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International Congress on image and Signal processing, BioMedical Engineering and Informatics

作者： Yi-Ze Zhang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781509037117

Signal peptides are significant important in targeting the translocation of integral membrane proteins and secretory proteins. Due the high similarity between the transmembrane helices and signal peptides, classifiers have limit ability to discriminate the signal peptides from the transmembrane helices. To solve this problem, the protein functional domain information is applied in this method. For accurately identify the cleavage sites along the sequence, a subset of potential cleavage sites was firstly screened out by statistical machine learning rules, and then the final unique site was picked out according to its evolution conservation score. This method has been benchmarked on multiple datasets and the experimental results have shown its superiority.

关键词： Sequence evolution Signal peptide N-terminal transmembrane helix Functional domain

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Fault detection and isolation of sensor in time-delay systems based on space geometry method

Fault detection and isolation of sensor in time-delay system...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Dianfeng Qiao Qianshuai Cheng Yandong Hou Institute of Image Processing and Pattern Recognition Henan University Kaifeng China

ISBN: (纸本)9781467386456

This paper investigates sensor fault problems in time-delay systems with uncertain disturbances. Using the measurement equation, the sensor fault can be translated into the state inputs. Subsequently, a cluster of residual generators is designed by employing the space geometric method. The corresponding filter parameters are obtained based on the space geometric approach, then using H ∞ optimization technique reduce the effects of disturbance inputs on the residuals, at the same time, the residual generator is designed so that the residual signals and sensor faults satisfy one to one correspondence, which can be used to detect and isolate the sensor faults in time-delay systems with disturbance. Simulation results suggest the effectiveness and robustness of our proposed approach.

关键词： Conferences Industrial electronics Decision support systems Fault detection Optimization

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Cross-modal subspace learning for fine-grained sketch-based image retrieval

arXiv

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arXiv 2017年

作者： Xu, Peng Yin, Qiyue Huang, Yongye Song, Yi-Zhe Ma, Zhanyu Wang, Liang Xiang, Tao Bastiaan Kleijn, W. Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China SketchX Lab School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Communications and Signal Processing Group Victoria University of Wellington New Zealand

Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are unsufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting

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Brain MRI Segmentation with Patch-based CNN Approach

Brain MRI Segmentation with Patch-based CNN Approach

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第35届中国控制会议

作者： Zhipeng CUI Jie YANG Yu QIAO Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in modern medical *** methods were developed for automatic segmenting of brain MRI but failed to achieve desired *** this paper,we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network(CNN).Each brain MRI acquired from a small portion of public dataset is firstly divided into *** of these patches are then used for training CNN,which is used for automatic segmentation of brain *** results showed that our approach achieved better segmentation accuracy compared with other deep learning methods.

关键词： CNN Deep Learning Brain MRI Segmentation Patch-based

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ABNORMAL EVENT DETECTION USING SPATIO-TEMPORAL FEATURE AND NONNEGATIVE LOCALITY-CONSTRAINED LINEAR CODING

ABNORMAL EVENT DETECTION USING SPATIO-TEMPORAL FEATURE AND N...

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IEEE International Conference on image processing

作者： Yu Zhao Lei Zhou Keren Fu Jie Yang Institution of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781467399623

In this paper, an approach using the spatio-temporal feature and nonnegative locality-constrained linear coding (NLLC) is proposed to detect abnormal events in videos. This approach utilizes position-based spatio-temporal descriptors as the low-level representations of a video clip. Each descriptor consists of the position information of a space-time interest point and an appearance feature vector. To obtain the high-level video representations, the nonnegative locality-constrained linear coding is adopted to encode each spatio-temporal descriptor. Then, the max pooling integrates all NLLC codes of a video clip to produce a feature vector. Finally, the support vector machine (SVM) is employed to classify the feature vector as abnormal or normal. Experimental results on two datasets have demonstrated the promising performance of the proposed approach in the detection of both global and local abnormal events.

关键词： Videos Encoding Feature extraction Event detection Support vector machines Computer vision image motion analysis Event detection Support Vector Network Videotapes image motion analysis abnormal event Eigenvector images computer vision Feature extraction video clips Nonnegative coding

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Semantic Channels for Fast Pedestrian Detection

Semantic Channels for Fast Pedestrian Detection

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IEEE Conference on Computer Vision and pattern recognition

作者： Arthur Daniel Costea Sergiu Nedevschi Image Processing and Pattern Recognition Research Center Technical University of Cluj-Napoca

ISBN: (纸本)9781467388528

Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestrian detection and semantic segmentation. As baseline for pedestrian detection we use sliding windows over cost efficient multiresolution filtered LUV+HOG channels. We use the same channels for classifying pixels into eight semantic classes. Using short range and long range multiresolution channel features we achieve more robust segmentation results compared to traditional codebook based approaches at much lower computational costs. The resulting segmentations are used as additional semantic channels in order to achieve a more powerful pedestrian detector. To also achieve fast pedestrian detection we employ a multiscale detection scheme based on a single flexible pedestrian model and a single image scale. The proposed solution provides competitive results on both pedestrian detection and semantic segmentation benchmarks at 8 FPS on CPU and at 15 FPS on GPU, being the fastest top performing approach.

关键词： Pedestrians Semantics Arts image scale short-distance central processing units segmentation Segmentation FES gene

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Short text classification based on LDA topic model

Short text classification based on LDA topic model

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International Conference on Audio, Language and image processing, ICALIP

作者： Qiuxing Chen Lixiu Yao Jie Yang Institution of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781509006557

As the rapid development of computer technology and network communication, short text data has increased enormously. Classifying the short text snippets is a great challenge to due to its less semantic information and high sparseness. In this paper, we proposed an improved short text classification method based on Latent Dirichlet Allocation topic model and K-Nearest Neighbor algorithm. The generated probabilistic topics help both make the texts more semantic-focused and reduce the sparseness. In addition, we present a novel topic similarity measure method with the topic-word matrix and the relationship of the discriminative terms between two short texts. A short text dataset for experiment validation is constructed by crawling the posts from Sina News website. The extensive and comparable experimental results obtained show the effectiveness of our proposed method.

关键词： Training Text categorization Resource management Probabilistic logic Semantics Classification algorithms Search engines

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Correlation filter tracking via bootstrap learning

Correlation filter tracking via bootstrap learning

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IEEE International Conference on image processing

作者： Kunqi Gu Tao Zhou Fanghui Liu Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

In recent years, correlation filter based trackers outperform better than other trackers. Nevertheless, they only employ one feature and a single kernel, so they are usually not robust in complex scenes. In this paper, we derive a multi-feature and multi-kernel correlation filter based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features and kernels to further improve the performance. A novel bootstrap learning method is utilized to obtain a strong classifier by fusing these weak kernel correlation filters (KCFs). Moreover, a new target scale estimation strategy is incorporated into our framework. The efficient and effective scale estimation method is based on target dictionary representation. The proposed method is tested on several videos and compared with seven state-of-the-art methods. Experimental results have provided further support to the effectiveness and robustness of the proposed method.

关键词： Kernel Target tracking Correlation Computational modeling Boosting Robustness Object tracking

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Fault detection and isolation of sensor in Markov jump systems based on space geometry method

Fault detection and isolation of sensor in Markov jump syste...

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Chinese Control Conference (CCC)

作者： Yandong Hou Dianfeng Qiao Qianshuai Cheng Ruirui Huang Institute of Image Processing and Pattern Recognition Henan University Kaifeng Henan CN

This paper investigates sensor fault problems in Markov jump systems with uncertain disturbances. Using the measurement equation, the sensor faults can be translated into the state inputs. Subsequently, a cluster of residual generators is designed by employing the space geometric method. The corresponding filter parameters are obtained based on the space geometric approach, then using H ∞ optimization technique reduce the effects of disturbance inputs on the residuals, at the same time, the residual generator is designed so that the residual signals and sensor faults satisfy one to one correspondence, which can be used to detect and isolate the sensor faults in Markov jump systems with disturbance. Simulation results demonstrate the efficiency of the proposed method.

关键词： Hafnium Decision support systems

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A robust cell tracking framework by fusing global and local optimization algorithms

A robust cell tracking framework by fusing global and local ...

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第28届中国控制与决策会议

作者： Shu Meng Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing the global optimal method in consecutive frames assignment and local optimal approach in spatial trajectory *** the process,the detection errors were recognized and cell moving trajectories were completed *** also introduced the concept of clustering to measure the correlation between established short trajectories and reduce the tracking errors caused by fast *** rare information of cells was used in the linkage,the system can work well with *** experimental results show the effectiveness of our approach with cells having different density and activity.

关键词： Multi-object tracking Bipartite graph Optimization Hierarchical tracking

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