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Fast Extraction of Feature Salience Maps for Rapid Video Data Analysis

Fast Extraction of Feature Salience Maps for Rapid Video Data Analysis. Nikos P. Pitsianis and Xiaobai Sun. Feature Salience Maps (FSMs). FSMs are used in separation or integration automatic or assisted visual search tasks target indication, object recognition, tracking

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Fast Extraction of Feature Salience Maps for Rapid Video Data Analysis

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  1. Fast Extraction of Feature Salience Maps for Rapid Video Data Analysis Nikos P. Pitsianis and Xiaobai Sun

  2. Feature Salience Maps (FSMs) • FSMs are used in • separation or integration • automatic or assisted visual search tasks • target indication, object recognition, tracking • Salient information in multiple feature dimensions • color, edge orientation, shape, texture, motion • selective tuning, feedback, attentional or intentional guidance • High volume and rate of video data, frame by frame • Involves many filtering steps at multiple spatial scales MUNDHENK, T. N., ITTI, L. Computational modeling and exploration of contour integration for visual saliency. Biological Cybernetics (2005). HPEC 2010

  3. Processing of feature maps on GPU • Direct domain • Filter-centric • Image-centric • Fourier domain • Based on the convolution theorem • Using CUDA SDK 3.1 • NVIDIA Tesla C1060 • 240 processing cores @ 1.3GHz • 4GB or GDDR3 • CUFFT CUDA FFT library • Asynchronous I/O and Streaming HPEC 2010

  4. Discussion • The extraction and use of salient information from static or dynamic images are recent and active research topics • The computation based on an extraction model serves two purposes • Test and validate the underlying neurobiological model for certain visual function in the visual system of the primate brain • Exploit the new understanding and model(s) for developing and improving artificial vision systems. • Challenges : • generation of motion features, which are much more computation intensive • visual tasks at the higher levels • segmentation, object recognition, tracking of moving targets. • data representation at higher levels, sparse and irregular, but still structured • Efficiency of high-level processing steps on GPUs HPEC 2010

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