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Toshiharu Kurosawa

dblp:23/1443 · DBLP profile ↗
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2ranked-venue papers
0as 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 · 1Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 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
Robot navigation and mapping · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › localization
dead reckoning
0.012004
Accurate Localization in Combination with Planet Observation and Dead reckoning for Lunar Rover · ICRA 2004
Robotics › Robot navigation and mapping
localization
0.012004
Accurate Localization in Combination with Planet Observation and Dead reckoning for Lunar Rover · ICRA 2004

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

sun and earth observation · 0.0extended kalman filter · 0.0
YearPublicationVenuePosition
2004 Accurate Localization in Combination with Planet Observation and Dead reckoning for Lunar Rover
abstract
This paper proposes a localization method to estimate position and azimuth of a lunar rover. In the proposed method, the position is precisely estimated by integration of an absolute and a relative position. The absolute position is measured by observing the sun and the earth, and the relative position is determined by dead reckoning. Effectiveness is confirmed by the results of simulations. Moreover, differences between proposed method and Extended Kalman Filter are shown by results of long-distance simulation.
Yoji Kuroda, Toshiharu Kurosawa, Akiyoshi Tsuchiya, Takashi Kubota
ICRA2
2001 High quality color correction method combining neural networks with genetic algorithms
abstract
A variety of color correction technologies have been applied to hardcopies to eliminate the crosstalk caused by unwanted absorption in colorants. However, since the accuracy of color correction is not sufficient, an improvement in color correction technology is required as an aspect of the appearance of color devices. This paper proposes a new color correction method combining neural networks with genetic algorithms. An evaluation experiment of RMS color difference has been done with the chart pattern of 1331 colors inside the printer color gamut. As a result, it has been shown that the proposed method is qualitatively and quantitatively superior to the conventional color correction masking method using the least squares method.
Tatsumi Watanabe, Akio Kojima, Yasuhiro Kuwahara, Toshiharu Kurosawa
ICIP (1)4