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检索条件"任意字段=3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023"

共 1699 条记录，以下是201-210 订阅

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Real Time Object Distance and Dimension Measurement using Deep Learning and OpenCV 3

Real Time Object Distance and Dimension Measurement using De...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Basavaraj, M.U. Raghuram, H. Mohana Rv College of Engineering® Electronics and Telecommunication Engineering Karnataka Bengaluru India Karnataka Bengaluru India

ISBN: (纸本)9781665462167

Deep learning is a subset of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data. Deep learning is focused on improving the AI process of having machines learning new things on its own. In this paper, proposed real time object dimension detection and dimension analysis using python. For ML and AI-based papers, python programming language offers consistency, simplicity, and access to excellent libraries and frameworks. It also offers platform freedom, flexibility, and a large developer community. Proposed work, explorations were done on some methodologies, including You Only Look Once (YOLO), for object identification that is meant for speed and real-time application use, and Region-Based Convolutional Neural Networks (R-CNNs) built for model performance and analysis. To make it better, a canny edge detection algorithm is being used. Canny edge detector is a multistage algorithm-based on edge detection operator that can identify a variety of edges in image. © 2023 IEEE.

关键词： Computer vision

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Convolutions vs. Sequences: Understanding Performances of Neural-Based Methods for Automatic Baybayin Script Recognition 3

Convolutions vs. Sequences: Understanding Performances of Ne...

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3rd international conference on Computer vision and Information Technology, CVIT 2022

作者： Dela Rosa, Cerwin Dexter L. Lagnilla, Kreed Zion Lorenzo G. Ramos, Jomari V. San Pedro, Austin Kenneth V. Imperial, Joseph Marvin R. National University 551 MF Jhocson Street Sampaloc ManilaPH 1008 Philippines

ISBN: (数字)9781510663046

ISBN: (纸本)9781510663039

Common approaches to vision-based tasks such as character and object recognition use Convolutional Neural Networks (CNNs) due to their practicality in processing images and theoretical grounding. In this work, we take a different perspective in the task of Baybayin script recognition by exploring vision Transformers, a new paradigm for processing images inspired by the Transformer model. We compare performances of CNNs and ViT and analyzed model confidence on a set of test images using Local Interpretable Model-Agnostic Explanations (LIME). Results show that, performance-wise, convolution-based architectures (CNNS) still outperform sequence-based methods (ViT) for discriminating Baybayin scripts with a nearly doubled performance of 84.5% to 48.8% in accuracy respectively. © 2023 SPIE.

关键词： Visual process modeling Transformers Optical character recognition image processing Convolutional neural networks Artificial neural networks Convolution image segmentation

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Fused image Classification using Pre-trained Deep Convolutional Neural Networks 3

Fused Image Classification using Pre-trained Deep Convolutio...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Patil, Milind S. Mane, Pradip B. Institute of Information Technology All India Shri Shivaji Memoril Society's Pune India Vishwakarama Institute of Information Technology E and Tc Engineering Department Pune India

ISBN: (纸本)9781665462167

image Quality Assessment (IQA) has got importance in the computer vision applications as it provides tool to evaluate and rate different image processing algorithms. image Fusion is a process in which information from multiple images is combined into a single image. Due to specific nature of fused images present IQA methods have limitations for evaluation of image Fusion algorithms. With the recent development of Deep Convolutional Neural Networks (Deep CNNs), No- reference image quality assessment is becomes reality. This article has proposed the pre-trained Deep CNNs based image fusion classification using Alexnet, VGG19, Inception V3 and ResNet-50. Four states-of-the-art image fusion algorithms used for image fusion are Laplacian Pyramid (LP), Shift Invariant DWT (SIDWT), Discrete Wavelet Transform (DWT) and Ratio Pyramid (RP). To achieve the effective IQA, sufficiently large dataset of synthetically fused images is created and same is evaluated by using Deep CNNs. The results show that recent deep CNN methods correctly classify the fused images into corresponding categories based on its fusion algorithms. The results are consistent with FR-IQA methods. ResNet-50 provides best classification accuracy with less number of epochs and time to converge due to sparse network connections. © 2023 IEEE.

关键词： image classification

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Automatic defect detection method for mobile phone curved glass based on machine vision 3

Automatic defect detection method for mobile phone curved gl...

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3rd international conference on Optics and image processing, ICOIP 2023

作者： Chen, Ning Guo, Gangxiang Guo, Bin Zhang, Yue Wang, Chao Qiu, Yuexin Wang, Yinghui Hu, Pengbing Zhao, Jianming Chen, Ting Zhejiang Institute of Metrology Hangzhou310018 China College of Metrological Technology and Engineering China Jiliang University Zhejiang Hangzhou310018 China Key Laboratory of Digital Precision Measurement Technology of Zhejiang Province Hangzhou310018 China Key Laboratory of Acoustics and Vibration Precision Measuring Technology for State Market Regulation China

ISBN: (纸本)9781510667426

An automatic defect detection method for the mobile phone curved glass based on machine vision is proposed and implemented to estimate the plane images, edge images, and R-angle images of the mobile phone curved glass. Moreover, different defect size can be obtained. The experimental results show the consistency with the image measurement instrument, and the common scratches, stains, scratches and bubbles on the curved glass surface of mobile phone can be accurately extracted by the proposed algorithm, with a dimensional accuracy within 20μm. © 2023 SPIE. All rights reserved.

关键词： image classification

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Capturing the Essence: Exploring Cutting-Edge Feature Extraction Methods in Digital image processing 3

Capturing the Essence: Exploring Cutting-Edge Feature Extrac...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Prabu, S. Natarajan, B. George, Roshan Sujitha, S. Bharathi, P Divya Rajeswari, S. School of Computer Science and Engineering VIT University Chennai India Amrita Vishwa Vidyapeetham Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India

ISBN: (纸本)9798350317022

Feature Extraction (FE) plays a vital role in the fields of image processing, machine learning and pattern recognition. Binarization, resizing, thresholding and normalization are the initial steps applied before extracting features from an image. FE methods are applied to images to extract the most essential features that help in detection, recognition and classification tasks. FE techniques are used in numerous applications, such as object recognition, character recognition and pattern recognition. This research study presents a comprehensive research study of different image FE techniques, including SURF, BRISK, PCA and SIFT, which have been employed for various applications in recent times. This research study explains each method's fundamental concepts and pros and cons and demonstrates in what scenario which feature extraction technique will be better. This research work also presents a summary table of FE techniques used in various applications with their accuracy percentage. © 2023 IEEE.

关键词： Edge Detection Feature Extraction Feature Selection image processing image Representation

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English Handwriting image Edge Detection Based on Fuzzy Logic 3

English Handwriting Image Edge Detection Based on Fuzzy Logi...

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3rd international conference on Computer vision and Pattern Analysis, ICCPA 2023

作者： Wang, Xueying Liu, Hongwei Wang, Yuanshen School of Mathematics and Big Data Chongqing University of Arts and Sciences Chongqing China

ISBN: (纸本)9781510667563

This paper mainly applies fuzzy logic to the edge detection of handwritten images in English. image edge detection is an important part of image processing. Through image edge detection, the amount of data can be greatly reduced, and the information that can be considered irrelevant can be eliminated, and the important structural attributes of the image are retained. It uses six traditional operators to detect the edge of English handwritten images: canny, log, sobel, sprewitt, zerocross, and roberts. By comparing the edge detection results of the two kinds of experiments, we can find that the canny operator has the best edge detection effect among the traditional operators. In the fuzzy logic experiment, with the growth of Sx and Sy, the edge detection effect gradually weakened. In this paper, the experiment with Sx = 0. 05 and Sy = 0. 05 has the best edge detection results. In conclusion, the edge detection effect of English handwritten images based on fuzzy logic is better than that of English handwritten images based on traditional methods. © 2023 SPIE.

关键词： Fuzzy logic

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TrOCR-Enhanced Language Chains: Bridging image-Based Text and Single Document Question Answering 3rd

TrOCR-Enhanced Language Chains: Bridging Image-Based Text an...

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3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Althaf, Patan Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819743582

This research aims to integrate TrOCR, an advanced Optical Character Recognition (OCR) technology, with the Langchain framework for Document question answering on image-based queries. The college has many number of students and they face many problems when it comes to the preparation for exams. There may be many question papers they have to prepare manually by searching the answers in books. Our model uses TrOCR to extract text from input images and the large text data is split using Langchain. Vector indexing is to process the text. This integration helps in information retrieval and question answering. The VectorstoreIndexCreator facilitates text vectorization and indexing, and HuggingFaceEmbeddings for semantic understanding. This research aims for information retrieval, document analysis, and natural language processing, offering a connection between image-based text and advanced language technology where users can have easy handling of the model and provides comprehensive answers for given complex questions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Question answering

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An image caption generation method based on improved attention mechanism 3

An image caption generation method based on improved attenti...

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3rd international conference on Computer vision, image and Deep Learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Su, Yingying Bian, Minjie Cheng, Chen Huang, Yuzhe Shanghai University Shanghai China

ISBN: (纸本)9781665459112

image captioning is a research hotspot in the field of image understanding. It spans the fields of computer vision and natural language processing. Most of the image captioning researches use deep learning technology, especially using Convolutional Neural Networks(CNN) as encoder-decoder structure for feature extraction. The existing captioning model is not of high quality, the important parts of the image are not sufficiently extracted features and the model is too complex. To address these problems, this paper introduces a new generation method based on attention mechanism. First, an efficient attention module is introduced to improve the feature extraction effect based on traditional attention mechanism. It reduces the model complexity and better extracts the important part of image features. The method uses convolutional features extracted from a pre-trained CNN model which integrate the features with the attention mechanism. Second, we also select the GRU language model as the decoder to construct descriptive sentences. Finally, experiments on the MSCOCO dataset and Flickr30k dataset show that the model proposed in this paper effectively improves the quality of descriptive sentences. © 2022 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Formalin Detection in Fish Using Deep Neural Networks 3

Comparative Analysis of Formalin Detection in Fish Using Dee...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Rashedul Islam, Sabrin Jeba, Samiha Maisha Dihan, Rakibul Hasan International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

For the people of Bangladesh, fish is a non-vegetarian and staple food. As Bangladesh is a riverine country, fish is available everywhere in our country. Fish rots easily. For this reason, the fish has to be preserved to fulfill people's nutritional needs. To meet the needs of the people of Bangladesh, fish is collected from the rivers and sometimes imported for this vast population. While importing fish or taking it from one place of the country to another, unscrupulous traders mixed formalin to prevent the fish from rotting. Formalin is very harmful to the human body. It creates so many health problems like cancer, miscarriage, tumors, etc. So, it is essential to detect formalin fish and non-formalin fish. There is a need for a more dependable and automated method of formalin detection because current approaches rely on human senses and are prone to inaccuracy. Digital image processing has become a promising technology for fish formalin detection in recent years. In this paper, an intelligent application is proposed. This model can identify formalin and fresh fish based on the images of fish eyes. The capability of digital image processing to offer a quick and non-intrusive examination technique is one of the main advantages of formalin detection. Without damaging sample preparation, it can swiftly and accurately identify the presence of formalin by examining photographs of the fish. This is especially helpful in large-scale operations when it is impractical to inspect each fish manually. Digital image processing algorithms are more accurate and consistent because they are not influenced by human biases or subjective judgments like human senses are. The architecture we proposed is VGG-16, and we compare it with EfficientNet-B3. VGG-16, renowned for its simplicity and effectiveness in various computer vision tasks, achieved a notable accuracy of 94.44%. © 2024 IEEE.

关键词： Deep neural networks

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improved Algorithm for Solar Power Systems for Efficient Tracking System 3

Improved Algorithm for Solar Power Systems for Efficient Tra...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Kalele, Girish Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350399264

Current evaluation of a pilot concentrated solar energy plant found that the monitoring technology was unable to precisely align the complex heliostats with the sun and direct the sunlight onto the goal. The inability of concentrating solar energy monitoring systems to both increase precision & lower operating costs was shown by a subsequent examination of solar tracking devices. A computers vision solar tracking systems is presented and put into practice in this work. It consists of vision controllers, camera, light sensors, Arduino microprocessor, two stepping motor, sticks, and a transparency plate. The light sensors determine whether sunlight is present, the webcam records a picture of the stick's shadows on the plate, & the sight processor interprets this information in order to derive the sun inclinations. The microprocessor receives the angle and uses them to properly position the heliostat using stepper motor. The suggested system may be put in a single heliostat or a collection of heliostats & is site independently. It follows the sun correctly and costs little to operate, according to initial testing. © 2023 IEEE.

关键词： Signal processing

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