Vector quantized binary features for visual pose measurement

Visual pose measurement computes the translation and orientation of an object based on an image. The problem is made difficult by background clutter, partial occlusions, and illumination variations. This paper presents a solution to these problems with a new algorithm for planar, visual pose measurement based on compressed, binary subtemplates. For a given object, we take a sequence of training images as the object rotates. On each training image, we detect binary edges and pick binary edge subtemplates as features to model the object. These features are compressed using the Lloyd algorithm, a conventional image compression technique. We detect the object in an image using a Hough transform. We demonstrate the algorithm on images with background clutter, partial occlusions, and illumination variations