http://www.hindawi.comThe latest articles from Hindawi Publishing Corporation© 2012, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/journals/ivp/2011/537372/We propose a novel approach for video classification that bases on the analysis of the temporal relationships between the basic events in audiovisual documents. Starting from basic segmentation results, we define a new representation method that is called Temporal Relation Matrix (TRM). Each document is then described by a set of TRMs, the analysis of which makes events of a higher level stand out. This representation has been first designed to analyze any audiovisual document in order to find events that may well characterize its content and its structure. The aim of this work is to use this representation to compute a similarity measure between two documents. Approaches for audiovisual documents classification are presented and discussed. Experimentations are done on a set of 242 video documents and the results show the efficiency of our proposals.Zein Al Abidin Ibrahim, Isabelle Ferrane, and Philippe JolyCopyright © 2011 Zein Al Abidin Ibrahim et al. All rights reserved.http://www.hindawi.com/journals/ivp/2011/689780/The gradual migration of television from broadcast diffusion to Internet diffusion offers countless possibilities for the generation of rich navigable contents. However, it also raises numerous scientific issues regarding delinearization of TV streams and content enrichment. In this paper, we study how speech can be used at different levels of the delinearization process, using automatic speech transcription and natural language processing (NLP) for the segmentation and characterization of TV programs and for the generation of semantic hyperlinks in videos. Transcript-based video delinearization requires natural language processing techniques robust to transcription peculiarities, such as transcription errors, and to domain and genre differences. We therefore propose to modify classical NLP techniques, initially designed for regular texts, to improve their robustness in the context of TV delinearization. We demonstrate that the modified NLP techniques can efficiently handle various types of TV material and be exploited for program description, for topic segmentation, and for the generation of semantic hyperlinks between multimedia contents. We illustrate the concept of cross-media semantic navigation with a description of our news navigation demonstrator presented during the NEM Summit 2009.Guillaume Gravier, Camille Guinaudeau, Gwénolé Lecorvé, and Pascale SébillotCopyright © 2011 Guillaume Gravier et al. All rights reserved.http://www.hindawi.com/journals/ivp/2011/642532/This paper proposes an approach for tracking multiple articulated targets using a combined data association and evolving population particle filter. A visual target is represented as a pictorial structure using a collection of parts together with a model of their geometry. Tracking multiple targets in video involves an iterative alternating scheme of selecting valid measurements belonging to a target from a clutter or other measurements that all fall within a validation gate. An algorithm with extended likelihood probabilistic data association and evolving groups of populations of particles representing a multiple-part distribution is designed. Variety in the particles is introduced using constrained genetic operators both in the sampling and resampling steps. We explore the effect of various model parameters on system performance and show that the proposed model achieves better accuracy than other widely used methods on standard datasets.Harish Bhaskar and Lyudmila MihaylovaCopyright © 2011 Harish Bhaskar and Lyudmila Mihaylova. All rights reserved.http://www.hindawi.com/journals/ivp/2010/183605/This paper addresses the problem of 3D face tracking from a monocular view. Dominant tracking algorithms in current literature can be classified as intensity-based or feature-based methods. Intensity-based methods track 3D faces based on the brightness constraint, assuming constant intensity of the face across adjacent frames. Feature-based trackers use local 2D features to determine sparse pairs of corresponding points between two frames and estimate 3D pose from these correspondences. We argue that using either approach alone neglects valuable visual information used in the other method. We therefore propose a novel hybrid tracking approach that integrates multiple visual cues. The hybrid tracker uses a nonlinear optimization framework to incorporate both feature correspondence and brightness constraints, and achieves reliable 3D face tracking in real-time. We conduct a series of experiments to analyze our approach and compare its performance with other state-of-the-art trackers. The experiments consist of synthetic sequences with simulated environmental factors and real-world sequences with estimated ground truth. Results show that the hybrid tracker is superior in both accuracy and robustness, particularly when dealing with challenging conditions such as occlusion and extreme lighting. We close with a description of a real-world human-computer interaction application based on our hybrid tracker.Wei-Kai Liao, Douglas Fidaleo, and Gerard MedioniCopyright © 2010 Wei-Kai Liao et al. All rights reserved.http://www.hindawi.com/journals/ivp/2010/124681/This paper presents a robust approach to detect multiple moving targets from aerial infrared (IR) image sequences. The proposed novel method is based on dynamic Gabor filter and dynamic Gaussian detector. First, the motion induced by the airborne platform is modeled by parametric affine transformation and the IR video is stabilized by eliminating the background motion. A set of feature points are extracted and they are categorized into inliers and outliers. The inliers are used to estimate affine transformation parameters, and the outliers are used to localize moving targets. Then, a dynamic Gabor filter is employed to enhance the difference images for more accurate detection and localization of moving targets. The Gabor filter's orientation is dynamically changed according to the orientation of optical flows. Next, the specular highlights generated by the dynamic Gabor filter are detected. The outliers and specular highlights are fused to indentify the moving targets. If a specular highlight lies in an outlier cluster, it corresponds to a target; otherwise, the dynamic Gaussian detector is employed to determine whether the specular highlight corresponds to a target. The detection speed is approximate 2 frames per second, which meets the real-time requirement of many target tracking systems.Fenghui Yao, Guifeng Shao, Ali Sekmen, and Mohan MalkaniCopyright © 2010 Fenghui Yao et al. All rights reserved.http://www.hindawi.com/journals/ivp/2010/195640/General mutual information- (MI-) based registration methods treat all voxels equally. But each voxel has a different utility depending upon the task. Because of its robustness to noise, low computation time, and agreement with human fixations, the Itti-Koch visual saliency model is used to determine voxel utility of renal perfusion data. The model is able to match identical regions in spite of intensity change due to its close adherence to the center-surround property of the visual cortex. Saliency value is used as a pixel's utility measure in an MI framework for rigid registration of renal perfusion data exhibiting rapid intensity change and noise. We simulated varying degrees of rotation and translation motion under different noise levels, and a novel optimization technique was used for fast and accurate recovery of registration parameters. We also registered real patient data having rotation and translation motion. Our results show that saliency information improves registration accuracy for perfusion images and the Itti-Koch model is a better indicator of visual saliency than scale-space maps.Dwarikanath Mahapatra and Ying SunCopyright © 2010 Dwarikanath Mahapatra and Ying Sun. All rights reserved.http://www.hindawi.com/journals/ivp/2010/626324/We address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which consistently matches or exceeds the performance of traditional unsupervised action categorization methods in various datasets. Our method is inspired by the recent success of link analysis techniques in the image domain. By applying these techniques in the space-time domain, we are able to naturally take into account the spatiotemporal relationships between the video features, while leveraging the power of graph matching for action classification. We present a comprehensive set of experiments demonstrating that our approach is capable of handling cluttered backgrounds, activities with subtle movements, and video data from moving cameras. State-of-the-art results are reported on standard datasets. We also demonstrate our method in a compelling surveillance application with the goal of avoiding fraud in retail stores.Haowei Liu, Rogerio Feris, Volker Krueger, and Ming-Ting SunCopyright © 2010 Haowei Liu et al. All rights reserved.http://www.hindawi.com/journals/ivp/2010/367181/3D retrieval has recently emerged as an important boost for 2D search techniques. This is mainly due to its several complementary aspects, for instance, enriching views in 2D image datasets, overcoming occlusion and serving in many real-world applications such as photography, art, archeology, and geolocalization. In this paper, we introduce a complete “2D photography to 3D object” retrieval framework. Given a (collection of) picture(s) or sketch(es) of the same scene or object, the method allows us to retrieve the underlying similar objects in a database of 3D models. The contribution of our method includes (i) a generative approach for alignment able to find canonical views consistently through scenes/objects and (ii) the application of an efficient but effective matching method used for ranking. The results are reported through the Princeton Shape Benchmark and the Shrec benchmarking consortium evaluated/compared by a third party. In the two gallery sets, our framework achieves very encouraging performance and outperforms the other runs.Thibault Napoléon and Hichem SahbiCopyright © 2010 Thibault Napoléon and Hichem Sahbi. All rights reserved.http://www.hindawi.com/journals/ivp/2009/945717/Active Appearance Models (AAMs) are able to align efficiently known faces under duress, when face pose and illumination are controlled. We propose Adapted Active Appearance Models to align unknown faces in unknown poses and illuminations. Our proposal is based on the one hand on a specific transformation of the active model texture in an oriented map, which changes the AAM normalization process; on the other hand on the research made in a set of different precomputed models related to the most adapted AAM for an unknown face. Tests on public and private databases show the interest of our approach. It becomes possible to align unknown faces in real-time situations, in which light and pose are not controlled.Renaud Séguier, Sylvain Le Gallou, Gaspard Breton, and Christophe GarciaCopyright © 2009 Renaud Séguier et al. All rights reserved.http://www.hindawi.com/journals/ivp/2009/820986/We present a method to improve the accuracy and speed, as well as significantly reduce the memory requirements, for the recently proposed Graph Partitioning Active Contours (GPACs) algorithm for image segmentation in the work of Sumengen and Manjunath (2006). Instead of computing an approximate but still expensive dissimilarity matrix of quadratic size, (Ns2Ms2)/(nsms), for a 2D image of size Ns×Ms and regular image tiles of size ns×ms, we use fixed length histograms and an intensity-based symmetric-centrosymmetric extensor matrix to jointly compute terms associated with the complete NsMs×NsMs dissimilarity matrix. This computationally efficient reformulation of GPAC using a very small memory footprint offers two distinct advantages over the original implementation. It speeds up convergence of the evolving active contour and seamlessly extends performance of GPAC to multidimensional images.Sumit K. Nath and Kannappan PalaniappanCopyright © 2009 Sumit K. Nath and Kannappan Palaniappan. All rights reserved.http://www.hindawi.com/journals/ivp/2009/248581/This paper introduces triangular wavelets, which are two-dimensional nonseparable biorthogonal wavelets defined on the regular triangular lattice. The construction that we propose is a simple nonseparable extension of one-dimensional interpolating wavelets followed by a straightforward generalization. The resulting three oriented high-pass filters are symmetrically arranged on the lattice, while low-pass filters have hexagonal symmetry, thereby allowing an isotropic image processing in the sense that three detail components are distributed uniformly. Applying the triangular filter to images, we explore applications that truly benefit from the triangular wavelets in comparison with the conventional tensor product transforms.Kensuke Fujinoki and Oleg V. VasilyevCopyright © 2009 Kensuke Fujinoki and Oleg V. Vasilyev. All rights reserved.http://www.hindawi.com/journals/ivp/2009/539713/A multichannel texture segmentation algorithm is presented based on the image pyramids produced with the Bamberger directional filter bank. An extensive evaluation of Bamberger pyramids and their design parameters is presented. The impact on segmentation performance of factors like the number of pyramid levels, number of directional channels, redundancy and filter specifications is considered. The proposed system is shown to provide some of the best results reported to date when compared with other multichannel representations under similar evaluation conditions. It is further shown that segmentation results using the maximally decimated directional filter bank rival those of the undecimated case. To the knowledge of the authors, such performance has not been previously observed for decompositions with decimated channels.Jose Gerardo Rosiles and Mark J. T. SmithCopyright © 2009 Jose Gerardo Rosiles and Mark J. T. Smith. All rights reserved.http://www.hindawi.com/journals/ivp/2009/532312/This paper addresses the properties of a subclass of sigmoid-based shrinkage functions: the non zeroforcing smooth sigmoid-based shrinkage functions or SigShrink functions. It provides a SURE optimization for the parameters of the SigShrink functions. The optimization is performed on an unbiased estimation risk obtained by using the functions of this subclass. The SURE SigShrink performance measurements are compared to those of the SURELET (SURE linear expansion of thresholds) parameterization. It is shown that the SURE SigShrink performs well in comparison to the SURELET parameterization. The relevance of SigShrink is the physical meaning and the flexibility of its parameters. The SigShrink functions performweak attenuation of data with large amplitudes and stronger attenuation of data with small amplitudes, the shrinkage process introducing little variability among data with close amplitudes. In the wavelet domain, SigShrink is particularly suitable for reducing noise without impacting significantly the signal to recover. A remarkable property for this class of sigmoid-based functions is the invertibility of its elements. This propertymakes it possible to smoothly tune contrast (enhancement, reduction).Abdourrahmane M. Atto, Dominique Pastor, and Grégoire MercierCopyright © 2009 Abdourrahmane M. Atto et al. All rights reserved.http://www.hindawi.com/journals/ivp/2009/173841/The SONAR images are perturbed by speckle noise. The use of speckle reduction filters is necessary to optimize the image exploitation procedures. This paper presents a new denoising method in the wavelet domain, which tends to reduce the speckle, preserving the structural features and textural information of the scene. Shift-invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in many fields of image processing. Generally, complex wavelet transforms, for example, the Double Tree Complex Wavelet Transform (DT-CWT) have these useful properties. In this paper, we propose the use of the DT-CWT in association with Maximum A Posteriori (MAP) filters. Such systems carry out different quality denoising in regions with different homogeneity degree. We propose a solution for the reduction of this unwanted effect based on diversity enhancement. The corresponding denoising algorithm is simple and fast. Some simulation results prove the performance obtained.Alexandru Isar, Sorin Moga, and Dorina IsarCopyright © 2009 Alexandru Isar et al. All rights reserved.http://www.hindawi.com/journals/ivp/2009/924287/Hidden Markov Models have been employed in many vision applications to model and identify events of interest. Their use is common in applications where HMMs are used to classify previously divided segments of video as one of a set of events being modelled. HMMs can also simultaneously segment and classify events within a continuous video, without the need for a separate first step to identify the start and end of the events. This is significantly less common. This paper is an exploration of the development of HMM frameworks for such complete event recognition. A review of how HMMs have been applied to both event classification and recognition is presented. The discussion evolves in parallel with an example of a real application in psychology for illustration. The complete videos depict sessions where candidates perform a number of different exercises under the instruction of a psychologist. The goal is to isolate portions of video containing just one of these exercises. The exercise involves rotating the head of a kneeling subject to the left, back to centre, to the right, to the centre, and repeating a number of times. By designing a HMM system to automatically isolate portions of video containing this exercise, issues such as the strategy of choice of event to be modelled, feature design and selection, as well as training and testing are reviewed. Thus this paper shows how HMMs can be more extensively applied in the domain of event recognition in video.Naomi Harte, Daire Lennon, and Anil KokaramCopyright © 2009 Naomi Harte et al. All rights reserved.http://www.hindawi.com/journals/ivp/2009/859371/We study the problem of video shot boundary detection using an adaptive edge-oriented framework. Our approach is distinct in its use of multiple multilevel features in the required processing. Adaptation is provided by a careful analysis of these multilevel features, based on shot variability. We consider three levels of adaptation: at the feature extraction stage using locally-adaptive edge maps, at the video sequence level, and at the individual shot level. We show how to provide adaptive parameters for the multilevel edge-based approach, and how to determine adaptive thresholds for the shot boundaries based on the characteristics of the particular shot being indexed. The result is a fast adaptive scheme that provides a slightly better performance in terms of robustness, and a five fold efficiency improvement in shot characterization and classification. The reported work has applications beyond direct video indexing, and could be used in real-time applications, such as in dynamic monitoring and modeling of video data traffic in multimedia communications, and in real-time video surveillance. Experimental results are included.Don Adjeroh, M. C. Lee, N. Banda, and Uma KandaswamyCopyright © 2009 Don Adjeroh et al. All rights reserved.http://www.hindawi.com/journals/ivp/2009/797052/This paper proposes an unsupervized offline video object segmentation method that introduces a number of improvements to existing work in the area. It consists of the following steps. The initial segmentation utilizes object color and motion variance to more accurately classify image pixels in the first frame. Histogram-based merging is then employed to reduce oversegmentation of the first frame. During object tracking, segmentation quality measures based on object color and motion contrast are taken. These measures are then used to enhance video objects through selective pixel reclassification. After object enhancement, cumulative histogram-based merging, occlusion handling, and island detection are used to help group regions into meaningful objects. Compared to two reference methods, greater success and improved accuracy in segmenting video objects are first demonstrated by subjectively examining selected frames from a set of standard video sequences. Objective results are obtained through the use of a set of measures that aim at evaluating the accuracy of object boundaries and temporal stability through the use of color, motion, and histograms.K. Ryan, A. Amer, and L. GagnonCopyright © 2009 K. Ryan et al. All rights reserved.http://www.hindawi.com/journals/ivp/2009/432853/This paper proposes a method to control texture resolution for rendering large-scale 3D urban maps. Since on the 3D maps texture data generally tend to be far larger than geometry information such as vertices and triangles, it is more effective to reduce the texture by exploiting the LOD (Level of Detail) in order to decrease whole data size. For this purpose, we propose a new method to control the resolution of the texture. In our method we classify the textures to four classes based on their salient features. The appropriate texture resolutions are decided based on the classification resulsts, their rendered sizes on a display, and their level of importance. We verify the validity of our texture classification algorithm by applying it to the large-scale 3D urban map rendering.Hiroyuki Inatsuka, Makoto Uchino, Satoshi Ueno, and Masahiro OkudaCopyright © 2009 Hiroyuki Inatsuka et al. All rights reserved.http://www.hindawi.com/journals/ivp/2009/856037/We present a novel profile-based focused crawling system for dealing with the increasingly popular social media-sharing websites. In this system, we treat the user profiles as ranking criteria for guiding the crawling process. Furthermore, we divide a user's profile into two parts, an internal part, which comes from the user's own contribution, and an external part, which comes from the user's social contacts. In order to expand the crawling topic, a cotagging topic-discovery scheme was adopted for social media-sharing websites. In order to efficiently and effectively extract data for the focused crawling, a path string-based page classification method is first developed for identifying list pages, detail pages, and profile pages. The identification of the correct type of page is essential for our crawling, since we want to distinguish between list, profile, and detail pages in order to extract the correct information from each type of page, and subsequently estimate a reasonable ranking for each link that is encountered while crawling. Our experiments prove the robustness of our profile-based focused crawler, as well as a significant improvement in harvest ratio, compared to breadth-first and online page importance computation (OPIC) crawlers, when crawling the Flickr website for two different topics.Zhiyong Zhang and Olfa NasraouiCopyright © 2009 Zhiyong Zhang and Olfa Nasraoui. All rights reserved.http://www.hindawi.com/journals/ivp/2009/140492/Image segmentation has become an indispensable task in many image and video applications. This work develops an image segmentation method based on the modified edge-following scheme where different thresholds are automatically determined according to areas with varied contents in a picture, thus yielding suitable segmentation results in different areas. First, the iterative threshold selection technique is modified to calculate the initial-point threshold of the whole image or a particular block. Second, the quad-tree decomposition that starts from the whole image employs gray-level gradient characteristics of the currently-processed block to decide further decomposition or not. After the quad-tree decomposition, the initial-point threshold in each decomposed block is adopted to determine initial points. Additionally, the contour threshold is determined based on the histogram of gradients in each decomposed block. Particularly, contour thresholds could eliminate inappropriate contours to increase the accuracy of the search and minimize the required searching time. Finally, the edge-following method is modified and then conducted based on initial points and contour thresholds to find contours precisely and rapidly. By using the Berkeley segmentation data set with realistic images, the proposed method is demonstrated to take the least computational time for achieving fairly good segmentation performance in various image types.Yuan Been Chen and Oscal T.-C. ChenCopyright © 2009 Yuan Been Chen and Oscal T.-C. Chen. All rights reserved.http://www.hindawi.com/journals/ivp/2008/749172/The two-dimensional discrete cosine transform (2-D DCT) and its subsequent quantization are widely used in standard video encoders. However, since most DCT coefficients become zeros after quantization, a number of redundant computations are performed. This paper proposes a hybrid statistical model used to predict the zeroquantized DCT (ZQDCT) coefficients for intratransform and to achieve better real-time performance. First, each pixel block at the input of DCT is decomposed into a series of mean values and a residual block. Subsequently, a statistical model based on Gaussian distribution is used to predict the ZQDCT coefficients of the residual block. Then, a sufficient condition under which each quantized coefficient becomes zero is derived from the mean values. Finally, a hybrid model to speed up the DCT and quantization calculations is proposed. Experimental results show that the proposed model can reduce more redundant computations and achieve better real-time performance than the reference in the literature at the cost of negligible video quality degradation. Experiments also show that the proposed model significantly reduces multiplications for DCT and quantization. This is particularly suitable for processors in portable devices where multiplications consume more power than additions. Computational reduction implies longer battery lifetime and energy economy.Jin Li, Moncef Gabbouj, and Jarmo TakalaCopyright © 2008 Jin Li et al. All rights reserved.http://www.hindawi.com/journals/ivp/2008/970353/A new edge-guided morphological filter is proposed to sharpen digital images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the performance of these detected edge deblurring filters is superior to that of other sharpener-type filters.T. A. Mahmoud and S. MarshallCopyright © 2008 T. A. Mahmoud and S. Marshall. All rights reserved.http://www.hindawi.com/journals/ivp/2008/145715/We present a trajectory mapping algorithm for a distributed camera setting that is based on statistical homography estimation accounting for the distortion introduced by camera lenses. Unlike traditional approaches based on the direct linear transformation (DLT) algorithm and singular value decomposition (SVD), the planar homography estimation is derived from renormalization. In addition to this, the algorithm explicitly introduces a correction parameter to account for the nonlinear radial lens distortion, thus improving the accuracy of the transformation. We demonstrate the proposed algorithm by generating mosaics of the observed scenes and by registering the spatial locations of moving objects (trajectories) from multiple cameras on the mosaics. Moreover, we objectively compare the transformed trajectories with those obtained by SVD and least mean square (LMS) methods on standard datasets and demonstrate the advantages of the renormalization and the lens distortion correction.Gabin Kayumbi and Andrea CavallaroCopyright © 2008 Gabin Kayumbi and Andrea Cavallaro. All rights reserved.http://www.hindawi.com/journals/ivp/2008/485921/A decision-based nonlinear algorithm for removal of strip lines, drop lines, blotches, band missing, and impulses in images is presented. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and estimation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. The algorithm uses an adaptive length window whose maximum size is 5×5 to avoid blurring due to large window sizes. However, the restricted window size renders median operation less effective whenever noise is excessive in which case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of mean square error [MSE], peak-signal-to-noise ratio [PSNR], and image enhancement factor [IEF] and compared with standard algorithms already in use. Improved performance of the proposed algorithm is demonstrated. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms required for removal of different artifacts.S. Manikandan and D. EbenezerCopyright © 2008 S. Manikandan and D. Ebenezer. All rights reserved.http://www.hindawi.com/journals/ivp/2008/380867/This paper investigates the application of digital image processing techniques to the detection of neurological disorder. Visual information extracted from the postures and movements of a human gait cycle can be used by an experienced neurologist to determine the mental health of the person. However, the current visual assessment of diagnosing neurological disorder is based very much on subjective observation, and hence the accuracy of diagnosis heavily relies on experience. Other diagnostic techniques employed involve the use of imaging systems which can only be operated under highly constructed environment. A prototype has been developed in this work that is able to capture the subject's gait on video in a relatively simple setup, and from which to process the selected frames of the gait in a computer. Based on the static visual features such as swing distances and joint angles of human limbs, the system identifies patients with Parkinsonism from the test subjects. To our knowledge, it is the first time swing distances are utilized and identified as an effective means for characterizing human gait. The experimental results have shown a promising potential in medical application to assist the clinicians in diagnosing Parkinsonism.Howard Lee, Ling Guan, and Ivan LeeCopyright © 2008 Howard Lee et al. All rights reserved.http://www.hindawi.com/journals/ivp/2008/524793/We incorporate the early zero-block detection technique into the UMHexagonS algorithm, which has already been adopted in H.264/AVC JM reference software, to speed up the motion estimation process. A nearly sufficient condition is derived for early zero-block detection. Although the conventional early zero-block detection method can achieve significant improvement in computation reduction, the PSNR loss, to whatever extent, is not negligible especially for high quantization parameter (QP) or low bit-rate coding. This paper modifies the UMHexagonS algorithm with the early zero-block detection technique to improve its coding performance. The experimental results reveal that the improved UMHexagonS algorithm greatly reduces computation while maintaining very high coding efficiency.Y. M. Lee, Y. J. Tsai, and Y. LinCopyright © 2008 Y. M. Lee et al. All rights reserved.http://www.hindawi.com/journals/ivp/2008/743103/The selection of the optimal features subset and the classification have become an important issue in the field of iris recognition. We propose a feature selection scheme based on the multiobjectives genetic algorithm (MOGA) to improve the recognition accuracy and asymmetrical support vector machine for the classification of iris patterns. We also suggest a segmentation scheme based on the collarette area localization. The deterministic feature sequence is extracted from the iris images using the 1D log-Gabor wavelet technique, and the extracted feature sequence is used to train the support vector machine (SVM). The MOGA is applied to optimize the features sequence and to increase the overall performance based on the matching accuracy of the SVM. The parameters of SVM are optimized to improve the overall generalization performance, and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes. Our experimental results indicate that the performance of SVM as a classifier is better than the performance of the classifiers based on the feedforward neural network, the k-nearest neighbor, and the Hamming and the Mahalanobis distances. The proposed technique is computationally effective with recognition rates of 99.81% and 96.43% on CASIA and ICE datasets, respectively.Kaushik Roy and Prabir BhattacharyaCopyright © 2008 Kaushik Roy and Prabir Bhattacharya. All rights reserved.http://www.hindawi.com/journals/ivp/2008/842029/Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.S. Chabrier, C. Rosenberger, B. Emile, and H. LaurentCopyright © 2008 S. Chabrier et al. All rights reserved.http://www.hindawi.com/journals/ivp/2008/248905/End users of large volume image datasets are often interested only in certain features that can be identified as quickly as possible. For hyperspectral data, these features could reside only in certain ranges of spectral bands and certain spatial areas of the target. The same holds true for volume medical images for a certain volume region of the subject's anatomy. High spatial resolution may be the ultimate requirement, but in many cases a lower resolution would suffice, especially when rapid acquisition and browsing are essential. This paper presents a major extension of the 3D-SPIHT (set partitioning in hierarchical trees) image compression algorithm that enables random access decoding of any specified region of the image volume at a given spatial resolution and given bit rate from a single codestream. Final spatial and spectral (or axial) resolutions are chosen independently. Because the image wavelet transform is encoded in tree blocks and the bit rates of these tree blocks are minimized through a rate-distortion optimization procedure, the various resolutions and qualities of the images can be extracted while reading a minimum amount of bits from the coded data. The attributes and efficiency of this 3D-SPIHT extension are demonstrated for several medical and hyperspectral images in comparison to the JPEG2000 Multicomponent algorithm.Emmanuel Christophe and William A. PearlmanCopyright © 2008 Emmanuel Christophe and William A. Pearlman. All rights reserved.http://www.hindawi.com/journals/ivp/2008/693053/We present in this article a comparative study of well-known supervised evaluation criteria that enable the quantification of the quality of contour detection algorithms. The tested criteria are often used or combined in the literature to create new ones. Though these criteria are classical ones, none comparison has been made, on a large amount of data, to understand their relative behaviors. The objective of this article is to overcome this lack using large test databases both in a synthetic and a real context allowing a comparison in various situations and application fields and consequently to start a general comparison which could be extended by any person interested in this topic. After a review of the most common criteria used for the quantification of the quality of contour detection algorithms, their respective performances are presented using synthetic segmentation results in order to show their performance relevance face to undersegmentation, oversegmentation, or situations combining these two perturbations. These criteria are then tested on natural images in order to process the diversity of the possible encountered situations. The used databases and the following study can constitute the ground works for any researcher who wants to confront a new criterion face to well-known ones.Sébastien Chabrier, Hélène Laurent, Christophe Rosenberger, and Bruno EmileCopyright © 2008 Sébastien Chabrier et al. All rights reserved.