%0 Journal Article
%T Grey-Level Cooccurrence Matrix Performance Evaluation for Heading Angle Estimation of Moveable Vision System in Static Environment
%A Zairulazha Zainal
%A Rizauddin Ramli
%A Mohd Marzuki Mustafa
%J Journal of Sensors
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/624670
%X A method of extracting information in estimating heading angle of vision system is presented. Integration of grey-level cooccurrence matrix (GLCM) in an area of interest selection is carried out to choose a suitable region that is feasible for optical flow generation. The selected area is employed for optical flow generation by using Horn-Schunck method. From the generated optical flow, heading angle is estimated and enhanced via moving median filter (MMF). In order to ascertain the effectiveness of GLCM, we compared the result with a different estimation method of optical flow which is generated directly from untouched greyscale images. The performance of GLCM is compared to the true heading, and the error is evaluated through mean absolute deviation (MAE). The result ensured that GLCM can improve the estimation result of the heading angle of vision system significantly. 1. Introduction Sensors are most the important components in measurement. With the data gathered from sensor, data analysis as well as control strategy implementation can be conducted. Due to the importance of the sensor, there are various works conducted in this field to cater issues that arise in this field, for instance, new design of force sensor [1, 2] and tactile display [3]. Particularly in the field of robotics, sensors provide information about a variable been measured in order to control the robot, which is most critical part. This information is processed in order to decide the way the robot should take an action with good performance. There are various kinds of sensors available for this purpose, for example, sonar sensor, position sensor, infrared sensor, and camera. Employment of a camera as a sensor, also known as robot vision system, is an interesting idea due to its capabilities to obtain useful information from surroundings through image acquisition. Depending on the application, extraction of information gathered from robot vision system can be executed in various ways. The extraction process commonly utilises image processing and analysis. A research in the field of vision system is ranging from hardware development and testing, algorithm development specifically on image processing and analysis, and strategy proposal regarding the way to use vision system for a specific task and its implementation. Conducted previous works include manipulation of image acquisition capabilities of the optical mouse to be used as a sensor [4¨C6], utilization of panoramic image in visual navigation [7], log-polar imaging application in robotic vision [8], application of sparse visual
%U http://www.hindawi.com/journals/js/2013/624670/