Abstract
This work describes a video coder based on a hybrid DPCM-vector quantization algorithm that is suited for bit rates ranging from 8-16 kb/s, The proposed approach involves segmenting difference images into variable-size and variable-shape blocks and performing segmentation and motion compensation simultaneously, The purpose of obtaining motion vectors for variable-size and variable-shape blocks is to improve the quality of motion estimation, particularly in those areas where the edges of moving objects are situated, For the larger blocks, decimation takes place in order to simplify vector quantization, For very active blocks, which are always of small dimension, a specific vector quantizer has been applied, the fuzzy classified vector quantizer (FCVQ), The coding algorithm described displays good performance in the compression of test sequences at the rates of 8 and 16 kb/s; the signal-to-noise ratios obtained are good in both cases, The complexity of the coder implementation is comparable to that of conventional hybrid coders, while the decoder is much simpler in this proposal.

Abstract
An algorithm for coding image sequences based on the segmentation of the differential image into variable size and variable shape blocks is described. The differential image is split into 4 × 4 blocks, that are grouped to form square and rectangular macro blocks of several areas. Each macro block is then vector quantised. Simulation results presented show that compression rates and subjective quality compare favourably with methods previously described.

Abstract
Automatic image registration is a process related to several application fields: remote sensing, medicine and computer vision, among others. Particularly in the field of remote sensing, the ever-increasing number of available satellite images requires automatic image registration methods, capable of correctly aligning a new image. An automatic image registration method - CHAIR (correlation-and Hough transform-based method of automatic image registration) - is proposed, the key concept of which relies on the 'correlation image' produced in both the horizontal and vertical directions. In particular, the computation of the distance of an identified diagonal brighter strip in the correlation image (through the Hough transform) to an offset (the main diagonal) allows for the determination of translational shifts and consequently control points. The set of obtained control points allows for the correction of several types of distortions. The geometric correction quality achieved by CHAIR was objectively evaluated through measures recently proposed, which allow for a more complete assessment of the obtained results. The CHAIR performance was evaluated on both synthetic and real data, with different spatial resolutions and spectral contents. CHAIR has been shown to be able to correctly align two images with a subpixel accuracy, having a priori a 'gold standard' image covering a considerable part of the image to be registered, and has also been shown to work for images of different sensors and/or different spectral bands, situations where traditional correlation methods often yield low and smooth peaks on the correlation surface. It is also able to account for elevation differences and to some extent for rotation and scale effects. Furthermore, it has been shown to have potential for registering synthetic aperture radar (SAR) with optical images.

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