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作者： Xu, Yang Kan, Haibin Han, Guangyue Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China The University of Hong Kong Faculty of Science Department of Mathematics Hong Kong Hong Kong Shanghai Engineering Research Center of Blockchain Shanghai200433 China Yiwu Research Institute Fudan University Yiwu City322000 China

Let H be the Cartesian product of a family of finite abelian groups. Via a polynomial approach, we give sufficient conditions for a partition of H induced by weighted poset metric to be reflexive, which also become necessary for some special scenarios. Moreover, by examining the roots of the Krawtchouk polynomials, we give sufficient conditions for a partition of H induced by combinatorial metric to be non-reflexive, and then give several examples of non-reflexive partitions. When H is a vector space over a finite field F, we consider the property of admitting MacWilliams identity (PAMI) and the MacWilliams extension property (MEP) for partitions of H. More specifically, under some invariance assumptions, we show that two partitions of H admit MacWilliams identity if and only if they are mutually dual and reflexive, and any partition of H satisfying MEP is in fact an orbit partition induced by some subgroup of Aut F(H), which is necessarily reflexive. Furthermore, we show that the aforementioned non-reflexive partitions induced by combinatorial metric do not satisfy MEP, which further enables us to disprove a conjecture proposed by Pinheiro et al., (2019). © 1963-2012 IEEE.

关键词： Directed graphs

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Hierarchical background subtraction using local pixel clustering

Hierarchical background subtraction using local pixel cluste...

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作者： Zhong, Bineng Yao, Hongxun Shan, Shiguang Chen, Xilin Gao, Wen Department of Computer Science and Engineering Harbin Institute of Technology China Digital Media Research Center Institute of Computing Tech. CAS Beijing China Key Laboratory of Intelligent Information Processing CAS Beijing China Digital Media Institute Peking University Beijing China

ISBN: (纸本)9781424421756

We propose a robust hierarchical background subtraction technique which takes the spatial relations of neighboring pixels in a local region into account to detect objects in difficult conditions. Our algorithm combines a per-pixel with a per-region background model in a hierarchical manner, which accentuates the advantages of each. This is a natural combination because the two models have complementary strengths. The per-pixel background model is achieved by mixture of Gaussians Models (GMM) with RGB feature. Although precisely describing background change in high resolution, it suffers from the sensitivity to quick variations in dynamic environment. To tolerate these quick variations, we further develop a novel GMM based per-region background model, which is updated by the cluster centers obtained from a k-means clustering of the pixels' RGB feature in the region. Numerical and qualitative experimental results on challenging videos demonstrate the robustness of the proposed method. © 2008 IEEE.

关键词： Pixels

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A Detection Method Toward Yellow Dragon Disease Spreads Using Spark and Deep Learning 3

A Detection Method Toward Yellow Dragon Disease Spreads Usin...

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3rd International Conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Xie, Wu Su, Zhenzhao Zhang, Huimin Kang, Ping Qin, Kun Fan, Yong Zhao, Quanyou Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541214 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Lingchuan Science and Technology Information Institute Guilin541299 China Guilin Hivision Technology Co. Ltd. Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541214 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin541004 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection methods of yellow dragon disease spread via wood lice transmission networks like social networks are very important for diverse citrus trees and farmers. Although current methods have some detection accuracy or low cost, the detection processes are relatively troublesome and the detection cycles are long, making it be a difficult problem to apply them in large-scale orange farms as practical scenarios to detect citrus yellow dragon disease in a timely manner. A new method toward detecting citrus yellow dragon disease spread utilizing Spark and deep learning is proposed for this problem. By obtaining citrus field video stream data through high-definition cameras, and transferring the stream data to the Spark cluster through Kafka like intelligent agents, it is practicable to use the structured streaming component under the Spark framework via big data ecosphere to process video or image stream data transmitted via monitoring. We construct a citrus yellow dragon disease detection model via YOLOv7, and use self-made citrus yellow dragon disease images as training and testing data sets. The preliminary experimental results show that the new methods achieved an accuracy of 83.14%. To reduce the occurrence of missed and false detections, the shallow detection heads are added to the feature fusion networks for improvement, extracting and fusing shallow network information to try to improve the detection effects of yellow dragon disease. Then replace the convolution operation in the ELAN (Effective Long-range Aggregation Network) module with deep separable convolution to reduce the number of model parameters. The preliminary experimental results show that compared to the original YOLOv7 model, our improved citrus yellow dragon disease detection model with YOLOv7 has an accuracy improvement of 2.43%, maintaining a higher detection accuracy with lower time than before. © 2024 SPIE.

关键词： Deep learning

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Extensive Techniques to Detect Defects in Tyres through Radiography

Extensive Techniques to Detect Defects in Tyres through Radi...

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IEEE International Conference on Multi Topic

作者： Muhammad Ahmad Shabir Muhammad Umair Hassan Xiangru Yu Jinping Li Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Jinan China School of Information Science and Engineering Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (数字)9781728140018

ISBN: (纸本)9781728140025

The present paper highlights the experimentation procedure and decomposition of vehicle tyres through the extensive technique of radiography by the incorporation of total changes occurring in the image, transformation of curvelet that is based on image enlargement, and moreover, canny edge detection will examine the patterns through outer surfaces and poor defects in different types of tyres. Keeping into knowledge, the feature appeared after proceeding radiography are decomposed into pattern recognition. In the present paper, the splitting of the tyre into various segments of pattern recognition through image processing is focused on the clear image for selection to detect defects. It is done because the images occurred in the form of segments and recognition to detect the defects in continuity becomes simpler. Suitable modifications is made around the corners of the image appeared and is enhanced by the help of curvelet coefficients initially before the image detection algorithm is carried out. In addition, the result of the analysis is also drawn by incorporating the method of detection called, canny edge and is helpful for the detection of abnormal textures. Moreover, the operator of Sobel and many other detection methods like canny edge algorithms are effective for making comparative and precise discussion on the results of the analysis in brief. It is indicated by the results of analysis through X-ray detection method that outer surfaces of tyre and weak defects inside the tyres are the two different types of detection procedures located clearly by the help of proposed methods.

关键词： Tires Radiography Feature extraction Image edge detection Wires Image segmentation Electronics packaging

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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A Features Decoupling Method for Multiple Manipulations Identification in Image Operation Chains

A Features Decoupling Method for Multiple Manipulations Iden...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Jiaxin Chen Xin Liao Wei Wang Zheng Qin College of Computer Science and Electronic Engineering Hunan University China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

Recently, many forensic techniques have been developed to detect the use of a certain processing operation. When utilizing several manipulations to alter an image, artifacts left by manipulations that have been applied later can potentially disguise traces left by manipulations that were applied earlier. Therefore, the detection of manipulations become difficult. In this paper, we focus on identifying the manipulations in an image operation chain composed of multiple manipulations in a certain order. To address this issue, we analyze the relationship between manipulations identification and blind signal separation. Then, we propose a features decoupling method based on blind signal separation, which decouples the coupled features due to the superimposed processing artifacts and exploits the decoupled features to identify multiple operations. The experiments carried out on two image operation chains confirm the effectiveness of the proposed method.

关键词： Location awareness Forensics Conferences Detectors Signal processing Feature extraction Blind source separation

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Singing voice detection using multi-feature deep fusion with CNN 7th

Singing voice detection using multi-feature deep fusion with...

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7th Conference on Sound and Music Technology, CSMT 2019

作者： Zhang, Xulong Li, Shengchen Li, Zijin Chen, Shizhe Gao, Yongwei Li, Wei School of Computer Science Fudan University Shanghai201203 China Institute of information photonics and optical communications Beijing University of Posts and Telecommunications Beijing100876 China Department of Musicology China Conservatory of Music Beijing100101 China Department of Music Engineering Shanghai Conservatory of Music Shanghai200031 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai201203 China

ISBN: (纸本)9789811527555

The problem of singing voice detection is to segment a song into vocal and non-vocal parts. Commonly used methods usually train a model on a set of frame-based features and then predict the unknown frames by the model. However, the multi-dimensional features are usually concatenated together for each frame, with little consideration of spatial information. Hence, a deep fusion method of the Multi-feature dimensions with Convolution Neural Networks (CNN) is proposed. A one dimension convolution is made on feature dimensions for each frames, then the high-level features obtained can be used for a direct binary classification. The performance of the proposed method is on par with the state-of-art methods on public dataset. © Springer Nature Singapore Pte Ltd 2020.

关键词： Convolution

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WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

arXiv

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

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Tyre Defect Detection Based on GLCM and Gabor Filter

Tyre Defect Detection Based on GLCM and Gabor Filter

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IEEE International Conference on Multi Topic

ISBN: (数字)9781728140018

ISBN: (纸本)9781728140025

Defect detection is a crucial issue at industrial level, and generally, the designers have to find out a way to select suitable features to detect the defects in textured material images. This work reports this issue using the gray-level co-occurrence matrix (GLCM) and Gabor filter texture segmentation. We first analyze the defects in the corresponding tyre texture images using GLCM and then we apply Gabor filter texture segmentation to illustrate further the impurities found in the material. Our proposed method matches the abnormal textured images with standard texture images, and we use the 5 GLCM features to calculate the score for an abnormal texture image. Further, our proposed method decides the defected tyres and presents the texture segmentation of abnormal image. We found that our proposed method performs well when compares with other methods of tyre images data.

关键词： Gabor filters Feature extraction Tires Inspection Image segmentation Fabrics Filter banks

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Adversarial Example Defense via Perturbation Grading Strategy

arXiv

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arXiv 2022年

作者： Zhu, Shaowei Lyu, Wanli Li, Bin Yin, Zhaoxia Luo, Bin Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China School of Communication & Electronic Engineering East China Normal University Shanghai China

Deep Neural Networks have been widely used in many fields. However, studies have shown that DNNs are easily attacked by adversarial examples, which have tiny perturbations and greatly mislead the correct judgment of DNNs. Furthermore, even if malicious attackers cannot obtain all the underlying model parameters, they can use adversarial examples to attack various DNN-based task systems. Researchers have proposed various defense methods to protect DNNs, such as reducing the aggressiveness of adversarial examples by preprocessing or improving the robustness of the model by adding modules. However, some defense methods are only effective for small-scale examples or small perturbations but have limited defense effects for adversarial examples with large perturbations. This paper assigns different defense strategies to adversarial perturbations of different strengths by grading the perturbations on the input examples. Experimental results show that the proposed method effectively improves defense performance. In addition, the proposed method does not modify any task model, which can be used as a preprocessing module, which significantly reduces the deployment cost in practical applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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