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| 1 | Image completion with perspective constraint based on a single image显示文摘Although image completion has been used in many image filling and editing problems for a long time, it is rarely applied in the scenario regarding the perspective effect which is demonstrated largely in the design of urban architectures. Thus this paper proposes a method to capture the perspective information automatically based on a single image and employs it to image completion tasks effectively. Actually it is difficult to automatize the extraction from only a single image, but it has been observed that architectures usually contain plenty of repetitions and symmetries which could be described through periodicity. Therefore,our approach first determines the initial vanishing points through the cascaded hough transformation. Then, a refinement scheme based on SIFT feature matching is proposed to obtain the accurate vanishing points. At last,a good perspective mesh is generated from the geometry calculation. The experiments show that this method could be used to automatically extract perspective information from a single image and recover the image in a visually plausible way. | HAO ChuanYan CHEN YaDang WU Wen WU EnHua | 2015 | Science China(Information Sciences)2015，58，9： | 2 |
| 2 | Secure P2P topology based on a multidimensional DHT space mapping显示文摘Distributed search and routing algorithms based on the DHT(distributed hash table) protocol have attracted considerable attention in structured P2P(peer-to-peer) research as a result of favorable properties such as distribution,self-organization,and high scalability.Compared with a traditional C/S(client/server) network,the probability of peers initiating malicious behavior increases dramatically because of their self-governing and dynamic characteristics,which also make it harder to satisfy the peers' security required by DHT.In this paper,we propose a new secure DHT protocol based on a multidimensional mapping mechanism.This mechanism maps peers to a multidimensional space by dividing the identifiers into groups.Moreover,a series of secure methods and routing algorithms are provided to achieve secure DHT in smaller spaces.Compared with state-ofthe-art approaches,the theoretical analysis and experimental results show that the multidimensional mapping mechanism can effectively improve the average success rate of a resource search by inhibiting malicious behavior. | SUN ZhiXin LUO BingQing CHEN YaDang BU Kai | 2013 | Science China(Information Sciences)2013，56，5： | 1 |
| 3 | Robust dense reconstruction by range merging based on confidence estimation显示文摘Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach. | Yadang CHEN Chuanyan HAO Wen WU Enhua WU | 2016 | Science China(Information Sciences)2016，59，9： | 0 |
| 4 | Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm显示文摘Collaborative filtering algorithms(CF)and mass diffusion(MD)algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload.However,both algorithms suffer from data sparsity,and both tend to recommend popular products,which have poor diversity and are not suitable for real life.In this paper,we propose a user internal similarity-based recommendation algorithm(UISRC).UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity.The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions.Simulation experiments on RYM and Last.FM datasets,the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms. | Zejun Yang Denghui Xia Jin Liu Chao Zheng Yanzhen Qu Yadang Chen Chengjun Zhang | 2021 | Journal on Internet of Things2021，3，2： | 0 |
| 5 | A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing显示文摘Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor networks.Quantum computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or communication.In this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with iterations.The proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional complexity.Moreover,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic feature.Sufficient simulations present the correctness,validity and efficiency of the proposed estimation method. | Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 6 | Build Gaussian Distribution Under Deep Features for Anomaly Detection and Localization显示文摘Anomaly detection in images has attracted a lot of attention in the field of computer vision.It aims at identifying images that deviate from the norm and segmenting the defect within images.However,anomalous samples are difficult to collect comprehensively,and labeled data is costly to obtain in many practical scenarios.We proposes a simple framework for unsupervised anomaly detection.Specifically,the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis(PCA),then build the distribution of normal features via the multivariate Gaussian(MVG),and determine whether the test image is an abnormal image according to Mahalanobis distance.We further investigate which features are most effective in detecting anomalies.Extensive experiments on the MVTec anomaly detection dataset show that the proposed method achieves 98.6%AUROC in image-level anomaly detection and outperforms previous methods by a large margin. | Mei Wang Hao Xu Yadang Chen | 2022 | Journal of New Media2022，4，4： | 0 |
| 7 | Global video object segmentation with spatial constraint module显示文摘We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory framework.To some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past frames.The algorithm uses a global context(GC)module to achieve highperformance,real-time segmentation.The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time.Moreover,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current frame.The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources.We added a refinement module to the decoder to improve boundary segmentation.Our model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset. | Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu | 2023 | Computational Visual Media2023，9，2： | 0 |
| 8 | Fast target-aware learning for few-shot video object segmentation显示文摘Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video sequences.The proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained. | Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU | 2022 | Science China(Information Sciences)2022，65，8： | 0 |
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