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A novel fast object tracking method based on hierarchical block matching

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A novel fast object tracking method based on hierarchical bl...

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Yang, Zhi-Hui Liu, Jie-Fei Institute of Image Processing and Pattern Recognition Beijing100144 China

ISBN: (纸本)9781509048977

Research on object tracking has been an active field because of its fundamental roles in surveillance and monitoring. In this paper, a new adaptive algorithm for fast target tracking based on hierarchical block matching is proposed. The improvement of the algorithm lies in three aspects. First, the hierarchical pyramid method is applied to enhance the search efficiency of the local features. In addition, at different pyramid level, an adaptive searching strategy is adopted according to the number of candidate features. Second, since image blocks make full use of local characters of features, the structure similarity method, associated with the relation coefficient in statistics, can be exploited to design a new matching criteria, which provides more precise matching results, especially in the complex scenes such as the change of illumination and partial occlusion. Lastly, traditional algorithms often track the object through adjacent frames, whereas our algorithm will search two frames with a variable interval on the basis of matching results. The intervals are obtained by using the bisection method and thus the searching time can be greatly saved. Simulation results show that the proposed block matching method has better performances in the searching time and accuracy in contrast to the classic Mean-Shift target tracking algorithm and Particle Filter target tracking algorithm under the complex circumstances such as illumination variation and partial occlusion. © 2017 IEEE.

关键词： Adaptive algorithms

Enlightening the relationship between distribution and regression fitting 2nd

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Enlightening the relationship between distribution and regre...

2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Yu, Hang Yin, Qian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China

ISBN: (纸本)9783319681207

Statistical distribution fitting and regression fitting are both classic methods to model data. There are slight connections and differences between them, as a result they outperform each other in different cases. A analysis model for processing natural data, say astronomical pulsar data in this paper, is proposed to improve data fitting method performance. Then the insight behind the comprehensive fitting model is given and discussed. © IFIP International Federation for Information processing 2017.

关键词： Data handling

Weighting features before applying machine learning methods to pulsar search 2nd

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Weighting features before applying machine learning methods ...

2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Wang, Dayang Yin, Qian Wang, Hongfeng Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China

ISBN: (纸本)9783319681207

In recent years, different Artificial Intelligence methods have been applied to pulsar search, such as Artificial Neural Network method, PEACE Sorting Algorithm, Real-time Classification method. In this paper, Weighting Feature method before applying machine learning (ML) was proposed. We give weight to each feature according to its ability to distinguish pulsar and non-pulsar candidates. The ability is determined by the separation degree of the distribution of pulsars and non-pulsars on particular feature. And then use the ML methods to classify different types of candidates. The results show that this method is significant. The accuracy of identifying pulsars and modeling time were both improved after weighting. © IFIP International Federation for Information processing 2017.

关键词： Pulsars

Affine Non-negative Collaborative Representation Based pattern Classification

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arXiv 2020年

作者： Yin, He-Feng Wu, Xiao-Jun Feng, Zhen-Hua Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—During the past decade, representation-based classification methods have received considerable attention in pattern recognition. In particular, the recently proposed non-negative representation based classification (NRC) method has been reported to achieve promising results in a wide range of classification tasks. However, NRC has two major drawbacks. First, there is no regularization term in the formulation of NRC, which may result in unstable solution and misclassification. Second, NRC ignores the fact that data usually lies in a union of multiple affine subspaces, rather than linear subspaces in practical applications. To address the above issues, this paper presents an affine non-negative collaborative representation (ANCR) model for pattern classification. To be more specific, ANCR imposes a regularization term on the coding vector. Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces. The experimental results on several benchmarking datasets demonstrate the merits of the proposed ANCR method. The source code of our ANCR is publicly available at https://***/yinhefeng/ANCR. Copyright © 2020, The Authors. All rights reserved.

关键词： Vectors

Microarray camera image segmentation with Faster-RCNN

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Microarray camera image segmentation with Faster-RCNN

International Conference on Applied System Innovation (ICASI)

作者： Jiancheng Zou Rui Song Institute of Image Processing and Pattern Recognition North China University of Technology Shijingshan District Beijing China

A novel method of image segmentation based on Faster-RCNN and microarray camera (3 × 3) is proposed in this paper, we use the microarray camera to obtain nine images in the same scene and use nine array images to achieve image segmentation. Through the microarray camera, we can obtain images quickly. The result of microarray camera and high-speed camera is similar, the shooting interval is short and the disparity information between array images is small. We use the Faster-RCNN algorithm and the GrabCut algorithm to segment target for the array images. Because of the size of microarray camera is small, it can be widely used on portable devices in the further. In comparison to Faster-RCNN network, the experimental results show that the proposed method is more efficient.

关键词： Cameras Lenses image segmentation Calibration Convolution Proposals Feature extraction

AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks

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arXiv 2019年

作者： Qin, Yulei Chen, Mingjian Zheng, Hao Gu, Yun Shen, Mali Yang, Jie Huang, Xiaolin Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Insa Lyon Lyon France Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom

Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation. Manual extraction of airway requires strenuous efforts due to the complicated structure and various appearance of airway. For automatic airway extraction, convolutional neural networks (CNNs) based methods have recently become the stateof-the-art approach. However, there still remains a challenge for CNNs to perceive the tree-like pattern and comprehend the connectivity of airway. To address this, we propose a voxel-connectivity aware approach named AirwayNet for accurate airway segmentation. By connectivity modeling, conventional binary segmentation task is transformed into 26 tasks of connectivity prediction. Thus, our AirwayNet learns both airway structure and relationship between neighboring voxels. To take advantage of context knowledge, lung distance map and voxel coordinates are fed into AirwayNet as additional semantic information. Compared to existing approaches, AirwayNet achieved superior performance, demonstrating the effectiveness of the network's awareness of voxel connectivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Computerized tomography

Adversarial attack type I: Cheat classifiers by significant changes

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arXiv 2018年

作者： Tang, Sanli Huang, Xiaolin Chen, Mingjian Sun, Chengjin Yang, Jie Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. Besides, with pretrained generative models, Type I attack on latent spaces is investigated as well. Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

Random Fourier features via fast surrogate leverage weighted sampling

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arXiv 2019年

作者： Liu, Fanghui Huang, Xiaolin Chen, Yudong Yang, Jie Suykens, Johan A.K. Department of Electrical Engineering [ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Institute of Medical Robotics Shanghai Jiao Tong University China School of Operations Research and Information Engineering Cornell University United States

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-of-the-art method that uses the leverage weighted scheme (Li et al. 2019), our new strategy is simpler and more effective. It uses kernel alignment to guide the sampling process and it can avoid the matrix inversion operator when we compute the leverage function. Given n observations and s random features, our strategy can reduce the time complexity for sampling from O(ns2 + s3) to O(ns2), while achieving comparable (or even slightly better) prediction performance when applied to kernel ridge regression (KRR). In addition, we provide theoretical guarantees on the generalization performance of our approach, and in particular characterize the number of random features required to achieve statistical guarantees in KRR. Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to (Li et al. 2019). Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

Deep model-based feature extraction for predicting protein subcellular localizations from bio-images

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Frontiers of Computer Science 2017年第2期11卷 243-252页

作者： Wei SHAO Yi DING Hong-Bin SHEN Daoqiang ZHANG School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

Protein subcellular localization prediction is im- portant for studying the function of proteins. Recently, as significant progress has been witnessed in the field of mi- croscopic imaging, automatically determining the subcellular localization of proteins from bio-images is becoming a new research hotspot. One of the central themes in this field is to determine what features are suitable for describing the pro- tein images. Existing feature extraction methods are usually hand-crafted designed, by which only one layer of features will be extracted, which may not be sufficient to represent the complex protein images. To this end, we propose a deep model based descriptor （DMD） to extract the high-level fea- tures from protein images. Specifically, in order to make the extracted features more generic, we firstly trained a convolu- tion neural network （i.e., AlexNe0 by using a natural image set with millions of labels, and then used the partial parame- ter transfer strategy to fine-tnne the parameters from natural images to protein images. After that, we applied the Lasso model to select the most distinguishing features from the last fully connected layer of the CNN （Convolution Neural Net- work）, and used these selected features for final classifica- tions. Experimental results on a protein image dataset vali- date the efficacy of our method.

关键词： partial parameter transfer subcellular location classification feature extraction deep model convolution neural network