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Many conventional demosaicking methods are based on hand-crafted filters. However, the filters yield false colors in salient regions like edges and textures. For acquisition of high quality images, we focus on neural networks. Neural networks lead to high accuracy in many fields. However, there are few methods in demosaicking field. For adaptation to demosaicking, we consider not only network's architecture but also the input. In this research, we utilize a Bayer image as input of our networks.

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This paper proposes a new approach for human action recognition exploring the temporal salience. We exploit features over the temporal saliency maps for learning the action representation using a local dense descriptor. This approach automatically guides the descriptor towards the most interesting contents, i.e. the salience region, and obtains the action representation using solely the saliency information.

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Neuromorphic vision sensing (NVS) hardware is now gaining traction as a low-power/high-speed visual sensing technology that circumvents the limitations of conventional active pixel sensing (APS) cameras. While object detection and tracking models have been investigated in conjunction with NVS, there is currently little work on NVS for higher-level semantic tasks, such as action recognition.

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Spherical cameras, which can acquire all-round information, are effective to estimate rotation for robotic applications. Recently, Convolutional Neural Networks have shown great robustness in solving such regression problems. However they are designed for planar images and cannot deal with the non-uniform distortion present in spherical images, when expressed in the planar equirectangular projection. This can lower the accuracy of motion estimation. In this research, we propose an Equirectangular-Convolutional Neural Network (E-CNN) to solve this issue.

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Free viewpoint video (FVV), owing to its comprehensive applications in immersive entertainment, remote surveillance and distanced education, has received extensive attention and been regarded as a new important direction of video technology development. Depth image-based rendering (DIBR) technologies are employed to synthesize FVV images in the “blind” environment. Therefore, a real-time reliable blind quality assessment metric is urgently required. However, existing stste-of-art quality assessment methods are limited to estimate geometric distortions generated by DIBR.

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Edge-preserving smoothing filter smoothes the textures while it preserves the information of sharp edges. In image processing, this filter is used as a fundamental process of many applications. In this paper, we propose a new approach for edge-preserving smoothing filter. Our method uses 2D filter to smooth images and we apply indicator function to restrict the range of filtered pixels for edge-preserving. To define the indicator function, we recalculate the distance between each pixel by using edge information. The nearby pixels in the new domain are used for smoothing.

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Moments are a kind of classical feature descriptors for image analysis. Orthogonal moments, due to their computation
efficiency and numerical stability, have been widely developed.We propose a set of orthogonal polynomials which are
derived from the parity of Hermite polynomials. The new orthogonal polynomials are composed of either odd orders
or even ones of Hermite polynomials. They, however, are orthogonal in different domains. The corresponding orthogonal

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