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2012 International Conference on Computerized Healthcare, ICCH 2012

作者： Cai, Fuyu Triest, Han Van Kang, Yan Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang City Liaoning Province 110004 China Key Laboratory of Medical Image Computing Northeastern University Ministry of Education United States

ISBN: (纸本)9781467351294

Optical Flows is the study of the relationship with image intensity changes in time and the motion of objects. In this paper, the gradient-based optical flow method is studied to determine the velocity of observed objects and the parameters which can influence the accuracy of the velocity are identified and discussed. According to the characteristics of this Algorithm, an improved iterative method is proposed to let the method be more flexible to ground truth data. To understand the principles behind this phenomenon, we create a phantom sequence to analyze and experiment the algorithm from both mathematical and geometrical aspect. The precision of our method can reach an excellent level with a fast computing speed. The experiments on the phantom images showed that the method work quickly and accurately. © 2012 IEEE.

关键词： Optical flows

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2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

作者： Zhou, Jianlong Xiao, Chun Key Laboratory of Intelligent Computing and Information Processing Xiangtan University Ministry of Education Hunan China Janelia Farm Research Campus Howard Hughes Medical Institute VA United States

ISBN: (纸本)9781467300223

A contour tree is a topological abstraction of a scalar field. Contour tree simplification (CTS) removes branches corresponding to noise, while making the size of the tree small enough for maintaining essential structure of data. This paper proposes a similarity-driven multi-level partial CTS (PCTS) approach. The PCTS preserves branches corresponding to structures of interest or specific objects specified by users, while removing other branches of the contour tree. A clustering method (e.g. k-means clustering) is used to cluster branch nodes into groups based on their similarities (i.e., similar locations) in the attribute space. As a result, the contour tree is simplified with multi-levels based on different clustering groups. Furthermore, various interfaces and rendering windows are provided and synchronized, which makes the simplification process more meaningful and efficient compared with traditional simplification methods using slide-bar based approaches. The proposed approach can be generalized to process branches with more than three measures. © 2012 IEEE.

关键词： Trees (mathematics)

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Niche improved particle swarm optimization on geometric constraint solving

Niche improved particle swarm optimization on geometric cons...

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12th International Conference on Computer-Aided Design and Computer Graphics, CAD/Graphics 2011

作者： Cao, Chunhong Tang, Chuan Zhao, Dazhe Zhang, Bin Han, ChunYan Collge of Information Science and Engineering Northeastern University Shenyang 110819 China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology 610059 China Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang 110819 China

ISBN: (纸本)9780769544977

Geometric constraint problem can be transformed to an optimization problem. We can solve the problem with niche improved particle swarm. Classical particle swarm optimization is likely to be trapped into local minima as well as premature. A niche improved particle swarm optimization (NIPSO) based on niche theory was developed. After the update of the particle velocity and position, the outlier particle was identified in the NIPSO by comparing the niche number of every particle, with which the crossover and selection operators were employed sequent for those particles, whose personal best values were less than that of the outlier particle. The experiment shows that it can improve the geometric constraint solving efficiency and possess better convergence property than the compared algorithms. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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Hessian based image structure adaptive gradient vector flow for parametric active contours

Hessian based image structure adaptive gradient vector flow ...

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2010 17th IEEE International Conference on image Processing, ICIP 2010

作者： Wang, Y.Q. Chen, W.F. Yu, T.L. Zhang, Y.T. Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Lab. of Image Processing Southern Medical University Guangzhou 510515 China Dept. of Radiology General Hospital Tianjin Medical University Tianjin 300052 China

ISBN: (纸本)9781424479948

Active contours have been one of the most successful methods for image segmentation during the last two decades, but one of the shortcomings of being unable to converge to concavity is a handicap to its effectiveness. In order to address this issue, the gradient vector flow (GVF) was put forth. Although there have been a great number of works on GVF, the image structure has seldom been incorporated into GVF algorithm. In this work, the image structure characterized by the Hessian matrix is incorporated into the GVF algorithm by reformulating the smoothness constraint of GVF into matrix form. In this way, the associated diffusion PDEs are anisotropic and the modified GVF snake can converge to very long concavity and preserve weak edge simultaneously. Experiments and comparisons are presented to demonstrate the properties of the proposed strategies. © 2010 IEEE.

关键词： image segmentation

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Sea Surface Temperature Prediction Approach Based on 3D CNN and LSTM with Attention Mechanism

Sea Surface Temperature Prediction Approach Based on 3D CNN ...

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International Conference on Advanced Communication Technology (ICACT)

作者： Baiyou Qiao Zhongqiang Wu Zhong Tang Gang Wu School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China

ISBN: (数字)9791188428069

ISBN: (纸本)9781665447027

Sea Surface Temperature (SST) is an important physical quantity of the ocean system. The accurate prediction of SST is essential for studying physical ocean phenomena and forecasting the ocean environment information. In this paper, a SST prediction approach based on 3-Diminsional Convolutional Neural Network (3D CNN) and Long Short-Term Memory (LSTM) network with attention mechanism is proposed, which considers the spatial correlation and temporal dependency of SST data. Firstly, the machine learning algorithm XGBoost is used to extract the long period time features of each SST data. Then the 3D CNN is used to capture the spatial correlations among SST field data composed of multiple observation points in a selected sea area, followed by the LSTM model to extract the time dependency features of the SST field time series data, and the attention mechanism is added to weight the output of each step of LSTM model to adjust the prediction results and improve the prediction accuracy of the approach. A series of experimental results show that the proposed approach has lower complexity, higher training efficiency and prediction accuracy, which is significantly better than the existing prediction models.

关键词： Sea surface Three-dimensional displays Correlation Computational modeling Predictive models Feature extraction Ocean temperature

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Feature Selection for Microarray Data via Community Detection Fusing Multiple Gene Relation Networks Information

Feature Selection for Microarray Data via Community Detectio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shoujia Zhang Wei Li Weidong Xie Linjie Wang College of Computer Science and Engineering Northeastern University Shenyang Chain Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang Chain

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In recent decades, the rapid development of gene sequencing and computer technology has increased the growth of high-dimensional microarray data. Some machine learning methods have been successfully applied to it to help classify cancer. In most cases, high dimensionality and the small sample size of microarray data restricted the performance of cancer classification. This problem usually issolved bysome feature selection methods. However, most of them neglect the exploitation of relations among genes. This paper proposes a novel feature selection method by fusing multiple gene relation network information based on community detection (MGRCD). The proposed method divides all genes into different communities. Then, the genes most associated with cancer classification are selected from each community. The proposed method satisfies both maximum relevances gene with cancer and minimum redundancy among genes for the selected optimal feature subset. The experiment results show that the proposed gene selection method can effectively improve classification performance.

关键词： Sequential analysis Redundancy Machine learning Feature extraction Graph neural networks Data models Task analysis

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Dual-Granularity Contrastive Learning for Session-based Recommendation

arXiv

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arXiv 2023年

作者： Wang, Zihan Wu, Gang Wang, Haotong School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

The data encountered by Session-based Recommendation System(SBRS) is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applied in SBRS owing to its capability of improving embedding learning under the condition of sparse data. However, existing CL strategies are limited in their ability to enforce finer-grained (e.g., factor-level) comparisons and, as a result, are unable to capture subtle differences between instances. More than that, these strategies usually use item or segment dropout as a means of data augmentation which may result in sparser data and thus ineffective self-supervised signals. By addressing the two aforementioned limitations, we introduce a novel dual-granularity CL framework. Specifically, two extra augmentation views with different granularities are constructed and the embeddings learned by them are compared with those learned from original view to complete the CL tasks. At factor-level, we employ Disentangled Representation Learning to obtain finer-grained data, with which we can explore connections of items on latent factor independently and generate factor-level embeddings. At item-level, the star graph is deployed as the augmentation method. By setting an additional satellite node, non-adjacent nodes can establish additional connections through satellite nodes instead of reducing the connections of the original graph, so data sparsity can be avoided. Compare the learned embeddings of these two views with the learned embeddings of the original view to achieve CL at two granularities. Finally, the item-level and factor-level embeddings obtained are referenced to generate personalized recommendations for the user. The proposed model is validated through extensive experiments on two benchmark datasets, showcasing superior performance compared to existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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TransCoop: Cooperation of Transformers and CNNs for Camouflaged Object Segmentation

TransCoop: Cooperation of Transformers and CNNs for Camoufla...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wu, Fucai Li, Xuanya Zhang, Yuan Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Xiangnan University Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Chenzhou423000 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Camouflaged object segmentation (COS) is a challenging task due to the existence of high intrinsic similarities between the object and background. To overcome this challenge, we pro-pose a new framework, called TransCoop, for COS through the cooperation of Transformers and convolutional neural net-works (CNNs). Specifically, Transformer is used to model global context and structural information for accurately positioning potential target objects. Meanwhile, a well-designed texture feature fusion module (TFFM) is used to fuse the features encoded by CNN with the shallow Transformer to fully mine the low-level features in the scene. Furthermore, we propose a noise removal module (NRM), which can eliminate the background noise of low-level features with the guidance of precise target location. Notably, our TFFM and NRM can effectively realize the interaction between Transformer and CNN features. Extensive experiments on four benchmark datasets demonstrate the superiority of our TransCoop against existing state-of-the-art methods. © 2022 IEEE.

关键词： Convolutional neural networks

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A novel Bot detection algorithm based on API call correlation

A novel Bot detection algorithm based on API call correlatio...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Xiaomei Dong Fei Liu Xiaohua Li Xiaocong Yu Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang China

In this paper, a novel Bot detection algorithm for Windows system based on API call correlation was proposed. the coefficient of product-moment correlation was utilized to calculate the correlation of different API calls. More other activities were correlated as well as keylogging. According to the characteristics of different Bot activities with API calls, the membership of different activities were calculated and integrated to form the fuzzy set of unknown process. Lattice Degree was applied to correlate the fuzzy set of unknown processed and the fuzzy sets of known processes. The type of the unknown processes was distinguished utilizing F Pattern Identification. Experimental results show that the algorithm can detect Bots with a high detection rate and can well distinguish between normal processes and Bot process with a low false positive degree.

关键词： Correlation Monitoring Lattices Fuzzy sets Software keyboards Computers

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NiuTrans: an open source toolkit for phrase-based and syntax-based machine translation 12

NiuTrans: an open source toolkit for phrase-based and syntax...

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Proceedings of the ACL 2012 System Demonstrations

作者： Tong Xiao Jingbo Zhu Hao Zhang Qiang Li Northeastern University and Key Laboratory of Medical Image Computing Ministry of Education Northeastern University

We present a new open source toolkit for phrase-based and syntax-based machine translation. The toolkit supports several state-of-the-art models developed in statistical machine translation, including the phrase-based model, the hierachical phrase-based model, and various syntax-based models. The key innovation provided by the toolkit is that the decoder can work with various grammars and offers different choices of decoding algrithms, such as phrase-based decoding, decoding as parsing/tree-parsing and forest-based decoding. Moreover, several useful utilities were distributed with the toolkit, including a discriminative reordering model, a simple and fast language model, and an implementation of minimum error rate training for weight tuning.

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