A computer vision system for the recognition of trees in aerial photographs

Increasing problems of forest damage in Central Europe set the demand for an appropriate forest damage assessment tool. The Vision Expert System (VES) is presented which is capable of finding trees in color infrared aerial photographs. Concept and architecture of VES are discussed briefly. The system is applied to a multisource test data set. The processing of this multisource data set leads to a multiple interpretation result for one scene. An integration of these results will provide a better scene description by the vision system. This is achieved by an implementation of Steven's correlation algorithm

Using visual attention in a Nao humanoid to face the RoboCup any-ball challenge

Visual attention is a natural tool which allows animals to locate relevant objects or areas in a given scene, discarding the rest of elements present and thus reducing the amount of information to deal with. In this paper we present the design an implementation of a visual attention mechanism based on a saliency map and its implementation in the Nao humanoid. This control mechanism is applied to solve one of the challenges proposed in the RoboCup competition named ”any-ball”. The results obtained are analysed and future works derived from that analysis are presente

A multimodal attention mechanism for autonomous mobile robotics

Proceeding of: IX Workshop on Physical Agents 2008, University of Vigo, Vigo, Spain, September, 11-12th, 2008.Whatever the mission of an autonomous mobile robot is, attention is a helpful cognitive capability when dealing with real world environments. In this paper we present a novel control architecture which enables an integrated and efficient filtering of multiple modality sensory information. The concept of context is introduced as the set of criteria that determines what sensory information is relevant to the current mission. The proposed attention mechanism uses these contexts as a mean to adaptively select the constrained cognitive focus of the robot within the vast multimodal sensory space available. This approach for artificial attention is tested in the domain of autonomous mapping.This research work has been supported by the Spanish Ministry of Education and Science CICYT under grant TRA2007-67374-C02-02.Publicad

Una implementación computacional de un modelo de atención visual Bottom-up aplicado a escenas naturales

El modelo de atención visual bottom-up propuesto por Itti et al., 2000 [1], ha sido un modelo popular en tantoexhibe cierta evidencia neurobiológica de la visión en primates. Este trabajo complementa el modelo computacional de este fenómeno desde la dinámica realista de una red neuronal. Asimismo, esta aproximación se basa la prominencia de los objetos del campo visual para la formación de una representación general (mapa de prominencia), esta representación es la entrada de una red neuronal dinámica con interacciones locales y globales de colaboración y competencia que convergen sobre las principales particularidades (objetos) de la escena

An Attention Based Method For Motion Detection And Estimation

The demand for automated motion detection and object tracking systems has promoted considerable research activity in the field of computer vision. A novel approach to motion detection and estimation based on visual attention is proposed in the paper. Two different thresholding techniques are applied and comparisons are made with Black's motion estimation technique based on the measure of overall derived tracking angle. The method is illustrated on various video data and results show that the new method can extract both motion and shape information

Object-based visual attention for computer vision

AbstractIn this paper, a novel model of object-based visual attention extending Duncan's Integrated Competition Hypothesis [Phil. Trans. R. Soc. London B 353 (1998) 1307–1317] is presented. In contrast to the attention mechanisms used in most previous machine vision systems which drive attention based on the spatial location hypothesis, the mechanisms which direct visual attention in our system are object-driven as well as feature-driven. The competition to gain visual attention occurs not only within an object but also between objects. For this purpose, two new mechanisms in the proposed model are described and analyzed in detail. The first mechanism computes the visual salience of objects and groupings; the second one implements the hierarchical selectivity of attentional shifts. The results of the new approach on synthetic and natural images are reported