1000 frame/sec Stereo Matching VLSI Processor with Adaptive Window-Size Control

科研費報告書収録論文(課題番号:17300009/研究代表者:亀山充隆/システムインテグレーション理論に基づく高安全知能自動車用VLSIの最適設計

FPGA implementation of a stereo matching processor based on window-parallel-and-pixel-parallel architecture

科研費報告書収録論文(課題番号:17300009/研究代表者:亀山充隆/システムインテグレーション理論に基づく高安全知能自動車用VLSIの最適設計

Using dispersion measures for determining block-size in motion estimation

Video compression techniques remove temporal redundancy among frames and enable high compression efficiency in coding systems. Reduction of temporal redundancy is achieved by motion compensation. In turn, motion compensation requires motion estimation. Block matching is perhaps the most reliable and robust technique for motion estimation in video coding. However, block matching is computational expensive. Different approaches have been proposed in order to improve block matching motion estimation accuracy and efficiency. In this paper a block-matching strategy for motion estimation is introduced. In the proposed approach the size of matching block is adapted according to the variability of the matching areas. That is, the block-size is constrained by variations of the image intensity. The variability is assessed using two variability measures: the variance and the mean absolute deviation. Results of computer experiments aimed at validating the performance of the proposed approach are also reported

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world