Faculty of engineering robotics technology mech 4041

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Unformatted text preview: parts are present. Faculty of Engineering Robotics Technology MECH 4041 13 B.Eng (Hons.) Mechatronics S. Venkannah Mechanical and Production Engineering Department The more prior information that is available to the segmentation process, the better the segmentation results that can be obtained. Prior information affects segmentation algorithms; if a large amount of prior information about the desired result is available, the boundary shape and relations with other image structures are specified very strictly and the segmentation must satisfy all these specifications. If little information about the boundary is known, the segmentation method must take more local information about the image into consideration and combine it with specific knowledge that is general for an application area. The most common problems of edge-based segmentation are caused by image noise or unsuitable information in an image; an edge presence in locations where there is no border, and no edge presence where a real border exists. Edge image thresholding Almost no zero-value pixels are present in an edge image, but small edge values correspond to non-significant gray level changes resulting from quantization noise, small lighting irregularities, etc. Simple thresholding of an edge image can be applied to remove these small values. The approach is based on an image of edge magnitudes processed by an appropriate threshold. Selection of an appropriate global threshold is often difficult and sometimes impossible; p-tile thresholding can be applied to define a threshold Alternatively, non-maximal suppression and hysteresis thresholding can be used as was introduced in the Canny edge detector. Edge relaxation Borders resulting from the previous method are strongly affected by image noise, often with important parts missing. Considering edge properties in the context of their mutual neighbors can increase the quality of the resulting image. All the image properties, including those of further edge existence, are iteratively evaluated with more precision until the edge context is totally clear - based on the strength of edges in a specified local neighborhood, the confidence of each edge is either increased...
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