19 January 2009 Photographic expert-like capturing by analyzing scenes with representative image set
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Abstract
In this work we propose a method to build digital still cameras that can take pictures of a given scene with the knowledge of photographic experts, professional photographers. Photographic expert' knowledge means photographic experts' camera controls, i.e. shutter speed, aperture size, and ISO value for taking pictures of a given scene. For the implementation of photographic experts' knowledge we redefine the Scene Mode of currently commercially available digital cameras. For example instead of a single Night Scene Mode in conventional digital cameras, we break it into 76 scene modes with the Night Scene Representative Image Set. The idea of the night scene representative image set is the image set which can cover all the cases of night scene with respect to camera controls. Meanwhile to appropriate picture taking of all the complex night scene cases, each one of the scene representative image set comes along with corresponding photographic experts' camera controls such as shutter speed, aperture size, and ISO value. Initially our work pairs off a given scene with one of our redefined scene modes automatically, which is the realization of photographic experts' knowledge. With the scene representative set we use likelihood analysis for the given scene to detect whether it is within the boundary of the representative set or not. If the given scene is classified within the representative set it is proceeded to calculate the similarities with comparing the correlation coefficient between the given scene and each of the representative images. Finally the camera controls for the most similar one of the representative image set is used for taking picture of the given scene, with finer tuning with respect to the degree of the similarities.
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D. Chung, S. Kim, J. Bae, S. Lee, "Photographic expert-like capturing by analyzing scenes with representative image set", Proc. SPIE 7252, Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques, 72520O (19 January 2009); doi: 10.1117/12.806133; https://doi.org/10.1117/12.806133
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