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We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust...
We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera view-points. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures,...
Non-local means is a state-of-the-art image denoising algorithm which uses weighted contribution of similar patches to denoise images. But its asymptotic complexity upper bounds the degree to which the algorithm can be accelerated. In this paper, we present an approximate version of the same which uses Locality Sensitive Hashing to reduce the complexity. Finally, we show that the new proposed approximate...
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