A real-time 720p feature extraction core based on Semantic Kernels Binarized
Michael Schaffner, Pascal Alexander Hager, Lukas Cavigelli, Pierre Greisen, Frank K. Gürkaynak, Hubert Kaeslin · 2013
Several image processing applications rely on a sparse set of correspondence points between stereo images to discern a sparse but robust depth structure of the scene. There exist several methods to extract and match correspondences, but they are all computationally extensive and require significant memory bandwidths. In this paper, we describe an efficient ASIC core that is able to detect up to 25 k interest points in real time on a 720p video stream using the recently proposed Semantic Kernels Binarized (SKB) algorithm. To keep the memory bandwidth low, an optimized method to calculate the filter responses in the interest point detection stage has been devised. Instead of the 2D integral image we use a local 1D integral image combined with an incremental updating scheme to calculate the box filters. The ASIC core is manufactured in 180 nm technology and has a complexity of 254 kGE. It runs at 100 MHz, has a power dissipation of 184 mW and is the central processing block for a larger FPGA based stereo vision system that calculates a sparse depth map by locating corresponding interest points between left and right images in real time.