Issue No. 06 - June (2012 vol. 34)
K. G. P. Derpanis , Dept. of Comput. Sci. & Eng., York Univ., Toronto, ON, Canada
R. Wildes , Dept. of Comput. Sci. & Eng., York Univ., Toronto, ON, Canada
This paper is concerned with the representation and recognition of the observed dynamics (i.e., excluding purely spatial appearance cues) of spacetime texture based on a spatiotemporal orientation analysis. The term “spacetime texture” is taken to refer to patterns in visual spacetime, (x,y,t), that primarily are characterized by the aggregate dynamic properties of elements or local measurements accumulated over a region of spatiotemporal support, rather than in terms of the dynamics of individual constituents. Examples include image sequences of natural processes that exhibit stochastic dynamics (e.g., fire, water, and windblown vegetation) as well as images of simpler dynamics when analyzed in terms of aggregate region properties (e.g., uniform motion of elements in imagery, such as pedestrians and vehicular traffic). Spacetime texture representation and recognition is important as it provides an early means of capturing the structure of an ensuing image stream in a meaningful fashion. Toward such ends, a novel approach to spacetime texture representation and an associated recognition method are described based on distributions (histograms) of spacetime orientation structure. Empirical evaluation on both standard and original image data sets shows the promise of the approach, including significant improvement over alternative state-of-the-art approaches in recognizing the same pattern from different viewpoints.
stochastic processes, image recognition, image representation, image sequences, image texture, state-of-the-art approach, visual spacetime texture representation, visual spacetime texture recognition, spatiotemporal orientation analysis, aggregate dynamic property, local measurement, spatiotemporal support, image sequence, stochastic dynamics, aggregate region property, image streaming, associated recognition method, spacetime orientation structure, empirical evaluation, original image data sets, Vehicle dynamics, Frequency domain analysis, Dynamics, Spatiotemporal phenomena, Visualization, Energy measurement, Pattern recognition, spatiotemporal orientation., Spacetime texture, image motion, dynamic texture, temporal texture, time-varying texture, textured motion, turbulent flow, stochastic dynamics, distributed representation
K. G. Derpanis and R. Wildes, "Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1193-1205, 2012.