Author
Haim Shvaytser
Bio: Haim Shvaytser is an academic researcher from Sarnoff Corporation. The author has contributed to research in topics: Hough transform & Probabilistic logic. The author has an hindex of 3, co-authored 4 publications receiving 97 citations.
Papers
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TL;DR: An efficient probabilistic algorithm for a Monte-Carlo approximation to the Hough transform that requires substantially less computation and storage than the standard Houghtransform when applied to patterns that are easily recognized by humans.
80 citations
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TL;DR: In this article, a simple combinatorial criterion for determining concept classes that cannot be learned in the sense of Valiant from a polynomial number of positive-only examples is presented. The criterion is applied to several types of Boolean formulae in conjunctive and disjunctive normal form, to the majority function, to graphs with large connected components, and to neural networks with a single threshold unit.
Abstract: We present a simple combinatorial criterion for determining concept classes that cannot be learned in the sense of Valiant from a polynomial number of positive-only examples. The criterion is applied to several types of Boolean formulae in conjunctive and disjunctive normal form, to the majority function, to graphs with large connected components, and to a neural network with a single threshold unit. All are shown to be nonlearnable from positive-only examples.
11 citations
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29 Jul 1990TL;DR: A method is described for deriving rules of inference from relations between probabilities of sentences in Nilsson's probabilistic logic.
Abstract: A method is described for deriving rules of inference from relations between probabilities of sentences in Nilsson's probabilistic logic.
1 citations
Cited by
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TL;DR: A survey of Hough Transform and its variants, their limitations and the modifications made to overcome them, the implementation issues in software and hardware, and applications in various fields is done.
646 citations
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TL;DR: The progressive probabilistic Hough transform minimizes the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points.
604 citations
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TL;DR: A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed that was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation.
Abstract: A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed. The MVE estimator identifies the least volume region containing h percent of the data points. The clustering algorithm iteratively partitions the space into clusters without prior information about their number. At each iteration, the MVE estimator is applied several times with values of h decreasing from 0.5. A cluster is hypothesized for each ellipsoid. The shapes of these clusters are compared with shapes corresponding to a known unimodal distribution by the Kolmogorov-Smirnov test. The best fitting cluster is then removed from the space, and a new iteration starts. Constrained random sampling keeps the computation low. The clustering algorithm was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation. >
290 citations
01 Jan 1997
TL;DR: An algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform is described, found to give improvements in accuracy, and a reduction in computation time and the number of false alarms detected.
Abstract: We describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform. The algorithm is compared to a standard implementation of the Hough Transform, and the Probabilistic Hough Transform. Tests are performed using both noise-free and noisy images, and several real-world images. The algorithm was found to give improvements in accuracy, and a reduction in computation time, memory requirements and the number of false alarms detected.
266 citations
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TL;DR: In this paper, the authors describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform (RHT) and compare it with three other Hough-based algorithms.
252 citations