Selective Motion Analysis Based on Dynamic Visual Saliency Map Model
We propose a biologically motivated motion analysis model using a dynamic bottom-up saliency map model and a neural network for motion analysis of which the input is an optical flow. The dynamic bottom-up saliency map model can generate a human-like visual scan path by considering dynamics of continuous input scenes as well as saliency of the primitive features of a static input scene. Neural network for motion analysis responds selectively to rotation, expansion, contraction and planar motion of the optical flow in a selected area. The experimental results show that the proposed model can generate effective motion analysis results for analyzing only an interesting area instead of considering the whole input scenes, which makes faster analysis mechanism for dynamic input scenes.
KeywordsIndependent Component Analysis Motion Analysis Independent Component Analysis Probability Mass Function Middle Temporal
Unable to display preview. Download preview PDF.
- 1.Giboson, J.J.: The perception of the visual world. Boston, Houghton Mifflin (1950)Google Scholar
- 2.Duffy, C.J., Wurtz, R.H.: Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large-field stimuli. Journal of Neurophysiology 65(6), 1329–1345 (1991)Google Scholar
- 3.Duffy, C.J., Wurtz, R.H.: Sensitivity of MST neurons to optic flow stimuli. II. Mechanisms of response selectivity revealed by small-field stimuli. Journal of Neurophysiology 65(6), 1346–1359 (1991)Google Scholar
- 4.Kazuya, T., Fukushima, K.: Neural network model for extracting optic flow. Neural Networks 18(5-6), 1–8 (2005)Google Scholar
- 7.Barlow, H.B., Tolhust, D.J.: Why do you have edge detection? Optical Society of America Technical Digest 23, 172 (1992)Google Scholar