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This paper presents an application of the Hough transform to the tasks of identifying irregular patterns. The presented method is based on the Hough transform for irregular objects, with a parameter space defined by translation, rotation and scaling operations. The technique may be used in a robotic system, identification system or for image analysis, directly on grey-level images. An example application of the Hough transform to a robot monitoring within computer vision systems is presented. A hardware implementation of the Hough technique is introduced which accelerates the calculations considerably. [ABSTRACT FROM AUTHOR]

This paper presents a new concept of skeletonization which produces a graph containing all the topological information needed to derive a skeleton of noisy shapes, the proposed statistical method is based on Legendre moment theory controlled by Maximum Entropy Principle (M.E.P.). We propose a new approach for estimating the underlying probability density function (p.d.f.) of input data set. Indeed the p.d.f. is expanded in terms of Legendre polynomials by means of the Legendre moments. Then the order of the expansion is selected according to the (M.E.P.). The points corresponding to the local maxima of the selected p.d.f. will be true points of the skeleton to be extracted by the proposed algorithm. We have tested the proposed Legendre Moment Skeletonization Method (LMSM) on a variety of real and simulated noisy images, it produces excellent and visually appealing results, with comparison to some well known methods. [ABSTRACT FROM AUTHOR]

A shape-adaptive search is defined based on the BISK scheme and it is applied to sign encoding and magnitude refinement of images. It can be generalized to a complete bitplane encoder whose performance is comparable to that of other state-of-the-art encoders. [ABSTRACT FROM AUTHOR]

Rate-distortion optimization is the key technique in video coding standards to efficiently determine a set of coding parameters. In the R-D optimization for H.264 I-frame encoder, the distortion (D) is measured as the sum of the squared differences (SSD) between the reconstructed and the original blocks, which is same as MSE. Recently, a new image measurement called Structural Similarity (SSIM) based on the degradation of structural information was brought forward. It is proved that the SSIM can provide a better approximation to the perceived image distortion than the currently used PSNR (or MSE). In this paper, a new rate-distortion optimization for H.264 I-frame encoder using SSIM as the distortion metric is proposed. Experiment results show that the proposed algorithm can reduced 2.2~6.45% bit rate while maintaining the perceptual quality. [ABSTRACT FROM AUTHOR]

In recent times, computer vision and pattern recognition (CVPR) technologies made automatic feature extraction, events detection possible in real-time, on-the-fly video processing and streaming systems. However, these multiple and computational expensive video processing tasks require specialized processors to ensure higher frame rate output. We propose a framework for achieving high video frame rate using a single processor high-end PC while multiple, computational video tasks such as background subtraction, object tracking, recognition and facial localization have been performed simultaneously. We show the framework in detail, illustrating our unique scheduler using latency insensitive tasks distribution and the execution content parameters generation function (PGF). The experiments have indicated successful results using high-end consumer type PC. [ABSTRACT FROM AUTHOR]

We describe in this paper the building of a vision sensor able to provide video capture and the associated global motion between two consecutive frames. Our objective is to propose embedded solutions for mobile applications. The global motion considered here is the one typically produced by handheld devices movement, which is required for our purpose of video stabilization. We extract this global motion from local motion measures at the periphery of the image acquisition area. Thanks to this peculiar and "task-oriented" configuration, the resulting system architecture can take advantage of CMOS focal plane processing capabilities without sacrificing the sensor fill factor. Our approach is currently implemented in a CMOS 0.13μm technology. [ABSTRACT FROM AUTHOR]

In conventional iris recognition camera, it is very difficult to capture the focused iris image at fast speed due to small range of DOF(Depth Of Field) in iris camera. So. we introduce a fast focusing method to capture user's focused iris image based on the corneal specular reflection and the human eye model. According to experimental results, we can reduce the focusing time of proposed method to be 450 ms on average. [ABSTRACT FROM AUTHOR]

The XETAL chip by Philips Electronics is a low-cost hardware-solution for image processing on pixel level. The architecture of XETAL focuses on a low-energy environment and it is therefore highly suited for integration into mobile vision and intelligent cameras. While hardware support for 2D-vision has reached the level of affordable state-of-the-art technology by thorough research, also real-time 3D-vision by stereo, based on the support by a low-cost and low-energy hardware, appears to be able to reach this level soon. [ABSTRACT FROM AUTHOR]

Image processing is widely used in many applications, including medical imaging, industrial manufacturing and security systems. In these applications, the size of the image is often very large, the processing time should be very small and the real-time constraints should be met. Therefore, during the last decades, there has been an increasing demand to exploit parallelism in applications. It is possible to explore parallelism along three axes: data-level parallelism (DLP), instruction-level parallelism (ILP) and task-level parallelism (TLP). This paper explores the limitations and bottlenecks of increasing support for parallelism along the DLP and ILP axes in isolation and in combination. To scrutinize the effect of DLP and ILP in our architecture (template), an area model based on the number of ALUs (ILP) and the number of processing elements (DLP) in the template is defined, as well as a performance model. Based on these models and the template, a set of kernels of image processing applications has been studied to find Pareto optimal architectures in terms of area and number of cycles via multi-objective optimization. [ABSTRACT FROM AUTHOR]

In this paper we present a recursive algorithm for the cleaning and the enhancing of historical documents. Most of the algorithms, used to clean and enhance documents or transform them to binary images, implement combinations of complicated image processing techniques which increase the computational cost and complexity. Our algorithm simplifies the procedure by taking into account special characteristics of the document images. Moreover, the fact that the algorithm consists of iterated steps, makes it more flexible concerning the needs of the user. At the experimental results, comparison with other methods is provided. [ABSTRACT FROM AUTHOR]

This paper aims to provide a document restoration and segmentation algorithm for the Historic Middle Persian or Pahlavi manuscripts. The proposed algorithm uses the mathematical morphology and connected component concept to segment the line, word, and character overlapped in the Middle-age Persian documents in preparation for OCR application. To evaluate the performance of the restoration algorithm, 200 pages of the Pahlavi documents are used as experimental data in our test. Numerical results indicate that the proposed algorithm can remove the noise and destructive effects. The results also show 99.14% accuracy on the baseline detection, 97.35% accuracy on the text line extraction and removing other lines overlaps, and 99.5% accuracy for segmenting the extracted text lines to their components. [ABSTRACT FROM AUTHOR]

In this paper we present a novel video denoising method based on a fuzzy logic recursive motion detection scheme. For each pixel a fuzzy quantity (motion confidence) is calculated, indicating the membership degree of the fuzzy set "motion". Next, this fuzzy quantity is used to perform adaptive temporal filtering, where the amount of filtering is inversely proportional to the determined membership degree. Since big motion changes reduce temporal filtering, a non-stationary noise will be introduced. Hence a new fuzzy spatial filter is applied subsequently in order to obtain the final denoised image sequence. Experimental results show that the proposed method outperform other state of the art non-multiscale video denoising techniques and are comparable with some multi-scale (wavelet) based video denoising techniques. [ABSTRACT FROM AUTHOR]

In this paper we discuss an extensive comparative study of 38 different classical and fuzzy filters for noise reduction, both for impulse noise and gaussian noise. The goal of this study is twofold: (1) we want to select the filters that have a very good performance for a specific noise type of a specific strength; (2) we want to find out whether fuzzy filters offer an added value, i.e. whether fuzzy filters outperform classical filters. The first aspect is relevant since large comparative studies did not appear in the literature so far; the second aspect is relevant in the context of the use of fuzzy techniques in image processing in general. [ABSTRACT FROM AUTHOR]

Multiresolution video denoising is becoming an increasingly popular research topic over recent years. Although several wavelet based algorithms reportedly outperform classical single-resolution approaches, their concepts are often considered as prohibitive for real-time processing. Little research has been done so far towards hardware customization of wavelet domain video denoising. A number of recent works have addressed the implementation of critically sampled orthogonal wavelet transforms and the related image compression schemes in Field Programmable Gate Arrays (FPGA). However, the existing literature on FPGA implementations of overcomplete (non-decimated) wavelet transforms and on manipulations of the wavelet coefficients that are more complex than thresholding is very limited. In this paper we develop FPGA implementation of an advanced wavelet domain noise filtering algorithm, which uses a non-decimated wavelet transform and spatially adaptive Bayesian wavelet shrinkage. The standard composite television video stream is digitalized and used as source for real-time video sequences. The results demonstrate the effectiveness of the developed scheme for real time video processing. [ABSTRACT FROM AUTHOR]

A new impulse detector design method for image impulse noise is presented. Robust statistics of local pixel neighborhood present features in a binary classification scheme. Classifier is developed through the evolutionary process realized by genetic programming. The proposed filter shows very good results in suppressing both fixed-valued and random-valued impulse noise, for any noise probability, and on all test images. [ABSTRACT FROM AUTHOR]

We assume the presence of mirror symmetry in the viewed scene. In this scene we consider planar point sets and their mirror reflections. We observe the existence of a homology that maps the image of such a planar point set to the image of its mirror reflection. We show how to compute the vertex and the axis of this homology. Finally, the homology is used to reduce image noise by "symmetrization". [ABSTRACT FROM AUTHOR]

In this paper, we present a new restoration technique for colour images. This technique is developed for restoring colour images that are corrupted with impulse noise. The estimated histograms for the colour component differences (red-green, red-blue and green-blue) are used to construct fuzzy sets. Those fuzzy sets are then incorporated in a fuzzy rule based system in order to filter out the impulse noise. Experiments finally show the shortcomings of the conventional methods in contrast to the proposed method. [ABSTRACT FROM AUTHOR]

The problem of denoising of video signals corrupted by additive Gaussian noise is considered in this paper. A novel 3D DCT-based video-denoising algorithm is proposed. Video data are locally filtered in sliding/running 3D windows (arrays) consisting of highly correlated spatial layers taken from consecutive frames of video. Their selection is done by the use of a block matching or similar techniques. Denoising in local windows is performed by a hard thresholding of 3D DCT coefficients of each 3D array. Final estimates of reconstructed pixels are obtained by a weighted average of the local estimates from all overlapping windows. Experimental results show that the proposed algorithm provides a competitive performance with state-of-the-art video denoising methods both in terms of PSNR and visual quality. [ABSTRACT FROM AUTHOR]

A new background modeling approach is presented in this paper. In most background modeling approaches, input images are categorized into foreground and background regions using pixel-based operations. Because pixels on the input image are considered individually, parts of foreground regions are frequently turned into the background, and these errors cause incorrect foreground detections. The proposed approach reduces these errors and improves the accuracy of a background modeling. Each input image is categorized into three regions in the proposed approach instead of two regions, background and foreground regions. The proposed approach divides traditional foreground regions into two sub-regions, intermediate background and foreground regions, using activity measurements computed from optical flows at each pixel. The other difference of the proposed approach is grouping pixels into objects and using those objects at the background updating procedure. Pixels on each object are turned into the background at the same rate. The rate of each object is computed differently depending on its category. By controlling the rate of turning input pixels into the background accurately, the proposed approach can model the background accurately. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new tracking method based on dynamic Bayesian network. Dynamic Bayesian network provides a unified probabilistic framework in integrating multi-modalities by using a graphical representation of the dynamic systems. For visual tracking, we adopt a dynamic Bayesian network to fuse multi-modal features and to handle various appearance target models. We extend this framework to multiple camera environments to deal with severe occlusions of the object of interest. The proposed method was evaluated under several real situations and promising results were obtained. [ABSTRACT FROM AUTHOR]

The performance improvement of a number of motion estimation algorithms are evaluated following vector/SIMD instruction set extensions for MPEG-2 TM5 video encoding. Simulation-based results indicate a substantial complexity metric reduction for Full-Search, Three Step Search, Four Step Search and Diamond Search making the later three appropriate for execution on a high performance embedded VLSI platform. A simple model is developed to explain the simulated results, and a compound performance/power metric, the complexity-power-product (CPP), is proposed for algorithmic optimisation in vectorized applications for low-power, consumer devices. [ABSTRACT FROM AUTHOR]

A generic bi-directional scheme is proposed that robustifies the estimation of the maximum-a-posteriori (MAP) sequence of states of a visual object. It enables creative, non-technical users to obtain the path of interesting objects in offline available video material, which can then be used to create interactive movies. To robustify against tracker failure the proposed scheme merges the filtering distributions of a forward tracking particle filter and a backward tracking particle filter at some timesteps, using a reliability-based voting scheme such as in democratic integration. The MAP state sequence is obtained using the Viterbi algorithm on reduced state sets per timestep derived from the merged distributions and is interpolated linearly where tracking failure is suspected. The presented scheme is generic, simple and efficient and shows good results for a color-based particle filter. [ABSTRACT FROM AUTHOR]

Mesh motion estimation is a tracking technique useful in particular for low bitrate compression, object-based coding and virtual views synthesis. In this article we present a new triangular mesh tracking algorithm preserving the mesh connectivity. Our method generalizes the rigid template tracking method proposed by Jurie and Dhome [5] to the case of non-rigid objects. Thanks to a learning step that can be done off-line for a given number of nodes, the tracking step can be performed in real time. [ABSTRACT FROM AUTHOR]

In this paper we present a new and complete scheme of mode decision and motion estimation compliant with H.264/AVC encoding and decoding systems based on a moving object segmentation. It is particularly suited for applications, like surveillance, where a moving object segmentation is available. The knowledge of the moving object areas and background areas allows to reduce the set of modes permitted by the standard. This sub-set is selected in order to obtain a more accurately motion estimation for active objects and less intensive for quasi-static background. The number of comparisons needed to find the best motion vectors is reduced, conforming an encoding process simple and fast, compliant with the real time requirements of a surveillance application. An improved prediction of the motion vector is computed based on the result of the object segmentation. This avoids erroneous predictions to be carried out. Results will show that the number of comparisons needed to perform inter prediction is reduced by 60%-70% depending on the sequence, keeping the same image quality and bit-rate obtained without using segmentation. [ABSTRACT FROM AUTHOR]

Shape recognition and motion estimation are two of the most difficult problems in computer vision, especially for arbitrary shapes undergoing severe occlusion. Much work has concentrated on tracking over short temporal scales and the analysis of 2D image-plane motion from a single camera. In contrast, in this paper we consider the global analysis of extended stereo image sequences and the extraction of specified objects undergoing linear motion in full 3D. We present a novel Hough Transform based algorithm that exploits both stereo geometry constraints and the invariance properties of the cross-ratio to accumulate evidence for a specified shape undergoing 3D linear motion (constant velocity or otherwise). The method significantly extends some of the ideas originally developed in the Velocity Hough Transform, VHT, where detection was limited to 2D image motion models. We call our method the 3D Stereo Velocity Hough Transform, 3DSVHT. We demonstrate 3DSVHT on both synthetic and real imagery and show that it is capable of detecting objects undergoing linear motion with large depth variation and in image sequences where there is significant object occlusion. [ABSTRACT FROM AUTHOR]

Motion estimation (ME) is by far the main bottleneck in real-time video coding applications. In this paper, a configurable complexity-bounded motion estimation (CCBME) algorithm is presented. This algorithm is based on prediction-refinement techniques, which make use of spatial correlation to predict the search center and then use local refinement search to obtain the final motion field. During the search process, the ME complexity is ensured bounded through three configuration schemes: 1) configure the number of predictors; 2) configure the search range of local refinement; 3) configure the subset pattern of matching criterion computation. Different configuration leads to different distortion. Through joint optimization, we obtain a near-optimal complexity-distortion (C-D) curve. Based on the C-D curve, we preserve 6 effective configurable modes to realize the complexity scalability, which can achieve a good tradeoff between ME accuracy and complexity. Experimental results have shown that our proposed CCBME exhibits higher efficiency than some well-known ME algorithms when applied on a wide set of video sequences. At the same time, it possesses the configurable complexity-bounded feature, which can adapt to various devices with a wide range of computational capability for real-time video coding applications. [ABSTRACT FROM AUTHOR]

Many vision problems require computing fast template motion in dynamic scenes. These problems can be formulated as exploration problems and thus can be expressed as a search into a state space based representation approach. However, these problems are hard to solve because they involve search through a high dimensional space. In this paper, we propose a heuristic algorithm through the space of transformations for computing target 2D motion. Three features are combined in order to compute efficient motion: (1) a quality of function match based on a holistic similarity measurement, (2) Kullback-Leibler measure as heuristic to guide the search process and (3) incorporation of target dynamics into the search process for computing the most promising search alternatives. The paper includes experimental evaluations that illustrate the efficiency and suitability for real-time vision based tasks. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance (fMRI) data are often corrupted with colored noise. To account for this type of noise, many pre-whitening and pre-coloring strategies have been proposed to process the fMRI time series prior to statistical inference. In this paper, a generalized likelihood ratio test for brain activation detection is proposed in which the temporal correlation structure of the noise is modelled as an autoregressive (AR) model. The order of the AR model is determined from experimental null data sets. Simulation tests reveal that, for a fixed false alarm rate, the proposed test is slightly (2-3%) better than current tests incorporating colored noise in terms of detection rate. [ABSTRACT FROM AUTHOR]

In this paper we introduce a system for assisting the analysis of capsule-endoscopy (CE) data, and identifying sequences of frames related to small intestine motility. The imbalanced recognition task of intestinal contractions was addressed by employing an efficient two-level video analysis system. At the first level, each video was processed resulting in a number of possible sequences of contractions. In the second level, the recognition of contractions was carried out by means of a SVM classifier. To encode patterns of intestinal motility a panel of textural and morphological features of the intestine lumen were extracted. The system exhibited an overall sensitivity of 73.53% in detecting contractions. The false alarm ratio was of the order of 59.92%. These results serve as a first step for developing assisting tools for computer based CE video analysis, reducing drastically the physician's time spent in image evaluation and enhancing the diagnostic potential of CE examination. [ABSTRACT FROM AUTHOR]

In this paper, we present an affine image coregistration technique for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data sets based on mutual information. The technique is based on a multi-channel approach where the diffusion weighted images are aligned according to the corresponding acquisition gradient directions. Also, in addition to the coregistration of the DT-MRI data sets, an appropriate reorientation of the diffusion tensor is worked out in order to remain consistent with the corresponding underlying anatomical structures. This reorientation strategy is determined from the spatial transformation while preserving the diffusion tensor shape. The method is fully automatic and has the advantage to be independent of the applied diffusion framework. [ABSTRACT FROM AUTHOR]

This paper describes a clustering approach for color image segmentation using fuzzy classification principles. The method uses classification to group pixels into homogeneous regions. Both global and local information are taken into account. This is particularly helpful in taking care of small objects and local variation of color images. Color, mean and standard deviation are used as a data source. The classification is achieved by a new version of self-organizing maps algorithm . This new algorithm is equivalent to classic fuzzy C-mean algorithm (FCM) whose objective function has been modified. Code vectors that constitute centers of classes, are distributed on a regular low dimension grid. In addition, a penalization term is added to guarantee a smooth distribution of the values of the code vectors on the grid. Tests achieved on color images, followed by an automatic evaluation revealed the good performances of the proposed method. [ABSTRACT FROM AUTHOR]

A fully unsupervised image segmentation algorithm is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited together with the cluster analysis and validity techniques. The true number of textures in a given image is determined by calculating the likelihood disparity of textures using the modified partition fuzzy degree (MPFD) function at one suitable scale. Then, possibilistic C-means (PCM) clustering is performed to determine the training sample data from different textures according to the true number of textures obtained. The unsupervised segmentation is changed into self-supervised one, and the HMTseg algorithm is used to achieve the final segmentation results. This algorithm is applied to segment a variety of composite texture images into distinct homogeneous regions and good segmentation results are reported. [ABSTRACT FROM AUTHOR]

Piecewise constant Mumford-Shah segmentation[17] has been rediscovered by Chan and Vese [6] in the context of region based active contours. The work of Chan and Vese demonstrated many practical applications thanks to their clever numerical implementation using the level-set technology of Osher and Sethian [18]. The current work proposes a Γ-convergence formulation to the piecewise constant Mumford-Shah model, and demonstrates its simple implementation by the iterated integration of a linear Poisson equation. The new formulation makes unnecessary some intermediate tasks like normal data extension and level-set reinitialization, and thus lowers the computational complexity. [ABSTRACT FROM AUTHOR]

A new segmentation strategy is proposed to precisely extract moving objects in video sequences. It is based on the automatic detection of the static elements, and its classification as background and foreground using static differences and contextual information. Additionally, tracking information is incorporated to reduce the computational cost. Finally, segmentation is refined through a Markov random field (MRF) change detection analysis including the foreground information, which allows improving the accuracy of the segmentation. This strategy is presented in the context of low quality sequences of surveillance applications but it could be applied to other applications, the only requirement being to have a static or quasi static background. [ABSTRACT FROM AUTHOR]

Active contours are useful tools for segmenting images. The classical formulation is given in the spatial domain and is based on a second order system. The formulation based on a frequency-domain analysis offers a new perspective for studying the convergence of the snake. This paper addresses an analysis and optimization for a snake-based segmentation algorithm. The study allows us to choose optimum values of the system dynamic parameters in the design of the active contour for improving its speed of convergence in a segmentation problem. [ABSTRACT FROM AUTHOR]

In this paper we present a new implementation of a rainfalling watershed segmentation algorithm. Our previous algorithm was a one-run algorithm. All the steps needed to compute a complete watershed segmentation were done in one run over the input data. In our new algorithm we tried another approach. We separated the watershed algorithm in several low-complexity relabeling steps that can be performed sequentially on a label image. The new implementation is approximately two times faster for parameters that produce visually good segmentations. The new algorithm also handles plateaus in a better way. First we describe the general layout of a rainfalling watershed algorithm. Then we explain the implementations of the two algorithms. Finally we give a detailed report on the timings of the two algorithms for different parameters. [ABSTRACT FROM AUTHOR]

Variational image segmentation combining boundary and region information was and still is the subject of many recent works. This combination is usually subject to arbitrary weighting parameters that control the boundary and region features contribution during the segmentation. However, since the objective functions of the boundary and the region features is different in nature, their arbitrary combination may conduct to local conflicts that stem principally from abrupt illumination changes or the presence of texture inside the regions. In the present paper, we investigate an adaptive estimation of the weighting parameters (hyper-parameters) on the regions data during the segmentation by using a Bayesian method. This permits to give adequate contributions of the boundary and region features to segmentation decision making for pixels and, therefore, improving the accuracy of region boundary localization. We validated the approach on examples of real world images. [ABSTRACT FROM AUTHOR]

A novel frequency domain technique for image blocking artifact reduction is presented in this paper. For each block, its DC and AC coefficients are recalculated for artifact reduction. To achieve this, a closed form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the Mean Squared Difference of Slope (MSDS), for every frequency separately. Experimental results illustrating the performance of the proposed method are presented and evaluated. [ABSTRACT FROM AUTHOR]

We describe a method for removing quantization artifacts (de-quantizing) in the image domain, by enforcing a high degree of sparseness in its representation with an overcomplete oriented pyramid. For this purpose we devise a linear operator that returns the minimum L2-norm image preserving a set of significant coefficients, and estimate the original by minimizing the cardinality of that subset, always ensuring that the result is compatible with the quantized observation. We implement this solution by alternated projections onto convex sets, and test it through simulations with a set of standard images. Results are highly satisfactory in terms of performance, robustness and efficiency. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to introduce a new method for flexible storage of still images. The complete design of the system is described with the scalable encoding, the distortion computation, the bits allocation strategy, and the method for the memory management. The main improvement is the full exploitation of a perceptual metric to assess precisely the introduced distortion when removing a layer in a scalable coding stream. Experimental results are given and compared with a system which uses the PSNR as distortion metric. [ABSTRACT FROM AUTHOR]

This contribution addressees the problem of the theoretical assessment of the bit rate reduction that can be achieved through foveated image compression for codecs operating in the DCT domain. Modeling the image components as Compound Gaussian Random Fields (CGRFs), we extend the mathematical analysis of the DCT coefficient distributions reported in [1] to foveated images. As a general result, we demonstrate that the DCT coefficients of low pass filtered image blocks can be effectively modelled with Laplacian distributions. This property allows us to express the Shannon rate reduction achievable with foveated compression in a simple and compact form as function of the foveal filter coefficients. Experiments results used to validate the theoretical analysis are also included. [ABSTRACT FROM AUTHOR]

The H.264 video compression standard provides tools for coding improvements of at least 2 dB, in terms of PSNR, and at least 50% in bit rate savings as compared with MPEG-2 video compression standard. It is expected that the H.264/MPEG-4 AVC will take over the digital video market, replacing the use of MPEG-2 in most digital video applications. The complete migration to the new video-coding algorithm will take several years given the wide scale use of MPEG-2 in the market place today. This creates an important need for transcoding technologies for converting the large volume of existent video material from the MPEG-2 into the H.264 format and vice versa. However, given the significant differences between the MPEG-2 and the H.264 encoding algorithms, the transcoding process of such systems is much more complex to other heterogeneous video transcoding processes. In this paper, we introduce and evaluate two versions of a fast intra-frame mode decision algorithm to be used as part of a high-efficient MPEG-2 to H.264 transcoder. In this work, we utilize an architecture of pixel domain video transcoding but we use the DC coefficient of the MPEG-2 DCT 8x8 blocks. Our evaluation results show that the proposed algorithm considerably reduces the complexity involved in the intra-frame prediction. [ABSTRACT FROM AUTHOR]

Lossy compression of noise-free and noisy images differs from each other. While in the first case image quality is decreasing with an increase of compression ratio, in the second case coding image quality evaluated with respect to a noise-free image can be improved for some range of compression ratios. This paper is devoted to the problem of lossy compression of noisy images that can take place, e.g., in compression of remote sensing data. The efficiency of several approaches to this problem is studied. Image pre-filtering is shown to be expedient for coded image quality improvement and/or increase of compression ratio. Some recommendations on how to set the compression ratio to provide quasioptimal quality of coded images are given. A novel DCT-based image compression method is briefly described and its performance is compared to JPEG and JPEG2000 with application to lossy noisy image coding. [ABSTRACT FROM AUTHOR]

The Full Search Block-Matching Motion Estimation (FSBME) algorithm is often employed in video coding for its regular dataflow and straightforward architectures. By iterating over all candidates in a defined Search Area of the reference frame, a motion vector is determined for each current frame macroblock by minimizing the Sum of Absolute Differences (SAD) metric. However, the complexity of the method is prohibitively high, amounting to 60-80% of the encoder's computational burden, and making it unsuitable for many real-time video applications. One means of alleviating the problem is to calculate SAD values using fewer bits (Reduced-Bit SAD), however the reduced dynamic range may compromise picture quality. The current work presents an algorithm, which corrects the RBSAD to full resolution under appropriate conditions. Our results demonstrate that the optimal conditions for correction include a knowledge of the motion vectors of neighboring blocks in space and/or time. [ABSTRACT FROM AUTHOR]

Currently, the growing of digital video delivery leads to compress at high ratio, the video sequences. Different coding algorithms like MPEG-4 introduce different artifacts (blocking, blurring, ringing) degrading the perceptual video quality. Such impairments are generally exploited by the No-Reference quality assessment. In this paper, we propose to use the principal distortions introduced by MPEG-4 coding to design a new reference free metric. Using the frequency and space features of each image, a distortion measure for blocking, blurring and ringing effects is computed respectively. On the one hand, the blocking measure and on the other hand, a joint measure of blurring and ringing effects are perceptually validated, which assures the relevance of distortion measures. To produce the final quality score, a new pooling model is also proposed. High correlation between the objective scores of the proposed metric and the subjective assessment ratings has been achieved. [ABSTRACT FROM AUTHOR]

Binary and grayscale mathematical morphology have many applications in different area. On the other hand, colour morphology is not widespread. The reason is the lack of a unique ordering of colour that makes the extension of grayscale morphology to colour images not straightforward. We will introduce a new majority sorting scheme that can be applied on binary, grayscale and colour images. It is based on the area of each colour or grayscale present in the image, and has the advantage of being independent of the values of the colours or grayvalues. We will take a closer look at the morphological pattern spectrum and will show the possible differences of the morphological pattern spectrum on colour images with the grayscale image pattern spectrum. [ABSTRACT FROM AUTHOR]

In this work we introduce a new technique for a frequently encountered problem in computer vision: image registration. The registration is computed by matching features, points and lines, in the reference image to their corresponding features in the test image among many candidate matches. Convex polygons are used to captivate the uncertainty of the transformation from the reference features to uncertainty regions in the test image in which candidate matches are to be found. We present a simple and robust method to check the consistency of the uncertainty transformation for all possible matches and construct a consistency graph. The distinction between the good matches and the rest can be computed from the information of the consistency graph. Once the good matches are determined, the registration transformation can be easily computed. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid foreground object detection method suitable for the marine surveillance applications. Our approach combines an existing foreground object detection method with an image color segmentation technique to improve accuracy. The foreground segmentation method employs a Bayesian decision framework, while the color segmentation part is graph-based and relies on the local variation of edges. We also establish the set of requirements any practical marine surveillance algorithm should fulfill, and show that our method conforms to these requirements. Experiments show good results in the domain of marine surveillance sequences. [ABSTRACT FROM AUTHOR]