Paper Info
Title
Motion Vector Estimation Of Textureless Objects Exploiting Reaction-Diffusion Cellular Automata
Abstract
Conventional motion estimation algorithms extract motion vectors from image sequences based on the image's local-brightness differences in consecutive images. Therefore, motion vectors are extracted along the moving edges formed by moving objects over their background. However, in the case of "textureless" moving objects, motion vectors inside the objects cannot be detected because no brightness (texture) differences exist inside the object. Severe issues may incur in motion-related imaging applications because motion-vectors of vast (inner) regions of textureless objects can not be detected, although the inner part is moving with the object's edges. To solve this problem, we propose an unconventional image-processing algorithm that generates spatial textures based on object's edge information, allowing the detection of the textures motion. The model is represented by a 2-D crossbar array of a 1-D reaction-diffusion (RD) model where 1-D spatial patterns are created inside objects and aggregated to form textures. Computer simulations confirm the approach, showing the formation of textures over approaching objects, which may open applications in machine vision and automated decision systems.
Year
Venue
Keywords
2016
INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING
Texture generation, cellular automata, reaction-diffusion systems, unconventional image processing
Field
DocType
Volume
Cellular automaton,Computer vision,Machine vision,Decision system,Theoretical computer science,Artificial intelligence,Motion estimation,Reaction–diffusion system,Brightness,Crossbar array,Mathematics,Motion vector
Journal
12
Issue
ISSN
Citations 
2-3
1548-7199
1
PageRank 
References 
Authors
0.48
0
5
Name
Order
Citations
PageRank
Miho Ushida110.48
Alexandre Schmid22911.91
Tetsuya Asai312126.53
Kazuyoshi Ishimura410.48
Masato Motomura59127.81