To characterize edges, this work proposes a zero-crossing detector that attempts to provide a more precise localization of edges together with robustness to brightness and contrast variation in images. Since the algorithm aims to be insensitive to image histogram manipulations, it is based on zero-crossings of the second derivative, and not on first derivative measurements. In order to provide good localization while preserving the true shape of the edges, the detector is based on precise mathematical expressions that represent image derivatives as a vector field, instead of using the Laplacian as a scalar approximation for the image second derivatives. This way, the proposed detector can be viewed as a Marr-Hildreth operator with a more exact mathematical formulation. Finally, to avoid false responses and provide thin edges, some additional checking is performed over the edge candidate points. The method showed good results when compared with well-known edge detectors.

The doubling of the data rate in stereo image systems causes problems for archiving and transmission, and compression is obviously desirable. In general, stereo codecs code one of the views as the reference using a conventional method like MPEG-2 assuming two basic sources, and the disparity map is calculated to aid the coding process. It can be coded using the DCT, another lossy method, or a lossless compression. The other view is either coded directly, or is restored from a combination of the reference view, the disparity data and a residual error image. In transform coding, inappropriate or coarse quantisation may cause coding artifacts. This work analyses and models the distribution of the 2D DCT coefficients of disparity maps and of disparity compensated residuals in the presence of occlusions, highlighting some points that should be considered when going from the monocular to the multiple-camera codec design process.

Human head models are used in many applications of computer graphics such as computer animation and cyberspace communications. Spherical harmonic shape representation decomposes the surface into its spatial surface frequency components by 3D multiresolution techniques. This paper demonstrates that spherical harmonic representation is particularly suited for modelling of the general 3D human head shape due to the originally rounded surface geometry. Operating from range data the SH representation ignores fine surface detail and retains the general shape suitable for texture mapping. The SH surface is represented with a limited number of bases function coefficients, analogous to Fourier series decomposition. It is shown that the total data amount to represent any human head SH model can be limited to a few thousand bits, with quantized and variable-length coded parameters. The SH models can be reconstructed from the parameters in wire-frame format at the desired polygon resolution and post-processed with conventional graphics utilities. 1

Multimedia and virtual reality systems give the user the illusion of three-dimensional contact. Unsurprisingly, this requires the transmission of more data than the monocular case. To bring participants together in a virtual space, or to allow a user to "see around" a 3-D object it is necessary to synthesise intermediate views from a stereoscopic image pair or sequence. In a stereo/video coding scheme, a block-based disparity map is usually coded and transmitted. This format is economic in computation and transmission, but likely to cause artifacts on any reconstructed intermediate view. To minimise the distortion of the reconstructed intensities, a nonlinear interpolator, which uses the single disparity available to each block of the image, generates a full-sized map that partially preserves original disparity edge information, producing a sharper intermediate view. The identification of occluded and non-occluded areas is also used to aid the intermediate view reconstruction.

The objective of stereo matching is, given a point in one view of a scene, to find the homologous point in another view from an adjacent camera. We use the discrete cosine transform (DCT) with evolutionary methods as an approach to solve the correspondence problem. The matching process is implemented by generating for each image block sets of DCT coefficients, which when reverse transformed become the estimated disparity map for that block. Assumed statistical properties of the AC coefficients are taken into account, while the DC component is estimated from a preliminary block matching stage. Matching is performed in the image domain using an intensity similarity measure. The job of optimising the DCT coefficients, whose inverse transform gives the disparity, is carried out by a biologically inspired optimisation technique: the genetic algorithm.

Edge and feature points are basic low level primitives for image processing. The justification is that such points convey a lot of structural information about the surfaces of the objects captured in the imaging process. Because of this, edge and feature detection are two of the most common operations in image analysis. Despite the variety of operators available for these purposes, there are still some drawbacks common to most of them, even the more sophisticated ones. Among them, the problems of connectivity and shape of junctions by edge detectors, and the dichotomy between reliability of detection and localization accuracy for feature detectors are particularly outstanding. The present work proposes an adaptive implementation of the SUSAN method for edge and feature detection. The aim is the development of an algorithm that offers some performance improvement in tackling these limitations.

The assumption that epipolar lines are parallel to image scan lines is made in many algorithms for stereo analysis. If valid, it enables the search for corresponding image features to be confined to one dimension and, hence, simplified. An algorithm that generates a vertically aligned stereo pair by warped resampling is described. The method uses grey scale image matching between the components of the stereo pair but confined to feature points.

Proposes a selective update scheme for both SA (simulated annealing) and ICMs (iterated conditional modes) which only visits sites within inhomogeneous neighborhoods. A second scheme is proposed to enhance the update confidence at each site by incorporating contextual information in terms of neighbor label class probability distributions, instead of their current realizations. The two update schemes reduce the computation demand and improve estimation accuracy. Both schemes are tested on a noise-contaminated Markov random field test image. The results show that ICM and SA with selective update achieve a computational savings of five times on average, without introducing noticeable degradation. The confidence enhanced update scheme, working with SA and ICM, much improves the final estimation accuracy. In particular for ICM, it produces similar results to those of SA, but uses only a fraction of the iterations needed by the latter.>

A sample selection procedure for a subband vector-quantization (VQ) interframe predictive coder incorporating short-kernel subband filter banks is presented. The coder is based on two-dimensional subband decomposition of the error image obtained after motion-compensated interframe prediction (MCIP). The luminance and chrominance subbands, generated after the decomposition process, form image vectors which are then quantized using conventional vector quantization. By using the sample selection procedure, selective quantization of the image vectors can be carried out with minimal distortion introduced into the reconstructed images. Any major spatial redundancies in the MCIP error image are exploited to achieve the low output bit rate desired.>