An Energy Computing Method Inspired from Visual Cognitive Function for Dynamic Behavioural Detection in Video Frames

An Energy Computing Method Inspired from Visual Cognitive Function for Dynamic Behavioural Detection in Video Frames

Zuojin Li (Chongqing University of Science and Technology, Chongqing, China), Jun Peng (Chongqing University of Science and Technology, Chongqing, China), Liukui Chen (Chongqing University of Science and Technology, Chongqing, China), Chen Gui (Chongqing University of Science and Technology, Chongqing, China) and Lei Song (Department of Computing, Unitec Institute of Technology, Mount Albert, New Zealand)
DOI: 10.4018/IJCINI.2014070101


The brain visual cortical simple cells have strong response to notable edges with directivity and contrast of light and dark, as well as the non-classical receptive fields of the neurons in visual cortex that have inhibition function to small light-spot stimulation. Because of this property, human vision system contrast sensitivity tends to dynamic videos. This paper, based on biological visual features, constructs an energy-computing model for dynamic video behaviors analysis, and designs computing methods for strengthening selectivity to directions of edges and inhibiting energy of non-significant areas in the images. The experiment is conducted on 30,000 frames of dynamic behaviors in video and shows 90% accuracy, which proves that the proposed method is capable to simulate the function of visual cortex simple cells, i.e. the enhancement to directional selection, and the inhabitation function of non-classical receptive fields, as well as extract energy features of dynamic behaviors in video. This contributes a choice for computer image processing and improves the understanding of machine vision.
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In recent years, cognitive information has become a hot topic in life science and computer science.Cognitive informatics (CI) is a Tran-disciplinary enquiry of computer science, information science, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing (Wang, 2007b, 2009c, 2009d; Wang, Kinsner, & Zhang, 2009; Wang &Chiew, 2010; Wang, 2011a;).. Cognitive informatics is a cutting-edge and multidisciplinary research areBaciu that tackles the fundamental problems shared by computational intelligence, modern informatics, computer science, AI, cybernetics, cognitive science, neuron-psychology, medical science, philosophy, formal linguistics, and life science (Wang,2007a, 2007b, 2008b, 2009c, 2010b). The development and the cross fertilization among the aforementioned science and engineering disciplines have led to a whole range of extremely interesting new research areas known as Cognitive informatics, which investigates the internal information processing mechanisms and processes of the natural intelligence – human brains and minds – and their engineering applications in computational intelligence. Cognitive informatics studies the natural intelligence and internal information processing mechanisms of the brain, as well as processes involved in perception and cognition. Cognitive informatics forges links between a number of natural science and life science disciplines with informatics and computing science. Moreover, how to construct information processing model by learning from brain information processing mechanism and cell function features has drawn enormous attention in computer science. (Wang, 2008a,2008b, 2009a, 2009b, 2009e, 2011b, 2010b, Wang, Baciu, Yao, Kinsner& Zhang, 2010),

Non-classical receptive field is a new cognitive outcome to human brain by biological scientists. It well explains the visual perception of a wide range of natural images. A large number of experiments demonstrate that outside the neuron perception fields in the retina, lateral geniculate nucleus (LGN) and cortex, there exists an area modulating cell reactions, much larger than traditional receptive fields. Stimuli of small light spots in these areas cannot trigger reaction of cells in receptive field; on the contrary, their existence will inhibit the reaction and consequently weaken the stimulus to the receptive field. To differentiate them with the classical receptive fields, these areas are referred to as “non-classical receptive fields”. Figure 1 (Nothdurft&Gallant,1999) shows the inhabitation effect of these fields on cells’ discharging reaction to stimuli. In this figure, (a) stands for the discharging rate of cells to isolated raster, (b) shows that of cells when raster to the same direction exits outside of the perception scope, (c) occurs when outside the scope there is raster to different directions, and (d) happens when there is no raster in the scope.

Figure 1.

Inhibition effect of non-classic receptive field on visual cells

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