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Chun-Pong Chau

dblp:48/3153 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › feature detection
hough transform		 0.012004
Generalized Hough Transform Using Regions with Homogeneous Color · Int. J. Comput. Vis. 2004
Image and video processing
image segmentation		 0.012004
Generalized Hough Transform Using Regions with Homogeneous Color · Int. J. Comput. Vis. 2004
Image and video processing
color image processing		 0.012004
Generalized Hough Transform Using Regions with Homogeneous Color · Int. J. Comput. Vis. 2004
YearPublicationVenuePosition
2004 Adaptive dual-point Hough transform for object recognition
Chun-Pong Chau, Wan-Chi Siu
 Comput. Vis. Image Underst.1
2004 Generalized Hough Transform Using Regions with Homogeneous Color
Chun-Pong Chau, Wan-Chi Siu
 Int. J. Comput. Vis.1
1999 Generalized Dual-Point Hough Transform for Object Recognition
abstract
In this paper, a modified version of dual-point generalized Hough transform has been proposed. Inspired by the result of an analysis of the shapes of objects, we are able to improve the efficiency of the dual-point generalized Hough transform. A characteristic angle, which is governed by the points selected in the recognition process, will be determined for the generation of the R-table. This angle is based on making the number of index entries in the R-table as large as possible, the number of entries per indexes as small as possible, and the number of access per point as small as possible. Experimental results show that it can improve the transform by increasing the detection accuracy and speed after the modification is made into the transform.
Chun-Pong Chau, Wan-Chi Siu
 ICIP (1)1