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.
Similar content being viewed by others
References
Kuno T., Sugiura H., Matoba N.: A new automatic exposure system for digital still cameras. IEEE Trans. Consumer Electron. 44(1), 192–199 (1998)
Myunghee Cho, S.L., Nam, B.D.: Fast auto-exposure algorithm based on numerical analysis. In: Proceedings SPIE Sensors, Cameras, and Applications for Digital Photography, vol. 3650, pp. 93–99 (1999)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423; 623–656 (1948)
Nayar S.K.: Computational cameras: redefining the image. Computer 39(8), 30–38 (2006)
Nayar, S., Branzoi, V.: Adaptive dynamic range imaging: Optical control of pixel exposures over space and time. In: IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 1168–1175 (2003)
Kuthirummal, S., Nayar, S.K.: Multiview radial catadioptric imaging for scene capture. In: ACM Transactions on Graphics (Proceedings of SIGGRAPH 2006), pp. 916–923 (2006)
Narasimhan S., Nayar S.: Enhancing resolution along multiple imaging dimensions using assorted pixels. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 518–530 (2005)
Zomet, A., Nayar, S.K.: Lensless imaging with a controllable aperture. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 339–346 (2006)
Kang S.B., Uyttendaele M., Winder S., Szeliski R.: High dynamic range video. ACM Trans. Graph. (Proc. SIGGRAPH 2003) 22(3), 319–325 (2003)
Bennett E.P., McMillan L.: Video enhancement using per-pixel virtual exposures. ACM Trans. Graph. (Proc. SIGGRAPH 2005) 24(3), 845–852 (2005)
Kang, H.W., Chen, X.Q., Matsushita, Y., Tang, X.: Space-time video montage. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1331–1338 (2006)
Sunkavalli K., Matusik W., Pfister H., Rusinkiewicz S.: Factored time-lapse video. ACM Trans. Graph. In: Proceedings of SIGGRAPH 2007 26(3), 101–111 (2007)
Bennett E.P., McMillan L.: Computational time-lapse video. ACM Trans. Graph. (Proc. SIGGRAPH 2007) 26(3), 102–107 (2007)
Baldi, P., Itti, L.: Attention: bits versus wows. In: Proceedings IEEE International Conference on Neural Networks and Brain, vol. 1, pp. 56–61 (2005)
Itti, L., Baldi, P.: A principled approach to detecting surprising events in video. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 631–637 (2005)
Kullback S.: Information Theory and Statistics. Wiley, New York (1959)
German, A., Jenkin, M.R., Lesperance, Y.: Entropy-based image merging. In: Proceedings of the Second Canadian Conference on Computer and Robot Vision, pp. 81–86 (2005)
Kadir T., Brady M.: Saliency, scale and image description. Int. J. Comput. Vis. 45(2), 83–105 (2001)
Shilston, R., Stentiford, F.: An attention based focus control system. In: 2006 IEEE International Conference on Image Processing, pp. 425–428 (2006)
Itti L., Koch C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vis. Res. 40(10–12), 1489–1506 (2000)
Koch C., Ullman S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985)
Lu, H., Zhang, H., Yang, S., Zheng, Z.: A novel camera parameters auto-adjusting method based on image entropy. In: RoboCup 2009: Robot Soccer World Cup XIII, pp. 192–203 (2009)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, G. Active entropy camera. Machine Vision and Applications 23, 713–723 (2012). https://doi.org/10.1007/s00138-011-0367-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-011-0367-3