Paper Info
Title
A Pattern Recognition Method for Automating Tactile Graphics Translation from Hand-Drawn Maps
Abstract
Tactile graphics are images that use raised surfaces so that a visually impaired person can feel them. Tactile maps are used by blind and partially sighted people when navigating around an environment, they are useful for explaining the layout of a building or outdoor environment. Tactile Map Automated Creation System (TMACS) is developed for producing tactile maps. Tactile maps, produced by TMACS, often contain many information of roads and streets. The structure of a map should be as simple as possible because of the readability of the map. TMACS, however, does not eliminate unnecessary information automatically, in a tactile map, necessary information such as the origin, destination, route, roads and landmarks should be presented. Thus it is difficult for the visually impaired to read a tactile map produced by TAMCS. The goal of our research is to propose a method for automatic translating hand-drawn maps to tactile maps. This is because a user can draw freely a map which contains necessary information, so that the readability of tactile maps is improved. In this paper, we discuss a pattern recognition method for extracting and classifying objects of hand-drawn maps.
Year
DOI
Venue
2013
10.1109/SMC.2013.711
SMC
Keywords
Field
DocType
tactile map automated creation,unnecessary information,pattern recognition method,tactile map,sighted people,tactile graphics,classifying object,automating tactile graphics translation,hand-drawn maps,outdoor environment,necessary information,hand-drawn map,image classification,computer graphics,object recognition,cartography,geography
Graphics,Computer vision,Pattern recognition,Computer science,Support vector machine,Readability,Artificial intelligence,Contextual image classification,Computer graphics,Tactile map,Cognitive neuroscience of visual object recognition
Conference
ISSN
Citations 
PageRank 
1062-922X
3
0.38
References 
Authors
3
2
Name
Order
Citations
PageRank
Jianjun Chen13912.52
Noboru Takagi24615.42