Telepointer is a powerful tool in the telemedicine system that enhances the effectiveness of long-distance communication. Telepointer has been tested in telemedicine, and has potential to a big influence in improving quality of health care, especially in the rural area. A telepointer system works by sending additional information in the form of gesture that can convey more accurate instruction or information. It leads to more effective communication, precise diagnosis, and better decision by means of discussion and consultation between the expert and the junior clinicians. However, there is no review paper yet on the state of the art of the telepointer in telemedicine. This paper is intended to give the readers an overview of recent advancement of telepointer technology as a support tool in telemedicine. There are four most popular modes of telepointer system, namely cursor, hand, laser and sketching pointer. The result shows that telepointer technology has a huge potential for wider acceptance in real life applications, there are needs for more improvement in the real time positioning accuracy. More results from actual test (real patient) need to be reported. We believe that by addressing these two issues, telepointer technology will be embraced widely by researchers and practitioners.
Malaysia is one of the world pineapple producers besides Thailand, Philippine, Indonesia, Brazil and South Africa. The government encourage farmers to have more production to meet increasing demand for export. Most of the pineapple production activities is still in manual process and rely on labor workers. In this paper, we proposed a system that can be used in production house to automatically detect the maturity index of pineapple. We implement image processing method to determine the maturity of a pineapple based on yellowish skin color. Binary ellipse mask has been used for extracting region of interest (ROI) as well as morphology normalized RGB to filter out the background and unwanted pixel image. Finally, linear method using threshold values has been selected to classify the maturity index. 910 pineapple images has been used at the development and testing stage and we obtained promising result with 94.29% good classification rate.
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