Object Recognition and Pose Estimation across Illumination Changes

In this paper, we present a new algorithm for color-based object recognition that detects objects and estimates their pose (position and orientation) in cluttered scenes observed under uncontrolled illumination conditions. As with so many other color-based object-recognition algorithms, color histograms are also fundamental to our approach; however, we use histograms obtained from overlapping subwindows, rather than the entire image. Furthermore, each local histogram is normalized using greyworld normalization in order to be as less sensitive to illumination as possible. An object from a database of prototype objects is identified and located in an input image by matching the subwindow contents. The prototype is detected in the input whenever many good histogram matches are found between the subwindows of the input image and those of the prototype. In essence, normalized color histograms of subwindows are the local features being matched. Once an object has been recognized, its 2D pose is found by approximating the geometrical transformation most consistently mapping the locations of prototype’s subwindows to their matched subwindow locations in the input image.