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3rd International Conference on Communication, image and Signal processing (CCISP)

作者： Lu, Tingting Zhang, Ming Kang, Tangbin Zhang, Anxue Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China

In this paper, we investigate the space-alternating generalized expectation-maximization (SAGE) algorithm with virtual carriers (VCs) in orthogonal frequency division multiplexing (OFDM) systems. The channel frequency response (CFR) at VCs cannot be estimated accurately due to edge effect after inverse discrete Fourier transform (IDFT). To solve the problem, an improved channel estimation method is introduced to minimize the errors of CFR via iterative technique. Then we apply the SAGE algorithm to estimate the direction of arrival (DOA) and time of arrival (TOA). The SAGE algorithm shows more excellent performance with higher resolution ability than subspace-based algorithms. Based on Monte Carlo trials, we test the performance of the improved method in terms of mean absolute error (MAE) at different signal-to-noise ratios (SNR). Simulation results indicate that the improved method behaves better at any SNR than the conventional DFT-based method.

关键词： Signal to noise ratio

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An Improved UAV Aerial image Mosaic Algorithm Based on GMS-RANSAC

An Improved UAV Aerial Image Mosaic Algorithm Based on GMS-R...

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IEEE International Conference on Signal and image processing (ICSIP)

作者： xiangyan Lan Baolong Guo Zhe Huang Suting Zhang School of Aerospace Science and Technology Xidian University Xi'an China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

The aerial image mosaic algorithm needs to ensure the real-time performance of the algorithm and the natural transition of image fusion. In order to improve the matching performance of the algorithm, an improved UAV aerial image registration algorithm based on GMS-RANSAC is proposed. The improved algorithm introduces the idea of partitioning, divides the image into meshes and then performs feature extraction on each mesh, uses the bidirectional BF algorithm and the GMS algorithm to perform accurate feature value matching, and finally uses the improved RANSAC algorithm for further feature purification to obtain a high-quality correct interior point set. This paper combines the characteristics of GMS algorithm to improve the RANSAC algorithm, reduce the number of iterations of the algorithm, and reduce the time complexity of the algorithm. The improved image registration algorithm has higher accuracy and shorter running time. After the registration is completed, the image is merged to obtain a mosaic image.

关键词： image registration Purification Feature extraction Real-time systems Partitioning algorithms Time complexity image fusion

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图像型全自动骨龄评测算法及应用研究进展

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西安邮电大学学报 2021年第4期26卷 65-78页

作者：贾阳陈伟光王海娟杜涵容韩俊岗靳晓辉杨斌西安邮电大学计算机学院陕西西安710121 西安邮电大学陕西省网络数据智能处理重点实验室陕西西安710121 西安邮电大学西安市大数据与智能计算重点实验室陕西西安710121 西安市红会医院放射科陕西西安710054

通过分析图像型全自动骨龄评测算法所依赖的医学基础理论,对比图像型全自动骨龄评测中必要的图像分割方法和特征预测模型。归纳总结基于深度学习的自动骨龄评测算法,包括各类深度神经网络模型、模型训练所使用的数据集和骨龄评测结果等... 详细信息

通过分析图像型全自动骨龄评测算法所依赖的医学基础理论,对比图像型全自动骨龄评测中必要的图像分割方法和特征预测模型。归纳总结基于深度学习的自动骨龄评测算法,包括各类深度神经网络模型、模型训练所使用的数据集和骨龄评测结果等。此外,介绍骨龄评测中使用的其他数据采集方法及图像型全自动骨龄评测的发展情况,分析常见的几种全自动骨龄评测软件及系统,并对目前图像型全自动骨龄评测中存在的问题进行讨论与展望。

关键词：全自动骨龄评测深度学习手骨分割方法 X线片

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Content based image retrieval via sparse representation and feature fusion 8

Content based image retrieval via sparse representation and ...

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8th IEEE Data Driven Control and Learning systems Conference, DDCLS 2019

作者： Liu, Han Wang, Wenqing Jiao, Pengfei Shanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China

ISBN: (纸本)9781728114545

During the last two decades, content-based image retrieval (CBIR) has been widely studied. The limitations of low-level feature representation of images have been a thorny issue in image retrieval problems. In this paper, a CBIR algorithm based on sparse representation and feature fusion is proposed, in which global features and local features are combined to retrieve the images. Firstly, the GIST features are used to roughly retrieve the images with similar scene information by measuring Canberra distance. Then, a representation of the local features extracted from the rough retrieval results is obtained by sparse coding and feature pooling. Finally, Euclidean distance is used to measure the similarity of the sparse feature vectors to obtain the retrieval results. The proposed method is tested on the Coil20 and Caltech256 image datasets. The experimental results demonstrate that the proposed algorithm can achieve comparable superior retrieval performance to the existing single feature-based image retrieval algorithms. © 2019 IEEE.

关键词： image fusion

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Fast robust matching algorithm based on BRISK and GMS 4

Fast robust matching algorithm based on BRISK and GMS

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2019 4th International Conference on Intelligent Computing and Signal processing, ICSP 2019

作者： Cen, Chen Li, Runqi Xu, xi Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing100124 China

In the image stitching technology, traditional feature extraction algorithms have uneven feature points distribution, many redundant features, time-consuming feature points precision matching and low image registration accuracy. In view of these problems, this paper proposes an improved image registration algorithm based on BRISK and GMS. In this method, the image is first divided into meshes and BRISK algorithm is used for extracting image features, then the Brute Force matching algorithm is used for rough image matching. Finally, the mesh motion estimation method is used for feature quantity statistics, and error matching is removed to obtain a set of fine matching feature points for image registration. This paper verified the robustness of the improved algorithm by comparing it with other methods in the Mikolajczyk data set. The experimental results show that this algorithm achieves higher matching accuracy on the basis of maintaining speed compared with the original algorithm, and the average accuracy is improved by 8.02%. It has better performance than the traditional algorithm, which can be used for occasions with higher requirements on registration accuracy and real-time performance. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： image registration

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IQ-STAN: image Quality Guided Spatio-Temporal Attention Network for License Plate Recognition

IQ-STAN: Image Quality Guided Spatio-Temporal Attention Netw...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Cong Zhang Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an P. R. China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

License plate recognition (LPR) is one of the essential components in intelligent transportation systems. Although the image processing algorithms for LPR have been extensively studied in the past several years, the recognition performance is still not satisfactory especially in unconstrained complex scenes. In order to tackle this issue, a novel deep multi-task learning-based method is proposed in this paper by introducing contextual information in multiple license plate frames. Specifically, an end-to-end trainable multi-task architecture, namely IQ-STAN, is developed by joint license plate recognition and image quality scoring. Moreover, we propose an image quality-guided spatio-temporal attention mechanism, which is utilized in the frame-level feature representation during the phase of plate recognition. Extensive experiments are conducted and the competitive results demonstrate the effectiveness of our proposed framework.

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基于多注意融合网络的输煤皮带异物识别方法

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西安科技大学学报 2024年第5期44卷 976-984页

作者：李利梁晶陈旭东寇发荣潘红光西安科技大学电气与控制工程学院陕西西安710054 西安市电气设备状态监测与供电安全重点实验室陕西西安710054 西安科技大学机械工程学院陕西西安710054

为改善现有输煤皮带异物识别算法网络参数量大、识别精度不高的问题,及时避免大块煤和矸石、锚杆等带来的安全隐患,提出了一种基于多注意融合网络的输煤皮带异物识别方法,使用低照度图像处理算法对数据集进行预处理,采用融合局部注意力... 详细信息

为改善现有输煤皮带异物识别算法网络参数量大、识别精度不高的问题,及时避免大块煤和矸石、锚杆等带来的安全隐患,提出了一种基于多注意融合网络的输煤皮带异物识别方法,使用低照度图像处理算法对数据集进行预处理,采用融合局部注意力残差块作为基本特征提取单元,在残差块中融入带有额外偏移量的可变形卷积以增加对不规则特征的描述,用注意力机制对全局特征图做期望最大化处理。结果表明:在Cifar 10数据集和矿用皮带传输异物识别数据集的识别准确率分别为93.7%和84.8%;与ShufflenetV2、MobileNetV2、ResNet 50、ResNet 110、Darknet 53算法相比,识别准确率分别提升了4.7%、3.9%、0.4%、0.5%、1.7%;与识别准确率相近的ResNet 50、ResNet 110算法相比,网络参数量和计算复杂度大大减小。识别方法能够快速识别输煤皮带异物,且具有较高的识别准确率,对保障煤矿运输系统的安全运行具有参考意义。

关键词：异物识别输煤皮带 Darknet网络可变形卷积注意力机制

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A unified framework for high precision and speed identification and tracking of space debris 71

A unified framework for high precision and speed identificat...

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71st International Astronautical Congress, IAC 2020

作者： Wei, Jiangtao Ning, xin Wang, Qihang Ma, Shichao National Key Laboratory of Aerospace Flight Dynamics Northwestern Polytechnical University Xi'an China Northwestern Polytechnical University NPU China

Automatic observation has become the development trend of optical observation of space debris, and corresponding automatic target identification without human intervention has become an urgent research topic. This paper studies the real-time instance segmentation and tracking of space debris based on ground-based and space-based observation systems. We provide a unified, flexible and universal high precision and speed real-time target recognition and tracking framework. This framework improves the recognition speed of continuous image sequences from 5fps (frames per second) to 27fps on the premise of ensuring high precision instance segmentation and category recognition. Our contributions are threefold: (i) we added a fast loop correlation detection module Siam-Mask into the deep network framework of Mask R-CNN instance segmentation recognition, and we innovatively divided the time-domain tasks of different modules of the framework in different threads;(ii) we insert the CBAM module into each convolutional layer in the ResNet and FPN network to improve the recognition accuracy of small targets and information loss targets;(iii) we apply the singular value decomposition technique to convolution feature compression to reduce the computational and storage requirements of the model. Experimental results show that this methodology can effectively realize the real-time detection and tracking of multi-scale debris in space, and reduce the computing cost and storage space as much as possible. This technology will promote the maturity of "visual navigation based" (VBN) technology. In-orbit satellites will be able to realize on-board processing of navigation algorithms to achieve near-real-time mapping of the movement trajectory of non-cooperative objects in space, which will form a complete "identification and tracking - motion analysis - capture - off-orbit" method of space debris removal. Copyright © 2020 by the International Astronautical Federation (IAF). All rights rese

关键词： Space debris

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image Artistic Style Transfer Based on Color Distribution Preprocessing 4th

Image Artistic Style Transfer Based on Color Distribution Pr...

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4th International Conference on Cognitive systems and Information processing, ICCSIP 2018

作者： Zhang, Yinshu Chen, Jiayi Si, xiangyu Tian, Zhiqiang Lan, Xuguang School of Software Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’anShaanxi710049 China

ISBN: (纸本)9789811379826

Style transfer is an increasingly popular field that can capture the styles of a particular artwork and use them to synthesize a new image with specific content. Previous NST algorithms have the limitation to transfer styles to correct regions in the output image. Therefore, some regions in the output image have deformed structures of the source image. In this paper, we propose a color preprocessing-based neural style transfer method to overcome the limitation. To reduce impacts caused by color differences between source image and style, we propose three models based on a color iterative distribution transform algorithm (IDT). The first one is named original color-preprocessed (OCp) model, which uses IDT to transform the color probability density function (PDF) of source image into that of style image. The second one is named exposure-corrected original color-preprocessed (EC-OCp) model, which adds an automatic detail-enhanced exposure correction module before OCp model. When source image is underexposed, EC-OCp model can achieve better results than OCp model. The third one is style color-preprocessed (SCp) model. It uses IDT to transform the color PDF of style image into that of source image. The original structures are well protected in the output image. According to experiments, the proposed models are robust to the source images with more conditions. Therefore, they have more usage values than the original method. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Color

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A neuro-heuristic approach for recognition of lung diseases from X-ray images

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EXPERT systems WITH APPLICATIONS 2019年 126卷 218-232页

作者： Ke, Qiao Zhang, Jiangshe Wei, Wei Polap, Dawid Wozniak, Marcin Kosmider, Leon Damasevicius, Robertas Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Xian Univ Technol Sch Comp & Engn Xian 710048 Shaanxi Peoples R China Silesian Tech Univ Inst Math Kaszubska 23 PL-44100 Gliwice Poland Virginia Commonwealth Univ Sch Pharm Bioanalyt Shared Resource Lab 410 N 12th St Richmond VA 23298 USA Kaunas Univ Technol Dept Software Engn Studentu 50 LT-51368 Kaunas Lithuania

Background and objective: The X-ray screening is one of the most popular methodologies in detection of respiratory system diseases. Chest organs are screened on the film or digital file which go to the doctor for evaluation. However, the analysis of x-ray images requires much experience and time. Clinical decision support is very important for medical examinations. The use of Computational Intelligence can simulate the evaluation and decision processes of a medical expert. We propose a method to provide a decision support for the doctor in order to help to consult each case faster and more precisely. Methods: We use image descriptors based on the spatial distribution of Hue, Saturation and Brightness values in x-ray images, and a neural network co-working with heuristic algorithms (Moth-Flame, Ant Lion) to detect degenerated lung tissues in x-ray image. The neural network evaluates the image and if the possibility of a respiratory disease is detected, the heuristic method identifies the degenerated tissues in the x-ray image in detail based on the use of the proposed fitness function. Results: The average accuracy is 79.06% in pre-detection stage, similarly the sensitivity and the specificity averaged for three pre-classified diseases are 84.22% and 66.7%, respectively. The misclassification errors are 3.23% for false positives and 3.76% for false negatives. Conclusions: The proposed neuro-heuristic approach addresses small changes in the structure of lung tissues, which appear in pneumonia, sarcoidosis or cancer and some consequences that may appear after the treatment. The results show high potential of the newly proposed method. Additionally, the method is flexible and has low computational burden. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Medical image processing Clinical decision support Neural networks Heuristic methods

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