Partly occluded objects can quickly be detected amid clutter.

An object-recognition algorithm analyzes data from two-dimensional (2D) images to locate and identify possibly complexly shaped three-dimensional (3D) objects in possibly highly cluttered scenes depicted in the images. More specifically, the algorithm implements a relatively simple, effective, and fast process for recognizing 2D objects that may be partly occluded and that have shapes that can be modeled by use of sets of line segments (see figure). Because the algorithm tolerates a fair amount of perspective distortion, it is also applicable to 3D objects represented by sets of viewpoint- dependent 2D models.

Books in a Test Image were modeled by use of line segments. Then in a process involving correspondences between model lines and lines detected in the image, the modeled books in the image were recognized.