. Here, the large amount of human dataset, which was collected from DV cameras, is pre-processed. The human body and its contour is successfully extracted from the image sequences.
Abstract: In this paper, we study on color transformation
method on website images for the color blind. The most common
category of color blindness is red-green color blindness which is
viewed as beige color. By transforming the colors of the images, the
color blind can improve their color visibility. They can have a better
view when browsing through the websites. To transform colors on
the website images, we study on two algorithms which are the
conversion techniques from RGB color space to HSV color space and
self-organizing color transformation. The comparative study focuses
on criteria based on the ease of use, quality, accuracy and efficiency.
The outcome of the study leads to enhancement of website images to
meet the color blinds- vision requirements in perceiving image
detailed.
Abstract: In this paper, a novel scheme is proposed for Ownership Identification and Color Image Authentication by deploying Cryptography & Digital Watermarking. The color image is first transformed from RGB to YST color space exclusively designed for watermarking. Followed by color space transformation, each channel is divided into 4×4 non-overlapping blocks with selection of central 2×2 sub-blocks. Depending upon the channel selected two to three LSBs of each central 2×2 sub-block are set to zero to hold the ownership, authentication and recovery information. The size & position of sub-block is important for correct localization, enhanced security & fast computation. As YS ÔèÑ T so it is suitable to embed the recovery information apart from the ownership and authentication information, therefore 4×4 block of T channel along with ownership information is then deployed by SHA160 to compute the content based hash that is unique and invulnerable to birthday attack or hash collision instead of using MD5 that may raise the condition i.e. H(m)=H(m'). For recovery, intensity mean of 4x4 block of each channel is computed and encoded upto eight bits. For watermark embedding, key based mapping of blocks is performed using 2DTorus Automorphism. Our scheme is oblivious, generates highly imperceptible images with correct localization of tampering within reasonable time and has the ability to recover the original work with probability of near one.
Abstract: Automatic segmentation of skin lesions is the first step
towards the automated analysis of malignant melanoma. Although
numerous segmentation methods have been developed, few studies
have focused on determining the most effective color space for
melanoma application. This paper proposes an automatic segmentation
algorithm based on color space analysis and clustering-based histogram
thresholding, a process which is able to determine the optimal
color channel for detecting the borders in dermoscopy images. The
algorithm is tested on a set of 30 high resolution dermoscopy images.
A comprehensive evaluation of the results is provided, where borders
manually drawn by four dermatologists, are compared to automated
borders detected by the proposed algorithm, applying three previously
used metrics of accuracy, sensitivity, and specificity and a new metric
of similarity. By performing ROC analysis and ranking the metrics,
it is demonstrated that the best results are obtained with the X and
XoYoR color channels, resulting in an accuracy of approximately
97%. The proposed method is also compared with two state-of-theart
skin lesion segmentation methods.
Abstract: Understanding the number of people and the flow of
the persons is useful for efficient promotion of the institution
managements and company-s sales improvements. This paper
introduces an automated method for counting passerby using virtualvertical
measurement lines. The process of recognizing a passerby is
carried out using an image sequence obtained from the USB camera.
Space-time image is representing the human regions which are
treated using the segmentation process. To handle the problem of
mismatching, different color space are used to perform the template
matching which chose automatically the best matching to determine
passerby direction and speed. A relation between passerby speed and
the human-pixel area is used to distinguish one or two passersby. In
the experiment, the camera is fixed at the entrance door of the hall in
a side viewing position. Finally, experimental results verify the
effectiveness of the presented method by correctly detecting and
successfully counting them in order to direction with accuracy of
97%.
Abstract: To distinguish small retinal hemorrhages in early
diabetic retinopathy from dust artifacts, we analyzed hue, lightness,
and saturation (HLS) color spaces. The fundus of 5 patients with
diabetic retinopathy was photographed. For the initial experiment, we
placed 4 different colored papers on the ceiling of a darkroom. Using
each color, 10 fragments of house dust particles on a magnifier were
photographed. The colored papers were removed, and 3 different
colored light bulbs were suspended from the ceiling. Ten fragments of
house dust particles on the camera-s object lens were photographed.
We then constructed an experimental device that can photograph
artificial eyes. Five fragments of house dust particles under the ocher
fundus of the artificial eye were photographed. On analyzing HLS
color space of the dust artifact, lightness and saturation were found to
be highly sensitive. However, hue was not highly sensitive.
Abstract: In this paper, a novel scheme is proposed for ownership identification and authentication using color images by deploying Cryptography and Digital Watermarking as underlaying technologies. The former is used to compute the contents based hash and the latter to embed the watermark. The host image that will claim to be the rightful owner is first transformed from RGB to YST color space exclusively designed for watermarking based applications. Geometrically YS ÔèÑ T and T channel corresponds to the chrominance component of color image, therefore suitable for embedding the watermark. The T channel is divided into 4×4 nonoverlapping blocks. The size of block is important for enhanced localization, security and low computation. Each block along with ownership information is then deployed by SHA160, a one way hash function to compute the content based hash, which is always unique and resistant against birthday attack instead of using MD5 that may raise the condition i.e. H(m)=H(m'). The watermark payload varies from block to block and computed by the variance factorα . The quality of watermarked images is quite high both subjectively and objectively. Our scheme is blind, computationally fast and exactly locates the tampered region.
Abstract: This work presents an approach for the construction of a hybrid color-texture space by using mutual information. Feature extraction is done by the Laws filter with SVM (Support Vectors Machine) as a classifier. The classification is applied on the VisTex database and a SPOT HRV (XS) image representing two forest areas in the region of Rabat in Morocco. The result of classification obtained in the hybrid space is compared with the one obtained in the RGB color space.
Abstract: Autofluorescence (AF) bronchoscopy is an
established method to detect dysplasia and carcinoma in situ (CIS).
For this reason the “Sotiria" Hospital uses the Karl Storz D-light
system. However, in early tumor stages the visualization is not that
obvious. With the help of a PC, we analyzed the color images we
captured by developing certain tools in Matlab®. We used statistical
methods based on texture analysis, signal processing methods based
on Gabor models and conversion algorithms between devicedependent
color spaces. Our belief is that we reduced the error made
by the naked eye. The tools we implemented improve the quality of
patients' life.
Abstract: While compressing text files is useful, compressing
still image files is almost a necessity. A typical image takes up much
more storage than a typical text message and without compression
images would be extremely clumsy to store and distribute. The
amount of information required to store pictures on modern
computers is quite large in relation to the amount of bandwidth
commonly available to transmit them over the Internet and
applications. Image compression addresses the problem of reducing
the amount of data required to represent a digital image. Performance
of any image compression method can be evaluated by measuring the
root-mean-square-error & peak signal to noise ratio. The method of
image compression that will be analyzed in this paper is based on the
lossy JPEG image compression technique, the most popular
compression technique for color images. JPEG compression is able to
greatly reduce file size with minimal image degradation by throwing
away the least “important" information. In JPEG, both color
components are downsampled simultaneously, but in this paper we
will compare the results when the compression is done by
downsampling the single chroma part. In this paper we will
demonstrate more compression ratio is achieved when the
chrominance blue is downsampled as compared to downsampling the
chrominance red in JPEG compression. But the peak signal to noise
ratio is more when the chrominance red is downsampled as compared
to downsampling the chrominance blue in JPEG compression. In
particular we will use the hats.jpg as a demonstration of JPEG
compression using low pass filter and demonstrate that the image is
compressed with barely any visual differences with both methods.
Abstract: Because of increasing demands for security in today-s
society and also due to paying much more attention to machine
vision, biometric researches, pattern recognition and data retrieval in
color images, face detection has got more application. In this article
we present a scientific approach for modeling human skin color, and
also offer an algorithm that tries to detect faces within color images
by combination of skin features and determined threshold in the
model. Proposed model is based on statistical data in different color
spaces. Offered algorithm, using some specified color threshold, first,
divides image pixels into two groups: skin pixel group and non-skin
pixel group and then based on some geometric features of face
decides which area belongs to face.
Two main results that we received from this research are as follow:
first, proposed model can be applied easily on different databases and
color spaces to establish proper threshold. Second, our algorithm can
adapt itself with runtime condition and its results demonstrate
desirable progress in comparison with similar cases.
Abstract: In this paper, a semi-fragile watermarking scheme is proposed for color image authentication. In this particular scheme, the color image is first transformed from RGB to YST color space, suitable for watermarking the color media. Each channel is divided into 4×4 non-overlapping blocks and its each 2×2 sub-block is selected. The embedding space is created by setting the two LSBs of selected sub-block to zero, which will hold the authentication and recovery information. For verification of work authentication and parity bits denoted by 'a' & 'p' are computed for each 2×2 subblock. For recovery, intensity mean of each 2×2 sub-block is computed and encoded upto six to eight bits depending upon the channel selection. The size of sub-block is important for correct localization and fast computation. For watermark distribution 2DTorus Automorphism is implemented using a private key to have a secure mapping of blocks. The perceptibility of watermarked image is quite reasonable both subjectively and objectively. Our scheme is oblivious, correctly localizes the tampering and able to recovery the original work with probability of near one.
Abstract: The colors of the human skin represent a special
category of colors, because they are distinctive from the colors of
other natural objects. This category is found as a cluster in color
spaces, and the skin color variations between people are mostly due
to differences in the intensity. Besides, the face detection based on
skin color detection is a faster method as compared to other
techniques. In this work, we present a system to track faces by
carrying out skin color detection in four different color spaces: HSI,
YCbCr, YES and RGB. Once some skin color regions have been
detected for each color space, we label each and get some
characteristics such as size and position. We are supposing that a face
is located in one the detected regions. Next, we compare and employ
a polling strategy between labeled regions to determine the final
region where the face effectively has been detected and located.
Abstract: Content-based Image Retrieval (CBIR) aims at searching image databases for specific images that are similar to a given query image based on matching of features derived from the image content. This paper focuses on a low-dimensional color based indexing technique for achieving efficient and effective retrieval performance. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique. Then the cluster (region) mode is used as representative of the image in 3-D color space. The feature descriptor consists of the representative color of a region and is indexed using a spatial indexing method that uses *R -tree thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. Alternatively, the images in the database are clustered based on region feature similarity using Euclidian distance. Only representative (centroids) features of these clusters are indexed using *R -tree thus improving the efficiency. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The results of these methods are compared. A JAVA based query engine supporting query-by- example is built to retrieve images by color.
Abstract: Panoramic view generation has always offered
novel and distinct challenges in the field of image processing.
Panoramic view generation is nothing but construction of bigger
view mosaic image from set of partial images of the desired view.
The paper presents a solution to one of the problems of image
seascape formation where some of the partial images are color and
others are grayscale. The simplest solution could be to convert all
image parts into grayscale images and fusing them to get grayscale
image panorama. But in the multihued world, obtaining the colored
seascape will always be preferred. This could be achieved by picking
colors from the color parts and squirting them in grayscale parts of
the seascape. So firstly the grayscale image parts should be colored
with help of color image parts and then these parts should be fused to
construct the seascape image.
The problem of coloring grayscale images has no exact solution.
In the proposed technique of panoramic view generation, the job of
transferring color traits from reference color image to grayscale
image is done by palette based method. In this technique, the color
palette is prepared using pixel windows of some degrees taken from
color image parts. Then the grayscale image part is divided into pixel
windows with same degrees. For every window of grayscale image
part the palette is searched and equivalent color values are found,
which could be used to color grayscale window. For palette
preparation we have used RGB color space and Kekre-s LUV color
space. Kekre-s LUV color space gives better quality of coloring. The
searching time through color palette is improved over the exhaustive
search using Kekre-s fast search technique.
After coloring the grayscale image pieces the next job is fusion of
all these pieces to obtain panoramic view. For similarity estimation
between partial images correlation coefficient is used.
Abstract: In this paper an algorithm is used to detect the color defects of ceramic tiles. First the image of a normal tile is clustered using GCMA; Genetic C-means Clustering Algorithm; those results in best cluster centers. C-means is a common clustering algorithm which optimizes an objective function, based on a measure between data points and the cluster centers in the data space. Here the objective function describes the mean square error. After finding the best centers, each pixel of the image is assigned to the cluster with closest cluster center. Then, the maximum errors of clusters are computed. For each cluster, max error is the maximum distance between its center and all the pixels which belong to it. After computing errors all the pixels of defected tile image are clustered based on the centers obtained from normal tile image in previous stage. Pixels which their distance from their cluster center is more than the maximum error of that cluster are considered as defected pixels.
Abstract: Automatic segmentation of skin lesions is the first step
towards development of a computer-aided diagnosis of melanoma.
Although numerous segmentation methods have been developed,
few studies have focused on determining the most discriminative
and effective color space for melanoma application. This paper
proposes a novel automatic segmentation algorithm using color space
analysis and clustering-based histogram thresholding, which is able to
determine the optimal color channel for segmentation of skin lesions.
To demonstrate the validity of the algorithm, it is tested on a set of 30
high resolution dermoscopy images and a comprehensive evaluation
of the results is provided, where borders manually drawn by four
dermatologists, are compared to automated borders detected by the
proposed algorithm. The evaluation is carried out by applying three
previously used metrics of accuracy, sensitivity, and specificity and
a new metric of similarity. Through ROC analysis and ranking the
metrics, it is shown that the best results are obtained with the X and
XoYoR color channels which results in an accuracy of approximately
97%. The proposed method is also compared with two state-ofthe-
art skin lesion segmentation methods, which demonstrates the
effectiveness and superiority of the proposed segmentation method.
Abstract: This paper proposes a copyright protection scheme for color images using secret sharing and wavelet transform. The scheme contains two phases: the share image generation phase and the watermark retrieval phase. In the generation phase, the proposed scheme first converts the image into the YCbCr color space and creates a special sampling plane from the color space. Next, the scheme extracts the features from the sampling plane using the discrete wavelet transform. Then, the scheme employs the features and the watermark to generate a principal share image. In the retrieval phase, an expanded watermark is first reconstructed using the features of the suspect image and the principal share image. Next, the scheme reduces the additional noise to obtain the recovered watermark, which is then verified against the original watermark to examine the copyright. The experimental results show that the proposed scheme can resist several attacks such as JPEG compression, blurring, sharpening, noise addition, and cropping. The accuracy rates are all higher than 97%.