Efficient methods for object recognition using the constellation model
We present efficient methods for object recognition, using the constellation model. This model represents objects as constellations of rigid features, with the variability between them being represented by a joint probability density function. Using simple, exhaustive methods, the computational requirements for this approach quickly become prohibitive. By using A* search methods, accompanied by some heuristics, large performance improvements are achievable in recognition, making real-time recognition on large images possible, using complex object models.