Adhan, Suchin
Loading...
Preferred name
Adhan, Suchin
Main Affiliation
Email
suchin.ad@kmitl.ac.th
4 results
Now showing 1 - 4 of 4
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, 3D extraction using DLT with one camera(2013-12-01); ; Sanpanich, ArthornThis paper proposes a method for accurate measurement of a three-dimensional of an eye's pupil by combining local stereo images which are acquired from multiway. A calibrated mini CCD camera is used to capture the side-by-side image of one eye by two plane mirrors to create a reflection of the two directions of the eye at a time. The center of mass is used for two-dimensional coordinates of the center of the pupil. The new and simple technique to solve the problem of geometric distortion is proposed by taking advantage of the Direct Linear Transformation (DLT), which requires only a single step in the refinement stage without changing or reinstalling a camera. The DLT algorithm was used for the three-dimensional coordinates of the center of the pupils from the previous two-dimensional coordinates for the three-dimensional position of the eyes is detected. The results are the statistical errors only slight, mean = 0.16°, S.D. = 0.15°, which shows that the system is suitable for high-precision medical applications for the movement of the human eye. © 2013 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Thai sign language recognition by using geometric invariant feature and ANN classification(2017-02-21); Hand sign language is the primary communication tool for people with hearing-impaired or deaf. People can use it to communicate effectively but the challenge is to communicate with the computer. Human computer interaction (HCI) will have a positive impact on their use. Thus, this is to bring the hand gestures in HCI as an important research area. This research focuses on 2D image recognition utilizing an evolved geometric invariant feature and also have developed a two-layer feedforward neural network to identify and translate hand gesture pose of the 42 letters in the Thai Sign Language (TSL) alphabet to Thai alphabets. We designed glove with six different colored markers for using in the experiment. The result shows that this system is able to recognize 42 TSL alphabets with an average accuracy of 96.19 %. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, American Sign Language recognition by using 3D geometric invariant feature and ANN classification(2014-01-20); Communication between normal and disabled person has been developed in several researches. The hand gesture is one of important communication for the deaf, especially American Sign Language (ASL) which is used in order to represent each alphabet (A-Z). This paper aims to translate ASL from static postures. Besides, this research also designs the glove with six different colored markers and develops algorithm for alphabet classification. Moreover the system is set by two cameras in order to extract 3D coordinate points from each marker. There are three main important processes for algorithm consisting of marker detection by using Circle Hough Transform, computation of all feasible triangle area patches constructed from 3D coordinate triplet that is novel feature, and feature classification using feedforward backpropagation of Artificial Neural Network. The experimental result shows average of accuracy is 95 percent that is high performance and feasibility for proposed method. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Alphabetic hand sign interpretation using geometric invariance(2014-01-20); Hand alphabet is an important sign language for disability people for a long time. This communication is also necessary for a normal people to understand the meaning as well. Hand language interpretation by applying a hand image posture classification is an active research theme to solve obstacle. In this research, we propose a promising technique to apply a B spline curvature concept for supporting a triangular-based feature extraction element in a hand interpretation process. Area, inner angle and adjacent area ratio which derived from a curvature reference set are created a feature string for each alphabet posture in the template. By testing with all 24 hand alphabets, our system provides a promising result in identification satisfactorily.