An Edge Detection Algorithm for Online Image Analysis Azzam Sleit, Abdel latif Abu Dalhoum, Ibraheem Al-Dhamari, Afaf Tareef Department of Computer Science, King Abdulla II School for Information Technology University of Jordan, Amman, Jordan [email protected], [email protected], [email protected], [email protected]Abstract: - Online image analysis is used in a wide variety of applications. Edge detection is a fundamental tool used to obtain features of objects as a prerequisite step to object segmentation. This paper presents a simple and relatively fast online edge detection algorithm based on second derivative. The proposed edge detector is less sensitive to noise and may be applied on color, gray and binary images without preprocessing requirements. The merits of the algorithm are demonstrated by comparison with Canny’s and Sobel’s edge detectors. Keywords: Edge detection, Canny’s edge detector, Sobel’s edge detector, Wavelet transforms, Derivative operators. 1. Introduction Several real life applications related to medical imaging, Geographical Information Systems (GIS) and Object Character recognition (OCR) depend on the discovery of edges surrounding objects since they hold desired features for the objects which appear in the images. Applying an edge detector to an image may significantly reduce the amount of data to be processed and may therefore filter out information that may be regarded as less relevant, while preserving the important structural properties of an image. An edge is a set of connected pixels that lie on the boundary between two regions reflecting discontinuities in the brightness of the image due to surface, depth, color, or illumination [1]. Sobel edge operator is one of the simplest operators known since 1968. It is a discrete differentiation operator which computes the approximated gradient of the image intensity. For each pixel of the image Sobel operator produces either the corresponding gradient vector or the norm of the corresponding gradient vector. The gradient approximation which Sobel operator produces is crude for high frequency variations in the image [2]. The Canny edge detector and its variations are considered the state-of-the-art edge detectors. Canny showed that the optimal filter is a sum of four exponential terms. He also showed that this filter can be well approximated by first-order derivatives of Gaussians. Canny edge detector is relatively complex and typically requires noise smoothing, edge enhancement, and edge localization [3]. There are many other edge detection algorithms which utilize more complex techniques such as k-means, neural networks and wavelet transform [4, 5, 6]. Such techniques have massive run-time requirements which make them inappropriate for online analysis of video steams or applications with large sets of images. Section 2 of this article proposes a fast and simple edge detection algorithm. Section 3 demonstrates experimental runs for the proposed algorithm including comparisons with Sobel and Canny. Section 4 concludes this article. 2. Proposed Edge Detector Real-time video and image processing is used in a wide variety of applications from video surveillance and traffic management to medical imaging applications. Edge detection is a fundamental tool used in most image processing applications to obtain information from the frames as a prerequisite step to feature extraction and object segmentation. This section presents a simple and relatively fast online edge detection approach based on the second derivative operator. The difference of the neighbor pixels is a good indicator of an edge in digital images. Second-order derivative operators such as Laplacian are sensitive to noise. However, we address this issue by the following steps: RECENT ADVANCES in APPLIED MATHEMATICS ISSN: 1790-2769 250 ISBN: 978-960-474-150-2
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An Edge Detection Algorithm for Online Image Analysis
Azzam Sleit, Abdel latif Abu Dalhoum, Ibraheem Al-Dhamari, Afaf Tareef
Department of Computer Science, King Abdulla II School for Information Technology