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2017 AN ALGORITHM TO DETECT THE RETINAL REGION OF INTEREST

Retina is one of the important layers of the eyes, which includes sensitive cells to colour and light and nerve fibers. Retina can be displayed by using some medical devices such as fundus camera, ophthalmoscope. Hence, some lesions like microaneurysm, haemorrhage, exudate with many diseases of the eye can be detected by looking at the images taken by devices. In computer vision and biomedical areas, studies to detect lesions of the eyes automatically have been done for a long time. In order to make automated detections, the concept of ROI may be utilized. ROI which stands for region of interest generally serves the purpose of focusing on particular targets. The main concentration of this paper is the algorithm to automatically detect retinal region of interest belonging to different retinal images on a software application. The algorithm consists of three stages such as pre-processing stage, detecting ROI on processed images and overlapping between input image and obtained ROI of the image.

International Workshop on GeoInformation Science
GEOADVANCES

E. Şehirli M. K. Turan Emrullah Demiral

57 31
Subject Area: Computer Science Broadcast Area: International Type: Abstract Language: English
2017 ESTIMATION OF POPULATION NUMBER VIA LIGHT ACTIVITIES ON NIGHT-TIME SATELLITE IMAGES

Estimation and accurate assessment regarding population gets harder and harder day by day due to growth of world population in a fast manner. Estimating tendencies to settlements in cities and countries, socio-cultural development and population numbers is quite difficult. In addition to them, selection and analysis of parameters such as time, work-force and cost seems like another difficult issue. In this study, population number is guessed by evaluating light activities in İstanbul via night-time images of Turkey. By evaluating light activities between 2000 and 2010, average population per pixel is obtained. Hence, it is used to estimate population numbers in 2011, 2012 and 2013. Mean errors are concluded as 4.14% for 2011, 3.74% for 2012 and 3.04% for 2013 separately. As a result of developed thresholding method, mean error is concluded as 3.64% to estimate population number in İstanbul for next three years.

International Workshop on GeoInformation Science
GEOADVANCES

M. K. Turan E. Yücer E. Şehirli Ismail Rakıp Karas

43 24
Subject Area: Computer Science Broadcast Area: International Type: Abstract Language: English