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Rotary Transformer for image Captioning 2

Rotary Transformer for Image Captioning

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2nd international conference on Optics and image processing (ICOIP)

作者： Qiu, Yile Zhu, Li Xi An Jiao Tong Univ Software Engn Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781510657274;9781510657267

image captioning tasks based on deep learning encompasses two major domains . computer vision and natural language processing. The Transformer architecture has achieved leading performance in the field of natural language processing, There have been studies using Transformer in image caption encoder and decoder, the results proving better performance compared to previous solutions. Positional encoding is an essential part in Transformer. Rotary Transformer proposed Rotary Position Embedding (RoPE), has achieved comparable or superior performance on various language modeling tasks. Limited work has been done to adapt the Roformer's architecture to image captioning tasks. The study conduct research based on the positional encoding of Transformer architecture, our proposed model consists of modified Roformer as an encoder and BERT as a decoder. With extracted feature as inputs as well as some training tricks, our model achieves similar or better performance on MSCOCO dataset compared to "CNN+RNN" models and regular transformer solutions.

关键词： image captioning Transformer sequence-to-sequence RoPE

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Research on the farthest scattering distance of blasting flying stones based on improved WOA-BP neural network 2

Research on the farthest scattering distance of blasting fly...

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Ning, Fangpeng Xu, Huarong Weng, Lifen Xiamen Institute of Technology School of Computer and Information Engineering Xiamen China Xiamen Institute of Technology School of Design and Art Xiamen China

ISBN: (纸本)9798350331417

The flying rocks in mining blasting operations pose a great threat to safety production, and the reasonable division of safety blasting scope is of great significance for production operations. In response to the problem of the farthest scattering distance of flying rocks in mining blasting operations, based on the excellent nonlinear mapping ability of BP neural network and mature research on its theory and application, WOA (whale optimization algorithm) is used to optimize the BP neural network, improving its weak global search ability and poor anti-interference ability. And adaptive weight adjustment is introduced, and an improved WOA-BP neural network model is applied to predict the farthest scattering distance of flying rocks in mining blasting. The minimum burden of blasting, unit consumption of blasting explosives, and maxi-mum charge weight of a single hole, which have a greater impact on the distance of flying rocks during blasting operation, are selected as input variables. The experimental results show that the average absolute error MAE, mean square error MSE, root mean square error RMSE, and average absolute percentage error MAPE of the improved WOA-BP model are 0.52283, 0.60303, 0.77655, and 2.8102%, respectively, which are better than the comparison model, indicating that the prediction accuracy of the model is high, it can provide guidance for actual mine blasting production. © 2023 IEEE.

关键词： Adaptive weight Blasting flying stone BP neural network component Mine blasting WOA

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Design of Burr Detection System for Inside the Shaft Holes Using computer vision 2

Design of Burr Detection System for Inside the Shaft Holes U...

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2nd international conference on Mechanics, Electronics, Automation and Automatic Control, MEAAC 2024

作者： Kawvijit, Nattapat Wanthong, Irin Srisertpol, Jiraphon Khaengkarn, Sorada School of Mechatronics Engineering Institution of Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (纸本)9781643685403

The automotive sector aims to reduce the production of scraps or burrs, which requires the adoption of a deburring procedure and the utilization of inspection techniques. The existing inspection procedures involve the use of visual examination techniques. Nevertheless, as a result of the substantial quantity of components that necessitate examination and the small dimensions of the hole. Inspectors get weariness after prolonged periods of work. It commits errors with such ease. This study aims to investigate and develop a system for the inspection of burrs in shaft holes resulting from drilling operations. A system was designed to support the workpiece for examination holes of two different diameters-5 millimeters and 3 millimeters. The process of inspection sequentially examines each hole individually after the workpiece is positioned within the fixture. The image will be captured by the system via web camera, subsequently imported into the image analysis procedure via the blob analysis method, and finally converted to binary format. In the case of a defective hole, including the burr, a clean hole image will display a larger pixel on the bright or white side compared to the white side pixels. The confusion matrix was utilized for the purpose of evaluating accuracy. The 320 x 240 resolution with F-Score of 0.933 was selected for the system due to its efficient processing speed (18.6 seconds), lack of false positives, and capacity to optimize storage space. Â© 2024 The Authors.

关键词： Inspection

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Face Detection and Recognition Using Digital image processing: "State of the Art" 2

Face Detection and Recognition Using Digital Image Processin...

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2nd international conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Singla, Neeraj Singla, Garvit Maggon, Navya Chitkara University Ete Dept. of Cse Rajpura India

ISBN: (纸本)9781665474160

With growing techniques and computer technologies emerging, biometric recognition system has gained immense popularity among the masses. Amongst the entire biometric recognition system, face detection has been one of the most important goals of computer vision, but only in the recent years, has it been prevalent by means of social tagging and image processing. Face recognition refers to the technique of identifying one's biometrics using their facial features, for example, eye color, nose and lips. In this paper, we shall discuss the various methodologies and approaches which can be used to implement the face detection system [1]. Due to the hike of video and image databases, it is the need of the hour for automatic understanding and examination of information by intelligent systems. It is the face through which we convey, identify feelings from person to person. Therefore, face recognition plays an vital role in our day-to-day life. Since sensitive data such as face id, biometrics are easily accessible to hackers so Digital image processing plays a very important role in Biometric recognition technique. Face recognition has the potential in a real time surveillance system. © 2022 IEEE.

关键词： Face recognition

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computer-aided Brain Age Estimation via Ensemble Learning of 3D Convolutional Neural Networks 12

Computer-aided Brain Age Estimation via Ensemble Learning of...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Malayeri, Ali Bahari Moradi, Mohammad Mahdi Dinani, Kian Jafari Shahid Beheshti University Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9781665412162

predicting brain age using Magnetic Resonant Imaging (MRI) and its difference with chronological age is useful for detecting Alzheimer's disease in the early stages. For having accurate brain age prediction with MRI, Deep learning could play an active role, but its performance is highly dependent on the amount of data and computes memory we access. In this paper, in order to approximate as accurately as possible, the age of the brain through T1 weighted structural MRI, a deep 3D convolutional neural network model is proposed. Furthermore, different techniques such as data normalization and ensemble learning have been applied to the suggested model for getting more accurate results. The system is trained and tested on the IXI database, which is being normalized by SPM12. Finally, this model is assessed through the Mean Absolute Error (MAE) metric, and the results demonstrate our model is capable of computing the approximation age of the subjects with an MAE, which is equal to 5.07 years. © 2022 IEEE.

关键词： Convolutional neural networks

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Design and Realization of computer image Intelligent Recognition System 2

Design and Realization of Computer Image Intelligent Recogni...

引用

2021 2nd international conference on Artificial Intelligence, computer Networks and Communications, AICNC 2021

作者： Xu, Guibing School of Computer Information Engineering Nanchang Institute of Technology Jiangxi 330000 China

The application of intelligent image recognition technology in life is more and more extensive, especially in the field of computer and multimedia, the research of machine vision system is becoming more and more mature, and the demand of human society for information processing is constantly *** article first analyzes the basic knowledge of digital images based on computer technology, including basic knowledge of digital images, basic knowledge of image filtering and image recognition algorithms. Secondly, this paper studies the design and implementation of computer image intelligent recognition system. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： image recognition

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Feature Line Based Feature Reduction of Polarimetric-Contextual Feature Cube for Polarimetric SAR Classification 12

Feature Line Based Feature Reduction of Polarimetric-Context...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Imani, Maryam Tarbiat Modares University Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Extraction of discriminative features is an efficient step in any classification problem such as synthetic aperture radar (SAR) images classification. Polarimetric SAR (PolSAR) images with rich spatial features in two first dimensions and polarimetric characteristics in the third dimension are rich source of information for providing classification maps from the ground surface. By applying the spatial operators such as morphological filters by reconstruction, data dimensionality of the PolSAR is increased and needs feature reduction. In this work, median-mean and feature line embedding (MMFLE) is proposed for dimensionality reduction of the polarimetric-contextual cube in PolSAR images. MMFLE is stable with respect to outliers by utilizing the median-mean line metric. By an appropriate definition of scatter matrices, MMFLE maximizes the class separability. In addition, MMFLE is specially a superior feature reduction method when a small training set is available because it uses the feature line metric to model the data variations and generate virtual samples. With 10 training samples per class, MMFLE achieves 94.15% and 83.01% overall classification accuracy, respectively in Flevoland and SanFranciso PolSAR datasets acquired by AIRSAR. © 2022 IEEE.

关键词： Synthetic aperture radar

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Classification and detection of printed product surface scratches based on machine vision 2

Classification and detection of printed product surface scra...

引用

2nd international conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Du, Yunfei Zuo, Cai Zhang, Yongbin School of Mechanical and Electrical Engineering Beijing Institute of Graphic Communication Beijing102600 China

ISBN: (纸本)9798350334784

Aiming at the surface defects of packaging and printing products caused in the process of packaging and printing production, a method based on machine vision to detect the scratch defects on the surface of printing products was proposed, and the common scratch defects on the surface of printing products were classified and defined. First, the image to be detected on the surface of the printed product is obtained through the industrial camera, and the image to be detected is grayed using the weighted value method. In terms of image denoising, Gaussian filtering is used for effective processing. Then, image binarization and canny edge detection operators are used to segment and extract the defect features of the pre-processed image. Finally, Fourier fast transform is used for image registration. The effectiveness of the proposed method is verified by using the matable software. The experimental results show that the accuracy of defect detection reaches 98.7% Compared with other defect detection methods, the proposed method has certain advantages in terms of convenience and accuracy and can realize rapid detection of scratches on the surface of printed products. © 2022 IEEE.

关键词： computer vision

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Exploring the frontier: recent advances and evaluations in vision transformers

Exploring the frontier: recent advances and evaluations in v...

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2nd international conference on Mechatronic Automation and Electrical Engineering (ICMAEE 2024)

作者： Qishun Zhang Bachelor of Technology McMaster University Hamilton L8S 4L8 Canada

ISBN: (数字)9781837242672

Research Background: Transformers, initially developed for natural language processing (NLP), gained prominence with their effective handling of arbitrarily long sequential data through a sequence-to-sequence model. Their self-attention mechanism, a core component, demonstrated remarkable success beyond NLP and significantly impacted computer vision. This led to the innovative adaptation of transformers in visual contexts, culminating in the creation of vision Transformers (ViTs). ViTs revolutionized image processing by treating images as sequences of patches, applying self-attention mechanisms directly to pixels. This Paper's Contributions: This paper aims to conduct a comprehensive review of the evolution of transformers in image processing, tracing the journey from initial experiments in training transformers on images to the latest advancements in hierarchical architectures and multi-scale ViTs. This survey not only highlights the superior performance and flexibility of ViTs across various computer vision tasks but also discusses their promising applications in diverse fields such as medical imaging and robotics. This review underscores the transformative impact of ViTs and outlines potential future directions in this dynamic field.

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A Survey of Deep learning-based image caption 2

A Survey of Deep learning-based Image caption

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2nd international conference on computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022

作者： Qiao, Lang Hu, Wenjin National Languages Information Technology Boulder Northwest Minzu University Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Gansu Lanzhou730000 China School of Mathematics and Computer Science Northwest Minzu University Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Gansu Lanzhou730000 China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

image caption is to use computer vision technology to extract the semantic content information contained in the image, and use natural language processing technology to generate a reasonable text caption. This paper takes the development of image description technology based on deep learning as the main line, and introduces the typical image description methods in each period. image description generally adopts an encoding-decoding structure, which is the most commonly used method at present. By improving the encoder or decoder, increasing the attention mechanism, introducing Transformer technology, the advantages and disadvantages of different methods are discussed and summarized. Data sets and evaluation indicators such as MS-COCO and Flicker commonly used in this field are introduced in detail. © 2022 IEEE.

关键词： computer vision

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