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Title: Shape Detection In Images Exploiting Sparsity
Authors: Gürbüz, Ali Cafer
Keywords: Sparsity
Hough Transform
Shape Detection
Basis pursuit
Convex optimization
Compressed sensing
Line detection
Issue Date: 2009
Publisher: IEEE
Source: 24th International Symposium on Computer and Information Sciences -- SEP 14-16, 2009 -- Guzelyurt, CYPRUS
Abstract: Detection of different kinds of shapes, i.e. lines, circles, hyperbolas etc., in varying kinds of images arises in diverse areas such as signal and image processing, computer vision or remote sensing. The generalized Hough Transform is a traditional approach to detect a specific shape in an image by transforming the problem into a parameter space representation. In this paper we use the observation that the number of shapes in an image is much smaller than the number of all possible shapes. This means the shapes are sparse in the parameter domain. Rather than forming the parameter space from the image as in the HT, we take a reverse approach and ask "which combination of parameter space cells represent my data best?". This leads us to generate a dictionary of shapes and use additional information about sparsity of shapes within a basis pursuit framework. The results indicate enhanced shape detection performance, increased resolution, joint detection of different shapes in an image and robustness to noise. In addition to this, combining the sparsity of shapes with the Compressive Sensing ideas shows that it is possible to directly find the shapes in an image from small number of random projections of the image without first reconstructing the image itself.
ISBN: 978-1-4244-5021-3
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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