Comparing Point and Block Representation in Computer Vision and Image Processing Tasks

The description of a digital image in terms of simple geometrical shapes, such as polygonal shapes, is a well established methodology that often proves useful for several image processing tasks, mainly to speed up image processing operations. The representation of binary images using rectangular blocks as primitives has been applied with great success to several computer vision and image processing tasks, such as fast implementation of morphological operations, fast implementation of the Hough transform, fast run length smoothing algorithm (RLSA) evaluation and fast projection profiles evaluation. Such decomposition can also contribute towards effective and fast shape analysis, image compression, image segmentation and feature identification. In this paper, we present an overview of the implementations of the most important image processing and vision tasks using binary image block representation. We focus on the acceleration achieved in comparison with the classical pixel based approaches.