NDLI: Shape detection in images exploiting sparsity

Content Provider	IEEE Xplore Digital Library
Author	Gurbuz, A.C.
Copyright Year	2009
Description	Author affiliation: Dept. of Electrical and Electronics Engineering, TOBB University of Economics and Technology, Ankara, 06560 (Gurbuz, A.C.)
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.
Starting Page	70
Ending Page	75
File Size	648161
Page Count	6
File Format	PDF
ISBN	9781424450213
DOI	10.1109/ISCIS.2009.5291916
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-14
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Dictionaries Image resolution Line detection Shape Image processing Noise shaping Basis pursuit Remote sensing Image coding Sparsity Convex optimization Signal processing Hough Transform Noise robustness Shape Detection Compressed sensing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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