DocumentCode :
2697113
Title :
A network for motion perception
Author :
Zhou, Y.T. ; Chellappa, R.
fYear :
1990
fDate :
17-21 June 1990
Firstpage :
875
Abstract :
A locally connected artificial neural network based on physiological and anatomical findings in the visual system is presented for motion perception. A set of velocity selective binary neurons is used for each point in the image. Motion perception is carried out by neuron evaluation using a parallel updating scheme. Two algorithms, batch and recursive, based on this network are presented for computing the flow field from a sequence of monocular images. The batch algorithm integrates information from all images simultaneously by embedding them into the bias inputs of the network, while the recursive algorithm uses a recursive-least-squares method to update the bias inputs of the network. Detection rules are also used to find the occluding elements. Based on information on the detected occluding elements, the network automatically locates motion discontinuities. The algorithms need to compute the flow field at most twice. Hence, fewer computations are needed and the recursive algorithm is amenable to real-time applications
Keywords :
computerised picture processing; neural nets; artificial neural network; batch algorithm; binary neurons; monocular images; motion perception; neuron evaluation; parallel updating; recursive algorithm; visual system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1990., 1990 IJCNN International Joint Conference on
Conference_Location :
San Diego, CA, USA
Type :
conf
DOI :
10.1109/IJCNN.1990.137804
Filename :
5726762
Link To Document :
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