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Fine-grained vehicle model recognition is a challenging problem in intelligent transportation systems due to the subtle intra-category appearance variation. In this paper, we demonstrate that this problem can be addressed by locating discriminative parts, where the most significant appearance variation appears, based on the large-scale training set. We also propose a corresponding coarse-to-fine method...
In this paper, we propose a novel approach to incorporate structure knowledge into Convolutional Neural Networks (CNNs) for articulated human pose estimation from a single still image. Recent research on pose estimation adopt CNNs as base blocks to combine with other graphical models. Different from existing methods using features from CNNs to model the tree structure, we directly use the structure...
Given 3D outdoor scenes acquired by a LIDAR sensor, we address the problem of semantic segmentation of 3D point clouds involving simultaneously segmenting and classifying the data. The capability of semantic segmentation is essential for several applications, such as autonomous robot navigation and 3D reconstruction of point clouds. In this paper, we present a higher-order class-specific CRF model...
To understand scenes and help autonomous robots and cars, researchers' attention is directed through the problem of classifying 3D point cloud. In this paper, we present a novel approach to semantically segment 3D point cloud of residential scenes captured by a lidar sensor. Our approach is based on a dual-scale analysis: a small-scale clustering and a large-scale grouping. Features used to train...
In this paper, the architect of biometric watermarking is researched. A feature embedding watermarking (FEW) system is resented firstly. A robust biometric watermarking method for iris image based on affine parameters estimation (APE) is presented. The determination of the general affine transform applied to an iris image relies on the determination of the regular grid of points for estimation of...
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