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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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A Quantitative Comparison of image Classification Models under Adversarial Attacks and Defenses 8

A Quantitative Comparison of Image Classification Models und...

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8th international conference on signal Processing and Integrated Networks, SPIN 2021

作者： Khullar, Kartikeya Kathuria, Sarthak Chahar, Nishant Gupta, Prince Kaur, Preeti Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9781665435642

In this paper, we present a comparison of the performance of two state-of-the-art model architectures under Adversarial attacks. These are attacks that are designed to trick trained machine learning models. The models compared in this paper perform commendable on the popular image classification dataset CIFAR-10. To generate these adversarial examples for the attack, we are using two strategies, the first one being a very popular attack based on the L metric. And the other one is a relatively new technique that covers fundamentally different types of adversarial examples generated using the Wasserstein distance. We will also be applying two adversarial defenses, preprocessing the input and adversarial training. The comparative results show that even these new state-of-the-art techniques are susceptible to adversarial attacks. Also, we concluded that more studies on adversarial defences are required and current defence techniques must be adopted in real-world applications. © 2021 ieee

关键词： image classification

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2021 ieee 6th international conference on Intelligent Computing and signal Processing, ICSP 2021

2021 IEEE 6th International Conference on Intelligent Comput...

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6th ieee international conference on Intelligent Computing and signal Processing, ICSP 2021

ISBN: (纸本)9780738143705

The proceedings contain 310 papers. The topics discussed include: single color image dehazing based on variational model;application research of submersible diaphragm pump based on nonmetal coiled tubing;a study on safety issues of human-machine transitions in autonomous vehicles;intelligent diagnosis method for intermittent fault of digital circuit based on dynamic power current;selection of communication frequency points for shortwave radio network based on VOACAP;research and practice of industrial sorting technology based on machine vision;a research on age of information minimization scheme of wireless sensor network assisted by UAV;improved affine projection algorithms with momentum term;NORA based group paging overload control method for massive MTC accesses under 5G networks;a data model and algebraic operation for multidimensional analysis of oil and gas exploration data;and research on image steganography information detection based on support vector machine.

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Fluorescence Feature image: Fluorescence Enhancement Method based on Illumination Adaptation

Fluorescence Feature Image: Fluorescence Enhancement Method ...

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international conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

作者： Lemin Shi Xin Feng Dianxin Song Ping Gong Ming Yue Yuan Si School of Computer Science and Technology Changchun University of Science and Technology Changchun China School of Life Science and Technology Changchun University of Science and Technology Changchun China College of Physics Changchun University of Science and Technology Changchun China

ISBN: (数字)9798350362107

ISBN: (纸本)9798350362114

In recent years, with the continuous development of computer technology in the field of molecular diagnosis, deep learning has shown great potential in enhancing fluorescence features in fluorescence in situ hybridization (FISH) images, allowing researchers to observe fluorescence distribution and abnormalities more clearly. Condition. of genetic material. Health status. Improves observation clarity and overall image quality of genetic material. However, due to problems such as uneven dyeing and lighting differences, the model training and generalization capabilities of deep learning are limited, thus affecting the feature recognition and accuracy of the algorithm. This method is approached from the perspective of image enhancement and feature recognition. By dynamically adjusting the lighting factors of the image to match the actual lighting distribution, this adjustment helps reduce the impact of lighting changes and makes the brightness of the image more uniform. At the same time, this method uses heat maps to quickly locate high-intensity signal areas in the image, that is, the parts with the most obvious fluorescence signals. By calculating 16 environments around these areas. Combined with the linear segmentation strategy and dynamically adjusting processing parameters, it not only highlights and enhances the clarity of image details, but also obtains accurate enhanced images. To avoid excessive enhancement of fluorescence characteristics, the fluorescence characteristics are compensated through the Cycle-Gan network to obtain the final compensation result. Finally, the effect of this method in image enhancement was verified through experimental analysis.

关键词： Deep learning Training image segmentation Accuracy Lighting Fluorescence Genetics Nanoscale devices Manufacturing image enhancement

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Cognitive internet of things (CIoT) a success for data collection 6

Cognitive Internet of things (CIoT) a success for data colle...

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6th international conference on image Information Processing, ICIIP 2021

作者： Fayaz, Fayaz Ahmad Malik, Arun Yatoo, Arshad Ahmad National Institute of Electronics Information Technology India Lovely Professional University Jalandhar India

ISBN: (纸本)9781665433617

In conjunction with data generated by intelligent machines, cognitive IoT uses cognitive computing technology and the actions these devices can accomplish. The Cognitive internet of Things (CIoT) is seen as the new IoT is combined with mental and mutual frameworks to facilitate success and intelligence. This leading research area has recently emerged as intelligent sensing. Researchers examine the sensing data performance problems with Smarter technologies, in which people usually use smart gadgets, contribute training datasets towards the Cognitive internet of things collected by sensors. Moreover, Cognitive Intent of Things (CIOT), shortcomings in the scope of sensing data, contribute to the loss of human life and civil instability. To answer this problem, we propose a new metric in this article, called the Quality of Information Coverage (QIC), which will personify information distribution and data sensing incentives to leverage the QIC. In addition, a market-based compensation system is being developed to pledge the QIC. To produce optimum kickbacks for CIoT and news outlets, we evaluate the optimal business solution and examine an acceptable representation. Then, by detailed computations, the results of a competition reward system are studied. The findings suggest that the way the method of reward management hits the balance point with a greater QIC than most current systems. The QIC told a system in this work guarantees that, relative to existing algorithms, the sample variance number obtained datasets for specific regions decreases by approximately less than 40 to 55 percent since these data sets are calibrated. Compared to these non-QIC-Aware algorithms, the average sale price is Sensing proposed should be less than 17 to 18 percent. © 2021 ieee.

关键词： internet of things

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Graph Refinement in Latent Space: A Hypergraph Convolution for Underwater Object Detection

Graph Refinement in Latent Space: A Hypergraph Convolution f...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Meghna Kapoor Badri Narayan Subudhi Ankur Bansal Indian Institute of Technology Jammu Jammu and Kashmir India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Underwater object detection presents significant challenges due to the intrinsic properties of light in aquatic environments. State-of-the-art methods often fail to capture the subtle details necessary for accurate detection in these scenarios. Recent advancements have shown promising results by reformulating relationships in graph space; however, most SOTA methods typically employ graph structures that are insufficient to represent the complex latent variables inherent in underwater environments. Hence, these models are unable to preserve the actual boundaries of object detection. To address these limitations, in this paper, we propose a novel end-to-end architecture that uses a graph refactoring aware deep learning based encoder-decoder architecture. The proposed approach uses a convolutional backbone to project the image into latent space, where an unsupervised initial graph is constructed. The hypergraph convolution is then utilized to optimize message passing between graph nodes, enhancing the representation of complex relationships of latent space. This helps in the retention of intricate details by modelling two or more latent variables as hyperedge by sharing the information among themselves. Finally, an image generation module maps the enhanced graph representation back to image space. The effectiveness of the proposed method is demonstrated through a comparative analysis against fourteen state-of-the-art methods on the benchmark underwater databases. The code for the proposed scheme can be found at https://***/immkapoor/hyper_graph.

关键词： Convolutional codes Convolution image synthesis Message passing Surveillance Object detection Vectors Robustness Speech processing Kernel

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DRSFANet: Dual-Path CNN with Residual and Frequency Attention for image Denoising

DRSFANet: Dual-Path CNN with Residual and Frequency Attentio...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Manish Kumar Suman Kumar Maji Anirban Saha CSIR Fourth Paradigm Institute Bangalore Karnataka India C.S.E. Department IIT Patna Bihta Patna Bihar India C.S.E. Department Indian Institute of Technology Patna Bihta Patna Bihar India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image noise, often resulting from disturbances during image acquisition or imperfections in the imaging device, notably degrades the quality of digital images. The challenge of removing this noise has been addressed through various techniques, from traditional filtering and prior-based methods to more recent deep learning approaches. In this paper, we introduce DRSFANet, an advanced dual-path convolutional neural network (CNN) specifically designed to tackle both synthetic Additive White Gaussian Noise (AWGN) and real-world noise. DRSFANet incorporates several innovative components: a residual feature extraction module (FEB) equipped with dilated convolutional layers to enhance the receptive field and mitigate gradient vanishing issues, and novel attention modules—Frequency-Plane Attention Block (FPAB) and Residual Attention Block (RAB)—which improve feature extraction in both frequency and spatial domains. Furthermore, the model features a downsampling (DS) block that effectively consolidates essential features prior to their integration into subsequent network stages. Comprehensive experimental evaluations reveal that DRSFANet outperforms several state-of-the-art denoising methods, demonstrating superior performance in both synthetic and real datasets through rigorous quantitative and qualitative analysis.

关键词： Training AWGN Frequency-domain analysis Noise Noise reduction Feature extraction Convolutional neural networks image denoising Testing Synthetic data

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Recalling Direct 2D-3D Matches for Large-Scale Visual Localization

Recalling Direct 2D-3D Matches for Large-Scale Visual Locali...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Song, Zhuo Wang, Chuting Liu, Yuqian Shen, Shuhan Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CASIA SenseTime Res Grp Beijing Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China SenseTime Res Hangzhou Peoples R China

ISBN: (纸本)9781665417143

Estimating the 6-DoF camera pose of an image with respect to a 3D scene model, known as visual localization, is a fundamental problem in many computer vision and robotics tasks. Among various visual localization methods, the direct 2D-3D matching method has become the preferred method for many practical applications due to its computational efficiency. When using direct 2D-3D matching methods in large-scale scenes, a vocabulary tree can be used to accelerate the matching process, which will also induce the quantization artifacts leading to reduce the inlier ratio and decrease the localization accuracy. To this end, in this paper two simple and effective mechanisms, called visibility-based recalling and space-based recalling, are proposed to recover lost matches caused by the quantization artifacts, thus can largely improve the localization accuracy and success rate without increasing too much computational time. Experimental results on long-term visual localization benchmarks demonstrate the effectiveness of our method compared with state-of-the-arts.

关键词： Location awareness Visualization Vocabulary Solid modeling Quantization (signal) Three-dimensional displays Pipelines

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An Efficient Hybrid Parallel Compression Approximate Multiplier 39

An Efficient Hybrid Parallel Compression Approximate Multipl...

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39th ieee international conference on Computer Design ICCD)

作者： Yao, Shangshang Zhang, Liang Wang, Qiong Shen, Li Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781665432191

Approximate computing has been widely used in many fault-tolerant applications. Multiplication as a key kernel in such applications, it is significant to improve the efficiency of approximate multiplier to achieve high computational performance. This paper proposes a novel approximate multiplier design based on using different compressors for different regions of partial products. We designed two Preprocessing Units (PUs) to explore the best efficiency via increasing the number of sparse partial products. Multiple 8-bit multipliers are designed using Verilog and synthesized under the 45-nm CMOS technology. Compared with the conventional Wallace Tree multiplier, experimental results indicate that one of our proposed multipliers reduce Power-Delay Product (PDP) by 58.5% at most with 0.42% normalized mean error distance. Moreover, a case study of image processing applications is also investigated. Our proposed multipliers can achieve a high peak signal-to-noise ratio of 51.87dB. Compared to the state-of-the-art, the proposed multiplier has a better comprehensive performance in accuracy, area and power consumption.

关键词： approximate computing approximate multiplier parallel compression image processing

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Multi-Objective Representation based Dynamic Prototype Learning for Unsupervised DCE-MRI Breast Tumor Segmentation

Multi-Objective Representation based Dynamic Prototype Learn...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Li Wang Lihui Wang Lei Tang Zixiang Kuai Jian Zhang Hongjiang Wei College of Computer Science and Technology Guizhou University Guiyang China Radiology Department Guizhou Provincial People’s Hospital Guiyang China Imaging Center Harbin Medical University Cancer Hospital Harbin China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unsupervised segmentation is a potential means to detect the breast tumors from DCE-MRI without using any annotated images, which can provide a coarse prior for several downstream tasks. However, the existing unsupervised segmentation methods are prone to collapse due to the presence of large background regions in breast DCE-MRI and the absence of effective constraints. To address these issues, we proposed a dynamic prototype learning network (DyPNet) based on nearest moving average (NMA) strategy and multi-cluster reconstruction (MCR) constraints for segmenting breast tumors from DCE-MRI unsupervisedly. Specifically, MCR is used to constraint the feature embeddings, ensuring the completeness and specificity of features for each cluster, and NMA is used to adaptively update the cluster prototypes according to the intensity of top-L intra-cluster samples. Using the Cosine similarity between the prototypes and the pixel embedding features, the image pixels can be clustered. By comparing the proposed method with several unsupervised segmentation models on different datasets, we demonstrated that the averaged DSC, HD95, and VM of the proposed method can be improved by 73.7%, 20.7% and 111.3% respectively. In addition, through the comparisons on downstream tasks, including one-shot, generalized and zero-shot segmentations, we further verified the effectiveness and superiority of the proposed method.

关键词： Training image segmentation Breast tumors Costs Clustering methods Prototypes signal processing Gray-scale Speech processing image reconstruction

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Boosting Face Recognition Performance with Synthetic Data and Limited Real Data

Boosting Face Recognition Performance with Synthetic Data an...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Wenqing Wang Lingqing Zhang Chi-Man Pun Jiu-Cheng Xie Macao SAR University of Macau Nanjing University of Posts and Telecommunications China

Face recognition is one of the most precise and straightforward methods to establish individual identity, and is important in our daily life. To solve the issues of privacy, bias, and collection difficulty caused by face recognition relying heavily on collecting a huge number of real face images from the internet, a seemingly promising idea is to employ GAN-generated synthetic faces as the training data. However, there are obvious surface gaps and domain gaps between real and synthetic face images, and cannot be replaced directly. In this paper, we attempt to boost face recognition simultaneously using synthetic data and limited real data. Specifically, we first design an augmented space for auto augmentation methods to augment synthetic images to alleviate the surface gap, then propose to disentangle the underlying style distributions through dual batch normalization layers so that both synthetic and real images can be learned jointly by convolution layers without mixing across domains. Extensive experiments demonstrate our method can achieve better results than training with large quantities of real data.

关键词： Training image recognition Face recognition Pipelines Training data Speech recognition Data models

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