DocumentCode :
3013352
Title :
Sensor noise modeling using the Skellam distribution: Application to the color edge detection
Author :
Hwang, Youngbae ; Kim, Jun-Sik ; Kweon, In-So
Author_Institution :
KAIST, Daejeon
fYear :
2007
fDate :
17-22 June 2007
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, we introduce the Skellam distribution as a sensor noise model for CCD or CMOS cameras. This is derived from the Poisson distribution of photons that determine the sensor response. We show that the Skellam distribution can be used to measure the intensity difference of pixels in the spatial domain, as well as in the temporal domain. In addition, we show that Skellam parameters are linearly related to the intensity of the pixels. This property means that the brighter pixels tolerate greater variation of intensity than the darker pixels. This enables us to decide automatically whether two pixels have different colors. We apply this modeling to detect the edges in color images. The resulting algorithm requires only a confidence interval for a hypothesis test, because it uses the distribution of image noise directly. More importantly, we demonstrate that without conventional Gaussian smoothing the noise model-based approach can automatically extract the fine details of image structures, such as edges and corners, independent of camera setting.
Keywords :
CCD image sensors; CMOS image sensors; Gaussian distribution; Poisson distribution; edge detection; image colour analysis; noise; CCD; CMOS camera; Gaussian smoothing; Poisson distribution; Skellam distribution; color edge detection; image noise; noise model-based approach; sensor noise modeling; CMOS image sensors; Cameras; Charge coupled devices; Charge-coupled image sensors; Color; Colored noise; Image edge detection; Optoelectronic and photonic sensors; Semiconductor device modeling; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
Conference_Location :
Minneapolis, MN
ISSN :
1063-6919
Print_ISBN :
1-4244-1179-3
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2007.383004
Filename :
4270029
Link To Document :
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