The DSC algorithm for edge detection

Jonghoon Oh; Chang Sung Jeong

doi:10.1007/978-3-540-30549-1_87

The DSC algorithm for edge detection

Jonghoon Oh, Chang Sung Jeong

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

Edge detection is one of the fundamental operations in computer vision with numerous approaches to it. In nowadays, many algorithms for edge detection have been proposed. However, most conventional techniques have assumed clear images or Gaussian noise images, thus their performance could decrease with the impulse noise. In this paper, we present an edge detection approach using Discrete Singular Convolution algorithm. The DSC algorithm efficiently detects edges not only original images but also noisy images which are added by Gaussian and impulse noise. Therefore, we evaluate that the performance of the DSC algorithm is compared with other algorithms such as the Canny, Bergholm, and Rothwell algorithm.

Original language	English
Pages (from-to)	967-972
Number of pages	6
Journal	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	3339
DOIs	https://doi.org/10.1007/978-3-540-30549-1_87
Publication status	Published - 2004
Event	17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence - Cairns, Australia Duration: 2004 Dec 4 → 2004 Dec 6

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-30549-1_87

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title = "The DSC algorithm for edge detection",

abstract = "Edge detection is one of the fundamental operations in computer vision with numerous approaches to it. In nowadays, many algorithms for edge detection have been proposed. However, most conventional techniques have assumed clear images or Gaussian noise images, thus their performance could decrease with the impulse noise. In this paper, we present an edge detection approach using Discrete Singular Convolution algorithm. The DSC algorithm efficiently detects edges not only original images but also noisy images which are added by Gaussian and impulse noise. Therefore, we evaluate that the performance of the DSC algorithm is compared with other algorithms such as the Canny, Bergholm, and Rothwell algorithm.",

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AU - Oh, Jonghoon

AU - Jeong, Chang Sung

PY - 2004

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N2 - Edge detection is one of the fundamental operations in computer vision with numerous approaches to it. In nowadays, many algorithms for edge detection have been proposed. However, most conventional techniques have assumed clear images or Gaussian noise images, thus their performance could decrease with the impulse noise. In this paper, we present an edge detection approach using Discrete Singular Convolution algorithm. The DSC algorithm efficiently detects edges not only original images but also noisy images which are added by Gaussian and impulse noise. Therefore, we evaluate that the performance of the DSC algorithm is compared with other algorithms such as the Canny, Bergholm, and Rothwell algorithm.

AB - Edge detection is one of the fundamental operations in computer vision with numerous approaches to it. In nowadays, many algorithms for edge detection have been proposed. However, most conventional techniques have assumed clear images or Gaussian noise images, thus their performance could decrease with the impulse noise. In this paper, we present an edge detection approach using Discrete Singular Convolution algorithm. The DSC algorithm efficiently detects edges not only original images but also noisy images which are added by Gaussian and impulse noise. Therefore, we evaluate that the performance of the DSC algorithm is compared with other algorithms such as the Canny, Bergholm, and Rothwell algorithm.

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JO - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

JF - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

T2 - 17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence

Y2 - 4 December 2004 through 6 December 2004

ER -

The DSC algorithm for edge detection

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