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检索条件"机构=Key Laboratory of Intelligent Perception and Image Understanding"

共 928 条记录，以下是91-100 订阅

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Edge detection approach based on directionlet transform

Edge detection approach based on directionlet transform

引用

2nd International Conference on Multimedia Technology, ICMT 2011

作者： Bai, Jing Zhou, Huaji Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an China

ISBN: (纸本)9781612847740

Due to possessing the multi-direction anisotropic basis functions, directionlet transform can capture the inherent geometrical feature of the image. A directionlet transform -based edge detection approach is proposed in this paper. In the approach, we filter along any two directions with two slopes to capture abundant edge information. In addition, we use the multiscale characteristics of directionlet transform. Combine the advantage of small and large scales to get the desirable edge. The experiments indicate that the proposed approach outperforms wavelet and any other traditional approaches. © 2011 IEEE.

关键词： Edge detection

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IMPROVED SIAMRPN++ WITH CLUSTERING-BASED FRAME DIFFERENCING FOR OBJECT TRACKING OF REMOTE SENSING VIDEOS

IMPROVED SIAMRPN++ WITH CLUSTERING-BASED FRAME DIFFERENCING ...

引用

2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Feng, Jie Hui, Bingyu Liang, Yuping Yao, Quanhe Zhang, Xiangrong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9781665403696

Deep learning (DL) based object tracking methods have achieved encouraging results on natural videos. However, directly applying these DL-based methods to the vehicle tracking of optical remote sensing videos (ORSV) still faces many challenges. Different with the vehicles in nature videos, most vehicles in ORSV are blurry, small in size, and highly similar to other vehicles. Furthermore, blurred vehicles easily blend into the background and are difficult to distinguish. To solve these problems, an improved Siamrpn++ with clustering-based frame differencing (CFD-SiamRPN++) is proposed. In CFD-SiamRPN++, a clustering method is used to divide the differencing map among adjacent frames into two clusters. By using the statistics of the clustering results, each cluster is judged to the target or not. According to the judgment of each cluster, the differencing map is refined by reducing the background noises and retaining moving information of vehicles. Then, the refined differencing map is fused with the original frame as the input of the tracking network to enhance the discriminative ability of small-sized and blurred vehicles. In the tracking phase, SiamRPN++ is selected as the tracking network due to its feature extraction capability with multi-scale feature fusion and efficient tracking performance. Experiment results based on Jilin-1 ORSV dataset show that the proposed method provides a competitive tracking performance over state-of art deep learning methods. © 2021 IEEE

关键词： Deep learning Target tracking Clustering methods Feature extraction Optical sensors Object tracking Background noise

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Tracking Multi-objects with Anchor-Free Siamese Network 5th

Tracking Multi-objects with Anchor-Free Siamese Network

引用

5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Hui, Bingyu Feng, Jie Yao, Quanhe Gu, Jing Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China

ISBN: (纸本)9783031149023

Single object tracker based on siamese neural network have become one of the most popular frameworks in this field for its strong discrimination ability and high efficiency. However, when the task switch to multi-object tracking, the development of siamese-network based tracking methods is limited by the huge calculation cost comes from repeatedly feature extract and excessive predefined anchors. To solve these problems, we propose a siamese box adaptive multi-object tracking (SiamBAN-MOT) method with parallel detector and siamese tracker branch, which implementation in an anchor-free manner. Firstly, ResNet-50 is constructed to extract shared features of the current frame. Then, we design a siamese specific feature pyramid networks (S-FPN) to fuse the multi-scale features, which improves detection and tracking performance with the anchor-free architecture. To alleviate the duplicate feature extraction, RoI align is operated to extract all trajectories’ template feature and search region feature in a single feature map at once. After that, anchor-free based Siamese tracking network outputs the tracking result of each trajectory according to its template and search region feature. Meanwhile, current frame’s detection results are obtained from the detector for the target association. Finally, a simple novel IOU-matching scheme is designed to map the tracking results to the detection results so as to refine the tracking results and suppress the drifts caused by siamese tracking network. Through experimental verification, our method achieves competitive results on MOT17. © 2022, IFIP International Federation for Information Processing.

关键词： Feature extraction

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Evolutionary game network reconstruction by memetic algorithm with l 1/2 regularization 11th

Evolutionary game network reconstruction by memetic algorith...

引用

11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Wu, Kai Liu, Jing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China

ISBN: (纸本)9783319687582

Evolutionary Game (EG) theory is effective approach to understand and analyze the widespread cooperative behaviors among individuals. Reconstructing EG networks is fundamental to understand and control its collective dynamics. Most existing approaches extend this problem to the l 1-regularization optimization problem, leading to suboptimal solutions. In this paper, a memetic algorithm (MA) is proposed to address this network reconstruction problem with l 1/2 regularization. The problem-specific initialization operator and local search operator are integrated into MA to accelerate the convergence. We apply the method to evolutionary games taking place in synthetic and real networks, finding that our approach has competitive performance to eight state-of-the-art methods in terms of effectiveness and efficiency. © Springer International Publishing AG 2017.

关键词： Evolutionary algorithms

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Low-complexity bandelet for SAR image compression

Low-complexity bandelet for SAR image compression

引用

MIPPR 2009 - Multispectral image Acquisition and Processing: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Yang, Shuyuan Wang, Zhaoxia Qi, Weidong Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478054

Synthetic aperture radar (SAR) images compression is very important in reducing the burden of data storage and transmission. Finding efficient geometric representations of images is a central issue in improving the efficiency of image compression. Bandelet provides an efficient way for image representation based on geometric regularity. In the second generation Bandelet, the multiscale decomposition of image is completed by 2D wavelet transform (WT) and the obtained subbands images are squared partitioned. Then a bottom to top CART algorithm is used to prune the quadtree, and finally an exhaustive searching algorithm is used to obtain the optimal direction in each square. This process is of high complexity in time and space though it can provide an efficient representation of images than WT. Considering this, we proposed a rapid implementation of Bandelet transform based on fixed size image partition, and then applied it to SAR image compression. Experiments results show that in relative to the second generation Bandelets, our proposed method has rapid implementation and comparable performance with chinalake and abq-apt in 0.5-2.0bpp. An improvement of PSNR(Peak Signal to Noise Ratio) and the preservation of edges and texture over JPEG2000 are obtained. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image compression

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A effective immune multi-objective algorithm for SAR imagery segmentation

A effective immune multi-objective algorithm for SAR imagery...

引用

MIPPR 2009 - Multispectral image Acquisition and Processing: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Yang, Dongdong Jiao, Licheng Gong, Maoguo Si, Xiaoyun Li, Jinji Feng, Jie Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xidian University 710071 China

ISBN: (纸本)9780819478054

A novel and effective immune multi-objective clustering algorithm (IMCA) is presented in this study. Two conflicting and complementary objectives, called compactness and connectedness of clusters, are employed as optimization targets. Besides, adaptive ranks clone, variable length chromosome crossover operation and k-nearest neighboring list based diversity holding strategies are featured by the algorithm. IMCA could automatically discover the right number of clusters with large probability. Seven complicated artificial data sets and two widely used synthetic aperture radar (SAR) imageries are used for test IMCA. Compared with FCM and VGA, IMCA has obtained good and encouraging clustering results. We believe that IMCA is an effective algorithm for solving these nine problems, which should deserve further research. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Multiobjective optimization

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Concatenated edge and co-occurrence feature extracted from Curvelet Transform for human detection

Concatenated edge and co-occurrence feature extracted from C...

引用

25th International Conference of image and Vision Computing New Zealand, IVCNZ 2010

作者： Han, Hong Fan, Youjian Jiao, Licheng Chen, Zhichao Key Laboratory of Intelligent Perception and Image Understanding Xidian University Ministry of Education Xi'an Shaanxi China

ISBN: (纸本)9781424496303

An efficient feature extraction method based on the Curvelet Transform for detecting human in static images is proposed in this paper. The edge features can be extracted with the block-based statistical information of each sub-band coefficients, and then the texture feature can be extracted from the co-occurrence of the lowest sub-band coefficients, all the extracted features are concatenated as the final feature vector of the images. All the training and test data are from the INRIA and MIT human dataset. The classification results with test data show that the proposed feature extraction method is suitable for human detection. From the detection results, it can be seen that, the detection accuracy of the proposed method is higher, meanwhile the false alarms are lower than the Histograms of Oriented Gradients(HOG) method. © 2010 IEEE.

关键词： Feature extraction

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Adaptive shrinkage despeckling for SAR image based on nonsubsampled contourlet transform

Adaptive shrinkage despeckling for SAR image based on nonsub...

引用

MIPPR 2009 - Multispectral image Acquisition and Processing: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Wang, Shuang Zhao, Hong Jiao, Licheng Hou, Biao Key Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478054

An improved Nonsubsampled Contourlet Transform (NSCT)-based method has been proposed in this paper, using subbands mask prior models and directional information for synthetic aperture radar (SAR) image despeckling. The NSCT is a shift-invariant directional wavelet transform which satisfies the demands of SAR image despeckling. Wedding coefficient statistic character of NSCT subbands and directional information captured by the transform, this paper applies anisotropic neighborhood models to the processing of adaptive shrinkage. The resulting algorithm strongly suppresses speckle, while preserving detailed information and image edges compared with other traditional despeckling methods. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Shrinkage

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Local graph cut criterion for supervised dimensionality reduction

Local graph cut criterion for supervised dimensionality redu...

引用

MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Zhang, Xiangrong Zhou, Sisi Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478078

Graph cut criterion has been proven to be robust and applicable in clustering problems. In this paper the graph cut criterion is applied to construct a supervised dimensionality reduction. A new graph cut, scaling cut, is proposed based on the classical normalized cut. Scaling cut depicts the relationship between samples, which makes it can handle the heteroscedastic and multimodel data in which LDA fails. Meanwhile, the solution to scaling cut is global optimal for it is a generalized eigenvalue problem. To obtain a more reasonable projection matrix and reduce the computational complexity as well, the localized k-nearest neighbor graph is introduced in, which leads to equivalent or better results compared with scaling cut. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Nearest neighbor search

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Kernel evolutionary algorithm for clustering 11th

Kernel evolutionary algorithm for clustering

引用

11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Jiang, Xiangming Ma, Jingjing Lei, Chao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China

ISBN: (纸本)9789811036132

In this paper, we propose a novel clustering algorithm named KECA based on kernel function and evolutionary optimization. As we know, Euclidean distance based similarity metrics can help clustering algorithms handle datasets with compact super-sphere distributions perfectly, but it is undesirable for the complex structural or irregular shaped datesets. Proper mapping function can map the data in original space to high-dimensional feature space, which exposes more features and sheds light on complex structural datasets. However, clustering in feature space is time-consuming and often suffers from curse of dimensionality. Fortunately, we can cluster the mapped data in feature space which performs nonlinearly in original space with the help of kernel function in our proposed KECA. What’s more, evolutionary algorithm is used in KECA to avoid local optimal. Experimental results on artificial as well as UCI datasets show the effectiveness and robustness of the proposed KECA in compare with the genetic algorithm-based clustering and the K-means clustering. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Clustering algorithms

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