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Masakazu Nagura

dblp:17/3357 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 1995
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 1 first-authorDatabases, data management, data science and information retrieval · 2

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%
Human-computer interaction and pervasive computing
1 paper
User interface design and tools · 100%

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

TopicWeightPapersLastEvidence papers
Image and video processing
document image analysis
0.011983
A Facsimile-Based Graphics Editing System by Auxiliary Mark Recognition · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Image and video processing › document image analysis
graphics recognition
0.011983
A Facsimile-Based Graphics Editing System by Auxiliary Mark Recognition · IEEE Trans. Pattern Anal. Mach. Intell. 1983

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

mark parameter list · 0.0graphic command generation · 0.0binary picture processing · 0.0
YearPublicationVenuePosition
1995 Drawing capturing system using image enhancement
abstract
The paper describes the properties of Ueda's (1985) image enhancement method for line drawings and its merit for practical use. This method can remove the line discontinuities or mis-connections caused by scanning errors. The method is applied to simple images to evaluate its effect quantitatively. The authors confirm that it is more efficient than any other methods, and propose a drawing capturing system based on this method that can build up high quality drawing databases faster than any other system.
Norio Nakamura, Kenichi Hosaka, Masakazu Nagura
ICDAR3
1993 Intelligent interactive map recognition using neural networks
abstract
An intelligent interactive map recognition method using the case learning ability of neural networks as proposed. It effectively uses operator judgements about local segmentations of map figures as neural network learning data. With this method, the recognition accuracy can be improved and the number of interactive operations needed in the recognition process can be decreased through the operation processes. The method is implemented with a single recognition algorithm which has no dependence on the kind of map figure. Experiment results of applying this method to the recognition of contour lines, longitudinal/latitudinal lines and sidewalks in a mapdraw show improved accuracy and a decreased number of interactions.>
Hiroyuki Arai, Shinji Abe, Masakazu Nagura
ICDAR3
1983 A Facsimile-Based Graphics Editing System by Auxiliary Mark Recognition
abstract
This paper describes a facsimile-based graphics editing system using handwritten mark recognition, and presents some experimental results with the system. In a manner different from usual graphics editors based on CRT displays and data tablets, only facsimiles are used as input and output devices in this system. As the first stage in processing, a graphic subject is first given as a set of line drawings and characters handwritten on a sheet of paper, and it is then input into the computer as a binary picture from a facsimile transmitter. Auxiliary editing information is input into the computer as handwritten marks or seal marks prepared on a separate sheet of paper. As the second stage, the marks are recognized and used to create a mark parameter list for the graphics editing. Third, referencing the mark parameter list, the graphics are expressed by using a set of graphic commands, and edited by the computer. Finally, a correct edited copy of the graphics is constructed by picture processing based on graphic commands, and it is output to a facsimile receiver. Very good results have been obtained for various kinds of hand-written graphics by using the system described here.
Masakazu Nagura, Yasuhito Suenaga
IEEE Trans. Pattern Anal. Mach. Intell.1