Abstract
Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate’s vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we review the gradient-based edge detection algorithms on Spiral Architecture. An edge point is defined as a hexagonal pixel at which the magnitude of the gradient of brightness function assumes a local maximum.
Highlights
Computer vision involves compositions of picture elements into edges, edges into object contours and object contours into scenes
Edge point from a continuous grey-level image represented as L(x, y; t) for given t is defined as a pixel at which the gradient magnitude of L(x, y; t) assumes a local maximum in the gradient direction
A toy duck and the Lena image represented on Spiral Architecture with 256 grey levels are used as the original image for edge detection
Summary
Computer vision involves compositions of picture elements (pixels) into edges, edges into object contours and object contours into scenes. The determination of edges depends on detection of edge points (pixels) of a 3-D physical object in a 2-D image This first step in the process is critical to the functioning of machine vision. Edge detection is based on the relationship a pixel has with its neighbours It extracts and localizes points (pixels) around which a large change in image brightness has occurred. A range filter gives more weights to those neighbouring pixels with light intensity that is more similar to the reference pixel value This method has been proved being more efficient for suppressing image noise for edge detection. Note that an edge point is a pixel at which the gradient magnitude assumes a local maximum This method is a more accurate detection mechanism where the gradient is implemented in a more accurate way in the discrete image space. We will list the problems for future research work
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