Yuji Iwahori
Chubu University
270 Papers
585 Citations
Yuji Iwahori is an academic researcher from Chubu University. The author has contributed to research in topics: Computer science & Photometric stereo. The author has an hindex of 14, co-authored 238 publications. Previous affiliations of Yuji Iwahori include Nagoya Institute of Technology & Tokyo Institute of Technology.
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Papers
A novel set of features for continuous hand gesture recognition
TL;DR: This work proposes to use a novel set of features: the length of an ellipse least-squares fitted to motion-trajectory points and the position of the hand to avoid the effects of variations in a gesture’s motion chain code (MCC).
Application Of Fuzzy Theory To Writer Recognition Of Chinese Characters
TL;DR: A new method to recognize waters in a short time with a simple algorithm that combines the combination of SOSUM and FPDAT, because it can absorb the instability of handwriting characters by membership functions.
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Reconstructing shape from shading images under point light source illumination
Yuji Iwahori,Hidezumi Sugie,Naohiro Ishii +2 more
- 16 Jun 1990
TL;DR: A photometric method called point source illuminating stereo is proposed for determining the 3D shape of an object from multiple shading images under point light source illumination by using the method of least squares and relying on the principle of the monocular vision and the inverse square law for illuminance.
A HOG-SVM Based Fall Detection IoT System for Elderly Persons Using Deep Sensor
TL;DR: In this article, a HOG-SVM based fall detection IoT system for elderly persons is proposed to ensure privacy and in order to be robust to changes of the light intensity, deep sensor is employed instead of RGB camera to get the binary images of elderly persons.
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Automatic Detection of Polyp Using Hessian Filter and HOG Features
Yuji Iwahori,Akira Hattori,Yoshinori Adachi,Manas Kamal Bhuyan,Robert J. Woodham,Kunio Kasugai +5 more
TL;DR: A new approach for the automatic detection of polyp regions in an endoscope image using a Hessian Filter and machine learning approaches is proposed and K-means++ is introduced to integrate the detection results in the classification.
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