A significant amount of research in the field of stereo vision has been published in the past decade. Considerable progress has been made in improving accuracy of results as well as achieving real-time performance in obtaining those results. Although much of the literature does not address it, many applications are sensitive to the tradeoff between accuracy and speed that exists among stereo vision algorithms. Overall, this work aims to organize existing efforts and encourage new ones in the development of stereo vision algorithms for resource limited systems. It does this through a review of the status quo as well as providing both software and hardware designs of new stereo vision algorithms that offer an efficient tradeoff between speed and accuracy. A comprehensive review and analysis of stereo vision algorithms is provided with specific emphasis on real-time performance and suitability for resource limited systems. An attempt has been made to compile and present accuracy and runtime performance data for all stereo vision algorithms developed in the past decade. The tradeoff in accuracy that is typically made to achieve real-time performance is examined with an example of an existing highly accurate stereo vision that is modified to see how much speedup can be achieved. Two new stereo vision algorithms, GA Spline and Profile Shape Matching, are presented with a hardware design of the latter also being provided, making Profile Shape Matching available to both embedded processor-based and programmable hardware-based resource limited systems.