Several strategies to retrieve depth information from a sequence of images have been described so far. In this paper a method that turns around the existing symbiosis between stereovision and motion is introduced; motion minimizes correspondence ambiguities, and stereovision enhances motion information. The central idea behind our approach is to transpose the spatially defined problem of disparity estimation into the spatial-temporal domain. Motion is analyzed in the original sequences by means of the so-called permanency effect and the disparities are calculated from the resulting two-dimensional motion charge maps. This is an important contribution to the traditional stereovision depth analysis, where disparity is got from the image luminescence. In our approach, disparity is studied from a motion-based persistency charge measure.
Recently, the Algorithmic Lateral Inhibition (ALI) method and the Accumulative Computation (AC) method have proven to be efficient in modelling at the knowledge level for general-motion-detection tasks in video sequences. More precisely, the task of persistent motion detection has been widely expressed by means of the AC method, whereas the ALI method has been used with the objective of moving objects detection, labelling and further tracking. This paper exploits the current knowledge of our research team on the mentioned problem-solving methods to model the Stereovision-Correspondence-Analysis (SCA) task. For this purpose, ALI and AC methods are combined into the Lateral Inhibition in Accumulative Computation (LIAC) method. The four basic subtasks, namely ''LIAC 2D Charge-Memory Calculation'', ''LIAC 2D Charge-Disparity Analysis'' and ''LIAC 3D Charge-Memory Calculation'' in our proposal of SCA are described in detail by inferential CommonKADS schemes. It is shown that the LIAC method may perfectly be used to solve a complex task based on motion information inherent to binocular video sequences.
A new method for active visual attention is briefly introduced in this paper. The method extracts motion and shape features from indefinite image sequences, and integrates these features to segment the input scene. The aim of this paper is to highlight the importance of the accumulative computation method for motion features extraction in the active selective visual attention model proposed. We calculate motion presence and velocity at each pixel of the input image by means of accumulative computation. The paper shows an example of how to use motion features to enhance scene segmentation in this active visual attention method.
Abstract. Autonomous robot guidance in dynamic environments requires, on the one hand, the study of relative motion of the objects of the environment with respect to the robot, and on the other hand, the analysis of the depth towards those objects. In this paper, a stereo vision method, which combines both topics with potential utility in robot navigation, is proposed. The goal of the stereo vision model is to calculate depth of surrounding objects by measuring the disparity between the two-dimensional imaged positions of the object points in a stereo pair of images. The simulated robot guidance algorithm proposed starts from the motion analysis that occurs in the scene and then establishes correspondences and analyzes the depth of the objects. Once these steps have been performed, the next step is to induce the robot to take the direction where objects are more distant in order to avoid obstacles.
Certainly, one of the prominent ideas of Professor Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research of Professor Mira and our team at University of Castilla-La Mancha has been that any bottom-up organization may be made operational using two biologically inspired methods called "algorithmic lateral inhibition", a generalization of lateral inhibition anatomical circuits, and "accumulative computation", a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulations of both methods, which have led to quite efficient solutions of problems related to motion-based computer vision. This article is dedicated to the memory of our close master and friend, Professor José Mira.
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