Idaku Ishii

Last Updated :2017/09/25

Affiliations, Positions
Graduate School of Engineering, Professor
E-mail
iishiirobotics.hiroshima-u.ac.jp

Basic Information

Academic Degrees

  • Doctor of Engineering, The University of Tokyo

Research Fields

  • Engineering;Electrical and electronic engineering;Measurement engineering

Research Keywords

  • Sensing
  • High-speed Vision
  • Robotics

Educational Activity

Course in Charge

  1. 2017, Undergraduate Education, 1Term, Research Tutorial IIB
  2. 2017, Undergraduate Education, Second Semester, Electronic Circuits
  3. 2017, Undergraduate Education, Intensive, Sensing Engineering
  4. 2017, Graduate Education (Master's Program) , First Semester, Multidisciplinary Seminar I
  5. 2017, Graduate Education (Master's Program) , First Semester, Multidisciplinary Seminar II
  6. 2017, Graduate Education (Master's Program) , Second Semester, Multidisciplinary Seminar I
  7. 2017, Graduate Education (Master's Program) , Second Semester, Multidisciplinary Seminar II
  8. 2017, Graduate Education (Doctoral Program) , First Semester, Multidisciplinary Seminar III
  9. 2017, Graduate Education (Doctoral Program) , First Semester, Multidisciplinary Seminar IV
  10. 2017, Graduate Education (Master's Program) , Year, Seminar on medical equipment engineering (B)
  11. 2017, Graduate Education (Master's Program) , Academic Year, Seminar on medical equipment engineering (B)
  12. 2017, Graduate Education (Master's Program) , Second Semester, Introduction of System and Electronics
  13. 2017, Graduate Education (Master's Program) , Year, Directed Study in System Cybernetics I
  14. 2017, Graduate Education (Master's Program) , Year, Directed Study in System Cybernetics II
  15. 2017, Graduate Education (Doctoral Program) , Year, Directed Study in System Cybernetics IV
  16. 2017, Graduate Education (Master's Program) , Year, Seminar in System Cybernetics I
  17. 2017, Graduate Education (Master's Program) , Year, Seminar in System Cybernetics II

Research Activities

Academic Papers

  1. Cyclic Motion Design and Analysis for a Passive Object Manipulation Using an Active Plate, Advanced Robotics, 29(7), 493-503, 20150422
  2. Simultaneous Vision-Based Shape and Motion Analysis of Cells Fast-Flowing in a Microchannel, IEEE Transactions on Automation Science and Engineering, 12(1), 204-215, 20150501
  3. Real-Time Image Mosaicing System Using a High-Frame-Rate Video Sequence, Journal of Robotics and Mechatronics, 27(1), 12-23, 20150220
  4. High-Frame-Rate Structured Light 4-D Vision for Fast Moving Objects, Journal of Robotics and Mechatronics, 126(3), 311-320, 201406
  5. A High-Frame-Rate Vision System with Automatic Exposure Control, IEICE Transactions on Information and Systems, E97-D(4), 936-950, 20140401
  6. Concrete Surface Strain Measurement Using Moire fringes, Construction and Building Materials, doi:10.1016/j.conbuildmat2013.12.035 (online first), 20140114
  7. 501-fps Face Tracking System, Journal of Real-Time Image Processing, 8(4), 379-388, 2013
  8. Dynamics-Based Stereo Visual Inspection Using Multidimensional Modal Analysis, IEEE Sensors Journal, 13(12), 4831-4843, 2013
  9. A Real-Time Microscopic PIV System Using Frame Straddling High-Frame-Rate Vision, Journal of Robotics and Mechatronics, 25(4), 586-595, 2013
  10. Fast Tracking System for Multi-colored Pie-shaped Markers, International Journal of Optomechatronics, 7(3), 160-180, 2013
  11. High Frame-rate Tracking of Multiple Color-patterned Objects, Journal of Real-Time Image Processing, doi: 10.1007/s11554-013-0349-y (online first) , 2013
  12. Fast FPGA-Based Multi-Object Feature Extraction, IEEE Transactions on Circuits and Systems for Video Technology, 23(1), 30-45, 2013
  13. A Self-Projected Light-Section Method for Fast Three-Dimensional Shape Inspection, International Journal of Optomechatronics, 6(4), 289-303, 2012
  14. Real-Time Optical Flow Estimation Using Multiple Frame-Straddling Intervals, Journal of Robotics and Mechatronics, 24(4), 686-698, 2012
  15. Accuracy of Gradient-Based Optical Flow Estimation in High-Frame-Rate Video Analysis, IEICE Transactions on Information and Systems, E95-D(4), 1130-1141, 2012
  16. A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling, IEICE Transactions on Information and Systems, E95-D(2), 636-645, 2012
  17. High-Frame-Rate Optical Flow System, IEEE Transactions on Circuits and Systems for Video Technology, 22(1), 105-112, 2012
  18. HFR-Video-Based Machinery Surveillance for High-Speed Periodic Operations, Journal of System Design and Dynamics, 5(6), 1310-1325, 2011
  19. Algorithm for Automatic Behavior Quantification of Laboratory Mice Using High-Frame-Rate Videos, SICE Journal of Control, Measurement, and System Integration, 4(5), 322-331, 2011
  20. A Structural Damage Quantification Method for HFR-Video-Based Modal Testing, Journal of System Design and Dynamics, 5(4), 624-641, 2011
  21. An Intelligent High-Frame-Rate Video Logging System for Abnormal Behavior Analysis, Journal of Robotics and Mechatronics, 123(1), 53-65, 2011
  22. Simultaneous Dynamics-Based Visual Inspection Using Modal Parameter Estimation, Journal of Robotics and Mechatronics, 23(1), 180-195, 2011
  23. Real-time Scratching Behavior Quantification System for Laboratory Mice Using High-speed Vision, Journal of Real-Time Image Processing, 4(2), 181-190, 2009
  24. Automatic Scratching Pattern Detection for Laboratory Mice using High-speed Video Images, IEEE Transactions on Automation Science and Engineering, 5(1), 176-182, 2008
  25. Dynamic Capturing Strategy for a 2-D Stick-Shaped Object Based on Friction Independent Collision, IEEE Transactions on Robotics, 23(3), 541-552, 2007
  26. Identification of Equine Subclinical Lameness Induced by Pressure to the Sole of Fore- or Hindlimb, Journal of Equine Veterinary Science, 27(10), 429-434, 2007
  27. A Sensitive Gait Parameter for Quantification of Arthritis in Rats, Journal of Pharmacological Sciences, 103(2), 113-116, 2007
  28. Higher Order Autocorrelation Vision Chip, IEEE Transactions on Electron Devices, 153(8), 1797-1804, 2006
  29. Non-Dimensional Analysis Based Design on Tracing Type Legged Robots, Journal of Robotics and Mechatronics, 18(3), 333-339, 2006
  30. Moment feature-based three-dimensional tracking using two high-speed vision systems, Advanced Robotics, 17(10), 1041-2056, 2003
  31. A Digital Vision Chip Specialized for High-speed Target Tracking, IEEE Transaction on Electron Devices, 50(1), 191-199, 2003
  32. Device and System Development of General Purpose Digital Vision Chip, Journal of Robotics and Mechatronics, 2(5), 515-520, 2001
  33. 1ms Sensory-Motor Fusion System, IEEE Transactions On Mechatoronics, 5(3), 244-252, 2000
  34. 電子情報通信学会論文誌D-I, J81-D-1 (2), 70-76, 1998
  35. LOC-Based High-Throughput Cell Morphology Analysis System, IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 12(4), 1346-1356, 20151001
  36. A SELF-PROJECTED STRUCTURED LIGHT SYSTEM FOR FAST THREE-DIMENSIONAL SHAPE INSPECTION, INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION, 30(5), 458-470, 2015
  37. High frame-rate tracking of multiple color-patterned objects, JOURNAL OF REAL-TIME IMAGE PROCESSING, 11(2), 251-269, 201602
  38. A hardware-oriented histogram of oriented gradients algorithm and its VLSI implementation, JAPANESE JOURNAL OF APPLIED PHYSICS, 56(4), 2017

Publications such as books

  1. 2012/03/14, Advanced Image Acquisition, Processing Techniques and Applications I (D. Ventzas ed.), In this chapter, a spatio-temporal selection type coded structured light projection method is proposed for the acquisition of three-dimensional images at a high frame rate. The proposed method can select adaptive space encoding types accoding to the temporal changes in the code images, Our proposed method was verified off-line using a testbed that was composed of a high-speed camera and ahigh-speed projector. We evaluated its effectiveness by producing experimental results for various three-dimensional objects moving quickly, at a high frame rate such as 1000 fps, which is too fast for the human eye to observe their three-dimensional motion in detail., In this chapter, a spatio-temporal selection type coded structured light projection method is proposed for the acquisition of three-dimensional images at a high frame rate. The proposed method can select adaptive space encoding types accoding to the temporal changes in the code images, Our proposed method was verified off-line using a testbed that was composed of a high-speed camera and ahigh-speed projector. We evaluated its effectiveness by producing experimental results for various three-dimensional objects moving quickly, at a high frame rate such as 1000 fps, which is too fast for the human eye to observe their three-dimensional motion in detail., A Coded Structured Light Projection Method for High-Frame-Rate 3D Image Acquisition, INTECH, 2012, 3, English, Idaku Ishii, 178, 16
  2. 2012/05/02, Human-Centric Machine Vision (M. Chessa, F. Solari and S.P. Sabatini, eds.), In this chapter, We have developed a real-time scratching behavior quantification system for laboratory mice; for this purpose, we introduced a specially designed high-speed vision system that can calculate the frame-to-frame difference for a 640 × 400 pixel image at a rate of 240 fps. An improved scratching quantification algorithm is implemented in the system and demonstrated experiment for detecting scratching behavior for 20min in 4 ICR mice. The analysis results demonstrate the system's effectiveness wih regard to accurate mice scratching quantification for real-time and long-time observation. For next step, the current method will be improved and an automated system will be developed for objective and quantitative evaluations of laboratory animal behaviors for practiealuse such as the development of new drugs for various diseases including atopic dermatitis., In this chapter, We have developed a real-time scratching behavior quantification system for laboratory mice; for this purpose, we introduced a specially designed high-speed vision system that can calculate the frame-to-frame difference for a 640 × 400 pi image at a rate of 240 fps. An improved scratching quantification algorithm is implemented in the system and demonstrated experiment for detecting scratching behavior for 20min in 4 ICR mice. The analysis results demonstrate the system's effectiveness wih regard to accurate mice scratching quantification for real-time and long-time observation. For next step, the current method will be improved and an automated system will be developed for objective and quantitative evaluations of laboratory animal behaviors for practiealuse such as the development of new drugs for various diseases including atopic dermatitis., Automatic Scratching Analyzing System for Laboratory Mice: SCLABA-Real, INTECH, 2012, 5, English, Yuman Nie, Idaku Ishii, Akane Tanaka and Hiroshi Matsuda, 188, 18
  3. 2000, Robotics Research (J.M. Hollerbach and D.E. Koditschek eds.), 1ms Sensory-Motor Fusion System , Springer, 2000, English, Masatoshi Ishikawa, Takashi Komuro, Akio Namiki, and Idaku Ishii, 6

Awards

  1. 2011年11月03日, ISOT2011 Best Student Paper Award, Yeung Yam General Chair, ISOT2011, 2000-FPS Multi-Object Recognition using SHIFT-Invariant Features
  2. Video Award Finalist, IEEE Int. Conf. on Robotics and Automation
  3. 2012年05月17日, IEEE ROBOTICS AND AUTOMATION SOCIETY,Best Automation Paper Award-Finalist, General Chair,ICRA2012・Awards Chair,ICRA2012
  4. 2015年12月13日, Best Student Paper Award IEEE/SICE International Symposium on System Integration (SII2015) , General Chair Award Chair