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Motiondriven segmentation by competitive neural processing
 Neural Processing Letters
, 2005
"... Abstract. Bioinspired energy models compute motion along the lines suggested by the neurophysiological studies of V1 and MT areas in both monkeys and humans: neural populations extract the structure of motion from local competition among MTlike cells. We describe here a neural structure that works ..."
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Abstract. Bioinspired energy models compute motion along the lines suggested by the neurophysiological studies of V1 and MT areas in both monkeys and humans: neural populations extract the structure of motion from local competition among MTlike cells. We describe here a neural structure that works as a dynamic filter above this MT layer for image segmentation and takes advantage of neural population coding in the cortical processing areas. We apply the model to the reallife case of an automatic watchout system for carovertaking situations seen from the rearview mirror. The egomotion of the host car induces a global motion pattern whereas an overtaking vehicle produces a pattern that contrasts highly with this global egomotion field. We describe how a simple, competitive, neural processing scheme can take full advantage of this motion structure for segmenting overtakingcars.
AlphaTrimmed Mean Radial Basis Functions And Their Application In Object Modeling
 CDROM Proc of the IEEE Workshop on Nonlinear Signal and Image Processing (NSIP'97
, 1997
"... In this paper we use Radial Basis Function (RBF) networks for object modeling in images. An object is composed from a set of overlapping ellipsoids and has assigned an output unit in the RBF network. Each basis function can be geometrically represented by an ellipsoid. We introduce a new robust stat ..."
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In this paper we use Radial Basis Function (RBF) networks for object modeling in images. An object is composed from a set of overlapping ellipsoids and has assigned an output unit in the RBF network. Each basis function can be geometrically represented by an ellipsoid. We introduce a new robust statistics based algorithm for training radial basis function networks. This algorithm relies on fftrimmed mean statistics. The use of the proposed algorithm in estimating ellipse parameters is analyzed. 1. INTRODUCTION Radial basis function neural network consists of a two layer feedforward structure employed for functional approximation and classification proposes. When used in pattern classification an RBF network successfully approximates the Bayesian classifier [1, 2]. In this case, the underlying probability functions are decomposed in a sum of kernel functions with localized support. The functions, implemented by the hidden units, are usually chosen as Gaussian. The intersection of an...