NDLI: Image mining for robot vision based on concept analysis

Content Provider	IEEE Xplore Digital Library
Author	Qizhi Xiao Kun Qin Zequn Guan Tao Wu
Copyright Year	2007
Description	Author affiliation: Sch. of Remote Sensing Inf. Eng., Wuhan Univ. Wuhan, Wuhan (Qizhi Xiao; Kun Qin; Zequn Guan)
Abstract	- In the process of image mining for robot vision, concept analysis is an important technique. The paper proposes a novel framework of image mining for robot vision based on concept lattice theory and cloud model theory. Concept lattice reflects the process of human's concept formation with mathematical formal language. Cloud model is a transformation model between qualitative concepts and quantitative numerical values. Image mining for robot vision is considered as a process of concept extraction from different granularities (image pixels, image pixel groups, image features, image objects, image files and image databases). The methods of image mining from image features(texture features, color features, shape features, spatial relationship features) are introduced in the paper, which include the following basic steps: firstly pre-process images, secondly use cloud model to extract concepts, lastly use concept lattice to extract a series of image knowledge(association rules, clustering rules and classification rules). At last, a software prototype is designed and developed, and some experiments confirm the validity of the proposed framework.
Starting Page	207
Ending Page	212
File Size	196856
Page Count	6
File Format	PDF
ISBN	9781424417612
DOI	10.1109/ROBIO.2007.4522161
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot vision systems Image analysis Lattices Clouds Pixel Formal languages Image databases Shape Software prototyping Software design concept analysis image mining robot vision concept lattice cloud model
Content Type	Text
Resource Type	Article

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