Reconfigurable computing is emerging as the new paradigm for satisfying the simultaneous demand for application performance and flexibility. The ability to customize the architecture to match the computation and the data flow of the application has demonstrated significant performance benefits compared to general purpose architectures. Computer vision applications are one class of applications that have significant heterogeneity in their computation and communication structures. At the low level, vision algorithms have regular repetitive computations operating on large sets of image data with predictable data dependencies. At the higher level, the computations have irregular dependencies. Computer vision application characteristics have significant overlap with the advantages of reconfigurable architectures. The main focus of the paper is on outlining the methodologies required to realize the potential of reconfigurable architectures for vision applications. After giving a broad introduction to reconfigurable computing, the advantages of utilizing reconfigurable architectures for vision applications are outlined and illustrated using example computations. The paper discusses the development of fundamental configurable computing models that abstract the underlying hardware for high-level application mapping. The Hybrid System Architecture Model and algorithms utilizing the model are illustrated to demonstrate a formal framework. The paper also outlines ongoing research and provides a comprehensive list of references for further reading.