When the boundary of a familiar object is shown by a series of isolated dots, humans can often recognize the object with ease. This ability can be sustained with addition of distracting dots around the object. However, such capability has not been reproduced algorithmically on computers. We introduce a new algorithm that groups a set of dots into multiple non-disjoint subsets. It connects the dots into a spanning tree using the proximity cue. It then applies the straight polygon transformation to an initial polygon derived from the spanning tree. The straight polygon divides the space into polygons recursively and each polygon can be viewed as grouping of a subset of the dots. The number of polygons generated is O( @math ). We also introduce simple shape selection and recognition algorithms that can be applied to the grouping result. We used both natural and synthetic images to show effectiveness of these algorithms.
Summary-Most extant theories of shape perception assume or assert that various contour attributes, and in particular, the orientation, curvature and linear extent of the contours provide essential object recognition cues. The present study examined this proposal using discrete dots that marked locations on the outer boundary of namable objects, providing shape-patterns similar to silhouettes. For each shape, the display initially provided only a sampling of the total number of dots in the boundary, and the number of dots was periodically increased until the participant named the object. There were three treatment conditions in which the initial display as well as the periodic increments consisted of continuous arrays (strings) of dots, randomly positioned dots, or evenly spaced dots. Analysis showed objects were recognized with the fewest percentage of dots with the evenly spaced condition, and participants needed the greatest percentage with the contiguous array condition. In many cases objects could be identified when very few evenly spaced dots were shown, thereby providing large spacing between the dots. It seems unlikely that known neural mechanisms could extract contour attributes, e.g., orientation, curvature, and linear extent, from such sparse stimulus patterns, which provides a challenge to the proposition that these are essential shape cues. It is believed that certain contour attributes, specifically orientation, curvature and linear extent, provide essential cues for object (shape) recognition. The present experiment examined this hypothesis by comparing stimulus conditions that differentially provided such cues. A spaced array of dots was used to mark the outside boundary of namable objects, and subsets were chosen that contained either contiguous strings of dots or randomly positioned dots. These subsets were briefly and successively displayed using an MTDC information persistence paradigm. Across the major range of temporal separation of the subsets, it was found that contiguity of boundary dots did not provide more effective shape recognition cues. This is at odds with the concept that encoding and recognition of shapes is predicated on the encoding of contour attributes such as orientation, curvature and linear extent.