Extending CLP(FD) with Interactive Data Acquisition for 3D Visual Object Recognition
This paper addresses the 3D object recognition problem modelled as a Constraint Satisfaction Problem. In this setting, each object view can be modelled as a constraint graph where nodes are object parts and constraints are topological and geometrical relationships among them. By modelling the problem as a CSP, we can recognize an object when all constraints are satis ed by exploiting results from the CSP eld. However, in classical CSPs variable domains have to be statically de ned at the beginning of the constraint propagation process. Thus, not only feature acquisition should be completed before the constraint solving process starts, but all image features should be extracted even if not belonging to signi cant image parts. In visual applications, this requirement turns out to be ine cient since visual features acquisition is a very time consuming task. We present an Interactive Constraint Satisfaction model for problems where variable domains may not be completely known at the beginning of the computation, and can be interactively acquired during the computational process. Some variable domain values may be already at disposal when the constraint propagation process starts, while others can be dynamically acquired during the computation only when needed (on demand). The constraint propagation process works on already known domain values and adds new constraints on unknown domain parts. These new constraints can be used to incrementally process new information without restarting the constraint propagation process from scratch each time new information is available. In addition, these constraints can guide the feature acquisition process, thus focussing attention on signi cant image parts. We present the Interactive CSP model and a propagation algorithm for it. We propose an implementation of the framework in Constraint Logic Programming on Finite Domains, CLP(FD).