Efficient parallel implementation of the Hough transform on a distributed memory system

doi:10.1016/0262-8856(89)90041-3

Image and Vision Computing

Volume 7, Issue 3, August 1989, Pages 167-172

https://doi.org/10.1016/0262-8856(89)90041-3 Get rights and content

Abstract

In parallel implementations of the Hough transform it is commonly suggested that either the transform space or the set of image features can be distributed among the processing elements. This paper proposes a method to link parallel access to feature points in the image, and parallel access to the transform space. A synchronous processing sequence is suggested so that both can be distributed. Realtime results have been achieved on a multiple instruction, multiple data (MIMD) distributed image memory and distributed transform memory architecture.

References (9)

PVC Hough
Methods and means for recognizing complex patterns
(1962)
DH Ballard
Generalizing the Hough transform to detect arbitrary shapes
Pattern Recogn.
(1981)
VF Leavers
AA Naqvi

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Cited by (8)

A survey of Hough Transform
2015, Pattern Recognition
Citation Excerpt :
In this paper we mainly discuss about parallel processing, one of the most popular among these. There are various configurations for parallel implementation of HT [110,38], differing in the way the image and the transform spaces are distributed among the processing elements (PEs). In Global image memory and distributed transform memory all PEs have access to all image pixel points with each computing the HT for a different segment of the transform space.
In 1962 Hough earned the patent for a method [1], popularly called Hough Transform (HT) that efficiently identifies lines in images. It is an important tool even after the golden jubilee year of existence, as evidenced by more than 2500 research papers dealing with its variants, generalizations, properties and applications in diverse fields. The current paper is a survey of HT and its variants, their limitations and the modifications made to overcome them, the implementation issues in software and hardware, and applications in various fields. Our survey, along with more than 200 references, will help the researchers and students to get a comprehensive view on HT and guide them in applying it properly to their problems of interest.
Which Hough Transform?
1993, Computer Vision and Image Understanding
The Hough transform is recognized as being a powerful tool in shape analysis which gives good results even in the presence of noise and occlusion. Major shortcomings of the technique are excessive storage requirements and computational complexity. Solutions to these problems form the bulk of contributions to the literature concerning the Hough transform. An excellent comprehensive review of available methods up to and partially including 1988 is given by Illingworth and Kittler (Comput. Vision Graphics Image Process. 44, 1988, 87-116). In the years following this survey much new literature has been published. The present work offers an update on state of the art Hough techniques. This includes comparative studies of existing techniques, new perspectives on the theory, very many novel algorithms, parallel implementations, and additions to the task-specific hardware. Care is taken to distinguish between research that aims to further basic understanding of the technique without necessarily being computationally realistic and research that may be applicable in an industrial context. A new trend in Hough transform work, that of the probabilistic Houghs, is identified and reviewed in some detail. Attempts to link the low level perceptive processing offered by the Hough transform to high level knowledge driven processing are also included, together with the many recent successful applications appearing in the literature.
Parallel hough transform algorithm performance
1991, Image and Vision Computing
Label inspection using the Hough transform on transputer networks
1991, Microprocessors and Microsystems
A variation of the Hough transform, the special normal/angle Hough transform (RθsHT) is described detailing its characteristics and advantages and suitability for a parallel implementation. The transputer is introduced and the application of the RθsHT on two popular types of transputer networks, a control-driven array and a demand-driven farm of transputers, is described. The methods employed for communication and the breakdown of the algorithms are described for each type of network. Timing results for the implementation on both networks are provided. The use of the RθsHT is then further examined with application to the problem of label inspection, and two types of labels are used to demonstrate how to pass or fail labels under inspection.
Fast parallel algorithm for detecting lines from image
2006, Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University
Intermediate-level feature extraction in novel parallel environments
2003, Machine Vision and Applications

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Image and Vision Computing

Efficient parallel implementation of the Hough transform on a distributed memory system

Abstract

Methods and means for recognizing complex patterns

Generalizing the Hough transform to detect arbitrary shapes

Pattern Recogn.

A survey of Hough Transform

Which Hough Transform?

Parallel hough transform algorithm performance

Label inspection using the Hough transform on transputer networks

Fast parallel algorithm for detecting lines from image

Intermediate-level feature extraction in novel parallel environments