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This paper proposes a novel method for the recognition of passports based on the fuzzy binarization and the fuzzy RBF network. First, for the extraction of individual codes for recognizing, this paper targets code sequence blocks including individual codes by applying Sobel masking, horizontal smearing and a contour tracking algorithm on the passport image. Then the proposed method binarizes the extracted blocks using fuzzy binarization based on the trapezoid type membership function. Then, as the last step, individual codes are recovered and extracted from the binarized areas by applying CDM masking and vertical smearing. This paper also proposes an enhanced fuzzy RBF network that adapts the enhanced fuzzy ART network for the middle layer. This network is applied to the recognition of individual codes. The results of the experiments for performance evaluation on the real passport images showed that the proposed method has the better performance compared with other approaches. [ABSTRACT FROM AUTHOR]

Based on the directionality of images and combining the direction information with multiple resolution analysis, an image enhancement idea via fusion based on directional filter banks is presented in this paper. Combining with LP analysis further, an image fusion method based on LPDFB is given in the paper. Using the experiments to compare the results, they prove its feasibility and validity. [ABSTRACT FROM AUTHOR]

The iris is considered one of the most reliable and stable biometrics as it is believed to not change significantly during a person's lifetime. Standard techniques for iris recognition, popularized by Daugman, apply Gabor wavelet analysis for feature extraction. In this paper, we consider an alternative method for iris recognition, the use of advanced distortion-tolerant correlation filters for robust pattern matching. These filters offer two primary advantages: shift invariance, and the ability to tolerate within-class image variations. The iris images we use in our experiments are from the CASIA database and also from an iris database we collected at CMU. In this paper, we perform automatic segmentation of the iris (which surrounds the pupil) from the rest of the eye, normalizing for scale and pupil dilation. We then use these segmented iris images to compare the recognition performance of various methods, including Gabor wavelet feature extraction, to correlation filters. [ABSTRACT FROM AUTHOR]

This paper presents the evaluation of face recognition performance using visual and thermal infrared (IR) face images with advanced correlation filter methods. Correlation filters are an attractive tool for face recognition due to features such as shift invariance, distortion tolerance, and graceful degradation. In this paper, we show that correlation filters perform very well when the face images are of significantly low resolution. Performing robust face recognition using low resolution images has many applications including human identification at a distance (HID). Minimum average correlation energy (MACE) filters and optimal trade-off synthetic discriminant function (OTSDF) filters are used in our experiments showing better performance over commercial face recognition algorithms such as FaceIt® based on Local Feature Analysis (LFA) using low resolution images. We also address the problems faced when using thermal images that contain eyeglasses which block the information around the eyes. Therefore we describe in detail a fully automated way of eyeglass detection and removal in thermal images resulting in a significant increase in thermal face recognition performance. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for analyzing the dynamic water motion and transforming the motion in natural scenes to non-photorealistic 2D cartoons. We limit the domain of the original water sequences containing only water surfaces with shallow relief, (the heights of the water wave are relatively small compared to the wave lengths) and one parallel light source. Within this constrained domain, we first automatically rectify the water wave sequence from a generic pose to orthogonal view direction. Then we clearly reveal the relationship between the rectified frames and the surface normal maps. Finally, as an application, a non-photorealistic rendering step is applied to transform the water motion to new cartoon sequences. several results are shown in the paper to demonstrate the quality and widely usability of this novel approach. [ABSTRACT FROM AUTHOR]

Concavity trees have been known for quite some time as structural descriptors of 2-D shape; however, they haven't been explored further until recently. This paper shows how 2-D shapes can be concisely, but reversibly, represented during concavity tree extraction. The representation can be exact, or approximate to a pre-set degree. This is equivalent to a lossless, or lossy compression of the image containing the shape. This paper details the proposed technique and reports near-lossless compression ratios that are 150% better than the JBIG standard on a test set of binary silhouette images. [ABSTRACT FROM AUTHOR]

The paper addresses the computer vision aspects of aligning a hydraulic cylinder prior to being hooked on a conveyer by a robotic arm. The robotic arm is programmed to assume the cylinder's clevis hole is perpendicular to the horizontal base of the stamping station; if the cylinder is not in this orientation, the arm will unsuccessfully attempt to hook the cylinder on the conveyor line, dropping it to the concrete floor. The approach is based on the use of the Hough transform for circle detection. A camera is mounted in a rotational orientation cradle and the different camera positions result in images in which the hole is seen as an ellipse that evolves to a circle as the correct angle is reached. The paper then discusses the effect of implementing circle detection on ellipses, and takes advantage of the count in the Hough parameter space that indicates the correct position. The approach has shown to be very efficient under the restrictions of positioning the cylinder in less than 35 seconds as well as achieving orientation errors less than +/- 5°. [ABSTRACT FROM AUTHOR]

Background updating is an important aspect of dynamic scene analysis. Two critical problems: sudden camera perturbation and the sleeping person problem, which arise frequently in real-world surveillance and monitoring systems, are addressed in the proposed scheme. The paper presents a multi-color model where multiple color clusters are used to represent the background at each pixel location. In the proposed background updating scheme, the updates to the mean and variance of each color cluster at each pixel location incorporate the most recently observed color values. Each cluster is assigned a weight which measures the time duration and temporal recurrence frequency of the cluster. The sleeping person problem is tackled by virtue of the observation that at a given pixel location, the time durations and recurrence frequencies of the color clusters representing temporarily static objects are smaller compared to those of color clusters representing the true background colors when measured over a sufficiently long history. The camera perturbation problem is solved using a fast camera motion detection algorithm, allowing the current background image to be registered with the background model maintained in memory. The background updating scheme is shown to be robust even when the scene is very busy and also computationally efficient, making it suitable for real-time surveillance and monitoring systems. Experimental results on real traffic monitoring and surveillance videos are presented. [ABSTRACT FROM AUTHOR]

In this paper, a new Target Density Function(TDF) is theorized to image the radar targets by a new estimation algorithm. TDF is represented in a specified manner. This method is developed by inspiring of ambiguity functions. TDF is obtained in the range and scanning angle plane different from Fowle-Naparst's methods. Target density function is produced via a linear phased array radar system. This is another gain of the method. In addition to scanning, targeting and imaging properties, by this way, the problem associated with beamforming is bypassed. Keywords: Sensor imaging, radar imaging, SAR-ISAR, phased array radar, dense-target environment, target density function. [ABSTRACT FROM AUTHOR]

This paper explores the statistics of least-significant bit (LSB) steganography. The problem of encoding a bit sequence (message) to match the statistics of a random bit-sequence (cover) is considered. A method of hiding information in the least significant bits (LSBs) of JPEG coefficients is described; the method mimics either the chi-square statistic of JPEG coefficients or their distribution. The method uses two-bit codes to encode the message bits. It is shown to be very effective on JPEG images of natural scenes. [ABSTRACT FROM AUTHOR]

Motion estimation (ME) is the main bottleneck and by far the most time-consuming module in real-time video coding application. Based on sufficient statistical analysis, a novel complexity-controllable ME algorithm is presented in this paper. The proposed algorithm consists of three effective stages: 1) initial stage with zero-motion detection, 2) predictive stage with early termination scheme, and 3) local refined stage by small diamond search. Furthermore, with given complexity constraints, a complexity-controllable scheme is introduced into the search process, which can achieve the preferable tradeoff between motion accuracy and ME complexity. Experimental results demonstrate that our proposed algorithm achieves similar performance with significantly reduced number of search points in comparison with some well-known ME algorithms, such as diamond search, hexagon-based search and enhanced hexagon-based search, etc. Moreover, due to its complexity-controllable feature, our algorithm can be adapted to various devices with a wide range of computational capability for real-time video encoder. [ABSTRACT FROM AUTHOR]

This paper describes a digital image warping method which reduces the geometric and optical distortions in several display devices such as wide screen CRTs, Projection TVs and Projectors. The 2-pass scan line warping algorithm is introduced and it effectively reduces the typical type of display distortions such as keystone, pincushion, or barrel types. The proposed warping algorithm also considers the image scaling function and renders arbitrary image scaling up or down with display distortion correction. The proposed architecture is successfully implemented in hardware and operates at the clock speed around 40 ~ 160MHz. Finally, it is successfully adopted in display distortion correction purposes. [ABSTRACT FROM AUTHOR]

This paper proposes an iterative methodology for real-time robust mosaic topology inference. It tackles the problem of optimal feature selection (optimal sampling) for global estimation of image transformations. This is called IGLOS: iterative global optimal sampling. IGLOS is a unified framework for robust global image registration (optimum feature selection and model computation are considered within the same methodology). The major novelty is that it does not rely on random sampling procedures. Furthermore, by considering an optimal subset of the total number of correspondences, it naturally avoids trivial solution. IGLOS can cope with any motion parameterization and estimation technique. Applications to underwater linear global mosaics and topology estimation are presented. [ABSTRACT FROM AUTHOR]

In a video surveillance system, moving object detection is the most challenging problem especially if the system is applied in complex environments with variable lighting, dynamic and articulate scenes, etc.. Furthermore, a video surveillance system is a real-time application, so discouraging the use of good, but computationally expensive, solutions. This paper presents a set of improvements of a basic background subtraction algorithm that are suitable for video surveillance applications. Besides we present a new evaluation scheme never used in the context of moving object detection algorithms. [ABSTRACT FROM AUTHOR]

Programmable graphics hardware have proven to be a powerful resource for general computing. Previous research has shown that using a GPU for local image processing operations can be much faster than using a CPU. The actual speedup obtained is influenced by many factors. In this paper, we quantify the performance gain that can be achieved by using the GPU for different image processing operations under different conditions. We also compare the strengths and weaknesses of two of the current leaders in mainstream GPUs - ATI's Radeon and nVidia's GeForce FX. Many interesting observations are obtained through the evaluation. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive scene-based nonuniformity and ghosting artifacts correction algorithm for infrared image sequences is presented. The method simultaneously estimates detector parameters and carry out the non-uniformity and ghosting artifacts correction based on the retina-like neural network approach. The method incorporates the use of a new adaptive learning rate rule into the estimation of the gain and the offset of each detector. This learning rule, together with the consideration of the dependence of the detector's parameters on the retinomorphic assumption used for parameter estimation, may sustain an efficient method that could not only increase the original method's ability for estimating the non-uniformity noise, but also increase the capability of mitigating ghosting artifacts. The ability of the method to compensate for nonuniformity and reducing ghosting artifacts is demonstrated by employing several infrared video sequences obtained using two infrared cameras. Keywords: Image Sequence Processing, Infrared Focal Plane Arrays, Neural Network. Topic: Vision and Image Processing, Signal Processing. [ABSTRACT FROM AUTHOR]

An extended visual secret sharing (EVSS) scheme with innocent looking (unsuspicious) cover images was firstly proposed by Naor and Shamir. Most recent papers about EVSS schemes are dedicated to get a higher contrast of the concealed secret or a less size of shadow image. The conventional EVSS scheme uses the whiteness of black and white sub pixels to represent the gray level of the covered image while we use the gray sub pixels instead to achieve the high-quality shadow image. The term "high-quality" means that the shadow has high-quality image such as a photo picture. [ABSTRACT FROM AUTHOR]

Digital images are increasingly being used as steganographic covers for secret communication. The Least Significant Bit (LSB) encoding is one of the most widely used methods for embedding a message in a digital image. However, the direct application of LSB encoding is vulnerable to steganalysis. For example, RS steganalysis is very efficient in detecting the presence of a message in a digital image and to estimate its approximate size. This paper presents a method robust to RS steganalysis, that makes the presence of a message unnoticeable. The method is based on the application of reversible histogram transformation functions to the image, before and after embedding the secret message. The method was tested on 4 greyscale images, with messages of 10%, 30% and 90% of the maximum embedding size. The proposed method proved to be effective in eluding RS steganalysis for all cases tested. [ABSTRACT FROM AUTHOR]

An aspect ratio invariant visual secret sharing (ARIVSS) scheme is a perfectly secure method for sharing secret images, by expanding a secret pixel to m sub pixels in shadow images, with m being the pixel expansion; meantime the aspect ratio of the recovered secret image is fixed. The advantage of ARIVSS is that there is no loss of information when the shape of the secret image is our information; for example, a secret image of a circle is compromised to an ellipse, if m does not have a square value. Two ARIVSS schemes, based on processing one and four pixel blocks, respectively, were previously proposed. In this paper, we have generalized the square block-wise approach, to further reduce pixel expansion. [ABSTRACT FROM AUTHOR]

There has been many hidden communication techniques proposed in the last few years. The focus was given to steganography to build such techniques. Utilizing stego-key(s) to hide secret messages into images strengthen the security of these techniques. However, adopting one of the available key-agreement protocols, to distribute stego-key(s) between the communicating parties, will destroy the infrastructure of the entire communication. The reason is that, these protocols perform their transactions on sight, while the desirable communications need to be completely hidden. In this paper, a key-generation unit is proposed to be added to the steganography general model. This unit utilizes a new key-agreement protocol, stego-KA, to help support the entire class of hidden communication techniques to exchange the sego-key(s) covertly. The proposed stego-KA protocol is based on Diffie-Hellman key establishment protocol and has significant advantages that support hidden communications. [ABSTRACT FROM AUTHOR]

In this paper an intelligent and efficient combination of several methods are employed for face and facial feature tracking with the motivation for real time applications. Face tracking algorithm is based on color and connected component analysis. It is scale, pose and orientation invariant, and can be implemented in real time in controlled environments. The more challenging problem of facial feature tracking uses intensity based adaptive clustering on facial feature sub-images. New search region estimation for each sub-image is proposed. The technique employs facial expression aware eye sub-image prediction. The simulation results indicate that facial feature tracking is efficient with an average tracking rate of 99% with a three pixel range under different head movements such as translation, rotation, tilt, and scale changes. Furthermore it is robust under varying facial expressions and non-uniform illumination. [ABSTRACT FROM AUTHOR]

Facial feature extraction is a fundamental problem in image processing. Correct extraction of features is essential for the success of many applications. Typical feature extraction algorithms fail for low resolution images which do not contain sufficient facial details. In this paper, a region-based super-resolution aided facial feature extraction method for low resolution video sequences is described. The region based approach makes use of segmented faces as the region of interest whereby a significant reduction in computational complexity of the super-resolution algorithm is achieved. The results indicate that the region-based super-resolution aided facial feature extraction algorithm provides significant performance improvement in terms of correctly detecting the location of the facial feature points. There are 6.4 fold reductions in the computational cost. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm detecting facial component to efficiently extract the FCP (Facial Characteristic Point). The FCP plays an important role in facial expression representation or recognition. For efficient FCP extraction using image processing, we analyze and improve the conventional algorithms detecting facial components that are the basis of the FCP extraction. The proposed algorithm includes face region detection without the effect of skin-color hair, eye region detection with weighted template, eyebrow region detection using a modified histogram, and mouth region detection using skin characteristics. [ABSTRACT FROM AUTHOR]

Biometric authentication has been considered a model for quantitatively establishing the discriminative power of biometric data. The dichotomy model classifies two biometric samples as coming either from the same person or from two different people. This paper reviews features, distance measures, and classifiers used in iris authentication. For feature extraction we compare simple binary and multi-level 2D wavelet features. For distance measures we examine scalar distances such as Hamming and Euclidean, feature vector and histogram distances. Finally, for the classifiers we compare Bayes decision rule, nearest neighbor, artificial neural network, and support vector machines. Of the eleven different combinations tested, the best one uses multi-level 2D wavelet features, the histogram distance, and a support vector machine classifier. Keywords: Biometric individuality, Dichotomy model, Histogram, Iris authentication. [ABSTRACT FROM AUTHOR]

In this paper, we propose a secure and efficient protocol to transmit fingerprint images from a fingerprint sensor to a client by exploiting characteristics of fingerprint images. To guarantee the integrity/confidentiality of the fingerprint images transmitted, a standard encryption algorithm is employed. Because the fingerprint sensor is computationally limited, however, such encryption algorithm may not be applied to the full fingerprint images in real-time. To reduce the computational workload on the resource-constrained sensor, we apply the encryption algorithm to a nonce for integrity and to a specific bitplane of each pixel of the fingerprint image for confidentiality. Experimental results show that the integrity/confidentiality of the fingerprint images can be guaranteed without any leakage of the ridge information. Also, the image-based selective bitplane encryption can be completed in real-time on embedded processors. Keywords: Biometrics, Fingerprint Verification, Embedded Processors. [ABSTRACT FROM AUTHOR]

Due to growing demands in such application areas as law enforcement, video surveillance, banking, and security system access authentication, automatic face recognition has attracted great attention in recent years. The advantages of facial identification over alternative methods, such as fingerprint identification, are based primarily on the fact that face is fairly easy to use and well accepted by people. However it is not robust enough to be used in most practical security applications because too sensitive to variations in pose and illumination. During the last few years, many algorithms have been proposed to overcome these problems using 2-D images, but very few has been made in order to address the problem of partial occlusions. In this paper, a fractal based technique is presented; the face image is partitioned in different regions of interest, each one is indexed by means of an IFS system. A new distance function is then introduced, in order to discard unuseful information. The proposed method turns out to be faster and more robust than other approaches in the state of the art. [ABSTRACT FROM AUTHOR]

The present paper introduces a novel set of facial biometrics defined in the frequency domain representing "facial asymmetry". A comparison with previously introduced spatial asymmetry measures suggests that the frequency domain representation provides an efficient approach for performing human identification in the presence of severe expressions and also for expression classification. Feature analysis indicates that asymmetry of the different regions of the face (e.g., eyes, mouth, nose) help in these two apparently conflicting classification problems. Another advantage of our frequency domain measures is that they are tolerant to some form of illumination variations. Error rates of less than 5% are observed for human identification in all cases. We then propose another asymmetry biometric based only on the Fourier domain phase and show a potential connection of asymmetry with phase. [ABSTRACT FROM AUTHOR]

In many realistic face recognition applications, such as surveillance photo identification, the subjects of interest usually have only a limited number of image samples a-priori. This makes the recognition a difficult task, especially when only one image sample is available for each subject. In such a case, the performance of many well known face recognition algorithms will deteriorate rapidly and some of the algorithms even fail to apply. In this paper, we introduced a novel scheme to solve the one training sample problem by combining a specific solution learned from the samples of interested subjects and a generic solution learned from the samples of many other subjects. A multi-learner framework is firstly applied to generate and combine a set of generic base learners followed by a second combination with the specific learner. Extensive experiments based on the FERET database suggests that in the scenario considered here, the proposed solution significantly boosts the recognition performance.This work is partially supported by a Bell University Lab research grant and CITO Student Internship Program. The authors would like to thank the FERET Technical Agent, the U.S. National Institute of Standards and Technology for providing the FERET database. [ABSTRACT FROM AUTHOR]

In this paper we propose a new range-based face recognition for significant improvement in the recognition rate using an optimized stereo acquisition system. The optimized 3D acquisition system consists of an eyes detection algorithm, facial pose direction distinction, and principal component analysis (PCA). The proposed method is carried out in the YCbCr color space in order to detect the face candidate area. To detect the correct face, it acquires the correct distance of the face candidate area and depth information of eyes and mouth. After scaling, the system transfers the pose change according to the distance. The face is finally recognized by the optimized PCA for each area with the facial pose elements detected. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance. [ABSTRACT FROM AUTHOR]

This paper presents a morphological- and color-based method for face localization in color images. Basically, it uses a skin-color segmentation technique in a novel color space, YCg'Cr', and the application of the shape information and the location of the facial features to the definition of a face model. First, a color-based segmentation technique detects the skin regions inside the image, and then, a combination of morphological operators and algorithms is used for completing the segmentation masks. At last, the feature extraction lets define a model based on the best-fit ellipse determined by the position of the eyes and mouth inside the image. Finally, the detection mask based on this ellipse is used for locating the face in the image under test. [ABSTRACT FROM AUTHOR]

Recognition of faces under varied poses has been a challenging area of research due to the complex dispersion of poses in feature space when compared to that of frontal faces. This paper presents a novel and robust pose-invariant face recognition method in order to improvise over existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the direction of face using facial features. The estimated pose vector is decomposed into X-Y-Z axes. Second, the input face is mapped by a deformable template using these vectors and the 3D CANDIDE face model. Finally, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses. Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses. [ABSTRACT FROM AUTHOR]

Neural Stem Cells (NSCs) have a remarkable capacity to proliferate and differentiate to other cell types. This ability to differentiate to desirable phenotypes has motivated clinical interests, hence the interest here to segment Neural Stem Cell (NSC) clusters to locate the NSC clusters over time in a sequence of frames, and in turn to perform NSC cluster motion analysis. However the manual segmentation of such data is a tedious task. Thus, due to the increasing amount of cell data being collected, automated cell segmentation methods are highly desired. In this paper a novel level set based segmentation method is proposed to accomplish this segmentation. The method is initialization insensitive, making it an appropriate solution for automated segmentation systems. The proposed segmentation method has been successfully applied to NSC cluster segmentation. [ABSTRACT FROM AUTHOR]

In this paper a novel approach to the impulsive noise removal in color images is presented. The proposed technique employs the switching scheme based on the impulse detection mechanism using the so called peer group concept. Compared to the vector median filter, the proposed technique consistently yields better results in suppressing both the random-valued and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of large images in real-time applications. The proposed filtering scheme has been successfully applied to the denoising of the cDNA microarray images. Experimental results proved that the new filter is capable of removing efficiently the impulses present in multichannel images, while preserving their textural features. [ABSTRACT FROM AUTHOR]

Since microcalcification clusters are primary indicators of malignant types of breast cancer, its detection is important to prevent and treat the disease. This paper proposes a method for detection of microcalcification clusters in mammograms using sequential Difference of Gaussian filters (DoG). In a first stage, fifteen DoG filters are applied sequentially to extract the potential regions, and later, these regions are classified using the following features: absolute contrast, standard deviation of the gray level of the microcalcification and a moment of contour sequence (asymmetry coefficient). Once the microcalcifications are detected, two approaches for clustering are compared. In the first one, several microcalcification clusters are detected in each mammogram. In the other, all microcalcifications are considered in a single cluster. We demonstrate that the diagnosis based on the detection of several microcalcification clusters in a mammogram is more efficient than considering a single cluster including all the microcalcifications in the image. [ABSTRACT FROM AUTHOR]

Image and statistical analysis are two important aspects of microarray technology. Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. In this paper, an automatic gridding and spot quantification technique is proposed, which takes a microarray image (or a sub-grid) as input, and makes no assumptions about the size of the spots, and number of rows and columns in the grid. The proposed method is based on a weighted energy maximization algorithm that utilizes three different energy functions. The method has been found to effectively detect the grids on microarray images drawn from databases from GEO, Stanford genomic laboratories and on some images obtained from private repositories. [ABSTRACT FROM AUTHOR]

Thin-Layer Chromatography images are used to detect and identify the presence of specific oligosaccharides, expressed by the existence, at different positions, of bands in the gel image. 1D gaussian deconvolution, commonly used for band detection, does not produce good results due to the large curvature observed in the bands. To overcome this uncertainty on the band position, we propose a novel feature extraction methodology that allows an accurate modeling of curved bands. The features are used to classify the data into two different classes, to differentiate normal from pathologic cases. The paper presents the developed methodology together with the analysis and discussion of the results. [ABSTRACT FROM AUTHOR]

X-ray images, such as mammograms, often contain high-intensity radiopaque artifacts in the form of horizontal or vertical stripes, often the result of the digitization process. These artifacts can contribute to difficulties in segmentation and enhancement algorithms. This paper presents an algorithm to suppress stripe artifacts based on weighted median filtering and shows how it affects post-processing segmentation. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic hepatic tumor segmentation method of a computed tomography (CT) image using composite hypotheses. The liver structure is first segmented using histogram transformation, multi-modal threshold, maximum a posteriori decision, and binary morphological filtering. Hepatic vessels are removed from the liver because hepatic vessels are not related to tumor segmentation. In order to find an optimal threshold, composite hypotheses and minimum total probability error are used. Then a hepatic tumor is segmented by using the optimal threshold value. In order to test the proposed method, 272 slices from 10 patients were selected. Experimental results show that the proposed method is very useful for diagnosis of the normal and abnormal liver. [ABSTRACT FROM AUTHOR]

This paper presents two novel feature selection techniques for the purpose of prostate tissue characterization based on Trans-rectal Ultrasound (TRUS) images. First, suspected cancerous regions of interest (ROIs) are identified from the segmented TRUS images using Gabor filters. Next, second and higher order statistical texture features are constructed for these ROIs. Furthermore, a representative feature subset with the best discriminatory power among the constructed features is selected using two artificial life techniques: the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO). Both the PSO and ACO are tailored to fit the binary nature of the feature selection problem. The results are compared to the results obtained using the Genetic Algorithm (GA) feature selection approach. When Support Vector Machine (SVM) classifier is applied for the purpose of tissue characterization, the features obtained using the PSO and ACO outperforms the features obtained using the GA, i.e., they are capable of discriminating between suspicious cancerous and non-cancerous in a better accuracy. The obtained results demonstrate excellent tissue characterization with 83.3% sensitivity, 100% specificity and 94% overall accuracy. [ABSTRACT FROM AUTHOR]

In the diagnosis of malignant melanoma, a skin cancer, the degree of irregularity along the skin lesion border is an important diagnostic factor. This paper presents a new measure of border irregularity based on conditional entropy. The measure was tested on 98 skin lesions of which 16 were malignant melanoma. The ROC analysis showed that the measure is 70% sensitive and 84% specific in discriminating the malignant and benign lesions. These results compare favourably with other measures and indicate that conditional entropy captures some distinguishing features in the boundary of malignant lesions. [ABSTRACT FROM AUTHOR]

One of the key issues in microarray analysis is to extract quantitative information from the spots, which represents gene expression levels in the experiments. The process of identifying the spots and separating the foreground from the background is known as microarray image segmentation. In this paper, we propose a new approach to microarray image segmentation, which we called the adaptive ellipse method, and shows various advantages when compared to the adaptive circle method. Our experiments on real-life microarray images show that adaptive ellipse is capable of extracting information from the images, which is ignored by the traditional adaptive circle method, and hence showing more flexibility. [ABSTRACT FROM AUTHOR]

This paper presents a new cDNA microarray image segmentation framework. The framework uses robust vector median filtering to generate a root sigLnal which is an image obtained from the input by repeatedly filtering it until no more changes occur. During the convergence to the root signal, the framework classifies the cDNA image data as either microarray spots or image background, and ideally separates the regular spots from the background. Thus, the obtained root signal represents the segmented microarray image. In addition, the framework excellently removes noise present in the cDNA microarray images and normalizes spots' intensities. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new scheme for extracting the contour of the carotid artery using ultrasound images. Starting from a user defined seed point within the artery, the scheme uses the fuzzy region growing algorithm to create a fuzzy connectedness map for the image. Then, the fuzzy connectedness map is thresholded using a threshold selection mechanism to segment the area inside the artery. Experimental results demonstrated the efficiency of the proposed scheme in segmenting carotid artery ultrasound images, and it is insensitive to the seed point location, as long as it is located inside the artery. [ABSTRACT FROM AUTHOR]

Orthogonal moments are recognized as useful tools for object representation and image analysis. It has been shown that the recently developed discrete orthogonal moments have better performance than the conventional continuous orthogonal moments. In this paper, a new set of discrete orthogonal polynomials, namely Hahn polynomials, are introduced. The related Hahn moment functions defined on this orthogonal basis set are investigated and applied to image reconstruction. In experiments, the Hahn moments are compared with the other two discrete orthogonal moments: Chebyshev and Krawtchouk moments. The simulation results show that the Hahn moment-based reconstruction method is superior to the other two discrete orthogonal moment-based methods. [ABSTRACT FROM AUTHOR]

A better understanding of cell behavior is very important in drug and disease research. Cell size, shape, and motility may play a key role in stem-cell specialization or cancer development. However the traditional method of inferring these values manually is such an onerous task that automated methods of cell tracking and segmentation are in high demand. Image cytometry is a practical approach to measure and extract cell properties from large volumes of microscopic cell images. As an important application of image cytometry, this paper presents a probabilistic model based cell tracking method to locate and associate HSCs in phase contrast microscopic images. The proposed cell tracker has been successfully applied to track HSCs based on the most probable identified cell locations and probabilistic data association. [ABSTRACT FROM AUTHOR]

Singular (unobservable) movements pose major challenges for consistent 3D human arm tracking using monocular image sequences. In this paper, we present an efficient and robust method for the detection and tracking recovery from one of the singular movements: rotation about humerus with outstretched arm. In our approach using a particle filter for 3D arm tracking, movement constraints (i.e. range of arm joint angles) are not enforced in particle generation. Instead, singularity detection is achieved by looking for particles with joint angles violating these constraints. Once such a singular movement has been detected, inverse kinematics method is used to recover correct arm tracking by transferring invalid particles from unconstrained movement parameter space into valid constrained space. Experimental results have demonstrated the efficacy of our approach in terms of explicit singularity detection, fast recovery of tracking and small number of particles. [ABSTRACT FROM AUTHOR]

This paper presents a system that tracks human heads in real-time under unconstrained environments where target occlusion, varying illumination, and cluttered backgrounds exist. Tracking is formulated as an active visual servo problem based on the integration of a saccade and a smooth pursuit processes. The head is modelled as an ellipse computed from the color clusters of candidate targets using a robust least square ellipse fitting algorithm. The Farnsworth Perceptually Uniform Color Model is employed to represent the color information of the visual objects. Kalman filtering is applied to the head ellipse to track the evolution of the position, size, and orientation of the target such that the occlusion of objects with similar color and shape as those of the target are effectively accommodated. Experiments with tracking scenarios demonstrate the effectiveness of the system. [ABSTRACT FROM AUTHOR]

This paper presents a robust approach to track multiple objects for low resolution, far-field visual surveillance applications. Multiple moving objects are detected by utilizing an adaptive background model and tracked by resolving the correspondence between their trajectory segments using proximity and appearance similarity measures. A new confidence measure is assigned to each possible match between objects and this information is maintained by a graph structure. This graph is utilized to prune and refine the trajectories. Kalman filter is used to handle discontinuities and occlusions. Proposed approach handles problems such as spurious objects, fragmentation, shadow, clutter and occlusions. [ABSTRACT FROM AUTHOR]

In this paper, a localized wavelet thresholding strategy which adopts context-based thresholding operators is proposed. Traditional wavelet thresholding methods, such as VisuShrink, LevelShrink and BayesShrink, apply the conventional hard and soft thresholding operators and only differ in the selection of the threshold. The conventional soft and hard thresholding operators are point operators in the sense that only the value of the processed wavelet coefficient is taken into consideration before thresholding it. In this work, it will be shown that the performance of some of the standard wavelet thresholding methods can be improved by applying a localized, context-based, thresholding strategy instead of the conventional thresholding operators. [ABSTRACT FROM AUTHOR]

In mineral processing industry, it is often useful to be able to obtain statistical information about the size distribution of ore fragments that move relatively to a static but noisy background. In this paper, we introduce a novel approach to estimate the 2D shapes of multiple moving objects in noisy background. Our approach combines adaptive Gaussian mixture model (GMM) for background subtraction and optical flow methods supported by temporal differencing in order to achieve robust and accurate extraction of the shapes of moving objects. The algorithm works well for image sequences having many moving objects with different sizes as demonstrated by experimental results on real image sequences. [ABSTRACT FROM AUTHOR]