S. Albert Antony Raj ¹, S. P. Victor ²

Affiliations
1 Department of Computer Applications, SRM University, Kattankulathur – 603 203, Tamilnadu, IN
2 Research Department of Computer Science, St.Xavier's College (Autonomous), Palayamkottai – 627002, Tamilnadu, IN

Abstract

For any two vertices u and v of a graph G = (V, E), any shortest path joining u and v is called a u-v geodesic. Closed interval I[u,v] of u and v is the set of those vertices belonging to at least one u-v geodesic. A subset S of V(G) is an geodetic set if every vertex of G lies in at least one closed interval between the vertices of S. The geodetic set of a minimum cardinality in G is called as minimum geodetic set. The cardinality of the minimum geodetic set is the geodetic number of G denoted by gn(G). For a non-trivial connected graph G, a set S ofV (G) is called an edge geodetic cover of G if every edge of G is contained in a geodesic joining some pair of vertices in S. The edge geodetic number egn (G) of G is the minimum order of its edge geodetic covers and any edge geodetic cover of order egn(G) is an edge geodetic basis. This paper introduces the algorithms to find geodetic numbers and edge geodetic numbers in connected graphs using dynamic programming approach.

Keywords

Diameter, Distance, Eccentricity, Edge Geodetic Cover, Edge Geodetic Number, Geodesic, Geodetic Number, Geodetic Set, Radius

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F. Fanax Femy ¹, S. P. Victor ²

Affiliations
1 Department of Computer Applications, Noorul Islam Center for higher Education, Kumaracoil. Kanyakumari – 629180, Tamil Nadu, IN
2 Computer Science Department, St. Xavier’s College (Autonomous), Palaymkottai – 627007, Tamil Nadu, IQ

Abstract

Background/Objectives: In this paper our proposed system easily detects the glaucoma affected eye from the fundus image database collected from the nearest eye hospital. Methods/Statistical Analysis: The feature extraction is done by contourlet transform and the best feature is selected for classification. Support vector machine is mainly applied for classification of images. Findings: In the conventional methods Wavelet Transform is applied for feature extraction of images and Support Vector Machine (SVM) adapted for classifying the Glaucoma images with non-affected and affected. The accuracy of classification is evaluated using existing and proposed techniques. Applications/Improvements: The proposed system is applied to find the Glaucoma disease of human eye with accurately which eliminates the human error to examine the disease of human eye. The system automatically finds the disease of human eye within the seconds from the applied fundus image.

Keywords

Feature Extraction in Glaucoma Images using Wavelet and Contourlet Transform

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