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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

共 3060 条记录，以下是21-30 订阅

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Analysis of Different Object Detection Techniques Used in image processing

Analysis of Different Object Detection Techniques Used in Im...

引用

2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Gour, Sonam Dubey, Shirish Mohan Sharma, Gaurav Joshi, Neetu Poornima College of Engineering Department of Computer Engineering Jaipur India

ISBN: (纸本)9798350373783

Computer vision research uses self-driving systems, robot surveillance, and science interpretation. A plethora of applications, including robotics, self-driving systems, video surveillance, and scene interpretation, have spurred intensive research in computer vision throughout the past ten years. visual recognition systems have attracted a lot of research attention and include localization, detection, picture categorization, and detection. Given the significant developments in neural networks, especially deep learning, it performs remarkably well. One area where a lot of success with computer vision is needed is object recognition. The purpose of this work is to conduct a methodical investigation into the relevance of object detection and its applications in the field of computer vision. This work gives a thorough introduction to object detection, as well as various approaches, computer vision basics, and applications, which will be useful to the image processing and computer vision research communities. © 2024 IEEE.

关键词： machine vision

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Implementation of Fast Gradient Sign Adversarial Attack on vision Transformer Model and Development of Defense Mechanism in Classification of Dermoscopy images 31

Implementation of Fast Gradient Sign Adversarial Attack on V...

引用

31st IEEE conference on Signal processing and Communications applications (SIU)

作者： Kanca, Elif Ayas, Selen Kablan, Elif Baykal Ekinci, Murat Karadeniz Tech Univ Yazilim Muhendisligi Trabzon Turkiye Karadeniz Tech Univ Bilgisayar Muhendisligi Trabzon Turkiye

ISBN: (纸本)9798350343557

In recent years, deep learning has been successfully applied in medical images due to its ability to learn high complex and multidimensional data. However, it is known that deep learning models are vulnerable to adversarial machine learning attacks, which add small imperceptible perturbation to the legitimate input image, causing the models to produce incorrect results. In this study, Fast Gradient Sign Method is applied to vision Transformer, a basic pre-trained transformer-based model, which is used for binary classification of a publicly available skin lesion dataset and the robustness of the model is analyzed. Then, the adversarial training approach is used to improve the robustness of the model against adversarial attacks. The experimental results show that the classification accuracy is reduced from 90.1% to 27.38% even for a small perturbation, and the adversarial training approach increases the model's robustness with an accuracy value of 96.61%.

关键词： vision transformer model fast gradient sign method adversarial machine learning adversarial training

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Raspberry Pi-powered door lock with facial recognition

Raspberry Pi-powered door lock with facial recognition

引用

2024 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Jha, Anjali Bulbule, Rajesh Nagrale, Nilesh Belambe, Tanavi Dit E&tc Dept. Pune India

ISBN: (纸本)9798350348460

Facial recognition is a widely-used process that aims to detect and verify an individual's identity. This technique is employed in various applications, such as image and video analysis, surveillance, and security systems. OpenCV is a computer vision technique that enables users to comprehend the storage, modification and feature extraction of images and videos. The advancement of artificial intelligence is heavily reliant on computer vision techniques, particularly in self-driving cars, robotics, and image-editing software. The OpenCV Library is a valuable tool for processing and displaying camera images. This research utilizes the Haar Cascade Classifier, a machine learning-based algorithm, to process a user's face image and detect faces accurately. © 2024 IEEE.

关键词： computer vision Facial recognition Haar Cascade Classifier image processing machine learning OpenCV robotics security systems self-driving cars

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Design of Multi view Natural Language Query System on machine vision 4

Design of Multi View Natural Language Query System on Machin...

引用

4th IEEE International conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Fang, Quanrong Investment Co. Ltd Beijing China

ISBN: (纸本)9798350359589

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. This article first discusses the system architecture of four modules: data input and preprocessing, visual feature extraction, natural language understanding and matching, and result generation and feedback. Then, the application of machine vision technology in the system was analyzed using two calculation formulas: grayscale conversion and binarization, and natural language processing technology was briefly discussed. Subsequently, a context understanding module was added to construct a multi view natural language query system based on machine vision. Finally, two sets of simulation experiments were conducted to draw the following conclusion: compared with traditional methods, the overall average improvement in image recognition accuracy indicators is about 14.3%, while the overall average improvement in response speed indicators is about 26.5%. This research system can effectively process images from different perspectives and match them with natural language queries. © 2024 IEEE.

关键词： machine vision technology multi view processing natural language query system design

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Word to Sentence visual Semantic Similarity for Caption Generation: Lessons Learned 18

Word to Sentence Visual Semantic Similarity for Caption Gene...

引用

18th International conference on machine vision and applications (MVA)

作者： Sabir, Ahmed Univ Politecn Cataluna TALP Res Ctr Barcelona Spain

ISBN: (纸本)9784885523434

This paper focuses on enhancing the captions generated by image captioning systems. We propose an approach for improving caption generation systems by choosing the most closely related output to the image rather than the most likely output produced by the model. Our model revises the language generation output beam search from a visual context perspective. We employ a visual semantic measure in a word and sentence level manner to match the proper caption to the related information in the image. This approach can be applied to any caption system as a post-processing method.

关键词： image enhancement

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Expanding Hyperspherical Space for Few-Shot Class-Incremental Learning

Expanding Hyperspherical Space for Few-Shot Class-Incrementa...

引用

IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Deng, Yao Xiang, Xiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

ISBN: (纸本)9798350318920;9798350318937

In today's ever-changing world, the ability of machine learning models to continually learn new data without forgetting previous knowledge is of utmost importance. However, in the scenario of few-shot class-incremental learning (FSCIL), where models have limited access to new instances, this task becomes even more challenging. Current methods use prototypes as a replacement for classifiers, where the cosine similarity of instances to these prototypes is used for prediction. However, we have identified that the embedding space created by using the relu activation function is incomplete and crowded for future classes. To address this issue, we propose the Expanding Hyperspherical Space (EHS) method for FSCIL. In EHS, we utilize an odd-symmetric activation function to ensure the completeness and symmetry of embedding space. Additionally, we specify a region for base classes and reserve space for unseen future classes, which increases the distance between class distributions. Pseudo instances are also used to enable the model to anticipate possible upcoming samples. During inference, we provide rectification to the confidence to prevent bias towards base classes. We conducted experiments on benchmark datasets such as CIFAR100 and miniimageNet, which demonstrate that our proposed method achieves state-of-the-art performance.

关键词： Algorithms Algorithms and algorithms formulations image recognition and understanding machine learning architectures

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Design and Implementation of Adaptive Gaussian Denoising Algorithm Based on FPGA 7

Design and Implementation of Adaptive Gaussian Denoising Alg...

引用

7th International conference on Automation Electronics and Electrical Engineering

作者： Chen, Xinghao Beihang Univ Sch Elect Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350377040;9798350377033

With the continuous progress of image processing and machine vision technology, the demand for efficient and real-time processing is becoming more and more prominent, especially in the field of high-noise image processing. In this study, an adaptive Gaussian filtering algorithm is proposed, which is implemented based on FPGA and aims to improve the computational efficiency and real-time performance of the image processing system. Compared with the traditional fixed-weight filter, this algorithm is able to dynamically adjust the filtering parameters according to different noise environments, effectively balancing noise suppression and image detail retention. We coded the algorithm using Verilog hardware description language and verified it on PYNQ-Z2 FPGA platform. The experimental results show that the adaptive algorithm outperforms the fixed-weight filtering method in terms of performance, especially in terms of noise suppression and detail preservation. Meanwhile, the FPGA hardware implements the reduction of filtering delay and optimization of resource consumption, making it well suited for real-time applications. This study demonstrates the promise of FPGA adaptive filtering for applications in medical imaging, remote sensing, and intelligent surveillance, which have stringent requirements for high-performance and high-efficiency processing. This research provides new hardware solutions for real-time, high-quality image processing in constrained environments.

关键词： FPGA image denoising gaussian filtering adaptive algorithm

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Recovering image Information from Colored Speckle under RGB Illumination in an Imaging System 7

Recovering Image Information from Colored Speckle under RGB ...

引用

7th International conference on machine vision and applications (ICMVA)

作者： Liu, Shuang Hanson, Steen G. Takeda, Mitsuo Wang, Wei Xian Technol Univ Sch Optoelect Engn Int Ctr Optic Res & Educ iCORE Xian 710032 Shaanxi Peoples R China Tech Univ Denmark DTU Foton Dept Photon Engn DK-4000 Roskilde Denmark Utsunomiya Univ Ctr Opt Res & Educ CORE 7-1-2 Yoto Utsunomiya Tochigi 3218585 Japan Heriot Watt Univ Sch Engn & Phys Sci Inst Photon & Quantum Sci Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9798400716553

Speckle field is one of the most information-rich light fields related to plentiful physical characteristics at present that can be used to provide high-resolution surface topography information or applied to image reconstruction, image enhancement and other fields. However, the most study of speckle image recovery for monochromatic wavelength ignores a large amount of real object information. In this paper, the amplitude information of colored speckle recovery in optical imaging is studied, as might be seen if red, green, and blue lasers illuminate a rough surface with different reflectivity at these three wavelengths. We derived the expression for color speckle distribution and designed an imaging system with a pupil stop, normal plane-wave incidence on the diffuser, and a camera to observe the colored speckled image. In order to analyze the simulation experiment, two aspects are studied: phase shift and average speckle size. The results show that more characteristic information is recovered from the colored speckle image.

关键词： Colored speckle image recovery image processing

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CONTROLLABLE UNIVERSAL EDGE-PRESERviNG image FILTERING 7

CONTROLLABLE UNIVERSAL EDGE-PRESERVING IMAGE FILTERING

引用

7th IEEE International conference on Multimedia Information processing and Retrieval (MIPR)

作者： Liang, Shijun Fu, Dongdong Michigan State Univ Dept Biomed Engn E Lansing MI 48824 USA Dolby Labs Inc Sunnyvale CA USA

ISBN: (纸本)9798350351439;9798350351422

In this study, we investigate the Deep image Prior (DIP) in enhancing image smoothing, a crucial component in numerous computer vision and graphics applications. Although deep learning has demonstrated remarkable achievements in these domains, it often falls short in flexibility and controllability, in contrast to traditional methods, which are more adaptable and typically exhibit subpar performance. Notably, some end-to-end deep learning models offer control over edge preservation, yet their performance remains marginally suboptimal. To address this shortcoming, we introduce an innovative network architecture that diverges from the traditional U-Net model, featuring a Laplacian pyramid as the encoder and a deep decoder as the decoding component, integrated with a bilateral filter loss to improve DIP. This design aids the network in rapidly assimilating essential low-frequency information. Our approach excels in retaining texture details, significantly improving image smoothing and related tasks beyond the capabilities of standard DIP methods. Moreover, our technique outperforms the leading unsupervised method, pyramid texture filtering, in texture filtering tasks and other applications.

关键词： image smoothing machine learning deep learning Deep image prior

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Model Pruning for Infrared-visible image Fusion 2

Model Pruning for Infrared-Visible Image Fusion

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2nd International conference on machine vision, image processing and Imaging Technology, MviPIT 2024

作者： Chen, Qi Feng, Rui State Grid Beijing Electric Power Company Beijing China

ISBN: (纸本)9798331543037

Infrared-visible image fusion combines complementary information from both modalities, enhancing scene perception in applications such as surveillance and autonomous driving. However, existing deep learning-based methods are often computationally expensive. Our approach involves training an over-parameterized fusion network, applying structured pruning to reduce model complexity, and fine-tuning the pruned model to maintain performance. The pruning process leverages the L2-norm of the Restormer blocks, ensuring that less critical components are removed while preserving essential fusion quality. Experiments on the benchmark datasets demonstrate that our approach achieves high fusion quality with significantly reduced computational costs. Ablation studies further validate the effectiveness of our pruning strategy. ©2024 IEEE.

关键词： image fusion

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