Asynch image sensor boosts machine vision
While conventional image sensors are optimized to produce images for the human eye, machines do not necessarily need the highest resolution but can benefit from different attributes such as high dynamic range, high speed and low data rate.
The company, founded in 2014, is based on 15 years of research at the University Pierre et Marie Curie and University of Vienna. It has received a number of awards and grants plus seed fundings including one of €750,000 (about $850,000) from Robert Bosch Venture Capital (Stuttgart, Germany) and CEA Investissement (Paris, France).
The sensors being developed by Chronocam are inspired by the action of the human eye, in that they acquire and process information asynchronously and in an efficient way. They are suitable for such machine vision tasks as 3D mapping, multi-object tracking, “always-on” visual input and are applicable in sectors such as automotive, industrial, aerospace and smart devices such as drones.
The initial image sensor is QVGA resolution (320 by 240 pixels) with a pixel size of 30-microns on a side and sampling circuit alongside each pixel. The sensor is not clocked and does not send frames of data, said Christop Posch, chief technology officer. Each of the pixels in the array acts independently and sends information that is time-based. In addition, the pixel only sends information when there is a significant change. The result is scene-dependent data compression that results in time-continuous but sparse stream of events sent over an asynchronous data bus. Chronocam calls the technology CCAM EyeOT.
Combined performance figures include up-date speeds equivalent to 100k frames per second, a dynamic range of greater than 120dB. The video compression is a factor of 100 up from conventional image sensors and a power consumption of less than 10mW.
The first sensor array is made for Chronocam by United Microelectronics Corp. (Hsinchu, Taiwan) and measures about 1cm by 0.8cm, said Posch. He said the next steps for the company could include reducing the size of the pixels to allow a lower cost sensor at QVGA resolution or a similar sized sensor at VGA resolution.
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