Abstract. The problem of reassembling an object from its parts or fragments has never been addressed with a unified computational approach, which depends on the pure geometric form of the parts and not application-specific features. We propose a method for the automatic reconstruction of a model based on the geometry of its parts, which may be computergenerated models or range-scanned models. The matching process can benefit from any other external constraint imposed by the specific application. Index Terms – Object reconstruction, complementary matching, depth buffer, virtual assemblage 1

Predicting the function of a protein from its three-dimensional (3-D) structure is a major intellectual and practical challenge. Despite the details inherent in the structure, extracting knowledge about what a protein does biologically and how it does it,

The task of identifying objects in images is a common requirement for many image processing systems. The identification of these objects (also called models) is in some contexts called automatic target recognition (ATR). Target Recognition algorithms are well suited to hardware field programmable gate array (FPGA) implementations because of their extensive use of bitlevel operations and their amenability to fine grain parallelism. Previous FPGA implementations have focused on full pixel comparison; which has very high algorithm complexity. This paper presents an alternate target recognition implementation for FPGAs of the geometric hashing (GH) algorithm. The GH algorithm has lower complexity for ATR where there are many possible objects to recognise in the scene because it is able to identify all rotations and dialations of all models simultaneously. Our implementation is novel in that it uses a memory pipeline processing (MPP) architecture to improve the concurrency of compute intensive components of the GH algorithm.