KINDAI UNIVERSITY


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ABE Koji

Profile

FacultyDepartment of Informatics / Graduate School of Science and Engineering Research
PositionAssociate Professor
DegreePh.D. in Engineering
Commentator Guidehttps://www.kindai.ac.jp/meikan/450-abe-kouji.html
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Last Updated :2020/09/30

Education and Career

Education

  •   1992 04  - 1996 03 , Kogakuin University
  •   1996 04  - 1998 03 , Grad. Sch. of Kogakuin University
  •   1998 04  - 2001 03 , Grad. Sch. of Kanazawa University

Academic & Professional Experience

  •   2010 04 ,  - 現在, Faculty of Science and Engineering, Department of Informatics, Kindai University
  •   2006 ,  - 2010 , Faculty of Science and Engineering, Department of Informatics, Kindai University
  •   2001 ,  - 2002 , Faculty of Engineering, Kanazawa University
  • Kanazawa University, Japan
  • Kanazawa Institute of Technology, Japan
  •   2003 ,  - 2006 , Assistant Prof.
  •   2006 , -current Lecturer
  •   2002 ,  - 2003 , Honorary Research Fellow
  •   2001 ,  - 2002 , Assistant Professor
  •   1998 ,  - 2001 , Research Associate
  • Kanazawa University of Economics
  • Integrated Information Institute,
  • Laboratory of Magnetic Field Control and Applications,
  • Institute for Image Data Research, University of Northumbria at Newcastle, UK
  • School of Science and Engineering, Kinki University, Japan

Research Activities

Research Areas

  • Informatics, Perceptual information processing
  • Informatics, Database science
  • Environmental science/Agricultural science, Agricultural environmental and information engineering
  • Environmental science/Agricultural science, Agricultural environmental and information engineering
  • Life sciences, Medical systems

Research Interests

  • Medical Image Processing, Multimedia Database, Pattern Recognition, Visual Information Retreival

Published Papers

  • Sound Classification for Hearing Aids Using Time-frequency Images, K. Abe, H. Masaki, H. Tian, Journal of Basic and Applied Physics, Journal of Basic and Applied Physics, 3(4), 159 - 166, Nov. 2014 , Refereed
  • Brain-computer Interface for Assisting Decision-making on Individual Preference by Switching Support Vector Machines, T. Misawa, S. Takano, K. Abe, T. Shimokawa, S. Hirobayashi, International Journal of Computational Science, International Journal of Computational Science, 4(6), 477 - 490, Dec. 2010 , Refereed
  • Performance Comparison of Flux-Concentration Type and Conventional Type Tubular Linear Induction Motor Using Three-Phase Equivalent Circuit Parameters, D. Roy, K. Abe, B. Basak, Journal of the Institution of Engineers (Electrical Engineering Division), Journal of the Institution of Engineers (Electrical Engineering Division), 89, 27 - 33, Jun. 2008 , Refereed
  • Discrimination of personal web pages by extracting subjective expressions, Takahiro Hayashi, Koji Abe, Debabrata Roy, Rikio Onai, International Journal of Business Intelligence and Data Mining, International Journal of Business Intelligence and Data Mining, 4(1), 62 - 77, 2009 , Refereed
    Summary:This paper presents a method for discriminating between personal and non-personal web pages. The method can support surveys of personal opinions about products and services. In the proposed method, subjective expressions are extracted from pages and then the pages are scored by quantitatively evaluating the subjectivity in the pages. We have evaluated performances of the proposed method using 1200 web pages collected from four categories of product, tourist spot, restaurant, and movie. Comparing the performances of the proposed method with categorisations by a general search engine, we have confirmed that the performances have been significantly better in every category. Copyright © 2009, Inderscience Publishers.
  • Recognition of plural grouping patterns in trademarks for CBIR according to the gestalt psychology, K Abe, H Iguchi, HY Tian, D Roy, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, E89D(6), 1798 - 1805, Jun. 2006 , Refereed
    Summary:According to the Gestalt principals, this paper presents a recognition method of grouping areas in trademark images modeling features for measuring the attraction degree between couples of image components. This investigation would be used for content-based image retrieval from the view of mirroring human perception for images. Depending on variability in human perception for trademark images, the proposed method finds grouping areas by calculating Mahalanobis distance with the features to every combination of two components in images. The features are extracted from every combination of two components in images, and the features represent proximity, shape similarity, and closure between two components. In addition, changing combination of the features, plural grouping patterns are output. Besides, this paper shows the efficiency and limits of the proposed method from experimental results. In the experiments, 104 participants have perceived grouping patterns to 74 trademark images and the human perceptions have been compared with outputs by the proposed method for the 74 images.

Works

  • Research for a similarity retreival of trademark images

Misc

  • Features for Measuring the Congestive Extent of Internal Hemorrhoids in Endoscopic Images, K. Abe, H. Takagi, M. Minami, H. Tian, Proc. of the 11th Australasian Data Mining Conference,   2013 11 , Refereed
  • Extraction of Essential Region in Gastric Area for Diagnosing Gastric Cancer Using Double Contrast X-ray Images, K. Abe, H. Nakagawa, M. Minami, H. Tian, Proc. of the 11th Australasian Data Mining Conference,   2013 11 , Refereed
  • Sound Classification for Hearing Aids Based on Time-frequency Image Processing, K. Abe, H. Sakaue, T. Okuno, K. Terada, Proc. of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 719, 724,   2011 08 , Refereed, 10.1109/PACRIM.2011.6032982
  • Estimation of Essential Objects in the Sports Ticker for Segmenting a Broadcasted Baseball Video into All the Plate Appearances, K. Abe, K. Miyashita, H. Murakami, T. Hayashi, H. Tian, Proc. of the 9th IASTED International Conference on Signal and Image Processing, 242, 247,   2007 08 , Refereed
  • A System for Drawing Monochrome Portraits for Criminal Detection, K. Abe, T. Okamine, S. Tanaka, T. Abe, H. Kimura, Proc. of The 4th Asia-Pacific Conference on Industrial Engineering and Management Systems, 1181, 1185,   2002 12 , Refereed
  • A Method to Extract The Region That Represents Meaning of Trademarks with Outer Frame, K. Abe, H. Kimura, H. Nagashima, Proc. of The 3rd International Conference on Engineering Design and Automation, 176, 179,   1999 08 , Refereed
  • Measurement ofworking hours in vdt work using a webcam, Koji Abe, Minori Yama, Masahide Minami, Haiyan Tian, Proceedings of the IASTED International Conference on Modelling, Simulation and Identification, MSI 2014, 121, 127,   2014 , Refereed, 10.2316/P.2014.820-012
    Summary:This paper presents a system for giving the warning information to the PC user in companies before going into working in front of the PC for long hours. This system is utilized in companies to avoid long VDT work. Although it would be not difficult to monitor the PC user by using motionsensing devices such as Kinect whether the user is doing VDT work, this way is not practical due to the high cost for companies. Assuming large-volume introduction of the system into companies, the proposed system monitors the PC user using a common webcam considering cost performance. The proposed method extracts just one feature for measuring area of the user's face and head from images captured at the interval of 1 min. and then discriminates whether the user appears in the camera frame or not. Besides, the proposed method measures hours of VDT work by counting the result of the discrimination for the user's appearance at every minute and gives a warning information to the user before the user is doing VDT work for long hours. Experimental results that accuracy ratios for the discrimination have been 93.9% on average have shown thatperformance of the proposed method is high enough.
  • Classification of real sounds for hearing aids based on time-Frequency image processing, Koji Abe, Hiroyoshi Masaki, Haiyan Tian, Proceedings of the IASTED International Conference on Modelling, Simulation and Identification, MSI 2014, 115, 120,   2014 , Refereed, 10.2316/P.2014.820-011
    Summary:This paper presents features of sound data for a sound classification equipped for hearing aids. The features are extracted by using image processing techniques to timefrequency images. As an application of hearing aids in mind, four classes of "classical music", "speech", "multitalker noise" and "speech in the noise" are prepared in order to classify the input signal of a hearing aid into useful classes. Although there are several possible ways to figure out which class the current input signal belongs to, an approach from image processing is utilized to find out appropriate features because 2D image (time-frequency image) can contain multifaceted information compared to 1D information (waveform or frequency response of sound), and can be regarded as comprehensive data. It is found that eight features are required to meet a certain quality of sound classification according to our investigation. Experimental results of the sound classification by some clustering machines using the proposed features have shown that accuracy of the classification was more than 95 % with every clustering machine.
  • Computer-Aided Diagnosis of Mass Screenings for Gastric Cancer Using Double Contrast X-ray Images, Koji Abe, Tetsuya Nobuoka, Masahide Minami, 2011 IEEE PACIFIC RIM CONFERENCE ON COMMUNICATIONS, COMPUTERS AND SIGNAL PROCESSING (PACRIM), 708, 713,   2011 , Refereed, 10.1109/PACRIM.2011.6032980
    Summary:In a mass screening for gastric cancer, diagnosticians read several hundred stomach X-ray pictures at a time. The existing systems of computer-aided diagnosis for the cancer mark the location of lesions appeared in X-ray images. However, the systems do not reduce the hard labor due to lack of the accuracy in the marking. Besides, to diagnose characteristics of legions, diagnosticians have to directly read X-ray pictures of abnormal cases even if the systems could show the location precisely. For the sake of decreasing the number of reading the pictures in the mass screenings, the proposed method discriminates normal cases using stomach X-ray images. In normal cases, folds on the stomach wall appear in parallel. Therefore, the proposed method measures features of parallelism extracting the folds from X-ray images. Experimental results of the discriminations for 43 images including 11 abnormal cases have shown that the proposed features are well effective for recognizing normal cases.
  • Recognition of grouping areas in trademarks according to Gestalt psychology using Mahalanobis distance, Koji Abe, Yoshimasa Daido, Hiromasa Iguchi, Haiyan Tian, Debabrata Roy, Proceedings of the IASTED International Conference on Human-Computer Interaction, 167, 172,   2005 , Refereed
    Summary:According to the Gestalt principals, in this paper, we model features for measuring the attraction degree between pairs of image components, and grouping areas in trademark images are recognized. This investigation would be used for content-based image retrieval from the view of mirroring human perception for images. Depending on variability in human perception for trademark images, the proposed method finds plural grouping areas by calculating Mahalanobis distance with the features. The features are extracted from every combination of two components in an image. We have evaluated the proposed method on 104 test images in experiments of comparing between outputs by the proposed method and by human perception, and we have got the concordance rate of 81.99%.
  • Recognition of grouping areas in trademarks considering proximity and shape similarity, K Abe, D Roy, JP Eakins, KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS, 3213, 614, 619,   2004 , Refereed
    Summary:According to the Gestalt principals, we model features for representing the attraction degree between pairs of image regions, and, grouping areas in trademarks are recognized. This investigation would be used for content-based image retrieval systems from the view of mirroring human perception for trademark images. Depending on variability in human perception for trademark images, the proposed method finds grouping areas considering combinations of the features. We have evaluated the effectiveness of the proposed method on 36 test images of abstract trademarks from the Japan Trademark Registry. Experimental results have shown over 80% in the images have been the same as humans perception.

Awards & Honors

  •   2009 , Journal of the Institution of Engineers (India), Certificate of Merit