This paper provides practitioners in the embedded systems community with an insight into computer vision challenges and techniques. It discusses algorithmic and
hardware-specific considerations when “outsourcing” computation to a DSP, FPGA, or onto an embedded
platform, and provides guidelines for how to best improve the runtime performance of computer vision applications. Embedded computers and computer vision systems are two of the largest growths markets in the industry. They take full advantage of Moore’s Law of shrinking form factors and increasing integration density and computational power. “Systems-on-a-chip” enjoy growing popularity because of the power-, cost-, and spacesaving combination of previously external chips onto the same die, including analog-to-digital converters, bus interfaces, I/O controllers, and even analog components. With the help of these integrated processing capabilities, computer vision has become a viable and preferable solution to many legacy and novel problem settings in industry, military, and consumer markets.