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Ikuji Honda

dblp:81/9703 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1983
—ORCID · none

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

Artificial intelligence and machine learning · 1

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 · 100%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › object recognition
curved object recognition
0.011983
Pseudodistance Measures for Recognition of Curved Objects · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Computer vision › Image recognition and object detection
object recognition
0.011983
Pseudodistance Measures for Recognition of Curved Objects · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Geometric modeling and processing › shape descriptor
fourier descriptors
0.011983
Pseudodistance Measures for Recognition of Curved Objects · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Geometric modeling and processing
shape description
0.011983
Pseudodistance Measures for Recognition of Curved Objects · IEEE Trans. Pattern Anal. Mach. Intell. 1983

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

pseudodistance measures · 0.0fourier series expansion · 0.0
YearPublicationVenuePosition
1983 Pseudodistance Measures for Recognition of Curved Objects
abstract
A method for the description and identification of curved objects is described in this paper. The shape of a curved object is considered as a surface which is an accumulation of horizontal section boundaries. This is described by the coefficients of a Fourier series expansion of the proposed representative function. Each complicated feature of the shape of the object is decomposed into five simple features: elongatedness, horizontal strain, section shape, torsion, and displacement. Differences of objects as patterns are defined as pseudodistances of corresponding features. Some essential properties such as completeness of the pseudodistance family are explained theoretically. Finally, experimental results of identification of wooden block models and a doll are displayed.
Yukio Sato, Ikuji Honda
IEEE Trans. Pattern Anal. Mach. Intell.2