2 February 2011 Low complexity orientation detection algorithm for real-time implementation
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Abstract
In this paper we describe a low complexity image orientation detection algorithm which can be implemented in real-time on embedded devices such as low-cost digital cameras, mobile phone cameras and video surveillance cameras. Providing orientation information to tamper detection algorithm in surveillance cameras, color enhancement algorithm and various scene classifiers can help improve their performances. Various image orientation detection algorithms have been developed in the last few years for image management systems, as a post processing tool. But, these techniques use certain high-level features and object classification to detect the orientation, thus they are not suitable for implementation on a capturing device in real-time. Our algorithm uses low-level features such as texture, lines and source of illumination to detect orientation. We implemented the algorithm on a mobile phone camera device with a 180 MHz, ARM926 processor. The orientation detection takes 10 ms for each frame which makes it suitable to use in image capture as well as video mode. It can be used efficiently in parallel with the other processes in the imaging pipeline of the device. On hardware, the algorithm achieved an accuracy of 92% with a rejection rate of 4% and a false detection rate of 8% on outdoor images.
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Vikram V. Appia, Vikram V. Appia, Rajesh Narasimha, Rajesh Narasimha, } "Low complexity orientation detection algorithm for real-time implementation", Proc. SPIE 7871, Real-Time Image and Video Processing 2011, 787108 (2 February 2011); doi: 10.1117/12.872236; https://doi.org/10.1117/12.872236
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