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.
Greene has conducted various studies on shape recognition using a device that allows controlling of a display of a 64x64 array of LEDs at sub-millisecond accuracy. In @cite_14 , a sparse set of dots uniformly sampled around the shape induced recognition of the shape more quickly than another set distributed non-uniformly around the shape. The result suggests that the maximum separation of dots affects the speed of shape recognition. In @cite_33 , using the same LED display, dots delineating common shapes were grouped into a group of four dots and they were flashed at millisecond accuracy. One treatment selected the four dots consecutively from the outline, thus provides contour cues. The other treatment selected the four dots randomly, thus depriving any contour cues. Subjects were divided into one of the two treatments and recognition accuracy was recorded for each shape. The results showed that there was no significant difference in the two treatment groups. The study suggests that contour attributes such as orientation, curvature, and length commonly used for perceptual grouping models are less important than the proximity cue for construction of shape outlines.
@cite_14 @cite_33
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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. 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. Psychophysical studies show that we can recognize objects using fragments of outline contour alone. This paper proposes a new automatic visual recognition system based only on local contour features, capable of localizing objects in space and scale. The system first builds a class-specific codebook of local fragments of contour using a novel formulation of chamfer matching. These local fragments allow recognition that is robust to within-class variation, pose changes, and articulation. Boosting combines these fragments into a cascaded sliding-window classifier, and mean shift is used to select strong responses as a final set of detection. We show how learning can be performed iteratively on both training and test sets to bootstrap an improved classifier. We compare with other methods based on contour and local descriptors in our detailed evaluation over 17 challenging categories and obtain highly competitive results. The results confirm that contour is indeed a powerful cue for multiscale and multiclass visual object recognition. We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13 for SVM and 7 for convolutional nets. On a segmentation recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16 7 error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second.