An efficient iterative pseudo point elimination technique to represent the shape of the digital image boundary

Visually, the environment is made up of a chaotic of irregular polygons. It is an important and intriguing issue in many fields of study to represent and comprehend the irregular polygon. However, approximating the polygon presents significant difficulties from a variety of perspectives. The method provided in this research eliminates the pseudo-redundant points that are not contributing to shape retention and then makes the polygonal approximation with the remaining high-curvature points, as opposed to searching for the real points on the digital image boundary curve. The proposed method uses chain code assignment to obtain initial segmentation points. Using integer arithmetic, the presented method calculates the curvature at each initial pseudo point using sum of squares of deviation. For every initial segmented pseudo point, the difference incurred by all the boundary points lies between its earlier pseudo point and its next initial pseudo point was taken into account. Then, this new proposal removes the redundant point from the subset of initial segmentation points whose curvature deviation is the lowest with each iteration. The method then recalculates the deviation information for the next and previous close pseudo points. Experiments are done with MPEG datasets and synthetic contours to show how well the proposed method works in both quantitative and qualitative ways. The experimental result shows the effectiveness of the proposed method in creating polygons with few points.