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In this work, we present a new multiple channel feature called Deep Compact Channel Feature (DCCF), which generates a compact, discriminative feature representation by a pre-trained deep encoder-decoder. With the combination of DCCF and boosted decision trees, a new object detector is proposed which achieved outstanding performance on standard pedestrian dataset INRIA and Caltech. Furthermore, a large...
The aim of this work is to propose a fusion procedure based on lidar and camera to solve the pedestrian detection problem in autonomous driving. Current pedestrian detection algorithms have focused on improving the discriminability of 2D features that capture the pedestrian appearance, and on using various classifier architectures. However, less focus on exploiting the 3D structure of object has limited...
A novel geometrically invariant watermarking scheme based on nonlinear anisotropic diffusion (NAD) detector of the feature points in images is proposed. The NAD feature points detector performs feature points detecting more accurately than Harris detector in an image, which correspondingly can definitely enhance the robustness and the imperceptibility of the watermarking scheme. According to the feature...
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