Automatic recognition of biological shapes with and without representations of shape
In this work it is described how to use the curvature function, the Fourier descriptors, and the coordinate functions of a contour to achieve automatic recognition of biological shapes. Those representations of shape and the coordinate functions were applied to recognize human corneal endothelial cells embedded in a sample of tissue. We assume that when the coordinates of the points of contours are analyzed directly, no representation of shape is being used. We applied scale-space filtering to the coordinate functions, to compensate the effects of scaling and to minimize the error due to quantization. A technique for compensating the effects of rotation, with or without the use of a representation of shape, is proposed. Our results show that, for a wide range of biological shapes, no representation of shape is required to solve or avoid the problems caused by translation, scaling, and rotation. We conclude that for certain applications the use of a representation of shape can provide some advantages. However, the coordinate functions of contours, evolved in scale-space, can be efficiently used, yielding even better results in applications of robotics and computer vision related to the recognition of biological shapes.
representations of shape computer vision object recognition shape analysis