Abstract
The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem. Extending the principles of image perception i.e. Weber-Fechner law, and Sheperd similarity law, we propose a new edge detection method and formulation that use perceived brightness and neighbourhood similarity calculations in the determination of robust object edges. The robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and Prewitt edge detection methods in an example face recognition problem showing statistically significant improvement in recognition accuracy and pixel noise tolerance.
| Original language | English |
|---|---|
| Article number | 7045586 |
| Pages (from-to) | 1336-1339 |
| Number of pages | 4 |
| Journal | IEEE Signal Processing Letters |
| Volume | 22 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - Sep 1 2015 |
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Keywords
- Edge detection
- local stimuli
- perceived brightness
- primal sketch model
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
- Applied Mathematics
Cite this
Spatial stimuli gradient sketch model. / Mathew, Joshin John; James, Alex Pappachen.
In: IEEE Signal Processing Letters, Vol. 22, No. 9, 7045586, 01.09.2015, p. 1336-1339.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Spatial stimuli gradient sketch model
AU - Mathew, Joshin John
AU - James, Alex Pappachen
PY - 2015/9/1
Y1 - 2015/9/1
N2 - The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem. Extending the principles of image perception i.e. Weber-Fechner law, and Sheperd similarity law, we propose a new edge detection method and formulation that use perceived brightness and neighbourhood similarity calculations in the determination of robust object edges. The robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and Prewitt edge detection methods in an example face recognition problem showing statistically significant improvement in recognition accuracy and pixel noise tolerance.
AB - The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem. Extending the principles of image perception i.e. Weber-Fechner law, and Sheperd similarity law, we propose a new edge detection method and formulation that use perceived brightness and neighbourhood similarity calculations in the determination of robust object edges. The robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and Prewitt edge detection methods in an example face recognition problem showing statistically significant improvement in recognition accuracy and pixel noise tolerance.
KW - Edge detection
KW - local stimuli
KW - perceived brightness
KW - primal sketch model
UR - http://www.scopus.com/inward/record.url?scp=84923320895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84923320895&partnerID=8YFLogxK
U2 - 10.1109/LSP.2015.2404827
DO - 10.1109/LSP.2015.2404827
M3 - Article
VL - 22
SP - 1336
EP - 1339
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
SN - 1070-9908
IS - 9
M1 - 7045586
ER -