In the era of multi-core, computer vision has emerged as an exciting application area which promises to continue to drive the demand for both more powerful and more energy efficient processors. Although there is still a long way to go, vision has matured significantly over the last few decades, and the list of applications that are useful to end users continues to grow. The parallelism inherent in vision applications makes them a promising workload for multi-core and many-core processors. While the vision community has focused many years on improving the accuracy of vision algorithms, a major barrier to the study of their computational properties has been the lack of a benchmark suite that simultaneously spans a wide portion of the vision space and is accessible in a portable form that the architecture community can easily use. We present the San Diego Vision Benchmark Suite (SD-VBS), a suite of diverse vision applications drawn from the vision domain. The applications are drawn from the current state-of-the-art in computer vision, in consultation with vision researchers. Each benchmark is provided in both MATLAB and C form. MATLAB is the preferred language of vision researchers, while C makes it easier to map the applications to research platforms. The C code minimizes pointer usage and employs clean constructs to make them easier for parallelization. Furthermore, we provide a spectrum of input sets that enable researchers to control simulation time, and to understand properties as inputs increase to leverage better processor performance. In this paper, we describe the benchmarks, show how their runtime is attributed to their constituent kernels, overview some of their computational properties - including parallelism - and show how they are affected by growing inputs. The benchmark suite will be made available on the Internet, and updated as new applications emerge.