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ICONIP
2008

Automatic Particle Detection and Counting by One-Class SVM from Microscope Image

13 years 5 months ago
Automatic Particle Detection and Counting by One-Class SVM from Microscope Image
Asbestos-related illnesses become a nationwide problem in Japan. Now human inspectors check whether asbestos is contained in building material or not. To judge whether the specimen contains asbestos or not, 3,000 particles must be counted from microscope images. This is a major labor-intensive bottleneck. In this paper, we propose an automatic particle counting method for automatic judgement system whether the specimen is hazardous or not. However, the size, shape and color of particles are not constant. Therefore, it is difficult to model the particle class. On the other hand, the non-particle class is not varied much. In addition, the area of non-particles is wider than that of particles. Thus, we use One-Class Support Vector Machine (OCSVM). OCSVM identifies "outlier" from input samples. Namely, we model the non-particle class to detect the particle class as outlier. In experiments, the proposed method gives higher accuracy and smaller number of false positives than a prel...
Hinata Kuba, Kazuhiro Hotta, Haruhisa Takahashi
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ICONIP
Authors Hinata Kuba, Kazuhiro Hotta, Haruhisa Takahashi
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