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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

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DocumentQA-Leveraging LayoutLMv3 for Next-Level Question Answering

DocumentQA-Leveraging LayoutLMv3 for Next-Level Question Ans...

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2023 international conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Chanti, S. Harika, P. Venkata Vinay, V. Guna Sri Charan, P. Ajad, Sk. Computer Science and Engineering Gmr Institute of Technology Andhra Pradesh India

ISBN: (纸本)9798350300857

With the increasing prevalence of digital documents in various domains, the demand for efficient and accurate question-answering (QA) systems has grown significantly. Traditional QA models primarily focus on text-based content, but the complex structure and visual elements present in documents pose unique challenges. This project explores the integration of LayoutLMv3, an advanced language and vision model, to elevate document question answering to the next level. The key role of LayoutLMv3 in document question answering (QA) lies in its ability to handle complex documents that contain not only textual information but also intricate visual elements like tables, images, and graphs. Traditional QA models typically focus on processing plain text and may struggle to comprehend the spatial arrangement and structure of documents. However, documents often contain vital information in their layout, which, if overlooked, can lead to inaccurate or incomplete answers. LayoutLMv3 overcomes this limitation by integrating both language and vision components into a single model. Through comprehensive tests on a variety of document datasets, this study shows the efficiency of DocumentQA. This study also analyzes the LayoutLMv3's interpretability and how it affects the way questions are answered. Through visualization techniques, various insights are gained on how the model analyses the document's layout, text, and interactions to generate accurate answers. The implications of this research extend beyond document QA, as LayoutLMv3's unique fusion of language and vision has broader applications in information retrieval, natural language processing, and document understanding tasks. The performance of LayoutLMv3 over LayoutLmv2 is also compared in this work showing efficient results. © 2023 IEEE.

关键词： Natural language processing systems

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Research on image Stitching for Parking Assistance System 19th

Research on Image Stitching for Parking Assistance System

引用

19th EAI international conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Liu, Sheng Yang, Yiqing Cao, Ting School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9783031651229

Currently, car parking assistance is limited to the small angle rear view imaging of the reversing radar, which can only provide the driver with a limited range of vision and is prone to safety hazards. It is necessary to stitch the surrounding images captured by the parking monitor by widening the monitoring field of view of the car parking and providing the driver with a complete rear panoramic image through the parking assistance system. In this research, we provide an enhanced SIFT image stitching technique that uses random k-d trees and the k-nearest neighbour matching to optimise SIFT feature matching to improve matching speed, the RANSAC algorithm to reject mismatched pairs to improve matching accuracy, and pre-processing to reduce distortion and eliminate stitching gaps by applying columnar projection and weighted fusion to the stitched images. The experimental findings demonstrate that the suggested approach may increase image stitching speed while also producing stitched and displayed images that are more accurate. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Nearest neighbor search

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Recombining vision Transformer Architecture for Fine-Grained Visual Categorization 29th

Recombining Vision Transformer Architecture for Fine-Grained...

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29th international conference on MultiMedia Modeling (MMM)

作者： Deng, Xuran Liu, Chuanbin Lu, Zhiying Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China

ISBN: (纸本)9783031278174;9783031278181

Fine-grained visual categorization (FGVC) is a challenging task in the image analysis field which requires comprehensive discriminative feature extraction and representation. To get around this problem, previous works focus on designing complex modules, the so-called necks and heads, over simple backbones, while bringing a huge computational burden. In this paper, we bring a new insight: vision Transformer itself is an all-in-one FGVC framework that consists of basic Backbone for feature extraction, Neck for further feature enhancement and Head for selecting discriminative feature. We delve into the feature extraction and representation pattern of ViT for FGVC and empirically show that simply recombining the original ViT structure to leverage multi-level semantic representation without introducing any other parameters is able to achieve higher performance. Under such insight, we proposed RecViT, a simple recombination and modification of original ViT, which can capture multi-level semantic features and facilitate fine-grained recognition. In RecViT, the deep layers of the original ViT are served as Head, a few middle layers as Neck and shallow layers as Backbone. In addition, we adopt an optional Feature processing Module to enhance discriminative feature representation at each semantic level and align them for final recognition. With the above simple modifications, RecViT obtains significant improvement in accuracy in FGVC benchmarks: CUB-200-2011, Stanford Cars and Stanford Dogs.

关键词： Fine-Grained Visual Categorization vision Transformer

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A Solder Crack Inspection System Using Machine Learning

A Solder Crack Inspection System Using Machine Learning

引用

2023 international conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Shimomura, Aoto Morimoto, Masakazu Nakano, Tomoya Hasegawa, Toshiaki Takegawa, Jun Graduate School of Engineering University of Hyogo Department of Electronics and Computer Science Japan Noritz Corporation China

ISBN: (纸本)9798350303780

Solder cracks are caused by repeated expansion and contraction due to temperature changes. When designing a new electronic board, a heat shock test is performed on the electronic board to identify areas where solder cracks are likely to occur. After the test, solder crack rank is determined by human visual inspection. In this paper, we propose a solder crack inspection system that can automatically estimate the crack rank using image processing and machine learning techniques. © 2023 IEEE.

关键词： Machine learning

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Multi-Layer Visual Perception for No-Reference image Quality Assessment 6

Multi-Layer Visual Perception for No-Reference Image Quality...

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6th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Qi, Junwei Wang, Yingzhen Wang, Qingchun Li, Yingsong College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei230601 China

ISBN: (纸本)9798350399059

In this work, we propose a no-reference image quality evaluation approach, aiming to solve the problem that the traditional convolutional neural network is insufficient to express the global information of the image. Therefore, in order to make the captured image features have the coherence of global context information, we combine a Transformer structure widely used in NLP (natural language processing) with the traditional CNN to realize the attention mechanism in the human visual perception characteristics. Firstly, the self-attention mechanism of Transformer is used to learn the global representation of the image from the multi-layer features extracted from different levels of CNN. Additionally, to forecast the ultimate image quality score, the global and local attributes are combined. Finally, the proposed algorithm is evaluated on three public datasets commonly used in CSIQ [1], TID2013 [2] and LIVE [3]. Compared with eight traditional no-reference image quality evaluation algorithms and five methods based on deep learning, the proposed algorithm achieves competitive results. © 2023 IEEE.

关键词： Convolutional neural networks

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A vision transformer-based approach for recognizing seven prevalent mango leaf diseases 26

A vision transformer-based approach for recognizing seven pr...

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26th international conference on computer and Information Technology, ICCIT 2023

作者： Rayed, Md. Eshmam Abdullah-Al-Akib Alfaz, Nazia Niha, Sadia Islam Islam, S. M. Sajibul American International University-Bangladesh Department of Computer Science & Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798350359015

Plant diseases, particularly affecting fruit crops, pose a significant challenge to the worldwide supply of fresh food due to their direct impact on the quality of fruits, resulting in an overall decline in agricultural production. The traditional approach of detecting leaf diseases in fruit plants requires farmers to undertake manual inspection which exhibits a lack of reliability and consistency. Moreover, the manual inspection procedure is prone to errors due to its reliance on the farmer's knowledge and skill. Mango referred to as the "king of all fruits", is renowned for its rich composition of various vitamins and vital nutrients. Mangoes are susceptible to many diseases that adversely damage their visual appeal, and flavor, and have significant implications on the overall economy. The identification of diseases affecting mango plant leaves using automated recognition remains a challenge due to the diverse range of symptoms and limited availability of data. There have been several deep learning-based research studies focused on identifying diseases in mango leaves;however, the majority of these studies have employed a convolutional neural network (CNN) trained on a small number of data. This study presents a vision Transformer (ViT) based approach to detect diseases in mango leaves using publicly available data namely MangoLeafBD. The ViT model has been selected as the detection model due to its parameter efficiency compared to deep CNN models. The ViT has produced remarkable overall classification accuracy of 100%, precision of 100%, recall of 100%, and f1-score of 100% for disease detection on mango leaves which is better than the existing CNN approaches on the MangoLeafBD dataset. This demonstrates that our approach has the potential to assist farmers in the field by providing automated, simple, and more reliable mango leaf disease diagnosis. © 2023 IEEE.

关键词： Deep learning image classification Mango leaf disease vision Transformer (ViT)

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A Completed Gaussian Extended Binary Pattern for Texture image Classification 2

A Completed Gaussian Extended Binary Pattern for Texture Ima...

引用

2nd international conference on Signal processing, computer Networks and Communications, SPCNC 2023

作者： Xu, Xiaochun Lin, Weizheng Chen, Dingrong Su, Nanling Tang, Leinuo Cai, Changxu College of Computer and Control Engineering Minjiang University Fuzhou350108 China International Digital Economy College Minjiang University Fuzhou350108 China

ISBN: (纸本)9798400716430

Texture image classification is a fundamental and challenging visual task and has wide range of applications. Binary pattern methods play an important role in texture feature extraction due to its ease of implementation and promising performance. To extract completed texture feature representation and improve the classification performance, this paper proposes a completed gaussian extend binary pattern for texture image classification. First, instead of the original pixel value, this paper uses the mean of local range to encode the binary pattern. Second, this paper introduces a novel gaussian sign pattern to fully represent the macro texture structure. Third, to achieve completed texture feature description, this paper presents a completed gaussian extended binary pattern, which combines the novel gaussian sign pattern, the local sign and magnitude pattern extracted from mean-processing texture image. To validate the effectiveness of the proposed completed gaussian extend binary pattern, experimental evaluations are conducted on three test subsets from Outex database. The evaluation results show that the proposed completed gaussian extended binary pattern achieves the state-pf-the-art classification performance. © 2023 ACM.

关键词： Feature extraction

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Jewelry Recognition via Encoder-Decoder Models 2

Jewelry Recognition via Encoder-Decoder Models

引用

2nd Edition IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Alcalde-Llergo, José M. Yeguas-Bolívar, Enrique Zingoni, Andrea Fuerte-Jurado, Alejandro Viterbo Italy University of Córdoba Computing and Numerical Analysis Córdoba Spain Gac Travel Córdoba Spain

ISBN: (纸本)9798350300802

Jewelry recognition is a complex task due to the different styles and designs of accessories. Precise descriptions of the various accessories is something that today can only be achieved by experts in the field of jewelry. In this work, we propose an approach for jewelry recognition using computer vision techniques and image captioning, trying to simulate this expert human behavior of analyzing accessories. The proposed methodology consist on using different image captioning models to detect the jewels from an image and generate a natural language description of the accessory. Then, this description is also utilized to classify the accessories at different levels of detail. The generated caption includes details such as the type of jewel, color, material, and design. To demonstrate the effectiveness of the proposed method in accurately recognizing different types of jewels, a dataset consisting of images of accessories belonging to jewelry stores in Córdoba (Spain) has been created. After testing the different image captioning architectures designed, the final model achieves a captioning accuracy of 95%. The proposed methodology has the potential to be used in various applications such as jewelry e-commerce, inventory management or automatic jewels recognition to analyze people's tastes and social status. © 2023 IEEE.

关键词： Classification Deep Learning Human Behavior image Captioning Jewelry Object Detection

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Facial Emotion Detection Using Haar Cascade and CNN Algorithm

Facial Emotion Detection Using Haar Cascade and CNN Algorith...

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2023 international conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Singh, Nongmeikapam Thoiba Rana, Samridh Kumari, Sonal Ritu Chandigarh University Department of Computer Science Engineering Punjab Mohali India

ISBN: (纸本)9798350333244

Due to its wide applications in psychology, healthcare, and safety, facial emotion recognition is a crucial machine vision issue that has to be studied. Emotion detection from facial expressions is considered a challenging task because it involves numerous sophisticated processes, including the detection, analysis, and recognition of facial characteristics. In this research, the Haar Cascade algorithm and the Convolutional Neural Network (CNN) are combined to create an innovative approach for identifying facial expressions of emotion. The Haar Cascade algorithm is used to detect facial characteristics, like eyes, nose, and mouth, from an input image. These detected features are then fed into the CNN algorithm, which is responsible for recognising the person's state of mind. The suggested strategy is tested on a publicly available dataset, and the findings reveal that it achieves 98.42% accuracy. This indicates that the approach is highly effective in accurately detecting facial emotions. © 2023 IEEE.

关键词： Emotion Recognition

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Learning with Balanced Criss-Cross Attention for Cross-Modality Crowd Counting 23

Learning with Balanced Criss-Cross Attention for Cross-Modal...

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5th international conference on Information Technology and computer Communications, ITCC 2023

作者： Zeng, Xin Zhang, Wanjun Wang, Huake Bian, Xiaoli Zhengzhou Vocational College of Finance and Taxation Zhengzhou China School of Computer and Information Engineering Henan University Kaifeng China School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798400700583

Cross-modality crowd counting is one of the most essential tasks in multimedia and image processing, which usually uses multi-sensor information as input in neural networks. Various approaches have been proposed to extract the alignment and relationships between the different modalities in the task of crowd counting. In this work, we explore how to further remedy the cross-modal discrepancies and learn latent relevance across different modalities. We present a novel RGBT crowd counting framework, namely Balanced Criss-Cross Attention Network (BCANet), to overcome the above limitations. To bridge the two modalities, we introduce a Balanced Criss-Cross Attention (BCA) module to encode complementary information across modalities. Lastly, we evaluate our BCANet via extensive experiments and demonstrate that it consistently achieves state-of-the-art results on RGBT-CC and DroneRGBT datasets. © 2023 ACM.

关键词： image processing

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