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Computational Vision CSCI 363, Fall 2012 Lecture 25 Biological Motion Processing 1 Motion Processing in V1 In V1, some simple cells and complex cells are tuned to direction of motion. I.e. they respond most strongly to motion in a given direction and their response falls off as the motion deviates from that direction. Tuning for 180 deg Firing Rate 120o 180o 240o Direction of Motion Direction Tuning Polar Plot (tuning for 180 deg) 2 V1 neurons tuned to temporal frequency V1 neurons appear to be tuned to temporal frequency. Their preferred speed depends on the spatial frequency of the pattern. v = wt/wx Firing Rate Temporal Frequency Neurons in V1 behave like motion energy filters. 3 Motion Processing in MT MT (The Middle Temporal Area) is thought to be important for processing motion information. Characteristics of MT neurons: 1) Cells tuned for direction of motion (more broadly tuned than V1 cells. 2) Cells tuned for speed. (Some cells specifically tuned for speed. Not dependent on spatial frequency). 3) Large receptive field sizes. (Some are 100x bigger than V1 receptive fields). They range from 1-2deg in diameter in the foveal region and increase in the periphery. 4 Speed Selectivity McKee and Nakayama have shown that people are very good at discriminating two different speeds independent of spatial frequency. The Weber fraction gives a measure of how big a change in speed is necessary to distinguish two different speeds. It is fairly constant over a broad range of speeds: DV/V = .05 MT may be the area that first computes speed independent of spatial frequency. 5 Direction Selectivity V1 cells are sensitive to the direction of motion of the spatial frequency components of a stimulus. For example, in plaid stimuli a V1 cell will respond when either sine wave is moving in its preferred direction, but will not respond for the pattern motion in its preferred direction. Some MT cells respond to pattern motion. They respond best when the pattern motion of a plaid is in their preferred direction. 20% of MT cells respond to the pattern motion. 40% respond to component motion. 40% are in between. 6 Responses to Plaids Moving plaid: + = Response to pattern motion MT response V1 response 7 (20% of cells) Motion opponency Psychophysical results suggest that neurons in the brain use a motion-opponent processing (e.g. left - right). Evidence: 1) We cannot see both left and right motion at the same time. 2) Motion after-effect: If you adapt to rightward motion, and then look away at static image, you see leftward motion MT cells appear to exhibit motion-opponency in their receptive fields: + This has implications for motion transparency. 8 MT cells have inhibitory surround - Many MT cells have an inhibitory surround. + Motion in the surround inhibits the response to motion in the center. The inhibitory surround may be involved in: • Figure-ground segmentation based on motion. • Motion parallax • Heading judgments. 9 Evidence that MT processes motion 1. Cells in MT prefer moving stimuli to static stimuli. 2. Lesions of MT cause loss of ability to discriminate motion direction: Newsome et al. performed an experiment to test this in monkeys. Stimulus: Moving dots--Some percentage move in a coherent direction (correlated dots), the rest move in random directions (noise) Task: Judge direction of motion (e.g. up vs. down). Measure: Percent correlation needed to discriminate the directions of motion. Result: After lesion of MT, monkeys require a greater percentage of correlated dots to make the discrimination (i.e. they were worse at the motion task). 10 Microstimulation experiments Another piece of evidence for MT being involved in motion comes from experiments in which MT cells are electrically stimulated with a micro-electrode (microstimulation). Salzman & Newsome (1994) showed that they could influence a monkey's perception of motion by stimulation of cells in MT. 11 2D Motion is just the Beginning 2D image motion contains information about: • Relative depth of surfaces • 3D motion of objects • 3D structure of objects • Direction of observer motion Among other things. 12 Structure From Motion Structure from motion originally studied rigorously by Wallach and O'Connell (1953). They studied wire-frame objects and examined peoples ability to judge the structure of the objects when moving. The ability to see a 3D structure from a moving 2D image is known as the Kinetic Depth Effect. Demo: http://www.michaelbach.de/ot/mot_ske/index.html 13 Inherent Ambiguity How do we compute a 3D motion from a 2D image motion? Given the 2D image motion, there are multiple possible 3D motions that could have generated it: ? Image plane v To solve for 3D motion from 2D image information, we must use a constraint. The Rigidity constraint assumes that the object is rigid. Next time: Incremental rigidity scheme. 14