novel process of digital curve evolution. To compute our similarity measure, we first establish the best possible correspondence of visual parts (without explicitly computing the visual parts). Then, the similarity between corresponding parts is computed and aggregated. We applied our similarity measure

Human perception of shape is based on visual parts of objects to a point that a single, significant visual part is su#cient to recognize the whole object. For example, if you see a hand in the door, you expect a human behind the door. Therefore, a cognitively motivated shape similarity measure

-alone behavioral description or as part of a more general design methodology that deals also with the system's other aspects, such as functional decomposition and data-flow specification. We also discuss some practical experience that was gained over the last three years in applying the statechart formalism

[Green and Bavelier, 2003] has grabbed national attention for suggesting that playing “action ” video and computer games has the positive effect of enhancing students ’ visual selective attention. But that finding is just one small part of a more important message that all parents and educators

. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We use these models to address the problem

w4 is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera. W4 employs a combination of shape analysis and tracking

We recorded electrical activity from 532 neurons in the rostral part of inferior area 6 (area F5) of two macaque monkeys. Previous data had shown that neurons of this area discharge during goal-directed hand and mouth movements. We describe here the properties of a newly discovered set of F5

Prior to each visually presented target letter string in a speeded word-nonword classification task, either BIRD, BODY, BUILDING, or xxx appeared as a priming event. When the target was a word, it was (a) a name of a type of bird on most BiRD-prime trials; (b) a name of part of a building on most

. The instrumentation is controlled by the Performance Consultant module, that automatically directs the placement of instrumentation. The Performance Consultant has a well-defined notion of performance bottlenecks and program structure, so that it can associate bottlenecks with specific causes and specific parts of a

with two moves. The first restricts attention to one visual cue, the shapes of objects; the second restricts attention to one problem, the initial guess at the identity of an object. We propose that the visual system decomposes a shape into parts, that it does so using a rule defining part boundaries