Learning and recognizing patterns of visual motion, color and form

Animal vision systems make use of information about an object's motion, color, and form to detect and identify predators, prey and mates. Neurobiological evidence from the macaque monkey indicates that visual processing is separated into two streams: the magnocellular primarily for motion, and the parvocellular primarily for color and form. Two computational systems are developed using key functional properties of the two postulated physiological streams. Each produces invariant representations that act as input to separate copies of a new learning and recognition architecture, Gaussian ARTMAP with covariance terms (GAC). Finally, perceptual experiments are conducted to explore the ability of the human form/color system to detect and recognize targets in photo-realistic imagery.; GAC, the component common to both computational systems, retains the on-line learning capabilities of previous ARTMAP architectures, but uses categories that have a location and orientation in the dimensions of the feature space. This architecture is shown to have lower error rates than Fuzzy ARTMAP and Gaussian ARTMAP for all data sets examined, and is used to cluster motion and spectral parameters.; For the motion system, local velocity measures of image features are obtained by the method of Convected Activation Profiles. This method is extended and shown to accurately estimate the velocity normal to rotating and translating lines, or of line ends, points, and curves. These local measures are grouped into neighborhoods, and the collection of motions within a neighborhood is described using orientation-invariant deformation parameters. Multiple parameters obtained by examining maneuvering objects are clustered, and motions that are characteristic of specific objects are identified.; For the form and color system, multi-spectral measurements are made invariant to some fluctuations of local luminance and atmospheric transmissivity by within-band and across-band shunting networks. The resulting color-processed spectral patterns are clustered to enhance the performance of a machine target detection algorithm.; Psychophysicists have examined human target detection capabilities primarily via scenes of polygonal targets and distractors on uniform backgrounds. Techniques are developed and experiments are performed to assess human performance of visual search for a complex object in a cluttered scene.