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

共 3257 条记录，以下是211-220 订阅

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ChangeSAM: Adapting the Segment Anything Model to Street Scene image Change Detection 27

ChangeSAM: Adapting the Segment Anything Model to Street Sce...

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27th IEEE International conference on Intelligent Transportation Systems, ITSC 2024

作者： Bauer, Adrian Krabbe, Jan-Christoph Kummert, Anton University of Wuppertal Germany

ISBN: (纸本)9798331505929

In the dynamic field of machine learning, foundation models have recently gained prominence, particularly for their application in natural language processing and computer vision. The foundational Segment Anything Model (SAM), known for its interactive image segmentation via prompts, serves as the basis for this study. We introduce ChangeSAM, a tailored adaptation of SAM for street scene image change detection (CD). ChangeSAM utilizes the versatility and vast knowledge of SAM, adapting it to effectively identify semantic changes in image pairs. Two architectural adaptations are introduced - Pre Decoder Fusion (PreDF) and Post Decoder Fusion (PostDF) - enabling ChangeSAM to process dual images. Enhancements through Prompt Tuning and Low-Rank Adaptation (LoRA) are integrated, achieving a balance between reusability, computational efficiency, and accuracy. Our evaluation on the VL-CMU-CD dataset shows that with minimal parameter adjustments, ChangeSAM achieves accuracy on par with fully fine-tuned models. This work contributes to the ongoing development of foundation models in practical applications, illustrating the viability and potential of adaptable, efficient models in scenarios with limited computational resources, such as intelligent vehicles. © 2024 IEEE.

关键词： Semantics

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Research on the Application of Generative Adversarial Networks in Aerial image Generation

Research on the Application of Generative Adversarial Networ...

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International conference on image processing, Computer vision and machine Learning (ICICML)

作者： Cai, H. X. Zhu, X. Y. Wen, P. C. Bai, L. T. Li, R. Q. Han, W. AVIC Xian Aeronaut Comp Tech Res Inst Xian Peoples R China

ISBN: (纸本)9781665464680

Computer vision is one of the important areas and directions of deep learning research, which requires different approaches to be chosen for different fields due to the complexity and diversity of vision tasks. In the field of aviation, the existing image resources are still far from the real needs due to the influence and constraints of realistic scenes and difficulties of image acquisition. More detailed and comprehensive images can better provide reliable technical support and basis for applications, and then make more accurate decisions on problems, which requires generating more effective images to expand the data. Generative Adversarial Networks (GAN) are the fastest growing and most effective generation method in recent years, so this experiment investigates the application of GAN on aviation data, taking images of airplanes, cars and ships as examples to conduct quantitative research. The effect on the effect of GAN is studied from the perspective of image size, number of images, number of iterations, and different categories of images, in order to obtain better parameter settings for generating effective images, which provides a theoretical and experimental basis for the subsequent application of GAN in the aviation field to generate more images with similar characteristics and solve the problem of insufficient data.

关键词： Generative Adversarial Networks image generation aerial applications quantitative research

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Automated Detection of Diabetic Retinopathy Segmented images using ResNet50 and VGG16 Deep Learning Algorithms 2

Automated Detection of Diabetic Retinopathy Segmented Images...

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2nd International conference on Inventive Computing and Informatics (ICICI)

作者： Betha, Sashi Kanth Seventline, J. B. GITAM Deemed Be Univ Visakhapatnam Andhra Pradesh India Vignans Inst Engn Women Dept ECE Visakhapatnam Andhra Pradesh India GITAM Deemed Be Univ Dept EECE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9798350373301;9798350373295

Diabetic retinopathy (DR), a severe complication arising from diabetes, make a significant threat to vision due to the deterioration of retinal blood vessels. This research work proposes a comprehensive methodology for the automated detection, grading, and segmentation of DR, leveraging advanced image processing, deep learning techniques and machine learning. The study utilizes the Indian Diabetic Retinopathy image dataset (IDRID), comprising 81 fundus images and labels, to rigorously evaluates the proposed methodology. Key steps include detailed image preprocessing, VGG16-based feature extraction, Random Forest classifier-based grading, and innovative segmentation techniques for lesion localization. The evaluation demonstrates exceptional performance, with both VGG16 and ResNet50 architectures achieving over 99% accuracy. The process of semantic segmentation enhances interpretability, supporting clinical decision-making in retinopathy diagnosis. While the results are promising, future validation on diverse datasets and careful consideration of ethical implications are essential for responsible deployment in clinical settings. The proposed methodology signifies a significant step toward precise diagnostics and improved patient outcomes in diabetic retinopathy and holds potential for broader applications in retinal disease diagnosis.

关键词： Diabetic retinopathy VGG16 Feature extraction image Preprocessing Segmentation

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Impact of Hybrid [CPU-GPU] Architecture on machine Learning-based image-to-image Translation Using HiDT

Impact of Hybrid [CPU-GPU] Architecture on Machine Learning-...

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2024 International conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Kantharaju, V. Chandrashekhar, B.N. Niranjanamurthy, M. Murthy, S.V.N. Bms Institute of Technology and Management Department of Ai & Ml Bengaluru India Amity University Amity School of Engineering and Technology Department of Cse Bangalore India S J C Institute of Technology Department of Cse Karnataka Chikkaballapur India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359688

image-to-image translation is the process of transforming an image from one domain to another, where the goal is to learn the mapping between an input image and an output image. This task has been generally performed by using a training set of aligned image pairs on fewer cores-based CPU-based architecture, which mainly aims to transfer images from a source domain to a target domain while preserving the content representations by consuming more execution time. Due to its broad range of applications in numerous computer vision and image processing problems, including image synthesis, segmentation, style transfer, restoration, and pose estimation, GPU-based image-to-image has attracted growing attention and made enormous progress in recent years. It can be utilized for a variation of principles, including photo enhancement, object transformation, season transfer, and collection style transfer. Only CPU and only GPU-based architecture are difficult in order to speed up the image processing task, especially during re-rendering the same scene under various illuminations characteristic for day, night, or dawn. To address this issue, in this work, we are proposing the Hybrid CPU-GPU-based architecture with HiDT technology for implementing the image translation works at tremendous speed. On the hybrid CPU-GPU-based architecture, it is possible to train a multi-domain image-to-image translation model with HiDT on variable size of dataset unaligned images without domain labels using this technology when it is integrated into an application. The speed of the mentioned application can be achieved by using emerging technologies such as pix2pixHD and HiDT on hybrid architecture, where pix2pixHD is a deep learning-based technique for high-resolution photorealistic image-to-image translation, and it is implemented in PyTorch. This article represents Impact of Hybrid Architecture on machine Learning-based image-toimage Translation Using HiDT. © 2024 IEEE.

关键词： Training Knowledge engineering image segmentation image resolution image synthesis Pose estimation Lighting

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A Survey on Attention-Based image Captioning: Taxonomy, Challenges, and Future Perspectives 4th

A Survey on Attention-Based Image Captioning: Taxonomy, Chal...

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4th International conference on machine Intelligence and Signal processing, MISP 2022

作者： Sharma, Himanshu Padha, Devanand Selwal, Arvind Department of Computer Science and Information Technology Central University of Jammu Samba181143 India

ISBN: (纸本)9789819900466

Generating textual descriptions for images is called image captioning (IC). IC is an innovative concept of Computer vision (CV) with several applications such as visual assistance, multi-modal search, machine vision, and many more. Enabling a system to generate image captions has witnessed an excess of progress aided by various state-of-the-art deep learning techniques. Recently, the attention-based image captioning (AIC) approaches have amplified the core field of IC with their promising results. The exponential growth of AIC approaches demands a thorough examination of its literature. There have been relatively lesser contributions in extensively surveying AIC systems up to this point. We review a comprehensive and analytical survey of AIC approaches reported in the literature and propose an alternative taxonomy for diverse AIC techniques. We also highlight a list of open challenges and directions for future work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Design of assembly line material sorting system based on machine vision 4

Design of assembly line material sorting system based on mac...

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4th International conference on Intelligent Design, ICID 2023

作者： Yuan, Qingsong He, Fuyun Su, Jande Xue, Ruiqiang Guangxi Normal University School of Electronics and Information Engineering Guilin541004 China

ISBN: (纸本)9798350342574

In order to solve the problems of low recognition accuracy and inaccurate automatic grasping effect in industrial production lines, a vision-based robotic arm grasping system was designed. openmv module was used to collect image data of objects in motion, and then the image data of objects were processed to determine the shape, color and position of the objects to be measured. Then the motion control module controls the six-axis manipulator to realize the sorting operation of the object. The device uses STM32 single chip microcomputer as the core controller to control the grasp of the robot arm and the rotation of the conveyor belt. Through image processing to complete the positioning, through coordinate transformation, inverse kinematics to complete the motion control. Finally, the materials of different colors were sorted, and the average grasping success rate reached 98%. The results show that the system has a strong practical significance for industrial production line. © 2023 IEEE.

关键词： Belt conveyors

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A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital image processing 6

A Survey on Automated Disease Diagnosis and Classification o...

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6th International conference on Information Systems and Computer Networks, ISCON 2023

作者： Khandelwal, Ankit Shukla, Aasheesh Sain, Mangal GLA University Electronics and Communication Dept. Mathura India Dongseo University Information Engineering Dept. Busan Korea Republic of

ISBN: (纸本)9798350346961

This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. This literature discusses various digital methods for classifying plant diseases. The first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. The second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. This review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.. © 2023 IEEE.

关键词： Computer vision

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Deepfake image and Video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Srinivasan, Srivanth Nischitha, P. Chavan, Akshita Mohana Rv College of Engineering® Bengaluru India Rv College of Engineering® Bengaluru India

ISBN: (纸本)9798331509828

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%. © 2025 IEEE.

关键词： Adversarial machine learning

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Deep Learning based image classification for Automated Face Spoofing Detection using machine Learning: Convolutional Neural Network 2

Deep Learning based Image classification for Automated Face ...

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2nd IEEE World conference on Communication and Computing (WCONF)

作者： Kumar, Biresh Manisha, Kumari Sinha, Anurag Kumar, Abhishek Kumar, Jeevan Amity Univ Amity Inst Informat Technol Ranchi Bihar India IGNOU Sch Comp & Informat Sci New Delhi India Jharkhand Univ Technol Comp Sci & Engn Ranchi Bihar India RVSCET RVS Coll Engn & Technol Jamshedpur Bihar India

ISBN: (纸本)9798350395334;9798350395327

Facial recognition technology has gained widespread use in various applications, raising concerns about the weakness of frameworks to confront mocking assaults. This study presents an implementation of face spoofing detection using machine learning techniques. The exploration utilizes a far-reaching system that envelops data combination, preprocessing, incorporate extraction, and model readiness. A diverse dataset comprising genuine and spoofed facial images, representing various spoofing techniques, is utilized. Feature extraction leverages Convolutional Brain Organizations (CNNs) to catch discriminative facial elements. The selected machine learning model is trained and fine-tuned, with a focus on achieving robustness against evolving spoofing methods. The evaluation of the implemented system involves rigorous testing on a separate dataset, utilizing estimations like precision, exactness, survey, and F1-score. The study investigates post-processing techniques and considerations for real-time deployment, ensuring practical applicability is done by the method convolutional neural network (CNN). Cross-approval is performed to evaluate the model's speculation capacities, and the deployment phase explores integration into real-world scenarios. Ethical considerations, user feedback, and compliance with data privacy regulations are integral components of the study.

关键词： Face Spoofing image Classifying Face Recognition Computer vision Deep Learning

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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

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26th Irish machine vision and image processing conference, IMVIP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

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