Eigen local color histograms for object recognition and orientation estimation

Color has been shown to be an important clue for object recognition and image indexing.(1) We present a new algorithm for color-based recognition of objects in cluttered scenes that also determines the 2D pose of each object. As with so many other color-based object recognition algorithms, color histograms are also fundamental to our new approach; however, we use histograms obtained from overlapping subwindows rather than the entire image.(2-4) An object from a database of prototypes is identified and located in an input image whenever there are many good histogram matches between the respective subwindow histograms of the input image and the image prototype from the database. In essence, local color histograms are the features to be matched. Once an object's position in the image has been determined, its 2D pose is determined by approximating the geometrical transformation most consistently mapping the locations of the prototype's subwindows to their matching locations in the input image.(5)