2008 Canadian Conference on Computer and Robot Vision 2008
DOI: 10.1109/crv.2008.31
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Segmentation of Rectangular Objects Lying on an Unknown Background in a Small Preview Scan Image

Abstract: We describe a method to segment rectangular objects that lie on a slightly textured background of an a-priori unknown colour. Our contribution consists of a fast and accurate background colour approximation method, a set of heuristics for accurate detection of rectangle sides, and procedures to generate imprecise hypotheses of rectangles, adjust hypotheses to fit the rectangles in the image, and verify or reject the hypotheses. Our algorithm is capable of detecting overlapping and touching objects such as phot… Show more

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Cited by 5 publications
(2 citation statements)
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“…Taking into account the rectangular shape of traps, multiple methods have been explored in the literature for similar tasks. In [19,20], the authors explored edge detection approaches to segment objects with rectangular shape, while, in [21], the authors presented a methodology with additional steps to validate the similarity of the object with a rectangle.…”
Section: Related Workmentioning
confidence: 99%
“…Taking into account the rectangular shape of traps, multiple methods have been explored in the literature for similar tasks. In [19,20], the authors explored edge detection approaches to segment objects with rectangular shape, while, in [21], the authors presented a methodology with additional steps to validate the similarity of the object with a rectangle.…”
Section: Related Workmentioning
confidence: 99%
“…In a recent publication, we proposed an algorithm for automatized segmentation of PV-modules characterized by highly sensitive lock-in thermography [36]. This method was stimulated by algorithms developed to detect the background and foreground in images obtained by commercial scanners [37][38][39]. The method proved to work even under extremely low signal to noise ratio of 1.09 (for more details see [36], i.p.…”
Section: Algorithmmentioning
confidence: 99%