Automated Object Detection in an Image

Recent developments in machine vision have demonstrated remarkable improvements in the ability of computers to properly identify objects in a viewing field. Most of these advances rely on color-texture analyses that require target objects to possess one or more highly distinctive, local features that can be used as distinguishing characteristics for a classification algorithm. Many objects, however, consist of materials that are widely prevalent across a variety of object categories. For example, many trees have leaves, many manmade objects are made of painted metal, and so forth, such that color-texture detectors configured/trained to identify leaves or painted metal are good for some categorizations, but not for others. Much less effort has been made to characterize objects based on shape, or the particular way the component features are arranged relative to one another in two-dimensional (2D) image space.