Analyzing the Performance-Hardware Trade-off of an ASIP-based SIFT Feature Extraction

One of the key problems in the field of Computer Vision is recovering the geometry from multiple views of the same scene. Once the homography of two images is known, the motion of a stereo camera system can be determined, images can be rectified or image registration can be performed. A feature-based approach to determine the homography between two images bases on the extraction and matching of SIFT features (SIFT, Scale-Invariant Feature Transform). By extracting image features from varying images of one scene and finding corresponding image features in both images, the homography of the scene can be determined. The extraction of image features, which provide sufficient quality for computation of the homography of a scene, leads to an algorithm complexity, that prevents real-time applications on conventional CPUs. Therefore, we present and discuss an application-specific instruction-set extensions for a Tensilica Xtensa LX5 ASIP to accelerate a SIFT feature extraction (ASIP, Application-Specific Instruction-set Processor). In total, the complete SIFT feature extraction, executed on an extended processor is accelerated by a factor of x125 compared to the baseline processor. At the same time, the accuracy of the SIFT features is preserved. In addition, the proposed processor extensions maintain the full flexibility of an ASIP for a fast integration of further feature extractors.