Colombo city, the commercial capital of Sri Lanka and also a popular touristdestination is currently under a redesigning program coupled with landscapingactivities. Therefore it is important to investigate the spatial distribution of thegreen cover and its change over the time so that the findings can effectively be used to identify priority areas for restoring and revitalizing the greenery of the city. This study was conducted on mapping the green cover in all 47 wards of Colombo city for the years 1956, 1982, 2001 and 2010 by analyzing aerial photographs and IKONOS maps using Geographic Information Systems. Investigations were further extended to identify the vegetation cover% and its change in each ward during the 54 year study period. According to the results, the green cover of Colombo city declined from 35.67% to 22.23% from 1956 to 2010. In 2010, the highest green cover (49.65%) was reported in Narahenpita and only three other wards (Kirillipone, Cinnamon Gardens, and Thimbirigasyaya) had green cover over 30% of the total land extent. In contrast, the green cover was less than 10% for ten wards, i.e., Kochchikade North, Kochchikade South, Grandpas North, Masangasweediya, Panchikawatte, Fort, Gintupitiya, New Bazaar, Maligawatte, and Aluthkade East. Under the current redesigning plans, those ten wards should be given the priority to increase the green cover to re-establish a healthy environment. Expansion of built-up areas and road network can be surmised as major reasons for the gradual reduction of green cover in Colombo city.
Active contours are a form of curves that deforms according to an energy minimising function and are widely used in computer vision and image processing applications to extract features of interests from raw images acquired using an image capturing device. One of the major limitations in active contours is its inability to converge accurately when the object of interest exhibits sharp corners. In this paper, a new technique of active contour model to extract boundaries of objects having sharp corners is presented. By incorporating a priori knowledge of significant corners of the object into the deforming contour, the proposed active contour is able to deform towards the boundaries of the object without surpassing the corners. The ability of the new technique to accurately extract features of interest of anatomical structures in medical X-ray images having sharp corners is demonstrated.