  Image segmentation refers to the separation of objects from the background,
and has been one of the most challenging aspects of digital image processing.
Practically it is impossible to design a segmentation algorithm which has 100%
accuracy, and therefore numerous segmentation techniques have been proposed in
the literature, each with certain limitations. In this paper, a novel
Falling-Ball algorithm is presented, which is a region-based segmentation
algorithm, and an alternative to watershed transform (based on waterfall
model). The proposed algorithm detects the catchment basins by assuming that a
ball falling from hilly terrains will stop in a catchment basin. Once catchment
basins are identified, the association of each pixel with one of the catchment
basin is obtained using multi-criterion fuzzy logic. Edges are constructed by
dividing image into different catchment basins with the help of a membership
function. Finally closed contour algorithm is applied to find closed regions
and objects within closed regions are segmented using intensity information.
The performance of the proposed algorithm is evaluated both objectively as well
as subjectively. Simulation results show that the proposed algorithms gives
superior performance over conventional Sobel edge detection methods and the
watershed segmentation algorithm. For comparative analysis, various comparison
methods are used for demonstrating the superiority of proposed methods over
existing segmentation methods.
