Ana E. Delgado

Visual surveillance by dynamic visual attention method

This paper describes a method for visual surveillance based on biologically motivated dynamic visual attention in video image sequences. Our system is based on the extraction and integration of local (pixels and spots) as well as global (objects) features. Our approach defines a method for the generation of an active attention focus on a dynamic scene for surveillance purposes. The system segments in accordance with a set of predefined features, including gray level, motion and shape features, giving raise to two classes of objects: vehicle and pedestrian. The solution proposed to the selective visual attention problem consists of decomposing the input images of an indefinite sequence of images into its moving objects, defining which of these elements are of the users interest at a given moment, and keeping attention on those elements through time. Features extraction and integration are solved by incorporating mechanisms of charge and discharge-based on the permanency effect-, as well as mechanisms of lateral interaction. All these mechanisms have proved to be good enough to segment the scene into moving objects and background.

Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation

To be able to understand the motion of non-rigid objects, techniques in image processing and computer vision are essential for motion analysis. Lateral interaction in accumulative computation for extracting non-rigid shapes from an image sequence has recently been presented, as well as its application to segmentation from motion. In this paper, we introduce a modi4ed version of the 4rst multi-layer architecture. This version uses the basic parameters of the LIAC model to spatio-temporally build up to the desired extent the shapes of all moving objects present in a sequence of images. The in5uences of LIAC model parameters are explained in this paper, and we 4nally show some examples of the usefulness of the model proposed. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Lateral interaction in accumulative computation: a model for motion detection

Some of the major computer vision techniques make use of neural nets. In this paper we present a novel model based on neural networks denominated lateral interaction in accumulative computation (LIAC). This model is based on a series of neuronal models in one layer, namely the local accumulative computation model, the double time scale model and the recurrent lateral interaction model. The LIAC model usefulness in the general task of motion detection may be appreciated by means of some signi:cant examples of object detection in inde:nite sequences of synthetic andreal images. c � 2002 Elsevier Science B.V. All rights reserved.

On motion detection through a multi-layer neural network architecture

A neural network model called lateral interaction in accumulative computation for detection of non-rigid objects from motion of any of their parts in indefinite sequences of images is presented. Some biological evidences inspire the model. After introducing the model, the complete multi-layer neural architecture is offered in this paper. The architecture consists of four layers that perform segmentation by gray level bands, accumulative charge computation, charge redistribution by gray level bands and moving object fusion. The lateral interaction in accumulative computation associated learning algorithm is also introduced. Some examples that explain the usefulness of the system we propose are shown at the end of this article.

Dynamic visual attention model in image sequences

A new computational architecture of dynamic visual attention is introduced in this paper. Our approach defines a model for the generation of an active attention focus on a dynamic scene captured from a still or moving camera. The aim is to obtain the objects that keep the observers attention in accordance with a set of predefined features, including color, motion and shape. The solution proposed to the selective visual attention problem consists in decomposing the input images of an indefinite sequence of images into its moving objects, by defining which of these elements are of the users interest, and by keeping attention on those elements through time. Thus, the three tasks involved in the attention model are introduced. The Feature-Extraction task obtains those features (color, motion and shape features) necessary to perform object segmentation. The Attention-Capture task applies the criteria established by the user (values provided through parameters) to the extracted features and obtains the different parts of the objects of potential interest. Lastly, the Attention-Reinforcement task maintains attention on certain elements (or objects) of the image sequence that are of real interest.

Knowledge modelling for the motion detection task: the algorithmic lateral inhibition method

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. q 2004 Published by Elsevier Ltd.

Algorithmic lateral inhibition method in dynamic and selective visual attention task: Application to moving objects detection and labelling

In a recent article, knowledge modelling at the knowledge level for the task of moving objects detection in image sequences has been introduced. In this paper, the algorithmic lateral inhibition (ALI) method is now applied in the generic dynamic and selective visual attention (DSVA) task with the objective of moving objects detection, labelling and further tracking. The four basic subtasks, namely feature extraction, feature integration, attention building and attention reinforcement in our proposal of DSVA are described in detail by inferential CommonKADS schemes. It is shown that the ALI method, in its various forms, that is to say, recurrent and non-recurrent, temporal, spatial and spatial-temporal, may perfectly be used as a problem-solving-method in most of the subtasks involved in the DSVA task. q 2005 Elsevier Ltd. All rights reserved.

Real-time motion detection by lateral inhibition in accumulative computation

Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. In the few last years, the neurally inspired lateral inhibition in accumulative computation (LIAC) method and its application to the motion detection task have been introduced. The article shows how to implement the tasks directly related to LIAC in motion detection by means of a formal model described as finite state machines. This paper introduces two steps towards that direction: (a) A simplification of the general LIAC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation of such a designed LIAC module, as well as an 8x8 LIAC module, has been tested on several video sequences, providing promising performance results.

A Model of Neural Inspiration for Local Accumulative Computation

This paper explores the computational capacity of a novel local computational model that expands the conventional analogical and logical dynamic neural models, based on the charge and discharge of a capacity or in the use of a D flip-flop. The local memory capacity is augmented to behave as an S states automaton and some control elements are added to the memory. The analogical or digital calculus equivalent part of the balance between excitation and inhibition is also generalised to include the measure of specific spatio-temporal features over temporal expansions of the input space (dendritic field). This model is denominated as accumulative computation and is inspired in biological short-term memory mechanisms. The work describes the model’s general specifications, including its architecture, the different working modes and the learning parameters. Then, some possible software and hardware implementations (using FPGAs) are proposed, and, finally, its potential usefulness in real time motion detection tasks is illustrated.

Neurally inspired mechanisms for the dynamic visual attention map generation task

In this paper we explore the molecular computation model based on a splicing system, A simulator of the programmable, two-input symbol finite automaton is described. The molecular finite state machine is implemented with three enzymes of the class IIS restriction enzymes: Fokl, BseMII, and BseXI.