Pregled bibliografske jedinice broj: 1022968
Image Representation and Analysis by Continuous Chebyshev Polynomials
Image Representation and Analysis by Continuous Chebyshev Polynomials // Proceedings of the 2019 Signal Processing Symposium (SPSympo)
Kraków, Poljska, 2019. str. 300-305 doi:10.1109/SPS.2019.8882089 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1022968 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Image Representation and Analysis by Continuous Chebyshev Polynomials
Autori
Vlašić, Tin ; Seršić, Damir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 2019 Signal Processing Symposium (SPSympo)
/ - , 2019, 300-305
Skup
2019 Signal Processing Symposium (SPSympo)
Mjesto i datum
Kraków, Poljska, 17.09.2019. - 19.09.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
polynomial image representation ; polynomial approximation ; sparse modeling ; finite rate of innovation ; compressed sensing ; edge detection ; gradient
Sažetak
This paper proposes a spline-like Chebyshev polynomial image representation obtained by block-based compressed sampling (CS) for image analysis and processing. Due to orthogonality and close relation with the discrete cosine transform, Chebyshev polynomials have near-zero redundancy measure and possess great compression properties. Consequently, they fit perfectly into CS paradigm. We propose a spline-like model, that equalizes a desired number of derivatives on each block boundary, to avoid blocking artifacts. This can be seen as an additional constraint in the standard CS optimization problem. Still, the proposed model provides a system of equations that is underdetermined for achieving sparse reconstruction using the l1 optimization. The proposed framework offers polynomial representation of the acquired image and allows further analysis and processing conducted on Chebyshev polynomial coefficients without the need of converting them into samples. In this paper, we demonstrate the efficiency of the proposed model by implementing an analytic Chebyshev polynomial gradient operator that can be used instead of discrete gradient approximations in edge detection methods. The proposed operator is compared to Sobel operator and we prove its potential by implementing it into an edge detection algorithm.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Projekti:
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )
HRZZ-IP-2014-09-2625 - Iznad Nyquistove granice (BeyondLimit) (Seršić, Damir, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
