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—Classification of objects is an important area of research and application in a variety of fields. In the presence of full knowledge of the underlying probabilities, Bayes decision theory gives optimal error rates. In those cases where this information is not present, many algorithms make use of distance or similarity among samples as a means of(More)
Many objects in images of natural scenes are so complex and erratic, that describing them by the familiar models of classical geometry is inadequate. In this paper, we exploit the power of fractal geometry to generate global characteristics of natural scenes. In particular we are concerned with the following two questions: 1) Can we develop a measure which(More)
We propose a feature, the Histogram of Oriented Normal Vectors (HONV), designed specifically to capture local geometric characteristics for object recognition with a depth sensor. Through our derivation , the normal vector orientation represented as an ordered pair of azimuthal angle and zenith angle can be easily computed from the gradients of the depth(More)
—When clustering produces more than one candidate to partition a finite set of objects O, there are two approaches to validation (i.e., selection of a " best " partition, and implicitly, a best value for c, which is the number of clusters in O). First, we may use an internal index, which evaluates each partition separately. Second, we may compare pairs of(More)