In general, message passing multiprocessors suffer from communication overhead and shared memory multiprocessors suffer from memory contention. Also, data I/O overhead limits performance. In particular, computer vision tasks that require massive computation are strongly affected by these disadvantages. This paper proposes new parallel architectures for computer vision, a Flexibly (Tightly/Loosely) Coupled Multiprocessor (FCM) and a Flexibly Coupled Hypercube Multiprocessor (FCHM) to alleviate these problems. FCM and FCHM have a variable address space memory in which a set of neighboring memory modules can be merged into a shared memory by a dynamically partitionable topology. FCM and FCHM are based on two different topologies: reconfigurable bus and hypercube. The proposed architectures are quantitatively analyzed using computational models and parallel vision algorithms are simulated on FCM and FCHM using the Intel's Personal SuperComputer (iPSC), a hypercube multiprocessor, showing significant performance improvements over that of iPSC.