The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 21, 2007

Filed:

May. 02, 2005
Applicants:

Ross Mitchell, Calgary, CA;

Hongmei Zhu, Richmond Hill, CA;

Yunyan Zhang, Calgary, CA;

Alan Law, Kelowna, CA;

Inventors:

Ross Mitchell, Calgary, CA;

Hongmei Zhu, Richmond Hill, CA;

Yunyan Zhang, Calgary, CA;

Alan Law, Kelowna, CA;

Assignee:

Calgary Scientific Inc., Calgary, Alberta, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G09G 5/00 (2006.01); G06T 15/10 (2006.01); G06K 9/34 (2006.01); G06K 9/40 (2006.01); G06K 9/36 (2006.01); G06T 15/50 (2006.01); G06K 9/32 (2006.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
Abstract

The present invention relates to a method and system for segmenting texture of multi-dimensional data indicative of a characteristic of an object. Received multi-dimensional data are transformed into second multi-dimensional data within a Stockwell domain based upon a polar S-transform of the multi-dimensional data. Principal component analysis is then applied to the second multi-dimensional data for generating texture data characterizing texture around each data point of the multi-dimensional data. Using a classification process the data points of the multi-dimensional data are partitioned into clusters based on the texture data. Finally, a texture map is produced based on the partitioned data points. The present invention provides image texture segmentation based on the polar S-transform having substantially reduced redundancy while keeping maximal data variation.


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