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.
This paper analyses the improvements that can be gained in the generalized Hough transform method for recognizing objects through the use of imperfect perceptual grouping techniques. In particular, we consider simple grouping techniques that determine pairs of points that are likely to belong to the same object using a criterion based on connectedness in the image edge map. It is shown that such imperfect grouping techniques can considerably improve both the efficiency and accuracy of object recognition. Experiments are described that demonstrate the improvements in performance.