Bimal Kumar Ray

Bimal Kumar Ray

The existing object classification techniques based on descriptive features rely on object alignment to compute the similarity of objects for classification. This paper replaces the necessity of object alignment through sorting of feature. The object boundary is normalized and segmented into approximately convex segments and the segments are then sorted in descending order of their length. The segment length, number of extreme points in segments, area of segments, the base and the width of the segments - a bag of features - is used to measure the similarity between image boundaries. The proposed method is tested on datasets and acceptable results are observed.

Bimal Kumar Ray

This paper proposes an algorithm for clipping line segment against an axis-aligned rectangular window. The conventional algorithms for line segment clipping treat the clipping boundary and/or the line segment to be clipped as line. The present algorithm treats the clipping boundary and the line segment to be clipped as line segment and using this strategy, it succeeds to avoid computation of false intersection points. A quadrilateral is constructed using the end points of a clipping boundary segment and the end points of the line segment to be clipped as its vertices. The concavity and convexity of the quadrilateral dictates whether a line segment actually intersects the clipping boundary. If the quadrilateral is found to be concave then the line segment is rejected, otherwise the point of intersection of the line segment with the clipping boundary is computed. Since a 'test & intersect' approach is used instead of a 'intersect & test', hence the proposed algorithm does not compute false intersection point thereby reducing the number of divisions required to obtain a clipped line segment. Only one routine can process line segments at any position. Improved performance is observed with respect to the Nicholl-Lee-Nicholl, Liang-Barsky, Cohen-Sutherland and Skala's algorithm through experiments with random line segments using a metric based on execution time.

Bimal Kumar Ray

This paper proposes a fast and unsupervised scheme for the polygonal approximation of a closed digital curve. It is demonstrated that the approximation scheme is faster than state-of-the-art approximation and is competitive with Rosin's measure and aesthetic aspects. The scheme comprises of three phases: initial segmentation, iterative vertex insertion, iterative merging, and vertex adjustment. The initial segmentation is used to detect sharp turns, that is, vertices that seemingly have high curvature. It is likely that some of the important vertices with low curvature might have been missed in the first phase; therefore, iterative vertex insertion is used to add vertices in a region where the curvature changes slowly but steadily. The initial phase may pick up some undesirable vertices, and thus merging is used to eliminate redundant vertices. Finally, vertex adjustment was used to enhance the aesthetic appearance of the approximation. The quality of the approximations was measured using the Rosin's method. The robustness of the proposed scheme with respect to geometric transformation was observed.