Abstract
This paper presents a fast and straightforward method to assess displacement fields on the surface of a planar specimen with sub-pixel resolution. First, pixel level displacements are obtained by cross correlation (CC) of two consecutive images, used as the image similarity measure. Next, sub-pixel displacement information is calculated from the CC coefficients matrix using a similarity interpolation method. Finally, sub-pixel estimation error cancellation is performed. Several experiments were conducted to validate the proposed method effectiveness, applied to chestnuts compression test for quality control purposes.

Abstract
In this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems to find a minima of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. No pior knowledge of the image is required.

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Abstract
Visual impairment and blindness caused by infectious diseases has been greatly reduced, but increasing numbers of people are at risk of age-related visual impairment. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage because appropriate information about the surrounding environment is not available. With the recent advances in inclusive technology it is possible to extend the support given to people with visual impairment during their mobility. In this context we propose a system, named SmartVision, whose global objective is to give blind users the ability to move around in unfamiliar environments, whether indoor or outdoor, through a user friendly interface. This paper is focused mainly in the development of the computer vision module of the SmartVision system.

Abstract
In this article, two artificial neural networks based models for motion detection and motion analysis in image sequence, are proposed and analyzed. The first model is applicable to sequences acquired by a static camera, and segments the images into two areas: moving objects and background. The second model, applicable to image sequences obtained by a moving camera, uses a neural network to identify the `interesting points' in the images (e.g. edges, corners), and a block matching algorithm to calculate the movement in these points. Several tests with real images have been performed to evaluate the models. The results are similar to those obtained by other methods but the proposed techniques show some advantages which are discussed in the paper.

Abstract