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International Conference on image, vision and Computing (ICIVC)

作者： Xin Wang Qiong Wang Guofang Lv Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9781728123264

Traditional super-resolution reconstruction methods usually process a low resolution image as a whole, which ignores the differences among image patches, resulting in ineffective reconstruction result. In this paper, we adopt the concept of patch as the basic unit of super resolution reconstruction, and establish a novel image super-resolution reconstruction method based on bionic intelligence. The original low resolution image is initially reconstructed by bicubic interpolation. Then, a biological visual attention mechanism inspired approach is applied to the initial reconstruction result to detect the salient regions. Subsequently, a super-resolution convolutional neural network model is applied to the salient image patches so as to get the precise reconstruction results. Extensive experiments manifest that the proposed method outperforms many current state-of-the-art schemes in reconstruction quality, and achieves fast speed for practical application.

关键词： Super-resolution Bionic intelligence Saliency detection Convolutional neural network super-resolution Reconstructive Surgical Procedures image patch bionics Reconstruction algorithm Low-resolution image image resolution Reconstructions Intelligence

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Stain-Adaptive Self-Supervised Learning for Histopathology image Analysis

arXiv

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arXiv 2022年

作者： Ye, Hai-Li Wang, Da-Han Department of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China Fujian Provincial Key Laboratory of Pattern Recognition and Image Understanding Xiamen361000 China

It is commonly recognized that color variations caused by differences in stains is a critical issue for histopathology image analysis. Existing methods adopt color matching, stain separation, stain transfer or the combination of them to alleviate the stain variation problem. In this paper, we propose a novel Stain-Adaptive Self-Supervised Learning(SASSL) method for histopathology image analysis. Our SASSL integrates a domain-adversarial training module into the SSL framework to learn distinctive features that are robust to both various transformations and stain variations. The proposed SASSL is regarded as a general method for domain-invariant feature extraction which can be flexibly combined with arbitrary downstream histopathology image analysis modules (e.g. nuclei/tissue segmentation) by fine-tuning the features for specific downstream tasks. We conducted experiments on publicly available pathological image analysis datasets including the PANDA, BreastPathQ, and CAMELYON16 datasets, achieving the state-of-the-art performance. Experimental results demonstrate that the proposed method can robustly improve the feature extraction ability of the model, and achieve stable performance improvement in downstream tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： image analysis

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NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection 3rd

NLFA: A Non Local Fusion Alignment Module for Multi-Scale Fe...

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3rd International Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

作者： Xue, Honghui Ma, Jinshan Cai, Zheyi Fu, Junfang Guo, Feng Weng, Wei Dong, Yunxin Zhang, Zhenchang College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Zhongke Zhongxin Intelligent Technology Co. Ltd Fuzhou China Fujian Newland Auto-ID Tech. Co. Ltd Fuzhou China Department of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9783031400698

Recently, in order to pursue better detection results, more convolutional layers and deeper networks are a direction pursued by everyone. However, more and more down-sampling convolution or up-sampling operations generate feature maps of different scales, which makes it difficult to avoid the loss of detailed information of the image, and the distribution of different scales features will be misaligned. In particular, the loss and dislocation of the target boundary information will affect the features learned by the model and reduce the accuracy. This paper proposes a feature alignment method based on non-local idea, and designed two modules—Non Local Align Module (NLA) and Channel Fusion Augment Module (CFA). At the same time, the neighborhood calculation algorithm is also designed for it, which strengthens the binding force on the calculation of boundary information. These two modules can be easily embedded into the current mainstream object detection network to improve the detection effect of the model. Compared to the previous model, the network using our NLA module and CFA module achieves better results than the original model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Efficient relaxed gradient support pursuit for sparsity constrained non-convex optimization

arXiv

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arXiv 2019年

作者： Shang, Fanhua Wei, Bingkun Liu, Hongying Liu, Yuanyuan Zhuo, Jiacheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China Department of Computer Science University of Texas Austin United States

Large-scale non-convex sparsity-constrained problems have recently gained extensive attention. Most existing deterministic optimization methods (e.g., GraSP) are not suitable for large-scale and high-dimensional problems, and thus stochastic optimization methods with hard thresholding (e.g., SVRGHT) become more attractive. Inspired by GraSP, this paper proposes a new general relaxed gradient support pursuit (RGraSP) framework, in which the sub-algorithm only requires to satisfy a slack descent condition. We also design two specific semi-stochastic gradient hard thresholding algorithms. In particular, our algorithms have much less hard thresholding operations than SVRGHT, and their average per-iteration cost is much lower (i.e., O(d) vs. O(dlog(d)) for SVRGHT), which leads to faster convergence. Our experimental results on both synthetic and real-world datasets show that our algorithms are superior to the state-of-the-art gradient hard thresholding methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

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A novel algorithm for many-objective dimension reductions: Pareto-PCA-NSGA-II

A novel algorithm for many-objective dimension reductions: P...

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Congress on Evolutionary Computation

作者： Ronghua Shang Kun Zhang Licheng Jiao Wei Fang Xiangrong Zhang Xiaolin Tian Department of Computer Science and Technology Jiangnan University Wuxi Jiangsu China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an China

Many-objective problem has more than 3 objectives. Because of the extraordinary difficulty of acquiring their Pareto optimal solutions directly, traditional methods will be out of operation for such problems. In recent years, many researchers have turned their attention to the study of this area. They are interested in two areas: acquiring some part of Pareto front which is useful to the researchers (Preferred Solutions) and reducing redundant objectives. In this paper, we combine two dimension reduction methods: the method based on Pareto optimal solution analysis and the method based on correlation analysis, to form a novel algorithm for dimension reduction. Firstly, the Pareto optimal solutions are acquired through NSGA-II. Then the objectives who contribute little to the number of non-dominated solutions are removed. At last, the dimension of objectives is reduced further according to their contribution to the principal component in PCA analysis. In this way, we can acquire the right non-redundant objectives with low time complexity. Simulation results show that the proposed algorithm can effectively reduce redundant objectives and keep the non-redundant objectives with low time.

关键词： Algorithm design and analysis Pareto optimization Principal component analysis Sociology Vectors

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Hyperspectral image Classification With Contrastive Graph Convolutional Network

arXiv

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arXiv 2022年

作者： Yu, Wentao Wan, Sheng Li, Guangyu Yang, Jian Gong, Chen The PCA Laboratory The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education The Jiangsu Key Laboratory of Image and Video Understanding for Social Security The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Recently, Graph Convolutional Network (GCN) has been widely used in Hyperspectral image (HSI) classification due to its satisfactory performance. However, the number of labeled pixels is very limited in HSI, and thus the available supervision information is usually insufficient, which will inevitably degrade the representation ability of most existing GCN-based methods. To enhance the feature representation ability, in this paper, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations, which is termed Contrastive Graph Convolutional Network (ConGCN), for HSI classification. First, in order to mine sufficient supervision signals from spectral information, a semi-supervised contrastive loss function is utilized to maximize the agreement between different views of the same node or the nodes from the same land cover category. Second, to extract the precious yet implicit spatial relations in HSI, a graph generative loss function is leveraged to explore supplementary supervision signals contained in the graph topology. In addition, an adaptive graph augmentation technique is designed to flexibly incorporate the spectral-spatial priors of HSI, which helps facilitate the subsequent contrastive representation learning. The extensive experimental results on four typical benchmark datasets firmly demonstrate the effectiveness of the proposed ConGCN in both qualitative and quantitative aspects. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectroscopy

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Multi-resolution image Analysis Using Reduced Quaternion Wavelet Matrix

Multi-resolution Image Analysis Using Reduced Quaternion Wav...

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2nd International Conference on Communication Technology(ICCT 2015)

作者： Shan Gai School of Computer Science and Engineering of Southeast University Jiangsu P.R.China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology

Quaternion wavelet transform(QWT) using matrix-valued wavelets(RQWM) can process the components of vector-valued signals such as color image. In this paper, we extend QWT to reduced quaternion wavelet transform(RQWT). In order to further encode color information as a whole, we proposed to generalize the framework of reduced quaternion wavelet transform matrix(RQWM), which contains reduced quaternion filter bank and multi-resolution analysis(MMRA). Finally, we construct a first color reduced quaternion filter bank: color Shannon RQWT.

关键词： Multi-channel wavelet Reduced quaternion wavelet Multi-wavelet

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A Deep Generalized Correlation Network for Bitemporal image Change Detection

A Deep Generalized Correlation Network for Bitemporal Image ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rongfang Wang Weidong Wang Jia-Wei Chen Licheng Jiao Hongxia Hao Key Laboratory of Intelligent Perception Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi'an China School of Computer Science and Technology Xidian University Xi'an China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Recently, many convolution neural networks have been successfully employed in bitemporal SAR image change detection. However, most methods are developed based on the traditional framework that exploits the changed region from a difference image (DI) that is usually subject to the speckle. To essentially solve this issue, in this paper, we propose a deep canonic correlation network for bitemporal SAR image. In the proposed network, bitemporal SAR images and its corresponding DI are taken as the inputs and then three deep neural networks are designed to employ their features, respectively. Then the changed regions are obtained by the exploited features. Finally, we compare the proposed method with other deep learning methods and perform the comparison on four sets of bitemporal SAR images. The experimental results show that our proposed method outperforms other methods.

关键词： Radar polarimetry Neural networks Synthetic aperture radar Training Feature extraction Correlation Remote sensing

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DUAL-CHANNEL CONVOLUTIONAL NEURAL NETWORK FOR POLARIMETRIC SAR imageS CLASSIFICATION

DUAL-CHANNEL CONVOLUTIONAL NEURAL NETWORK FOR POLARIMETRIC S...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Wenqiang Hua Shuang Wang Wen Xie Yanhe Guo Xiaomin Jin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an China

This paper presents a new dual-channel convolutional neural network (Dc-CNN) for Polarimetric synthetic aperture radar (PolSAR) image classification when labeled samples are small. First, a neighborhood minimum spanning tree (MST) is used to enlarge the labeled sample set. Then, in order to obtain the abundant spatial information, a new dual-channel CNN is designed to PolSAR image to acquire different spatial features. This network model contains two parallel CNN structures, which can extract different features used two multiscale convolution structure. Experiments results show that compared with other methods, the proposed method shows a satisfactory classification result.

关键词： Feature extraction Deep learning Convolution Training Convolutional neural networks image classification Covariance matrices

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Progressive Quantization of Compressive Sensing Measurements

Progressive Quantization of Compressive Sensing Measurements

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Data Compression Conference (DCC)

作者： Liangjun Wang Xiaolin Wu Guangming Shi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of E. E. Xidian University China Department of Electrical & Computer Engineering McMaster University Canada

Compressive sensing (CS) is recently and enthusiastically promoted as a joint sampling and compression approach. The advantages of CS over conventional signal compression techniques are architectural: the CS encoder is made signal independent and computationally inexpensive by shifting the bulk of system complexity to the decoder. While these properties of CS allow signal acquisition and communication in some severely resource-deprived conditions that render conventional sampling and coding impossible, they are accompanied by rather disappointing rate-distortion performance. In the present work we propose a novel coding technique that rectifies, to certain extent, the problem of poor compression performance of CS and at the same time maintains the simplicity and universality of the current CS encoder design. The main innovation is a scheme of progressive fixed-rate scalar quantization with binning that enables the CS decoder to exploit hidden correlations between CS measurements, which was overlooked in the existing literature. Experimental results are presented to demonstrate the efficacy of the new CS coding technique.

关键词： Quantization Decoding Encoding Complexity theory image coding Current measurement Rate-distortion

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