Abstract
A main requirement in clinical gait analysis is the ability to accurately identify gait events; especially, the initial contact of the heel with the floor and the toe off. The knowledge of the major events of the gait cycle is needed, for instance, in biomechanical data normalization and in the calculation of several temporal/distance parameters. The most common technologies for gait event detection are foot switches and force platforms; however, if the same performance could be achieved, it would be preferable to use the information collected by visual sensors to detect the main gait events. This paper proposes a procedure to be used in the detection of not just stance phase events (i.e. initial contact, opposite toe off, heel rise, opposite initial contact), but also of swing phase events (i.e. toe off, feet adjacent, tibia vertical) through the analysis of visual gait data acquired by image cameras. Moreover, in this paper, it is compared the performance of detecting the initial contact and toe off using our visual methodology and the information obtained from force platforms.

Abstract
In this paper we present a management model to deal with the problem of tracking missing features during long image sequences using Computational Vision. Some usual difficulties related with missing features are that they may be temporarily occluded or might even have disappeared definitively, and the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This approach may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step, and it can deal with the appearance, occlusion and disappearance of features especially useful for tracking in long image sequences. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented.

Abstract
In this paper we present a management model to deal with the problem of tracking a large number of features during long image sequences. Some usual difficulties are related to this problem: features may be temporarily occluded or might even have disappeared definitively; the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This methodology may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented.

Abstract

Abstract
In this paper we address the problem of tracking feature points along image sequences. To analyze the undergoing movement we use a common approach based on Kalman filtering which performs the estimation and correction of the feature point's movement in every image frame. The criterion proposed to establish correspondences, between the group of estimates in each image and the new data to include, minimizes the global matching cost based on the Mahalanobis distance. In this paper, along with the movement tracking, we use a management model which is able to deal with the occlusion and appearance of feature points and allows objects tracking in long sequences. We also present some experimental results obtained that validate our approach.

Abstract