CARS 2017—Computer Assisted Radiology and Surgery Proceedings of the 31st International Congress and Exhibition Barcelona, Spain, June 20–24, 2017

• DOI: 10.1007/s11548-017-1588-3
• Publication Date: 2017-05-19T00:09:53Z
• Authors: Ebrahim Nasr-Esfa...; Nader Karimi; S. M. Reza Sorous...; Mohammad H. Jafari; M. Khorsandi; Shadrokh Samavi; Kayvan Najarian
• Keywords: FOS: Computer and information sciences; ddc:610; Image segmentation; Angioectasia; Computer Vision and Pattern Recognition (cs.CV); Bleeding; Computer Science - Computer Vision and Pattern Recognition; 610; 03 medical and health sciences; 0302 clinical medicine; TEORIA DE LA SEÑAL Y COMUNICACIONES; Capsule endoscopy

Hand gesture is one of the most important means of touchless communication between human and machines. There is a great interest for commanding electronic equipment in surgery rooms by hand gesture for reducing the time of surgery and the potential for infection. There are challenges in implementation of a hand gesture recognition system. It has to fulfill requirements such as high accuracy and fast response. In this paper we introduce a system of hand gesture recognition based on a deep learning approach. Deep learning is known as an accurate detection model, but its high complexity prevents it from being fabricated as an embedded system. To cope with this problem, we applied some changes in the structure of our work to achieve low complexity. As a result, the proposed method could be implemented on a naive embedded system. Our experiments show that the proposed system results in higher accuracy while having less complexity in comparison with the existing comparable methods.