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In this article, we propose a pose transfer framework that can deal with large body motion to generate dance videos. To solve the problem of body shape deformation from large movements, a multiple scale region extractor (MSRE) is proposed. The features of each body region can be extracted from multiple layers of the encoder according to the body key points and passed through shortcuts to the decoder to reduce the spatial information loss. We add a region style loss calculated by the style representations of the body regions to the loss function to improve the quality of the generated images. In addition, the concept of learnable region normalization is integrated into the proposed framework to prevent introducing undesired mean and variance shifts by the corrupted regions during normalization. The experiments have shown that the proposed system can significantly improve the pose generation results compared with existing methods, especially when there are large body movements in the dancing poses. [ABSTRACT FROM AUTHOR]

In this article, we propose a system for automatically classifying and cleaning location images in large-scale image databases uploaded by arbitrary users. Detecting incorrect scenes uploaded by users and maintaining the correctness of the database through automatic data cleaning are essential because human inspection is not feasible for verifying massive amounts of data. In this study, we compared different feature extractors using deep convolutional neural networks trained using big data. We designed a multilevel extractor to improve feature extraction. Moreover, a detector based on multiresolution dissimilarity calculation was designed to overcome the issue of large intraclass distances and successfully identify incorrect scenes. The proposed system was validated using a highly challenging dataset with 138,000 images collected from Google Places. The experiments show that the multilevel extractor and the detector based on multiresolution dissimilarity calculation can improve the accuracy in identifying incorrect scenes and achieve satisfying data cleaning results. [ABSTRACT FROM AUTHOR]

In this paper, we propose an intelligent highway surveillance system that performs self-diagnosis and detects conditions when the camera is seriously tampered by raindrops at night. The system also provides solutions to analyze the traffic flow under the challenging nighttime raindrop-tampered conditions. To deal with the challenging scenes, we extract effective features via salient region detection and block segmentation. The extracted features are used to train a support vector machine to achieve self-diagnosis. For traffic flow analysis, we use the extracted features in the region of interest and construct a regression model to get an estimated vehicle count for each frame. The vehicle counts in consecutive frames form a vehicle count sequence. We propose a mapping model to acquire the desired per-minute traffic flow from the vehicle count sequence. The model utilizes state transfer likelihoods and takes into account the length of the segmented vehicle count sequence. The experiments on highly challenging data sets have demonstrated that the proposed system can effectively estimate the traffic flow for raindrop-tampered highway surveillance cameras at night. [ABSTRACT FROM PUBLISHER]

A GaN-based light-emitting diode (LED) grown on a nanocomb-shaped patterned sapphire substrate (PSS) is fabricated and studied. Nanocomb-shaped patterns are transferred on a sapphire substrate using a well-ordered anodized aluminum oxide (AAO) thin film as a mask for the inductively coupled plasma etching process. This well-ordered AAO thin film with a high aspect ratio is grown on a sapphire substrate by an oxalic acid-based electrochemical system and a three-step anodization. The strain state generated during epitaxial growth could be effectively alleviated by the use of nanocomb-shaped PSS. The treading dislocation density could be reduced. Thus, the enhanced crystalline quality is obtained. In addition, due to the presence of photonic crystal-like air buffer layer, part of reflected photons upward the top side could be scattered by this layer. Therefore, more photons could be extracted outside. Experimentally, at 20 mA, as compared with a conventional LED grown on a planar sapphire substrate, the studied LED grown on a nanocomb-shaped PSS shows 53.8% and 43.7% enhancements in light output power and external quantum efficiency as well as a reduced leakage current. [ABSTRACT FROM AUTHOR]

An interesting approach to improving the light extraction efficiency of high-power GaN-based light-emitting diodes (LEDs) by the use of a 3-D-photonic crystal (3-D-PhC) backside reflector is studied. A 3-D-PhC backside reflector is formed by coating a self-assembled SiO2 nanosphere monolayer between the hybrid reflector and backside of a sapphire substrate. The 3-D-PhC structure is used to enhance light scattering. At 350 mA, as compared with a conventional LED (with a hybrid reflector while only without the 3-D-PhC structure), the studied device exhibits 23.6% enhancement in the light output power without the degradation of electrical properties. Therefore, the performance of high-power GaN-based LEDs could be further improved by using a 3-D-PhC backside reflector. [ABSTRACT FROM AUTHOR]