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NEURAL MULTILAYER STRUCTURE FOR MOTION PATTERN SEGMENTANTION

Bio-inspired energy models compute motion following the guidelines suggested by the neurophysiological studies of V1 and MT areas of monkeys and human behaviour that use neural populations to extract the local motion structure through local competition of MT like cells. In this paper we present a neural structure that works as dynamic filter on the top of this MT layer for image segmentation and can take advantage of the neural population coding in the cortical processing areas. The test bed application addressed in this work is an automatic watch up system for overtaking situations seen from the rearview mirror. The ego-motion of the host car induces a global motion pattern whereas an overtaking vehicle produces a motion pattern highly contrasted with this global ego-motion field. We described how a simple neural processing scheme can take full advantage of this motion structure for segmenting overtaking cars in this scenario. INTRODUCTION Motion processing is a relevant task for the survival challenge of most of living beings therefore their visual systems have specific areas for motion processing [1]. Primary visual areas are modeled using spatio-temporal receptive filters to compute motion [2, 3, 4] as suggested by neuro-physiological data [5]. We use an energy model based on the work of Simoncelli and Heeger (S&H) that has strong neurophysiological bases. They modeled how the cortical areas (V1 and MT cells) can extract the motion structure through neural local computation and competition. With this model they obtained results that agreed with neuro-physiological data [4, 6]. The output layer uses neural velocity population coding, which is inefficient compared with more mathematical based algorithms but represents and advantage if the post-processing is done through neural computation as presented in this paper. The MT cells are highly sensitive to a very specific movement direction and speed. This characteristic is based on a high interconnectivity among the cortical layers. Hence it produces smooth and homogeneous motion patterns. We propose a post-processing structure that takes advantage of these properties. Motion estimation based on local operators is normally very noisy and requires further post-processing before addressing any segmentation. In this paper we describe how a simple connectivity pattern facilitates the neural computation of noisy motion information. This connection pattern makes individual cells behave as dynamic filters that are sensitive to more reliable movement features than simple spatio-temporal correlations. This post-processing layer is composed by cells that collect the output activity from MT cells sensitive to similar motion primitives. We also describe how this can enhance the capability of segmenting rigid body motion by connecting MT cells of local neighbourhoods throughout the visual field. The application of this neural processing strategy in real world problems is also illustrated. In particular, promising results have been obtained for an overtaking car segmentation task. This problem is currently being addressed by many application driven research groups [7]. Besides, in this scenario the motion processing plays an important role, since an overtaking car exhibits a forward motion pattern clearly contrasted against the global backward motion pattern observed in the rear-view mirror due to the ego-motion of the host car. VELOCITY ESTIMATION USING A NEURONAL COMPUTATION SCHEME The Simoncelli & Heeger model [4, 6], consists of two primary stages corresponding to cortical areas V1 and MT. The computation in these layers is highly parallel and regular. A linear model is used for V1 simple cells that exhibit specific selectivity for stimulus orientation and spatial frequency. 1 Copyright © #### by ASME