Stereovision depth analysis by two-dimensional motion charge memories

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

Stereovision disparity analysis by two-dimensional motion charge map inspired in neurobiology

Up to date several strategies of how to retrieve depth information from a sequence of images have been described. In this paper a method that is inspired in Neurobiology and that turns around the symbiosis existing between stereovision and motion is introduced. A motion representation in form of a two-dimensional motion charge map, based in the so-called permanency memories mechanism is presented. For each pair of frame of a video stereovision sequence, the method displaces the left permanency stereo-memory on the epipolar restriction basis over the right one, in order to analyze the disparities of the motion trails calculated

Modelling the stereovision-correspondence-analysis task by lateral inhibition in accumulative computation problem-solving method.

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

Motion-based stereovision model with potential utility in robot navigation.

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

Dynamic stereoscopic selective visual attention (dssva): integrating motion and shape with depth in video segmentation

Depth inclusion as an important parameter for dynamic selective visual attention is presented in this article. The model introduced in this paper is based on two previously developed models, dynamic selective visual attention and visual stereoscopy, giving rise to the so-called dynamic stereoscopic selective visual attention method. The three models are based on the accumulative computation problem-solving method. This paper shows how software reusability enables enhancing results in vision research (video segmentation) by integrating earlier works. In this article, the first results obtained for synthetic sequences are included to show the effectiveness of the integration of motion and shape features with depth parameter in video segmentation

Permanency memories in scene depth analysis

There are several strategies of how to retrieve depth information from a sequence of images, like depth from motion, depth from shading and depth from stereopsis. In this paper, we introduce a new method to retrieve depth based on motion and stereopsis. A motion detection representation helps establishing further correspondences between different motion information. This representation bases in the permanency memories mechanism, where jumps of pixels between grey level bands are computed in a matrix of charge accumulators. For each frame of a video stereovision sequence, the method fixes the right permanency stereo memory, and displaces the left permanency stereo memory by pixel on the epipolar restriction basis over the right one, in order to analyze the disparities of the motion trails calculated. By means of this functionality, for all possible displacements of one permanency memory over the other, the correspondences between motion trails are checked, and the disparities are assigned, providing a way to analyze the depths of the objects present in the scene

A historical perspective of algorithmic lateral inhibition and accumulative computation in computer vision

Certainly, one of the prominent ideas of Professor José 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 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 formulation of both methods. Finally, all of the works of our group related to this methodological approximation are mentioned and summarized, showing that all of them support the validity of this approximation

Revisiting algorithmic lateral inhibition and accumulative computation

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

Vision technology/algorithms for space robotics applications

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed

A proposal for local and global human activities identification

There are a number of solutions to automate the monotonous task of looking at a monitor to find suspicious behaviors in video surveillance scenarios. Detecting strange objects and intruders, or tracking people and objects, is essential for surveillance and safety in crowded environments. The present work deals with the idea of jointly modeling simple and complex behaviors to report local and global human activities in natural scenes. In order to validate our proposal we have performed some tests with some CAVIAR test cases. In this paper we show some relevant results for some study cases related to visual surveillance, namely ?speed detection?, ?position and direction analysis?, and ?possible cashpoint holdup detection?