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• W2319660319 abstract "Shape is probably the single most important feature for object detection and much research has gone into developing deformable shape models. However, contours extracted by bottom-up edge detectors are notoriously unreliable, especially in natural images. In this paper, we present a very simple, yet powerful method for model creation, hypothesis generation, and hypothesis verification, which is competitive with much more complex methods. Let s be a descriptor in some high-dimensional space of an edge fragment representing part of an object contour. In Bayesian terms, this fragment was likely to be generated by some class c∈C if the conditional likelihood P(s|c) is greater for c than that for any other class, P(s|c′ 6= c), including the background class. P(s|c) can be estimated non-parametrically, for example by using Gaussian kernels associated with some nearby samples in the feature space. In [2] it was shown that a good approximation can be obtained by using only the nearest sample. A fragment is discriminative if it is much more likely to belong to a class c than any other class c′ 6= c. We thus define the discriminative power of a fragment by:" @default.
• W2319660319 created "2016-06-24" @default.
• W2319660319 creator A5053472065 @default.
• W2319660319 creator A5073695921 @default.
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• W2319660319 date "2015-01-01" @default.
• W2319660319 modified "2023-09-23" @default.
• W2319660319 title "Shape Detection with Nearest Neighbour Contour Fragments" @default.
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• W2319660319 doi "https://doi.org/10.5244/c.29.59" @default.
• W2319660319 hasPublicationYear "2015" @default.
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