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Active entropy camera

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Abstract

Automatic exposure control (AEC) is traditionally based on the sampled intensity level in an image frame. However in certain situations, such intensity-based AEC does not achieve the desired results (with a maximum entropy) as shown in several examples in this paper. In most cases, maximizing entropy is the goal of a machine-vision system. We want the imaging system that captures the most amount of information, so that as many feature points as possible are presented in the captured image. To solve this problem, we propose an entropy-based exposure control technique. This technique adjusts the camera exposure setting to maximize the amount of information captured in the image frame. The amount of information is measured by Shannon entropy. The proposed entropy calculation is accelerated by graphics processing units (GPUs) to achieve a real-time performance. Experimental results show that under a number of conditions our AEC performs better than the traditional intensity-based AEC, preserving more detail, and capturing more information.

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Authors and Affiliations

  1. Centre for Intelligent Machines, McGill University, 3480 University Street, Montreal, QC, H3A 2A7, Canada

    Guangyu Wang

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  1. Guangyu Wang
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Correspondence to Guangyu Wang.

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Wang, G. Active entropy camera. Machine Vision and Applications 23, 713–723 (2012). https://doi.org/10.1007/s00138-011-0367-3

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  • DOI: https://doi.org/10.1007/s00138-011-0367-3

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