Title :
Compressive sensing of parameterized shapes in images
Author :
Gurbuz, Ali Cafer ; McClellan, James H. ; Romberg, Justin ; Scott, Waymond R., Jr.
Author_Institution :
Georgia Inst. of Technol., Atlanta, GA
fDate :
March 31 2008-April 4 2008
Abstract :
Compressive sensing (CS) uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals. The Hough transform is often used to find lines and other parameterized shapes in images. This paper shows how CS can be used to find parameterized shapes in images, by exploiting sparseness in the Hough transform domain. The utility of the CS-based method is demonstrated for finding lines and circles in noisy images, and then examples of processing GPR and seismic data for tunnel detection are presented.
Keywords :
Hough transforms; image reconstruction; Hough transform; compressible signal reconstruction; compressive sensing; parameterized shapes; sparse signal reconstruction; Computer vision; Dictionaries; Ground penetrating radar; Image coding; Image converters; Image processing; Image reconstruction; Noise shaping; Pattern recognition; Shape; Basis pursuit; Compressive Sensing; Convex optimization; Hough Transform; Shape Detection; line detection;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2008.4518018