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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4715 条记录，以下是21-30 订阅

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HYBRID SINGLE INPUT AND MULTIPLE OUTPUT METHOD FOR COMPRESSING FEATURES TOWARDS MACHINE VISION TASKS 31

HYBRID SINGLE INPUT AND MULTIPLE OUTPUT METHOD FOR COMPRESSI...

引用

2024 International conference on image processing

作者： Zhang, Zifu Li, Shengxi Liu, Tie Xu, Mai Xu, Tao Guan, Zhenyu Lv, Zhuoyi Beihang Univ Beijing Peoples R China Vivo Mobile Commun Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

With the advance of deep learning in the BigData era, image/video coding for machines (VCM) as called for proposals by the moving picture experts group (MPEG) now becomes the pivotal technique for extensive intelligent vision tasks. However, existing VCM methods typically focus on compressing features independently at each scale, ignoring the redundancy of features across multiple scales. This paper thus introduces a simple yet effective architecture called hybrid single input and multiple output (H-SIMO) for VCM, which can significantly reduce the redundancy across scales of features. More specifically, as the pyramid structure is commonly employed for localising multi-scale objects, our HSIMO method proposes to compress all features by inputting a single-scale feature while retaining the ability to decompress all the features. Moreover, an entropy model is seamlessly integrated into the training process to efficiently reduce the statistical redundancy of features. During the testing phase, the hybrid coding method, in conjunction with the versatile video coding (VVC), is employed to compress the features from both images and videos. We comprehensively evaluate the performance of our H-SIMO method in two standard machine vision tasks: object detection and instance segmentation, in which the experimental results verify the superior performances of our H-SIMO method.

关键词： Video coding for machines feature compression image compression

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Exploring stochastic Autoregressive image Modeling for Visual Representation 37

Exploring Stochastic Autoregressive Image Modeling for Visua...

引用

37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Qi, Yu Yang, Fan Zhu, Yousong Liu, Yufei Wu, Liwei Zhao, Rui Li, Wei Tsinghua Univ Beijing Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China

ISBN: (纸本)9781577358800

Autoregressive language modeling (ALM) has been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved comparable results with other self-supervised approaches in computer vision (e.g., contrastive learning, masked image modeling). In this paper, we try to find the reason why auto-regressive modeling does not work well on vision tasks. To tackle this problem, we fully analyze the limitation of visual autoregressive methods and proposed a novel stochastic auto-regressive image modeling (named SAIM) by the two simple designs. First, we serialize the image into patches. Second, we employ the stochastic permutation strategy to generate an effective and robust image context which is critical for vision tasks. To realize this task, we create a parallel encoder-decoder training process in which the encoder serves a similar role to the standard vision transformer focusing on learning the whole contextual information, and meanwhile the decoder predicts the content of the current position so that the encoder and decoder can reinforce each other. Our method significantly improves the performance of autoregressive image modeling and achieves the best accuracy (83.9%) on the vanilla ViT-Base model among methods using only imageNet-1K data. Transfer performance in downstream tasks also shows that our model achieves competitive performance. Code is available at https://***/qiy20/SAIM.

关键词： stochastic systems

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A Robust Large-scale Dataset for Assessing Light Field image Quality

A Robust Large-scale Dataset for Assessing Light Field Image...

引用

9th International conference on Signal and image processing (ICSIP)

作者： Ma, Jian Liu, Zhuochi Xu, Yaozong Yu, Jiajun Wang, Yang Liu, Yuhao Anhui Univ Sch Internet Hefei Peoples R China

ISBN: (纸本)9798350350920

In recent years, light field imaging has gained significant attention in the scientific community due to its ability to provide a more immersive representation of the 3D world. However, ensuring the quality of light field images is crucial for their subsequent processing and applications. Deep learning methods, leveraging neural networks, have shown promising performance in image Quality Assessment (IQA). However, the unique characteristics of light field data pose a challenge for existing IQA methods. To address this challenge, we propose a Robust Large-scale Dataset for Assessing Light Field image Quality, named RLSD, specifically designed for evaluating the quality of light field images. The dataset comprises both real and synthetic scenes, covering a wide range of key low attributes and including three representative distortions: compression, noise, and blur. To obtain subjective evaluations, we adopt the single stimulus continuous quality evaluation (SSCQE) method and compute the Mean Opinion Score (MOS). We performed statistical analysis on the dataset and experimental results indicate that our proposed RLSD dataset includes various common scenes and distortion levels, making it suitable for designing and evaluating LF-IQA algorithms. The dataset is publicly available at the following link: "https://***/s/1kJmx4qsy8ywLPba-HwGCEg" (password: XY28).

关键词： Light field image quality assessment RLSD distortion Single stimulus continuous quality evaluation

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Graph anomaly detection using graph data enhancement under stochastic configuration networks

Graph anomaly detection using graph data enhancement under s...

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2024 International conference on Advanced image processing Technology, AIPT 2024

作者： Tan, Qingyou Wang, Guiping Institute of Information Science and Engineering Chongqing Jiaotong University Chongqing400074 China

ISBN: (纸本)9781510682542

This study aims to improve the performance of graph data anomaly detection. To address the limitations of traditional methods in terms of accuracy and robustness of anomaly detection, this paper proposes a new method based on random configuration networks and graph data enhancement techniques. The method utilizes random configuration networks for feature learning on graph data, combined with graph data augmentation methods to enhance the characterization of the data to improve the performance of anomaly detection. This paper conducted experimental validation on several publicly available datasets, and the results show that our method outperforms traditional methods in both anomaly detection performance. The contribution of this research is to propose a novel and effective approach while bringing randomized configuration networks into graph data anomaly detection is vision. © 2024 SPIE.

关键词： Graph embeddings

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A method for stochastic human action prediction based on denoising diffusion probability model

A method for stochastic human action prediction based on den...

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2024 International conference on Computer Vision and image processing, CVIP 2024

作者： Wang, Yulin Mou, Gang Zhao, Yuewen Song, Tao Chongqing Vocational College of Public Transportation Chongqing402260 China Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9781510688384

This paper proposes a stochastic human action prediction method based on denoising diffusion probability model to address the shortcomings of current motion prediction methods, such as insufficient diversity and deviation of prediction results from reasonable motion intervals. Firstly, a spatio-temporal Transformer denoising diffusion prediction network is constructed to effectively capture the local relationships between 3D joints in each frame, improving the consistency between predicted actions and historical action sequences. Secondly, the graph convolutional network (GCN) and GCN loss are introduced in the discrete cosine transform space to design a prediction action sequence refinement module, which refines the prediction results, improves the accuracy of the prediction results, and further solves problems such as prediction action lag and discontinuity. Finally, the method proposed in this paper was evaluated on the benchmark dataset, and the evaluation results showed that the proposed method was significantly superior to existing prediction methods in terms of accuracy and fidelity. © 2025 SPIE.

关键词： Prediction models

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image Encryption Using A Novel Orbital-Extraction Permutation Technique and Chaotic Key Generation 7

Image Encryption Using A Novel Orbital-Extraction Permutatio...

引用

IEEE 7th International conference on Advanced Technologies, Signal and image processing (ATSIP)

作者： Khan, Sikandar Ullah, Safee Ahmad, Jawad Ullah, Amjad Arshad, Arshad Khan, Muhammad Shahbaz HITEC Univ Dept Electrican Engn Taxila Pakistan Edinburgh Napier Univ Sch Comp Engn & Built Environm Edinburgh Midlothian Scotland Glasgow Caledonian Univ Sch Comp Engn & Built Environm Glasgow Lanark Scotland

ISBN: (纸本)9798350351491;9798350351484

This paper presents an image encryption scheme that introduces a novel permutation technique named as orbital-extraction permutation. The proposed encryption scheme contains three important modules, i.e., the key generation module, the orbital-extraction permutation module, and dynamic chaotic substitution module. The key generation module utilizes the 2D Henon map, a highly non-linear and unpredictable chaotic map, to generate cryptographic keys. The orbital-extraction permutation module reshuffles the pixels of the plain text image in a complex manner disrupting the inherent correlation within the neighboring pixels of the input image. The proposed permutation technique serves as a strong diffusion stage. Furthermore, for the confusion part of the encryption scheme, bit-XOR operations and chaotic substitution methods have been employed. The proposed scheme has been evaluated for key statistical security parameters. Results indicate the enhanced security and robustness of the proposed scheme with an information entropy of 7.9974 and a correlation coefficient of 0.007.

关键词： Cryptography

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DEEP SUB-image SAMPLING BASED DEFENSE AGAINST SPATIAL-DOMAIN ADVERSARIAL STEGANOGRAPHY 49

DEEP SUB-IMAGE SAMPLING BASED DEFENSE AGAINST SPATIAL-DOMAIN...

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49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Huang, Xinyu Cao, Yuwen Ohtsuki, Tomoaki Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China Keio Univ Dept Informat & Comp Sci Tokyo Japan

ISBN: (纸本)9798350374520;9798350374513

Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vulnerable and has the risk of being attacked via adversarial steganography when performing the image classification tasks;ii) Pre-processing based methods aiming to remove adversarial perturbations from cover images jeopardize the accuracy performance, as the involved steganographic signal will be wiped off as well. In this context, to defend against adversarial attacks by an adversary, we propose an adversarial steganography detection scheme based on the pre-processing and feature migration. In brief, sub-images are sampled to obtain the dimensionality of the extracted features, which are usually used to expand them while reducing the effect brought by adversarial perturbations. In particular, by computing statistical features together with normalizing the features, our approach can improve the classification accuracy of the samples. Our experimental results show that the proposed approach is capable of detecting adversarial steganographic image with an accuracy gain of up to 35.9% over the state-of-the-art methods.

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Improvement of image-based Lane Marker Detection Algorithm using Sensor Fusion

Improvement of Image-based Lane Marker Detection Algorithm u...

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IEEE Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Abarghooei, Arash Ahmadi, Mojtaba Carleton Univ Mech & Aerosp Engn Ottawa ON Canada

ISBN: (纸本)9798350371635;9798350371628

Assistive driving systems like Lane Keeping Assist predominantly utilize image processing for lane marker detection to localize the vehicle, however, image data faces challenges, such as sensitivity to weather and road conditions and a low update rate due to computational demands. To address these issues, this study proposes a novel approach for local positioning by fusing image data with dashboard speed, IMU (accelerometer and gyroscope sensors) measurements, and GPS data. An Extended Kalman Filter as a stochastic estimator is used to estimate road-vehicle states and enhance lane marker detection accuracy under various conditions. The algorithm's performance was tested across diverse driving scenarios, including different speeds, road curvatures, and poor visibility conditions simulating Canada's winter weather. Results indicate that the proposed sensor fusion algorithm significantly reduces RMS and maximum error in estimating lane lateral offset, relative heading angle, and velocity, especially where image-based methods falter due to noise or temporary loss of functionality.

关键词： lane marker detection sensor fusion extended Kalman filter inertia navigation system

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Study of UAV image Denoising Based on Adaptive Two-Dimension Unsaturated System 23

Study of UAV Image Denoising Based on Adaptive Two-Dimension...

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9th International conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Li, Yong Sun, Ximin Jia, Jiangkai Wang, Shuai Li, Huichao Sun, Bo Zheng, Bin State Grid E-commerce Technology Co. Ltd. Tianjin300387 China

ISBN: (纸本)9781450399029

The unmanned aerial vehicle equipment is inevitably interfered by environmental noise in the process of image acquisition. Suppress noise to enhance images is a hot topic that scholars strive to study. The stochastic resonance theory can transform the noise signal energy of specific intensity into useful signal energy. Therefore, this paper proposes an image denoising algorithm based on adaptive two-dimensional unsaturated stochastic resonance system. By building a dynamic nonlinear system model, the peak signal to noise ratio and structural similarity of the output image are used as the dual evaluation model of the adaptive system, and the optimal parameters of the model are automatically obtained by adjusting the parameters of the dynamic nonlinear system. Compared with median filtering, mean filtering and two-dimensional traditional stochastic resonance methods, the image restoration effect of adaptive two-dimensional unsaturated stochastic resonance method is closer to the original image, and the histogram and peak signal to noise ratio of the output image are also significantly better than the other two methods. The research results show that in image processing, the proposed adaptive two-dimensional unsaturated stochastic resonance system has better denoising effect and better robustness to the change of noise intensity. © 2023 Copyright held by the owner/author(s).

关键词： image denoising

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Adaptive parametric multi-source remote sensing image fusion based on improved HSI 4

Adaptive parametric multi-source remote sensing image fusion...

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4th International conference on Optics and image processing, ICOIP 2024

作者： Yu, Pengyang Ma, Chao Zhang, Qiuyu Ma, Jianwei College of information engineering Henan University of Science and Technology Luoyang471000 China

ISBN: (纸本)9781510682481

A single image can not fully describe the information of the target, and the practical application value is low. Fusion of multi-source data to generate images with richer information and higher quality has become a technical research frontier direction in the field of image intelligent processing in recent years. Aiming at the shortcomings of the current photoelectric image and 3D scene image fusion methods, such as poor fusion quality, the existence of artifacts, and the need to manually adjust the parameters for different scenes, in order to obtain more ideal fusion effects and improve the adaptability of the algorithms to the images of different scenes, an adaptive parameter fusion method based on the improvement of HSI is proposed. Firstly, the photoelectric images and 3D scene images are acquired and denoised and aligned;then the statistical characteristics and histogram distribution of the images are considered comprehensively, and adaptive parameterization is used to determine the ClipLimit parameter of CLAHE for image enhancement, and finally the fusion is performed by the improved HSI color model, and the fusion results are tested, and the method improves the quality of the fusion of multi-source aerial images and the fusion effect is significantly better than that of the other fusion methods. © 2024 SPIE.

关键词： image fusion

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