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Frank C. D. Tsai

dblp:91/30 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 1996
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 3 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.

Artificial intelligence
1 paper
Image recognition and object detection · 50% 3D vision · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › feature matching › geometric matching
affine correspondence		 0.011993
Robust affine invariant matching with application to line features · CVPR 1993
Computer vision › 3D vision › feature matching
geometric matching		 0.011993
Robust affine invariant matching with application to line features · CVPR 1993
Computer vision › Image recognition and object detection › object recognition
model-based object recognition		 0.011993
Robust affine invariant matching with application to line features · CVPR 1993
Computer vision › Image recognition and object detection
object recognition		 0.011993
Robust affine invariant matching with application to line features · CVPR 1993

Methods — techniques the papers use, named apart from their topics

hough transform · 0.0geometric hashing · 0.0affine invariants · 0.0
YearPublicationVenuePosition
1996 A Probabilistic Approach to Geometric Hashing Using Line Features
Frank C. D. Tsai
 Comput. Vis. Image Underst.1
1994 Geometric hashing with line features
Frank C. D. Tsai
 Pattern Recognit.1
1993 Robust affine invariant matching with application to line features
abstract
Line features in geometric hashing are discussed. Lines are used as the primitive features to compute the geometric invariants, combining the Hough transform with a variation of geometric hashing as a technique for model-based object recognition in seriously degraded single intensity images. The effect of uncertainty of line features on the computed invariants for the case where images are formed under affine viewing transformations is analytically determined. The system is implemented with experiments on polygonal objects, which are modeled by lines. It is shown that the technique is noise resistant and suitable in an environment containing many occlusions.>
Frank C. D. Tsai
 CVPR1