Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C.V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Swi

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https://doi.org/10.1007/978-981-13-1939-6rounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only hu

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Frontiers in Chinese Linguisticsindoors. Particularly, we mainly aim at dealing with the corridor-like scenarios where the RGB-D camera mounted on the robot can capture apparent planar structures such as floor or walls. The novelties of our method lie in two-folds. First, to fully exploit the planar structures for odometry estimat

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Frontiers in Chinese Linguisticsnal way of manual rendering using professional software is very labor intensive and time consuming. In this paper, we present a novel capsule based conditional generative adversarial network that can automatically synthesize an indoor image with realistic and aesthetically pleasing rendering effect

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https://doi.org/10.1007/978-981-13-1939-6 a deep convolutional neural network (CNN) consisting of several residual blocks (ResBlocks). With cascade training, DN-ResNet is more accurate and more computationally efficient than the state of art denoising networks. An edge-aware loss function is further utilized in training DN-ResNet, so that

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Sarah E. Deery MD, MPH,Raul J. Guzman MD trained on synthetically generated silhouette images, and at test time, deep learning methods or standard video segmentation tools are used to extract silhouettes from real data. The system is tested on animal videos from several species, and shows accurate reconstructions of 3D shape and pose.

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Creatures Great and SMAL: Recovering the Shape and Motion of Animals from Video trained on synthetically generated silhouette images, and at test time, deep learning methods or standard video segmentation tools are used to extract silhouettes from real data. The system is tested on animal videos from several species, and shows accurate reconstructions of 3D shape and pose.