|Abstract:||Digital image processing generally refers to the use of computer algorithms that deal with
operations on or analysis of digital images. It covers a wide range of intriguing sub-topics. While these topics may vary from pattern recognition, classification to synthesis and generation, most techniques recognize the input as a raster image by default, treat it as a two-dimensional signal and applying standard signal-processing techniques to it. In this
paper, we attempt to solve specific problems of image segmentation, classification and enhancement by a hybrid approach. We perceive an input image not only as a 2D grid of pixels, but also as a connected graph, a collection of heterogeneous data points, or a surface mesh embedded in 3D space depending on the specific target problem. The broader framework makes it possible to combine image processing techniques with techniques that are typically applied to other research areas including digital geometry processing, machine learning, surface modeling and optimization. We demonstrate by experiments and comparisons that these methods, in concert with standard image operations such as filtering or feature detection, work particularly well and yield satisfactory results for the original target problems.