Catálogo de publicaciones - libros

H.264/MPEG4-AVC achieves higher compression gain in comparison to its predecessors H.263 and MPEG4 part 2. This gain partly results from the improvement of motion compensation tools especially the variable block size, the 1/4-pel motion accuracy and the access to multiple reference frames. A particular mode among all Inter modes is the Skip mode. For this mode, no information is transmitted except the signaling of the mode itself. In our previous work we have proposed a competing framework for better motion vector prediction and coding, also including the Skip mode. This proposal has recently been adopted by the Video Coding Expert Group (VCEG) in the Key Technical Area-software (KTA) of H.264, which is the starting point for future ITU standardization activities. In this paper we propose an extension of this method based on the adaptation of two families of predictors for the Skip mode according to the video content and to statistical criteria. A systematic gain upon the previous method, with an average of 8.2% of bits saved compared to H.264 standard, is reported.

Video compression techniques can be classified into scalable and non-scalable. Scalable coding is more suitable in variable band-width scenarios because it improves the quality of the reconstructed video. On the other hand, the scalability has a cost in terms of coding efficiency and complexity. This paper describes a JPEG2000-and-MCTF-based fully scalable video codec (FSVC) and analyzes a set of experiments to measure the cost of the scalability, comparing two different FSVC encoders: open-loop FSVC and closed-loop FSVC. In the open-loop version of FSVC, the encoder uses the original images to make the predictions. The closed-loop scheme generates the predictions with reference images identical to those obtained by the decoder at a given bitrate. Numerical and visual results demonstrate a small loss of the coding efficiency for the open-loop scheme. Moreover, the inclusion of the closed-loop increases the complexity of the encoder and produces poor performance at high bitrates.

In this paper, we propose a new computationally efficient fuzzy rule-based line doubling algorithm which provides effective visual performance. In the proposed scheme, spatio-temporal mode selector and fuzzy rule-based correlation dependent interpolation techniques are utilized for the 2-D input signal. The basic idea is to classify the field dynamically into background or foreground area. The proposed method interpolates missing pixels using temporal information in the background area, and then interpolates remaining pixels using spatial information in the foreground area using fuzzy rule.

Stereo vision is one of the central research problems in computer vision. The most difficult and important issue in this area is the stereo matching process. One technique that performs this process is the Graph-Cuts based algorithm and which provides accurate results . Nevertheless, this approach is too slow to use due to the redundant computations that it invokes. In this work, an Adaptive Graph-Cuts based algorithm is implemented. The key issue is to subdivide the image into several regions using quadtrees and then define a global energy function that adapts itself for each of these subregions. Results show that the proposed algorithm is 3 times faster than the other Graph-Cuts algorithm while keeping the same quality of the results.

This paper presents a novel binary speed term for tracking objects with the help of active contours. The speed, which can be 0 or 1, is determined by local nonlinear filters, and not by the strength of the gradient as is common for active contours. The speed has been designed to match the nature of a recent fast level-set evolution algorithm. The resulting active contour method is used to track objects for which probability distributions of pixel intensities for the background and for the object cannot be reliably estimated.

This paper presents an innovative road modeling strategy for video-based driver assistance systems. It is based on the real-time estimation of the vanishing point of sequences captured with forward looking cameras located near the rear view mirror of a vehicle. The vanishing point is used for many purposes in video-based driver assistance systems, such as computing linear models of the road, extraction of calibration parameters of the camera, stabilization of sequences, etc. In this work, a novel strategy for vanishing point estimation is presented. It is based on the use of an adaptive steerable filter bank which enhances lane markings according to their expected orientations. Very accurate results are obtained in the computation of the vanishing point for several type of sequences, including overtaking traffic, changing illumination conditions, paintings in the road, etc.

To test the effectiveness of Self-Eigenroughness, which is derived from performing principal component analysis (PCA) on each texture roughness individually, in texture recognition with respect to Eigenroughness, which is derived from performing PCA on all texture roughness; we present a novel fitness function with adaptive threshold to evaluate the performance of each subset of genetically selected eigenvectors. Comparatively studies suggest that the former is superior to the latter in terms of recognition accuracy and computation efficiency.

The problem of inspecting composite materials to detect internal defects is felt in many industrial contexts both for quality controls through production lines and for maintenance operations during in-service inspections. The analysis of the internal defects (not detectable by a visual inspection) is a difficult task unless invasive techniques are applied. For this reason in the last years there has been an increasing interest for the development of low cost non-destructive inspection techniques that can be applied during normal routine tests without damaging materials but also with automatic analysis tools. In this paper we have addressed the problem of developing an automatic signal processing system that analyzes the time/space variations in a sequence of thermographic images and allows the identification of internal defects in composite materials that otherwise could not be detected. First of all a preprocessing technique was applied to the time /space signals to extract significant information, then an unsupervised classifier was used to extract uniform classes that characterize a range of internal defects. The experimental results demonstrate the ability of the method to recognize different regions containing several types defects.

The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. First, the problem-oriented modification of the previously proposed fused Bayesian-regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill-poseness due to system-level and model-level uncertainties. Second, the modification of the Hopfield-type maximum entropy neural network (NN) is proposed that enables such NN to perform numerically the robust adaptive FBR technique via efficient NN computing. Finally, we report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.

A new technique for global and local skew correction in binary documents is proposed. The proposed technique performs a connected component analysis and for each connected component, document’s local skew angle is estimated, based on detecting a sequence of other consecutive connected components, at certain directions, within a specified neighborhood. A histogram of all local skew angles is constructed. If the histogram has one peak then global skew correction is performed, otherwise the document has more than one skews. For local skew correction, a page layout analysis is performed based on a boundary growth algorithm at different directions. The exact global or local skew is approached with a least squares line fitting procedure. The accuracy of the technique has been tested using many documents of different skew and it is compared with two other similar techniques.