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检索条件"机构=Laboratory of Image Processing and Intelligent Control"

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Fast detection and symptom analysis of cracks in highway asphalt pavement

Fast detection and symptom analysis of cracks in highway asp...

引用

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

作者： Hong, Hanyu Zhang, Xiuhua Yu, Jiuyang Laboratory of Image Processing and Intelligent Control Wuhan Institute of Technology Wuhan 430074 China Institute for Technology Research and Innovation Deakin University VIC 3217 Australia

ISBN: (纸本)9780819478078

A method for real-time fast detection and symptom analysis of cracks in highway asphalt pavement is proposed. At the first step, the fissure characteristic of the acquired and de-noised pavement images is analyzed and then extracted by the method based on gray value comparison between the current pixel and its neighboring pixels, the false cracks are deleted by using the computed measurement of crack features, thus the true fissures are detected. The most important step is the symptom analysis of the cracks in the pavement image, all the data could be analyzed and be the basis for the agencies to remedy and manage the pavement. Quantities of images are processed and the results show that the proposed method can detect the pavement distress information actually, and has robustness and availability. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Feature extraction

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Cortical Neural P Systems 15th

Cortical Neural P Systems

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Fu, Zhongzheng Zhang, Luping Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

P systems are distributed and parallel computing models inspired from living cells. In this work, a variant of spiking neural P systems, cortical neural P systems, are proposed in the frame work of P systems combining the topology structure of basic P system and information processing style in spiking neurons. The computational power of cortical neural P systems is investigated. It is proved that cortical neural P systems are equivalent to Turing machines as number generators. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Turing machines

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A fast quantum mechanics based contour extraction algorithm

A fast quantum mechanics based contour extraction algorithm

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Medical Imaging 2009 - image processing

作者： Lan, Tian Sun, Yangguang Ding, Mingyue Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475107

A fast algorithm was proposed to decrease the computational cost of the contour extraction approach based on quantum mechanics. The contour extraction approach based on quantum mechanics is a novel method proposed recently by us, which will be presented on the same conference by another paper of us titled "a statistical approach to contour extraction based on quantum mechanics". In our approach, contour extraction was modeled as the locus of a moving particle described by quantum mechanics, which is obtained by the most probable locus of the particle simulated in a large number of iterations. In quantum mechanics, the probability that a particle appears at a point is equivalent to the square amplitude of the wave function. Furthermore, the expression of the wave function can be derived from digital images, making the probability of the locus of a particle available. We employed the Markov Chain Monte Carlo (MCMC) method to estimate the square amplitude of the wave function. Finally, our fast quantum mechanics based contour extraction algorithm (referred as our fast algorithm hereafter) was evaluated by a number of different images including synthetic and medical images. It was demonstrated that our fast algorithm can achieve significant improvements in accuracy and robustness compared with the well-known state-of-the-art contour extraction techniques and dramatic reduction of time complexity compared to the statistical approach to contour extraction based on quantum mechanics. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Wave functions

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Double recursive algorithm based on fuzzy entropy for image thresholding

Double recursive algorithm based on fuzzy entropy for image ...

引用

MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Xiuhua Hong, Hanyu Laboratory of Image Processing and Intelligent Control Wuhan Institute of Technology Wuhan 430074 China Institute for Technology Research and Innovation Deakin University VIC 3217 Australia

ISBN: (纸本)9780819478085

A double recursive algorithm based on fuzzy entropy for image thresholding is proposed. The inner recursive step is to calculate the threshold between the given gray level interval, and the outer recursive step is to calculate the optimal for the image with small objects. Experiments on steel billet image show that the proposed algorithm can threshold image fast and exactly, which has effectiveness and application for real time image segmentation. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image segmentation

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Representative Graph Neural Network 16th

Representative Graph Neural Network

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Yu, Changqian Liu, Yifan Gao, Changxin Shen, Chunhua Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The University of Adelaide Adelaide Australia

ISBN: (纸本)9783030585709

Non-local operation is widely explored to model the long-range dependencies. However, the redundant computation in this operation leads to a prohibitive complexity. In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy. Instead of propagating the messages from all positions, our RepGraph layer computes the response of one node merely with a few representative nodes. The locations of representative nodes come from a learned spatial offset matrix. The RepGraph layer is flexible to integrate into many visual architectures and combine with other operations. With the application of semantic segmentation, without any bells and whistles, our RepGraph network can compete or perform favourably against the state-of-the-art methods on three challenging benchmarks: ADE20K, Cityscapes, and PASCAL-Context datasets. In the task of object detection, our RepGraph layer can also improve the performance on the COCO dataset compared to the non-local operation. Code is available at https://***/RepGraph. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Improving the degradation of digital logistic map via external control

Improving the degradation of digital logistic map via extern...

引用

International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2014

作者： Deng, Ya-Shuang Hu, Han-Ping School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan 430074 China

ISBN: (纸本)9783038350606

In this paper, the degradation problem of digital Logistic map is investigated. Chen chaotic system is applied to anti-control the digital Logistic map and a controller that combines an external state control along with a modular function is designed. Results of the experiments show that the external control can greatly improve the dynamical and statistical properties. Thus, it can be used in cryptography applications. © (2014) Trans Tech Publications, Switzerland.

关键词： Chaotic systems

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HSDet: A Representative Sampling Based Object Detector in Cervical Cancer Cell images 15th

HSDet: A Representative Sampling Based Object Detector in Ce...

引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Yan, Xiaoli Zhang, Zhongsi Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Automated detection of cervical cancer cells has the potential to reduce error and increase productivity in cervical cancer screening. However, the existing object detection methods to detect the cervical cancer cells make inadequate use of negative samples and fail to balance the number of hard samples and easy samples. In this work, we propose a image-level sampling method, called pair sampling, which can extract the representative negative samples and generate the sample pair image. Then, based on the sample pair image, we propose the hybrid sampling, a proposal-level sampling method, to balance the number of hard samples and easy samples. Combining the proposed sampling methods, we design a representative sampling based cervical cell detector, which can detect the cervical cancer cells effectively. In order to comprehensively evaluate our method, we use a dataset which is consisted of 16000 annotated cervical cell images with size of 1024 × 1024. The experimental result shows that our method achieves 57.1% mean Average Precision (mAP), and it is higher than cascade R-CNN and Faster R-CNN by 4.7% and 5.8%, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A Robust Recognition and Segmentation Algorithm for Retaining Walls in Mining Scenes

A Robust Recognition and Segmentation Algorithm for Retainin...

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2023 China Automation Congress, CAC 2023

作者： Zeng, Tian Xiao, Li Peng, Gang Wang, Aoze Wang, Zhuo Sun, Zhigang School of Artificial Intelligence and Automation Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China

ISBN: (纸本)9798350303759

The key for bulldozers to realize automatic operation in mine scenes is whether they can accurately identify and accurately segment the retaining walls, however, because the point cloud dataset of the mine site is too few, resulting in the deep learning-based identification and segmentation algorithm cannot be applied in this scene. At the same time, because of the rugged road surface of the mine scene, the existence of a lot of dust, numerous disturbances, retaining wall features are not obvious and other problems, the algorithms proposed by previous scholars are not perfect enough to solve the problem. We propose a point cloud recognition and segmentation algorithm based on clustering and evaluation function of integrated features. Ours firstly compensates the skew of the point cloud map with RANSAC and down samples the data by gridding the point cloud. Then reduces the influence of dust and truck materials in the scene by normal vector and variance information. Finally, screens out the candidate target class by density clustering, and identifies and segments the retaining wall by integrated feature. Our proposed algorithm is validated in several different real mine scenarios and the results show that ours has high accuracy and strong robustness. © 2023 IEEE.

关键词： Retaining walls

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Three-dimensional double directional filter for tracking and detecting moving dim weak target

Three-dimensional double directional filter for tracking and...

引用

MIPPR 2005: image Analysis Techniques

作者： Li, Meng Zhang, Tianxu Yang, Weidong Mao, Haiceng State Education Commission Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

The aim of the present work is to assess the performance of three-dimensional Double Directional Filtering (TDDDF) algorithm for detecting and tracking a weak moving dim target against a complex cluttered background in infrared image sequences. This paper proposes an novel TDDDF to improve the integrated signal-to-clutter ratio (ISCR) and enhance the three-dimensional directional filter's (TDDF) target energy accumulation ability further. Since the TDDDF do well to whitening noise (or quasi whitening noise) but not so sensitive to complex cloudscene background, prior to the filtering, a newly pre-whitening method termed Spatial-Temporal Adaptive Filtering algorithm is used here to suppress clutter background. Extensive experiment results demonstrate the proposed algorithm's ability in detecting weak dim point target against cloud-cluttered background. Finally, performance comparisons of the proposed algorithm and TDDF, on real IR image data, are presented in which the advantages of the proposed TDDDF filters are shown.

关键词： Tracking (position)

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Runway detection of an unmanned landing aerial vehicle based on vision

Runway detection of an unmanned landing aerial vehicle based...

引用

MIPPR 2005: SAR and Multispectral image processing

作者： Wang, Hongqun Peng, Jiaxiong Li, Lingling Institute for Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074

When an monocular vision-based unmanned aerial vehicle (UAV) based on vision is flown to the final approach fix to intercept the glide slope without the navigation of Global Positioning System (GPS), the position and orientation of the airport runway in image must be detected accurately so as to a host of suitable procedures have to be followed. The optimum length of the final approach is about five miles from the runway threshold. The front view of the runway, which is achieved at the moment, is very illegible. The approaching marking (cross bar) of the runway are showed as some white spots of high intensity and the complicated backgrounds of the airport are included in the images. In this case, spots with high intensity should be extracted and classified, some of these spots are just the images of the background noises and the pseudo-targets, which can't be separated with the spots of the runway as in the view there is no significant characteristic difference among them ostensibly. Fortunately, in the terrestrial coordinate space, most of the runway marks are located at the apexes of a rectangle, having some geometric relationships. The relationship among the projection coordinates of the runway spots in the images can be determined according to the perspective principle, the constraint condition of the rectangle as well as the front shot constraint condition of the target, by using this relationship, the runway approaching marks can be separated, the position and the direction of the runway in the images can be identified. In this paper, the clustering management is adopted so as to greatly reduce the computing time. The consequence of the experiments shows that by this algorithm, even from a place far away from the runway whose marks are unclear, we also can effectively detect the runway.

关键词： Unmanned vehicles

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