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Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors. In this paper, we propose a novel 3D shape representation learning approach, Directionally Convolutional Network (DCN), to solve the shape segmentation problem. DCN extends convolution operations from images to the surface mesh of 3D shapes. With DCN, we learn effective shape representations...
Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel Convolutional Neural Network (CNN)-based...
Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are...
The conventional internal model control and PID (IMC-PID) provides convenient tuning parameter to adjust the response speed and robustness of the closed-loop system because it has only one tuning parameter. But when the characteristics variation and uncertainty factors are included in the control system, it is difficult to accomplish satisfactory control performance by using conventional IMC-PID controllers...
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