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Title: Finding sparse parametric shapes from low number of imase measurements
Other Titles: Seyrek Parametrik Şekillerin Görüntülerden Az Öblçüm Altlnda Tespiti
Authors: İlhan, I.
Gürbüz, A. C.
Keywords: circle detection
compressive sensing
Hough transform
line detection
shape detection
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015, 16 May 2015 through 19 May 2015, , 113052
Abstract: Detection of parametric shapes i.e. line, circle, ellipse etc. in images is one of the most significant topics in diverse areas such as image and signal processing, pattern recognition and remote sensing. Compressive Sensing(CS) theory details how the signal is sparsely reconstructed in a known basis from low number of linear measurement. Sparsity of parametric shapes in parameter space offers to detect parametric shapes from low number of linear measurements under frameworks proposed by CS methods. Joint detedon performance of different parametric shapes in image is studied under different small number of measurements and noise level. Because of being both discrete image space and discretized parameter space, effect of offgrid, one of the most important problem in CS,is analysed in terms of shape detection. Results show that parametric shapes can robustly be found with a few measurements and effects of offgrid are seen as distribution of target energy in parameter space. © 2015 IEEE.
ISBN: 9781467373869
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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