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The Kernel-based Hough transform uses the same (,) parameterization proposed by Duda and Hart but operates on clusters of approximately collinear pixels. For each cluster, votes are cast using an oriented elliptical-Gaussian kernel that models the uncertainty associated with the best-fitting line with respect to the corresponding cluster.
While at SRI International, Duda and Peter E. Hart were the authors of "Pattern Classification and Scene Analysis", originally published in 1973. This classic text is a widely cited reference, and the first edition was in print for over 25 years until being superseded by the second edition in 2000. [2] Duda is an IEEE Fellow and a AAAI Fellow. [3]
The algorithm is widely used in robotics [4] to perform simplification and denoising of range data acquired by a rotating range scanner; in this field it is known as the split-and-merge algorithm and is attributed to Duda and Hart.
Richard O. Duda: Professor Emeritus of Electrical Engineering at San Jose State University renowned for his work on sound localization and pattern recognition. While at SRI, Duda and Peter E. Hart were the authors of "Pattern Classification and Scene Analysis", originally published in 1973. This classic text is a widely cited reference.
Probability matching is a decision strategy in which predictions of class membership are proportional to the class base rates.Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, then the observer using a probability-matching strategy will predict (for unlabeled examples) a class label of "positive" on 60% of instances ...
Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. [16] In 1981 a report was given on using teaching strategies so that an artificial neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. [17]
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The Dunn index, introduced by Joseph C. Dunn in 1974, is a metric for evaluating clustering algorithms. [1] [2] This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index, in that it is an internal evaluation scheme, where the result is based on the clustered data itself.