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

Knowledge modelling for the motion detection task

In this article knowledge modelling at the knowledge level for the task of moving objects detection in image sequences is introduced. Three items have been the focus of the approach: (1) the convenience of knowledge modelling of tasks and methods in terms of a library of reusable components and in advance to the phase of operationalization of the primitive inferences; (2) the potential utility of looking for inspiration in biology; (3) the convenience of using these biologically inspired problem-solving methods (PSMs) to solve motion detection tasks. After studying a summary of the methods used to solve the motion detection task, the moving targets in indefinite sequences of images detection task is approached by means of the algorithmic lateral inhibition (ALI) PSM. The task is decomposed in four subtasks: (a) thresholded segmentation; (b) motion detection; (c) silhouettes parts obtaining; and (d) moving objects silhouettes fusion. For each one of these subtasks, first, the inferential scheme is obtained and then each one of the inferences is operationalized. Finally, some experimental results are presented along with comments on the potential value of our approach

Foetal age and weight determination using a lateral interaction inspired net

The clinical estimate of the foetal weight is probably one of the most difficult parameters to obtain in the prenatal control. Only very accurate foetal body measurements reflect the gestation age and the weight of the foetus. A model is presented that performs an automated foetal age and weight determination from ultrasound by means of biparietal diameter, femur lenght and abdominal circumference parameters. The model proposed in this paper exploits the data in the images in three general steps. The first step is image preprocessing, to highlight useful data in the image and suppress noise and unwanted data. The next step is image processing, which results in forming regions that can correspond to structures or structure parts. The last step is image understanding, where knowledge on the specific problem is injected

Length-speed ratio (lsr) as a characteristic for moving elements real-time classification

In this article, the length?speed ratio (LSR) is proposed as a basic characteristic for the real-time detection of moving objects. We define the LSR of a uniform moving zone as the relation between its length in the direction of motion and the speed of this motion. For a given zone of the image with uniform gray level (or patch), the greater its length in the direction of motion and the smaller its speed, the greater its LSR. A moving element is generally composed of various zones of uniform gray levels (or patches), which move with the same speed but which have different lengths in the direction of motion and which therefore have a characteristic set of LSR values. In this article, this ?LSR footprint? is proposed as the basic characteristic for the detection and subsequent classification of moving elements in image sequences. The problem of detecting a moving element in a sequence of images is transformed into the recognition of a pattern on a static image, namely the LSR footprint. We also specify how to obtain this characteristic in real time, we discuss its invariants and we consider the cases for which LSR detection of movement is applicable. We also present its use in some significant examples and we compare it with other methods applicable to similar computational problems

Comparison of accumulative computation with traditional optical flow

Segmentation from optical flow calculation is nowadays a well-known technique for further labeling and tracking of moving objects in video streams. A likely classification of algorithms to obtain optical flow based on the intensity of the pixels in an image is in (a) differential or gradient-based methods and (b) block correlation or block matching methods. In this article, we are going to carry out a qualitative comparison of three well-known algorithms (two differential ones and a correlation one). We will do so by means of the optical flow obtaining method based on accumulated image differences known as accumulative computation

Neurally inspired mechanisms of the dynamic visual attention map generation task

A model for dynamic visual attention is briefly introduced in this paper. A PSM (problem-solving method) for a generic ?Dynamic Attention Map Generation? task to obtain a Dynamic Attention Map from a dynamic scene is proposed. Our approach enables tracking objects that keep attention in accordance with a set of characteristics defined by the observer. This paper mainly focuses on those subtasks of the model inspired in neuronal mechanisms, such as accumulative computation and lateral interaction. The subtasks which incorporate these biologically plausible capacities are called ?Working Memory Generation? and ?Thresholded Permanency Calculation?

Step-by-step description of lateral interaction in accumulative computation

In this paper we present a method for moving objects detection and labeling denominated Lateral Interaction in Accumulative Computation (LIAC). The LIAC method usefulness in the general task of motion detection may be appreciated by means of some step-by-step descriptions of significant examples of object detection in video sequences of synthetic and real images

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

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