This paper presents an approach to the local stereovision matching problem using edge segments as features with four attributes. From these attributes we compute a matching probability between pairs of features of the stereo images. A correspondence is said true when such a probability is maximum. We introduce a nonparametric strategy based on Parzen's window (1962) to estimate a probability density function (PDF) which is used to obtain the matching probability. This is the main finding of the paper. A comparative analysis of other recent matching methods is included to show that this finding can be justified theoretically. A generalization of the proposed method is made in order to give guidelines about its use with the similarity constraint and also in different environments where other features and attributes are more suitable.

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This paper presents an approach to the local stereovision matching problem using edge segments as features with four attributes. Based on these attributes we compute a matching probability between pairs of features of the stereo images. A correspondence is said to be true when this probability is maximum. The probability value is a weighted sum of the attributes. We use two combined ADALINE neural networks to compute the weight for each attribute. A comparative analysis among other recent matching methods is illustrated.

Super-Resolution (SR) consists in processing an image or a set of images in order to enhance the resolution of a video sequence or a single frame. In this paper, fusion SR techniques are considered, where High-Resolution (HR) images are constructed from several observed Low-Resolution (LR) images, thereby increasing the high-frequency components and removing the degradations caused by the recording process of LR imaging acquisition devices. This paper follows a strategy combining the selection of the most appropriate frames and adaptive sized Macro-Blocks (MBs) together with a Multi-Camera (MC) system. This proposal optimizes the spatial and temporal correlations between the recorded sequences, and minimizes the appearance of annoying artifacts at the same time, improving the quality of the super-resolved HR sequence and reducing the computational cost by more than a factor of two. This type of image enhancement systems has many applications in Consumer Electronics appliances related to imaging. More specifically, many camera suppliers are incorporating Super-Resolution in their high-end products 1 .