DocumentCode :
2552231
Title :
Visual Motion Detecting and Deblurring Based on Mathematical Morphology and Ensemble Learning
Author :
Xing Chao ; Li Yanjun ; Zhang Ke
Author_Institution :
Sch. of Astronaut., Northwestern Polytech. Univ., Xi´an, China
fYear :
2010
fDate :
23-25 Sept. 2010
Firstpage :
1
Lastpage :
4
Abstract :
The problem of blurring caused by object motion in a gray level image is analyzed, and an algorithm combining image segmentation and blind deconvolution based on statistical features of object and background is introduced to estimate visual motion and restore the image. Suspected regions with slowly changing intensity of pixels are segmented on the base of gradient and curvature of the image. Simple connected regions are selected by the use of mathematical morphological algorithm, and convolution kernels of regions larger than a given threshold are inferred through ensemble learning. Motion patterns of objects can be determined and the blurred region can be restored. Experimental results show the effectiveness of the algorithm for visual motion estimation and deblurring in a gray level image.
Keywords :
convolution; image restoration; image segmentation; mathematical morphology; motion estimation; blind deconvolution; convolution kernels; ensemble learning; gray level image; image restoration; image segmentation; mathematical morphology; object motion; statistical features; visual motion deblurring; visual motion detection; visual motion estimation; Algorithm design and analysis; Deconvolution; Image restoration; Image segmentation; Kernel; Motion segmentation; Pixel;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications Networking and Mobile Computing (WiCOM), 2010 6th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-3708-5
Electronic_ISBN :
978-1-4244-3709-2
Type :
conf
DOI :
10.1109/WICOM.2010.5600575
Filename :
5600575
Link To Document :
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