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RISS 인기검색어
- 검색키워드
- 저자 : Wayne Shin-Wei Huang (검색결과 2 건)
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A Novel Flag-Language Remote Control Design for a Laparoscopic Camera Holder Using Image Processing
Kateryna Zinchenko,Wayne Shin-Wei Huang,Kai-Che Liu,Kai-Tai Song 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
Minimally invasive surgeries(MIS) possess obvious advantages for patients but require more specially trained personnel than common, open procedure. With modern advances in robotics, it becomes possible to ease these requirements by introducing robotized assistants into operation. However, for robot-assisted solo MIS, it is crucial to have appropriate methods for robot control. This work presents a design of flag language as a novel human-to-robot communication method based on image processing of the endoscope video during real-time intervention. The proposed system comprises autonomous positioning of endoscope holder combined with recognition of surgeon intention by analyzing surgical instrument postures. The proposed algorithm has been evaluated using experimental setup as well as video clips from laparoscopic operation. Experimental results show that the flag posture can be detected satisfactorily with speed of 12 frames per second, as well as system robustness under moderate lighting and noise conditions.
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Image Tracking of Laparoscopic Instrument Using Spiking Neural Networks
Chun-Ju Chen,Wayne Shin-Wei Huang,Kai-Tai Song 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10
Minimally Invasive Surgery (MIS) has become more and more popular in recent years. An endoscopic image tracking system will assist surgeons to adjust the field of view autonomously in MIS. In this paper, we propose a novel image tracking algorithm based on natural features of surgical instruments. We suggest to use texture and geometric features in laparoscopic instrument imagery and to adopt a spiking neural network approach for object detection; considering color will be affected by lighting and the white balance conditions in the endoscope imagery. To enhance tracking performance, we further design a Kalman filter to combine with the neuro-based tracker. The instrument can be detected more robustly despite of deformation of the instrument image during surgery. A laparoscopic video has been tested to verify the developed methods. Experimental results show that two instruments can be distinguished and tracked simultaneously in the surgical video.
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