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

共 3300 条记录，以下是161-170 订阅

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E2Evideo: End to End video and image Pre-processing and Analysis Tool 30th

E2Evideo: End to End Video and Image Pre-processing and A...

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30th International conference on MultiMedia Modeling, MMM 2024

作者： Alawad, Faiga Halvorsen, Pål Riegler, Michael A. Department of Holistic Systems Simula Metropolitan Center for Digital Engineering Oslo Norway

ISBN: (纸本)9783031533013

In this demonstration paper, we present "e2evideo" a versatile Python package composed of domain-independent modules. These modules can be seamlessly customised to suit specialised tasks by modifying specific attributes, allowing users to tailor functionality to meet the requirements of a targeted task. The package offers a variety of functionalities, such as interpolating missing video frames, background subtraction, image resizing, and extracting features utilising state-of-the-art machine learning techniques. With its comprehensive set of features, "e2evideo" stands as a facilitating tool for developers in the creation of image and video processing applications, serving diverse needs across various fields of computer vision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Python

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Enhanced Feature Extraction for image Dehazing: A Comparative Study between Deep Learning Architectures and FFA-NET 2

Enhanced Feature Extraction for Image Dehazing: A Comparativ...

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

作者： Chaudhary, Sarthak Gupta, Samridh Iniyan, S. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai 603203 Tamil Nadu India SRM Inst Sci & Technol Dept Comp Technol Sch Comp Chennai 603203 Tamil Nadu India

ISBN: (纸本)9798350373301;9798350373295

The problem of poor visibility in foggy images has spurred various image de-hazing strategies. As the need for high-quality images grows, especially for autonomous systems, this research aims to leverage different Deep Learning (DL) architectures to draw out key details from images, localizing this retrieved data to mitigate the impact of haze. The work explores using DL methods, particularly contrasting the regression and classification models of Convolutional Neural Networks (CNN), to remove haze from foggy images. This work sets the stage for further developments in image processing, particularly in conditions with poor visibility. It opens opportunities for improving image quality in various applications, such as autonomous driving and outdoor robotics, where clarity of vision is crucial. The final stage of the proposed model involves three specific pre-processing methods: contextual regularization, air light estimation and boundary constraint for optimal results. The next stage sets out to determine the best DL model for producing clear images from de-hazed ones.

关键词： machine Learning (ML) Convolutional Neural Networks (CNN) Feature Fusion Attention Network(FFA-NET) Deep Learning Dark Channel Prior(DCP) Artificial Intelligence (AI)

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PROP - where learning is made fun

PROP - where learning is made fun

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

作者： Rajeshwari, P. Banushree, B. Nair, Niveditha G. Nivya, Nivya Krupa, Shri Dayananda Sagar College of Engineering Department of Electronics and Telecommunication Engineering Bangalore India

ISBN: (纸本)9798350348460

Writing in air has become a significant research area in image processing and pattern recognition, contributing to automation and improving human-machine interfaces in various applications. Object tracking, a crucial task in Computer vision, is increasingly popular due to faster computers, high-quality video cameras, and automated video analysis demands. This study provides a comparison of virtual air canvas using OpenCV and Mediapipe and air canvas application using OpenCV and Numpy in python. Further it dwells into usage of Augmented Reality for educational purposes and how if these two if are integrated together will revolutionize teaching and learning © 2024 IEEE.

关键词： Augmented Reality Computer vision Mediapipe Numpy Object Tracking virtual Air Canvas

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Research and design of a machine vision-based silk cocoon quality inspection system 10

Research and design of a machine vision-based silk cocoon qu...

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10th IEEE International conference on Cyber Security and Cloud Computing and 9th IEEE International conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yang, Chengjun Peng, Jansheng Cai, Jiahong Tang, Yuna Zhou, Ling Yan, Yaosheng Hechi University School of Artificial Intelligence and Manufacturing YiZhou City China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan City China

ISBN: (纸本)9798350312461

Silk cocoon is one of the critical textile raw materials, and its quality has a significant impact on production and processing. In view of the problems such as time-consuming, labor-intensive, and low efficiency in the existing silk cocoon quality inspection methods, this paper proposes a machine vision-based silk cocoon quality inspection system. For different types of silk cocoons, multiple machine vision techniques are used for image processing and feature extraction. The quality characteristics of silk cocoons are discriminated and analyzed by machine learning algorithms to achieve automatic detection of the cocoon quality. Experimental results show that the proposed system has high accuracy and fast detection speed and can meet the requirements of automated detection in the silk cocoon production process. © 2023 IEEE.

关键词： Feature extraction

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Design and Development of Industrial vision Sensor (IviS) for Next Generation Industrial applications 19

Design and Development of Industrial Vision Sensor (IVIS) fo...

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19th IEEE-India-Council International conference (INDICON)

作者： Daniel, Jerry J. Thomas, Lijo Lajitha, C. S. Mathew, Jacob T. Jithin, S. Mohan, Anju Kumar, Kichu S. C DAC Control & Instrumentat Grp Thiruvananthapuram Kerala India

ISBN: (纸本)9781665473507

Industrial automation is undergoing a tremendous change due to the proliferation of the concepts, the Internet of Things (IoT), Cyber-Physical Systems (CPS) and tactile internet, which enables the interconnections of factory floor devices and enterprise network on a wider and fine-grained scale. vision Sensor deployments are getting great momentum in factories, as it improves the quality and productivity of the systems being inspected. Smart vision Sensors[1] removes the need of the additional infrastructures for running the image processing algorithms and vision applications, by directly running the vision logic on the device and control/monitor the various parameters on the field based on the image processing outputs. Industrial vision sensor (IviS) is an industrial smart camera, which has a CMOS image sensor[2] and a powerful on-board processing system capable of supporting machine vision applications, for improving the product and process qualities and thereby improve the yield and profit. IviS is capable of extracting applicationspecific information from the captured images and make decisions based on the image processing algorithms implemented on the system, to realize stand-alone intelligent and decision-making automation system. In this paper we present the design and development of IviS, its application domains and preliminary test results.

关键词： CPS IviS CMOS

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Essential Depth Map processing Algorithms for Real-time applications: An Overview 26

Essential Depth Map Processing Algorithms for Real-time Appl...

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

作者： Budak, Ugurcan Ilola, Lauri Kondrad, Lukasz Alface, Patrice Rondao Fasogbon, Peter Schwarz, Sebastian Nokia Technologies Finland

ISBN: (纸本)9781837242672

RGB-D cameras provide both depth (D) and colour (RGB) data as the output simultaneously in real-time. The depth data provided by the camera typically contains imperfections, such as holes and noise. Improving the quality of depth data is a widely researched topic of practical importance for multimedia, computer vision, and robotics. Typically, RGB-D cameras provide color data with higher resolution and quality compared to depth data. In this paper, we propose to enhance the depth map, which can be a pre-processing step of real-time volumetric streaming. First, we survey and categorize the depth improvement methods into two, and then test them to improve the quality of the depth data in real-time. We present the empirical results, which are informative for selecting a suitable depth map refinement method. Our experiments show that there is a trade-off between improvement level and real-time capability of the methods. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： RGB color model

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Multi-Modal Learning with Joint image-Text Embeddings and Decoder Networks 7

Multi-Modal Learning with Joint Image-Text Embeddings and De...

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IEEE 7th International conference on Industrial Cyber-Physical Systems (ICPS)

作者： Chemmanam, Ajai John Jose, Bijoy A. Moopan, Asif Cochin Univ Sci & Technol CPS Lab Dept Elect Cochin Kerala India Vuelogix Technol Pvt Ltd Kochi Kerala India

ISBN: (纸本)9798350363029;9798350363012

Advances in machine learning and neural networks have transformed natural language processing (NLP) and computer vision (CV) applications. Recent research efforts have begun to bridge the gap between the two domains. In this work, we propose a semi supervised Multi-Modal Encoder Decoder Network (MMEDN) to capture the relationship between images and textual descriptions, allowing us to generate meaningful descriptions of images and retrieve images from a database using cross-modality search. The semi-supervised training approach, which combines ground truth text descriptions and pseudotext generated by the text decoder within the model, requires far fewer image-text pairs in the training data and can directly add new raw images without manual text labelling for training. This approach is particularly useful for active learning environments, where labels are expensive and hard to obtain. We show that our model performs well with qualitative evaluations. We applied our model for finding images of a person from large databases and generating descriptions of people involved in an event for adding to an automatically generated report. The model was able to retrieve relevant images and generate accurate descriptions, demonstrating its applicability to more practical use cases.

关键词： Multi-modal learning Cross-modal retrieval Encoder-decoder architectures Computer vision Natural Language processing

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Prediction of Handwritten Classification using CNN Techniques 9

Prediction of Handwritten Classification using CNN Technique...

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9th International conference on Signal processing and Communication, ICSC 2023

作者： Sanu, Kumar Bhandari, Rahul Computer Science and Engineering Chandigarh University Chandigarh India

ISBN: (纸本)9798350383201

Today's computer vision industry makes extensive use of image recognition. A popular method of image recognition is digit recognition. The recognition of handwritten numbers is one of the most well-known difficulties in computer vision and machine learning applications. In essence, this model proposes an online approach for recognizing handwritten digits (HDR) by utilizing convolutional neural networks (CNN).As a training sample, the technique uses the MINST dataset, which contains centered 28x28 gray scale images of handwritten numbers. It has 10,000 test cases and 60,000 training examples. This paper shows the accuracy rate and loss of the model. Lastly, execute the trials using a variety of random handwritten 28x28 pixel digits. © 2023 IEEE.

关键词： Computer vision

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Development of Enhance-Net Deep Learning Approach for Performance Boosting on Medical images 5th

Development of Enhance-Net Deep Learning Approach for Perfor...

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5th International conference on Data Science, machine Learning and applications

作者： Manoharan, Geetha Solanke, D. R. Acharjee, Purnendu Bikash Nayak, Chinmaya Kumar Sharma, Mukesh Kumar Sahu, Dillip Narayan SR Univ Sch Business Warangal Telangana India SGB Amravati Univ Dept Appl Elect Amravati 444602 Maharashtra India CHRIST Univ Pune Maharashtra India Sri Sri Univ Fac Emerging Technol Cuttack Odisha India Rungta Coll Pharmaceut Sci & Res Bhilai 490024 Chhattisgarh India Gangadhar Meher Univ Dept MCA Sambalpur Odisha India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. The A rapidly expanding area of study is the Deep Learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. The musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated.

关键词： Deep learning Medical image Enhance- net Dataset

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Cutting-Edge image Recognition Leveraging Deep Learning and machine Learning for Enhanced Accuracy

Cutting-Edge Image Recognition Leveraging Deep Learning and ...

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2024 International conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Shrivastava, Abhishek Kumar, vinesh Maurya, Jay Prakash School of Mechanical Engineering VIT Bhopal University Sehore India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computing Science & Engineering VIT Bhopal University Sehore India

ISBN: (纸本)9798331517953

This paper investigates advanced techniques in image recognition and classification by integrating deep learning and machine learning approaches to achieve higher accuracy. Through the implementation of sophisticated training algorithms, the study demonstrates enhanced performance in recognizing and categorizing images across various data models. A major turning point in the development of image identification technology came in 2012 when deep neural networks were introduced. These networks surpassed earlier cutting-edge algorithms and completely changed the computer vision industry. This progress has brought us closer to achieving human-level accuracy in tasks such as identity verification. The role of large datasets like imageNet is crucial, as they provide the foundation for the success of deep learning. With continuous research pushing the limits of picture identification and producing major advances in human knowledge, deep learning has a huge influence on business, society, and technology. Additional research in this area might lead to creative uses that revolutionize our relationship with our surroundings. Key topics discussed include data pre-processing, post-processing, model optimization, and accuracy enhancement. The findings highlight the potential of cutting-edge technologies to advance image classification and recognition in various sectors, such as medical imaging and visual analysis. The approach emphasizes scalability and adaptability, ensuring that models can be effectively applied to real-world scenarios. Future research will focus on refining these models to handle even more complex image datasets, further enhancing their practical utility and reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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