Matching an object depicted in multiple images is a useful operation in the image, video, and animation arts. In some approaches, pixels of a source image and pixels of a target image are compared against one another to identify the depicted object. The task of matching or tracking a depicted object in different images is more complex when the object's orientation or scale has changed. For example, assume the first image depicts an approaching car that is off in the distance, and a second image depicts the same car a moment later, closer (and thus larger in appearance). In the example, the car changed both its orientation within the image and also its scale. In some conventional approaches, e.g., scale-invariant feature transform (SIFT), a local feature computer vision algorithm is applied to match an object (e.g., a car) depicted in a first image to the object in a second image. However, SIFT uses edges and blobs to track objects and does not work well if the object being has smooth surfaces.