NDLI: Dynamic Texture Detection Based on Motion Analysis

Content Provider	Springer Nature Link
Author	Fazekas, Sándor Amiaz, Tomer Chetverikov, Dmitry Kiryati, Nahum
Copyright Year	2008
Abstract	Motion estimation is usually based on the brightness constancy assumption. This assumption holds well for rigid objects with a Lambertian surface, but it is less appropriate for fluid and gaseous materials. For these materials an alternative assumption is required. This work examines three possible alternatives: gradient constancy, color constancy and brightness conservation (under this assumption the brightness of an object can diffuse to its neighborhood). Brightness conservation and color constancy are found to be adequate models. We propose a method for detecting regions of dynamic texture in image sequences. Accurate segmentation into regions of static and dynamic texture is achieved using a level set scheme. The level set function separates each image into regions that obey brightness constancy and regions that obey the alternative assumption. We show that the method can be simplified to obtain a less robust but fast algorithm, capable of real-time performance. Experimental results demonstrate accurate segmentation by the full level set scheme, as well as by the simplified method. The experiments included challenging image sequences, in which color or geometry cues by themselves would be insufficient.
Starting Page	48
Ending Page	63
Page Count	16
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	82
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2008-11-15
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dynamic texture Motion analysis Optical flow Level set Real-time processing Brightness conservation Pattern Recognition Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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