Topic
Hough transform
About: Hough transform is a research topic. Over the lifetime, 9824 publications have been published within this topic receiving 185841 citations.
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01 Dec 2004
TL;DR: This work focuses on low-level processing on the three-dimensional world tackling the pespective n-point problem motion invariants and their applications and the need for speed - real-time electronic hardware systems.
Abstract: Introduction - vision, the challenge. Part 1 Low-level processing: images and imaging operations basic image filtering operations thresholding techniques locating objects via their edges binary shape analysis boundary pattern analysis. Part 2 Intermediate-level processing: line detection circle detection the Hough transform and its nature ellipse detection hole detection polygon and corner detection. Part 3 Application level processing: abstract pattern matching techniques the three-dimensional world tackling the pespective n-point problem motion invariants and their applications automated visual inspection statistical pattern recognition biologically inspired recognition schemes texture image acquisition the need for speed - real-time electronic hardware systems. Part 4 Perspectives on vision: machine vision, art or science?.
1,198 citations
TL;DR: A novel method for detecting and localizing objects of a visual category in cluttered real-world scenes that is applicable to a range of different object categories, including both rigid and articulated objects and able to achieve competitive object detection performance from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.
Abstract: This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance.
The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion.
An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.
1,084 citations
TL;DR: This work proposes a new method for curve detection that has the advantages of small storage, high speed, infinite parameter space and arbitrarily high resolution, and the preliminary experiments have shown that the new method is quite effective.
1,080 citations
TL;DR: This correspondence illustrates the ideas of the Adaptive Hough Transform, AHT, by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2-D parameter spaces and shows that the method is robust to the addition of extraneous noise.
Abstract: We introduce the Adaptive Hough Transform, AHT, as an efficient way of implementing the Hough Transform, HT, method for the detection of 2-D shapes. The AHT uses a small accumulator array and the idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the Hough parameter spaces. The method is substantially superior to the standard HT implementation in both storage and computational requirements. In this correspondence we illustrate the ideas of the AHT by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2-D parameter spaces. We show that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape.
671 citations
TL;DR: The development and implementation of an algorithm for automated text string separation that is relatively independent of changes in text font style and size and of string orientation are described and showed superior performance compared to other techniques.
Abstract: The development and implementation of an algorithm for automated text string separation that is relatively independent of changes in text font style and size and of string orientation are described. It is intended for use in an automated system for document analysis. The principal parts of the algorithm are the generation of connected components and the application of the Hough transform in order to group components into logical character strings that can then be separated from the graphics. The algorithm outputs two images, one containing text strings and the other graphics. These images can then be processed by suitable character recognition and graphics recognition systems. The performance of the algorithm, both in terms of its effectiveness and computational efficiency, was evaluated using several test images and showed superior performance compared to other techniques. >
664 citations