Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9848
Title: Finding Sparse Parametric Shapes From Low Number of Image Measurements
Authors: İlhan, İhsan
Gürbüz, Afi Cafer
Keywords: shape detection
compressive sensing
Hough transform
line detection
circle detection
off-grid
Issue Date: 2015
Publisher: IEEE
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 detecion 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.
Description: 23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY
URI: https://hdl.handle.net/20.500.11851/9848
ISBN: 978-1-4673-7386-9
ISSN: 2165-0608
Appears in Collections:WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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