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Imaging System Research Division


Located in San Jose, CA, the Imaging System Research Division is home to a research group whose mission is to contribute to create added value in current Canon products and expand new business and applications by researching and developing systems and algorithms in computational imaging and big data analytics.

The Imaging Systems Research Division is home to a research group whose mission is to contribute to create added value in current Canon products and expand new business and applications by researching and developing systems and algorithms in computational imaging and open world computer vision. Located in Silicon Valley, the Imaging Systems Research Division in San Jose, CA leverages the resources of this high-tech region as it investigates emerging technologies. Valuable work performed here contributes to Canon's strong patent portfolio. The group also engages in collaborative relationships with world renowned local universities.


Big Data Analysis

Data is becoming larger, devices are becoming smarter, with new 3D printers...

How do we leverage Big Data to make cameras smarter?

Open world computer vision is the combination of the state-of-the-art artificial intelligence algorithms with information inferred from databases. This can improve the performance of object identification and classification, which can be applied, for example, to network camera solutions.


Computational Imaging

The goal of the computational imaging research is to come with theoretical and experimental methods to explore unconventional ways to combine digital imaging capture and novel image processing techniques in order to understand better the nature of scenes we capture and provide proof-of-concept and prototyping for core technologies that enables improvement of imaging capabilities and performance. This work is centered on illumination-based physical sensing in which research is conducted on technologies that can be used to measure arbitrary shapes and materials in 3-D.

  • A. Lin, F. H. Imai, Efficient spectral imaging based on imaging systems with scene adaptation using tunable color pixels, Proc. of 19th IS&T/SID Color and Imaging Conference, pp. 332-338, 2011

  • F. H. Imai, Computational spectral imaging based on adaptive spectral imaging, Proc. of 4th International Workshop, CCIW 2013, Springer, LNCS 7786, p. 35 ff, 2013

  • S-K. Tin, Spectral reflectance by structured light: a simulation study using OptiX, Presented at GPU Technology Conference, 2013

  • F. H. Imai, Material sensing based on spectral decomposition, Proc. of the 12th Congress of the International Color Association, MCS 2013 Symposium, pp.367-370, 2013

  • J. Ye, F. Imai, High resolution multi-spectral image reconstruction on light field via sparse representation, in the Technical Digest of Imaging System and Applications, Optical Society of America, paper IT3A.4, 2015

  • J. Yu, S. Skaff, L. Peng, F. Imai, Leveraging knowledge-based inference for material classification, Proceedings of ACM Multimedia, pp. 1243-1246, 2015

  • C. Liu, S. Skaff, M. Martinello, Learning discriminative spectral bands for material classification, in Advances in Visual Computing, ISVC 2015, Eds. G. Goos, J. Hartmanis, J. van Leeuwen, Springer, pp. 671-681, 2015

  • S. Skaff, S-K. Tin, M. Martinello, Learning optimal incident illumination using spectral bidirectional reflectance distribution function images for material classification, Journal of Imaging Science and Technology 59, Number 6, pp. 604-5-1-60405-9(9), 2015

  • S-K. Tin, J. Ye, M. Nezamabadi, C. Chen, 3D reconstruction of mirror-type objects using efficient ray coding, in Proceedings of IEEE ICCP , pp. 1-11, 2016

  • E. Levine, M. Martinello, M. Nezamabadi, High-precision multi-view camera calibration using a rotation stage, accepted oral paper to be published in the Proceedings of IEEE ICIP, 2016

  • J. Cai, J. Yu, F. Imai, Q. Tian, Towards temporal adaptive representation for video action recognition, accepted paper to be published in the Proceedings of IEEE ICIP, 2016