A Neural Network for Motion Detection of Drift-Balanced Stimuli
نویسنده
چکیده
This paper briefly describes an artificial neural network for preattentive visual processing. The network is capable of determiuing image motioll in a type of stimulus which defeats most popular methods of motion detect.ion a subset of second-order visual motion stimuli known as drift-balanced stimuli(DBS). The processing st.ages of the network described in this paper are integratable into a model capable of simultaneous motion extractioll. edge detection, and the determination of occlusion.
منابع مشابه
Motion detection by a moving observer using Kalman filter and neural network in soccer robot
In many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. Duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. In thispaper, we propose a novel method to effectively overcome this problem using Neural Networks and Kalman Filtering theory. Thistechnique u...
متن کاملGyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods
In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...
متن کاملDrift-balanced random stimuli: a general basis for studying non-Fourier motion perception.
To some degree, all current models of visual motion-perception mechanisms depend on the power of the visual signal in various spatiotemporal-frequency bands. Here we show how to construct counterexamples: visual stimuli that are consistently perceived as obviously moving in a fixed direction yet for which Fourier-domain power analysis yields no systematic motion components in any given directio...
متن کاملCrack Detection of Timoshenko Beams Using Vibration Behavior and Neural Network
Abstract: In this research, at first, the natural frequencies of a cracked beam are obtained analytically, then, location and depth of a crack in beam is identified by neural network method. The research is applied on a beam with an open crack for three different boundary conditions. For this purpose, at first, the natural frequencies of the cracked beam are obtained analytically, to get the ex...
متن کاملA New Method for Intrusion Detection Using Genetic Algorithm and Neural Network
The article attempts to have neural network and genetic algorithm techniques present a model for classification on dataset. The goal is design model can the subject acted a firewall in network and this model with compound optimized algorithms create reliability and accuracy and reduce error rate couse of this is article use feedback neural network and compared to previous methods increase a...
متن کامل