P. Sujatha ¹, P. V. S. Janardhanam ²

Affiliations
1 Asst. Professor, Dept. of Geography, Bharathi Women’s College (A), Chennai-108, TN, IN
2 Intelligent Network & Value Added Service, Telecom Development Company Afghanistan Ltd., Kabul, AF

Abstract

The urban population in Chennai city is expected to increase tremendously by 2030. Currently, a sizable proportion of the population in most urban area lives in slum areas. The increasing slum population in urban areas are seen an indication of worsening living conditions and increasing poverty in urban areas. The increasing concentration of population in slums and urban poverty has elicited a strong interest in urban health conditions in general and the health of slum dwellers and the urban poor in particular. The present study provides spatial-information on an extent of poverty by slum/non-slum residence according to the census; demographic and socio-economic characteristics of slum/non-slum residents and the urban poor; Household living conditions; health and health care for the Chennai city. The perceptional study has been adopted for the future planning of urban health/living conditions.

Keywords

Urban Population, Slum Population, Non-Slum Population, Urban Poverty, Urban Health

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P. Sujatha ¹, K. K. Sudha ²

Affiliations
1 Department of Computer Science, Vels University, Chennai - 600 117, Tamil Nadu, IN
2 School of Computing Sciences, Vels University, Chennai - 600 117, Tamil Nadu, IN

Abstract

Objectives: This paper provides the performance analysis of three edge detection operators on the basis of intensity value and high gradient. Methods: Edge detection is considered to be the building blocks of image processing for object detection and it is an important technique in image segmentation. Sobel, Prewitt and Roberts are some of the edge detection operators discussed in this paper. MATLAB R2013a is used to analyze the performances of these operators using an input image. Findings: In order to classify the operators, the limitations are identified and the performance analysis is done on the basis of obtained results. Based on the intensity value, Prewitt produces better result than Sobel and Roberts. At the same time, Sobel locates the edges with high gradient. Likewise, Roberts produces quick result due to small filter. Application/Improvement: The performance of these operators has been analyzed and realized that each operator is recognized as the best under various conditions.

Keywords

Edge Detection Operators, Image Segmentation, Prewitt, Roberts, Sobel

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