High-quality cameras are a standard feature of mobile platforms, but the computational capabilities of mobile processors limit the applications capable of exploiting them. Emerging mobile application domains, for example Mobile Augmented Reality (MAR), rely heavily on techniques from computer vision, requiring sophisticated analyses of images followed by higher-level processing. An important class of image analyses is the detection of sparse localized interest points. The Scale Invariant Feature Transform (SIFT), the most popular such analysis, is computationally representative of many other feature extractors. Using a novel code-generation framework, we demonstrate that a small set of optimizations produce high-performance SIFT implementations for three very different architectures: a laptop CPU (Core 2 Duo), a low-power CPU (Intel Atom), and a low-power GPU (GMA X3100). We improve the runtime of SIFT by more than 5X on our low-power architectures, enabling a low-power mobile device to extract SIFT features up to 63% as fast as the laptop CPU.