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This paper addresses the problem of 3D pose estimation for multiple people in a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. The proposed approach achieves significant performance gains from the state-of-the-art (96.3% vs. 90.6% and 96.9% vs. 88% on the Campus and Shelf datasets, respectively), while being efficient for real-time applications.

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Time-resolved fluorescence and absorption measurements are performed on hypericin complexed with human serum albumin, HSA (1:4, 1:1 and approximately 5:1 hypericin: HSA complexes). Detailed comparisons with hypocrellin A/HSA complexes (1:4 and 1:1) are made. Our results are consistent with the conclusions of previous studies indicating that hypericin binds to HSA by means of a specific hydrogen-bonded interaction between its carbonyl oxygen and the N1-H of the tryptophan residue in the IIA subdomain of HSA. (They also indicate that some hypericin binds nonspecifically to the surface of the protein.) A single-exponential rotational diffusion time of 31 ns is measured for hypericin bound to HSA, indicating that it is very rigidly held. Energy transfer from the tryptophan residue of HSA to hypericin is very efficient and is characterized by a critical distance of 94 A, from which we estimate a time constant for energy transfer of approximately 3 x 10(-15) s. Although it is tightly bound to HSA, hypericin is still capable of executing excited-state intramolecular proton (or hydrogen atom) transfer in the approximately 5:1 complex, albeit to a lesser extent than when it is free in solution. It appears that the proton transfer process is completely impeded in the 1:1 complex. The implications of these results for hypericin (and hypocrellin A) are discussed in terms of the mechanism of intramolecular excited-state proton transfer, the mode of binding to HSA and the light-induced antiviral and antitumor activity.

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Small traffic sign recognition is a challenging problem in computer vision, and its accuracy is important to the safety of intelligent transportation systems (ITS). In this paper, we propose the multiscale region-based convolutional neural network (MR-CNN). At the detection stage, MR-CNN uses a multiscale deconvolution operation to up-sample the features of the deeper convolution layers and concatenates them to those of the shallow layer to construct the fused feature map. The fused feature map has the ability to generate fewer region proposals and achieve higher recall values. At the classification stage, we leverage the multi-scale contextual regions to exploit the information surrounding a given object proposal and construct the fused feature for the fully connected layers. The fused feature map inside the region proposal network (RPN) focuses primarily on improving the image resolution and semantic information for small traffic sign detection, while outside the RPN, the fused feature enhances the feature representation by leveraging the contextual information. Finally, we evaluated MR-CNN on the largest dataset, Tsinghua-Tencent 100K, which is suitable for our problem and more challenging than the GTSDB and GTSRB datasets. The final experimental results indicate that the MR-CNN is superior at detecting small traffic signs, and that it achieves the state-of-the-art performance compared with other methods.

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