Image matching algorithms in stereo vision using address-event-representation: A theoretical study and evaluation of the different algorithms

Image processing in digital computer systems usually considers the visual information as a sequence of frames. These frames are from cameras that capture reality for a short period of time. They are renewed and transmitted at a rate of 25–30 fps (typical real-time scenario). Digital video processing has to process each frame in order to obtain a filter result or detect a feature on the input. In stereo vision, existing algorithms use frames from two digital cameras and process them pixel by pixel until it is found a pattern match in a section of both stereo frames. Spike-based processing is a relatively new approach that implements the processing by manipulating spikes one by one at the time they are transmitted, like a human brain. The mammal nervous system is able to solve much more complex problems, such as visual recognition by manipulating neuron's spikes. The spike-based philosophy for visual information processing based on the neuro-inspired Address-Event-Representation (AER) is achieving nowadays very high performances. In this work we study the existing digital stereo matching algorithms and how do they work. After that, we propose an AER stereo matching algorithm using some of the principles shown in digital stereo methods.