A Locally Adapting Technique for Edge Detection using Image Segmentation
| Autor: | Leora Dresselhaus-Cooper, B. T. Meehan, Marylesa Howard, Margaret C. Hock |
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| Rok vydání: | 2018 |
| Předmět: |
Morphological gradient
Segmentation-based object categorization business.industry Applied Mathematics 0211 other engineering and technologies Scale-space segmentation Pattern recognition Image processing 02 engineering and technology Image segmentation Edge detection Computational Mathematics Image texture Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Range segmentation business 021101 geological & geomatics engineering Mathematics |
| Zdroj: | SIAM Journal on Scientific Computing. 40:B1161-B1179 |
| ISSN: | 1095-7197 1064-8275 |
| DOI: | 10.1137/17m1155363 |
| Popis: | Rapid growth in the field of quantitative digital image analysis is paving the way for scientific researchers to make precise measurements about objects in an image. To compute quantities from an image such as the density of compressed materials or the velocity of a shockwave, object boundaries must first be determined. Images containing regions that each have a spatial trend in intensity are of particular interest here. For edge detection, we present a supervised, statistical image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms, specifically for images where the regions are known but precise boundary locations are unknown. The segmentation of a pixel is determined by comparing its intensity to distributions from nearby pixel intensities, and a gradient of the segmented image indicates edge locations. Because of the statistical nature of the algorithm, we use maximum likelihood estimation to quantify uncertainty about ea... |
| Databáze: | OpenAIRE |
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