Support Vector Machines and Gabor Kernels for Object Recognition on a Humanoid with Active Foveated Vision

Object recognition requires a robot to perform a number of nontrivial tasks such as finding objects of interest, directing its eyes towards the objects, pursuing them, and identifying the objects once they appear in the robot's central vision. We have recently developed a recognition system on a humanoid robot which makes use of foveated vision to accomplish these tasks [1]. In this paper we present several substantial improvements to this system. We present a biologically motivated object representation scheme based on Gabor kernel functions and show how to employ support vector machines to identify known objects in foveal in1ages based on this representation. A mechanism for visual search is integrated into the system to find objects of interest in peripheral images. The framework also includes a control scheme for eye n1ovcn1ents, which arc directed using the results of attentive processing in peripheral images

Support Vector Machines and Gabor Kernels for Object Recognition on a Humanoid with Active Foveated Vision

Object recognition requires a robot to perform a number of nontrivial tasks such as finding objects of interest, directing its eyes towards the objects, pursuing them, and identifying the objects once they appear in the robot's central vision. We have recently developed a recognition system on a humanoid robot which makes use of foveated vision to accomplish these tasks [1]. In this paper we present several substantial improvements to this system. We present a biologically motivated object representation scheme based on Gabor kernel functions and show how to employ support vector machines to identify known objects in foveal images based on this representation. A mechanism for visual search is integrated into the system to find objects of interest in peripheral images. The framework also includes a control scheme for eye movements, which are directed using the results of attentive processing in peripheral images

Development of a foveated vision system for the tracking of mobile targets in dynamic environments

Mestrado em Engenharia MecânicaEste trabalho descreve um sistema baseado em percepção activa e em visão foveada, projectado para identificar e seguir objectos móveis em ambientes dinâmicos. O sistema inclui uma unidade pan & tilt para facilitar o seguimento e manter o objecto no centro do campo visual das câmaras, cujas lentes grandeangular e tele-objectiva proporcionam uma visão periférica e foveada do mundo, respectivamente. O método Haar features é utilizado para efectuar o reconhecimento dos objectos. O algoritmo de seguimento baseado em template matching continua a perseguir o objecto mesmo quando este não mais está a ser reconhecido pelo módulo de identificação. Algumas técnicas utilizadas para melhorar o template matching são também apresentadas, nomeadamente o Filtro Gaussiano e a Computação Rápida de Filtro Gaussiano. São indicados resultados relativos ao seguimento, identificação e desempenho global do sistema. O sistema comporta-se muito bem, mantendo o processamento de, pelo menos, 15 fotogramas por segundo em imagens de 320x240, num computador portátil normal. São também abordados alguns aspectos para melhorar o desempenho do sistema. ABSTRACT: This work describes a system based on active perception and foveated vision, intended to identify and track moving targets in dynamic environments. The full system includes a pan and tilt unit to ease tracking and keep the interesting target in the two cameras’ view, whose wide / narrow field lenses provide both a peripheral and a foveal view of the world respectively. View-based Haar-like features are employed for object recognition. A template matching based tracking technique continues to track the object even when its view is not recognized by the object recognition module. Some of the techniques used to improve the template matching performance are also presented, namely Gaussian Filtering and Fast Gaussian computation. Results are presented for tracking, identification and global system’s operation. The system performs well up to 15 frames per second on a 320 x 240 image on an ordinary laptop computer. Several issues to improve the system’s performance are also addressed