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Refined Feature Fusion-Based Transformer Network for Hyperspectral and LiDAR classification 2

Refined Feature Fusion-Based Transformer Network for Hypersp...

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

作者： Shen, Xinxin Zhu, Hongyu Meng, Chao Chinese Flight Test Establishment Xi'an China

ISBN: (纸本)9798350331417

Multimodal remote sensing images describe the same surface scene from different viewpoints, and can gain more reliable information about the earth's surface. As a result, fusion classification of multi-modal remote sensing images has gradually become a hot area of research in recent years. There is a large body of work on devising better fusion algorithms. However, if the features of single-modal data sources cannot be thoroughly explored, the classification performance of the network will be significantly degraded. In this paper, a refined feature fusion-based Transformer network(RFFT) for hyperspectral and LiDAR classification is proposed, which effectively compensates for the aforementioned shortcomings. In particular, a shallow specific feature extraction network(SSFE) is designed. It can use a more appropriate feature extractor to obtain the shallow feature of the individual sources. A refined spectral Tokenizer module (RSTM) is then proposed to finely explore richer unique spectral information through grouping and parallel perception. Finally, a spectral intergroup-spatial perception fusion(SISP) is built to capture global multimodal feature dependencies and perform a complete fusion. Experiments results on the public Houston2013 and Trento datasets show that the proposed RFFT performs well. © 2023 IEEE.

关键词： featrue fusion hyperspectral LiDAR multimodal remote sensing image fusion classification Remote sensing

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Automated segmentation of retinal neuroepithelial layer and pigment epithelium in macular OCT images using U-Net and ISC 2

Automated segmentation of retinal neuroepithelial layer and ...

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

作者： Chen, Yiwei He, Yi Ye, Hong Xing, Lina Zhang, Xin Shi, Guohua Chinese Academy of Sciences Jiangsu Key Laboratory of Medical Optics Suzhou Institute of Biomedical Engineering and Technology Suzhou215163 China Division of Life Sciences and Medicine Hefei230026 China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9798350331417

Retinal optical coherence tomography (OCT) images can be used for segmenting the retinal neuroepithelial layer and pigment epithelium. Segmentation is important for both ophthalmic diagnosis and the study of eye diseases. We propose an automated segmentation method for the retinal neuroepithelial layer and pigment epithelium in macular OCT images based on the widely used U-Net machine learning architecture and recently proposed integration of self-attention and convolution (ISC) approach. To verify the effectiveness of our method, we performed an experiment and compared its results with the corresponding ground truths in qualitative and quantitative terms. Our method is able to accurately segment the retinal neuroepithelial layer and pigment epithelium in macular OCT images, as demonstrated by the experimental results. © 2023 IEEE.

关键词： Biomedical optics image segmentation integration of self-attention and convolution optical coherence tomography retinal imaging U-Net

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Special Graph Group Contrastive Learning for Recommendation 2

Special Graph Group Contrastive Learning for Recommendation

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

作者： Gao, Yinjie Wan, Chang Zheng, Zhonglong Zhejiang Normal University School of Computer Science and Technology Jinhua China

ISBN: (纸本)9798350331417

While certain recommender systems based on Graph Contrastive Learning (GCL) have shown promising outcomes, there remain two significant issues. First, though current GCL approaches effectively generate perturbed views, they fail to fully leverage the valuable structural information in the interaction graph. Second, the majority of existing GCL models lack the ability to circumvent a high number of the pairwise comparison between positive and negative samples during the model training. To address the aforementioned challenges, we propose a special approach for recommender systems called Graph Group Contrastive Learning (GGCL). GGCL leverages a grouping corruption strategy to effectively utilize the structural information from the interaction graph. Therefore, nodes with different views exhibit distinct qualities through the message passing in neural networks. On that basis of the grouping strategy, Binary Cross-Entropy Loss is utilized in GGCL to optimize the loss function of GCL and accelerate the model training. Our proposed model has been extensively evaluated on diverse datasets, revealing its superior performance when compared to other recommender systems. © 2023 IEEE.

关键词： Contrastive Learning Data Mining Machine Learning Recommender System

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Deep Convolutional Neural Networks for Rail Surface Defect Perception 2

Deep Convolutional Neural Networks for Rail Surface Defect P...

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

作者： Wang, Yinjie Wang, Ting Yang, Zihao Ren, Shuoqi Gouliu, Xiaohui Gao, Jialei Lanzhou Jiaotong University School of Mechanical Engineering Lanzhou China Lanzhou Jiaotong University School of Mathematics and Physics Lanzhou China Lanzhou Jiaotong University School of Materials Science and Engineering Lanzhou China

ISBN: (纸本)9798350331417

Rail surface defect inspection is a vital element for ensuring railway safety. We critically analyze the application of deep convolutional neural networks (CNN) in Rail Surface Defect Perception (RSDP). We scrutinize 43 studies, revealing how CNN, YOLO, R-CNN, and Semantic Segmentation techniques, have revolutionized RSDP by surpassing traditional inspection methods in speed, accuracy, and efficiency. Our examination underscores the necessity for comprehensive and standardized datasets to support the varied and complex nature of rail surface defects. Additionally, we highlight the persistent challenges such as limited defect samples and the imperative for real-time detection capabilities. The paper details the performance and adaptation of various CNN models for RSDP, charting a course for future inquiry. We advocate for enhanced datasets, unified defect classification, and refined deep learning models, aiming to bolster the progress in CNN-driven RSDP technology for enhanced railway safety. © 2023 IEEE.

关键词： component Convolutional Neural Network image Segmentation R-CNN Rail Surface Defect Perception YOLO

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Improving Weeds Detection in Pastures Using Illumination Invariance Techniques 2nd

Improving Weeds Detection in Pastures Using Illumination In...

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2nd international conference on Advances in Computing Research, ACR 2024

作者： Alyatimi, Ali Hassan Al-Dala’in, Thair Chung, Vera Anaissi, Ali Sadgrove, Edmund J. School of Computer Science The University of Sydney Camperdown Australia Jizan Saudi Arabia Western Sydney University Sydney City Campus Penrith Australia School of Science and Technology University of New England ArmidaleNSW2351 Australia

ISBN: (纸本)9783031569494

computers have various applications in relation to the classification of weeds, including computer vision. This paper demonstrates the use of illumination invariance techniques and shadow reduction in images to improve the accuracy of machine learning (ML) models using support vector machines. The paper’s main aim is to identify the benefits of image optimisation utilising adjusting dark images. More specifically, the paper uses brightness and contrast adjustment to fix images and then compares the results of a dataset that underwent image pre-processing and a dataset that did not. Ensuring the clearness of an object in an image is essential if a ML model is to identify it accurately. Many issues within image datasets can hinder the accuracy of ML classification models, for example, illumination invariance and shadowed images, which entail underexposed dark pictures being projected onto the target in the absence of light sources. The paper uses several techniques and technologies to analyse the data, including cross-validation, a confusion matrix, the pre-processing technique, the TensorFlow framework and training conducted in both the central processing unit and graphics processing units. The results of these analyses show that the brightness has significantly enhanced the accuracy of the ML model. In addition, applying image pre-processing to the shadow has resulted in a slight improvement of 1% in this regard. In conclusion, this paper presents evidence concerning ML-based solutions for improving the accuracy of classification models by enhancing images of weeds using pixel brightness transformations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Luminance

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Objective Evaluation of Presence in Virtual Reality Based on Visual Features 2

Objective Evaluation of Presence in Virtual Reality Based on...

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

作者： Quan, Wei Li, Linxuan Liang, Zhen Zheng, Mingming Changchun University of Science and Technology School of Computer Science and Technology Changchun China

ISBN: (纸本)9798350331417

The sense of presence in virtual reality is a crucial factor for evaluating the quality and effectiveness of virtual reality products. In order to assess it accurately, an objective assessment method for virtual reality presence was proposed. An analysis of influencing factors was conducted. Features such as texture, color, and contrast from virtual reality videos were extracted. Discomfort experienced during virtual reality immersion can divert user attention, break the immersive experience and affect the sense of presence. Therefore, VR sickness scores were introduced as a feature in the assessment. We establish a virtual reality presence assessment model based on Support Vector Regression (SVR). Experimental results indicate that texture features have a significant impact on the sense of presence in virtual reality, and color and contrast also play a role. The inclusion of the sickness score in the model improved assessment accuracy. The assessment results showed a high level of consistency with subjective evaluations of the sense of presence in virtual reality, achieving performance metrics higher than other related studies, with a PLCC of 0.781. © 2023 IEEE.

关键词： Sense of presence in virtual reality Support vector regression Virtual reality Virtual reality sickness

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Potato Plant Disease Detection and Classification for Improved Agriculture 2

Potato Plant Disease Detection and Classification for Improv...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Chaudary, Sarthak Bakshi, Khushi Iniyan, S. Department of Computer Science and Engineering SRM Institute of Science and Technolgy Kaatankulathur Tamilnadu Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technolgy Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Agriculture is evolving, and deep learning techniques, especially convolutional neural networks, play a pivotal role. They streamline disease detection, providing farmers with timely information to deploy effective disease control strategies and amplify crop yields. Imagine a world where diseases in plants are detected with unparalleled speed and precision. In the age of technological advancement, agriculture stands at the brink of transformation. The proposed work delves into the world of innovation, by harnessing the power of cutting-edge technologies to safeguard crops. Embarking on a journey where image processing, artificial intelligence, and machine learning join forces to become the guardians of the farmlands. This is the vision that drives the project. The magic of image processing techniques, enabling to swiftly identify and categorize leaf diseases. As a result, farmers can now take immediate, well-informed actions, preventing their crops from getting plagued by diseases and ensuring a healthy yield of crops. The project introduces artificial intelligence and machine learning as formidable allies in the quest to diagnose crop diseases at an early stage. These intelligent algorithms detect disease symptoms even before the human eye can perceive them. These models, powered by convolutional neural networks, meticulously analyze leaf images, extract essential features, and classify diseases with remarkable accuracy. By fusing image processing and deep learning, the approach provides a holistic solution to the complex task of plant disease detectionin agriculture. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Convolutional Neural Networks (CNN) Deep Learning images Machine Learning (ML)

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Traffic Monitoring and Violation Detection Using Deep Learning 2nd

Traffic Monitoring and Violation Detection Using Deep Learni...

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2nd international conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Sargar, Omkar Jain, Saharsh Chittupalli, Sravan Tatipamula, Aniket Department of Electronics Engineering Veermata Jijabai Technological Institute Mumbai India Department of Computer Science Veermata Jijabai Technological Institute Mumbai India AIRPIX Geoanalytics Navi Mumbai India

ISBN: (纸本)9789819934805

The traffic density on roads has been increasing rapidly for the past few decades, which has in turn been reflected in the increase in traffic violations and accidents. Official reports from various governments and private entities bolster the fact that, indeed, the current methods for traffic monitoring are inept to deal with the huge traffic density [1, 2]. These methods, which traditionally included the deployment of traffic police personnel at a select few junctions where the traffic density is high, ignore the majority of the other roads. Traffic monitoring systems that exploit image processing, computer vision and deep learning techniques thus come out to be a viable and optimal solution to monitor traffic and detect violations. These systems can easily be integrated with the architecture of law enforcement to penalize violators in real time. The proposed method—which utilizes YOLOv3 and SORT—is effective and accurate in detecting several violations like—over-speeding, wrong-way driving, signal jumping, driving without helmet and triple seat violation. It also helps to keep track of the count of vehicles, their types and also the number of axels for multi-axle vehicles, thus, asserting itself as a novel and indigenous solution to a widely recognized problem. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Real-Time Retrieving Vedic Sanskrit Text into Multi-Lingual Text and Audio for Cultural Tourism Motivation 2

Real-Time Retrieving Vedic Sanskrit Text into Multi-Lingual ...

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2nd international conference for Advancement in Technology, ICONAT 2023

作者： Chand, Anchal Agarwal, Piyush Sharma, Sachin Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India

ISBN: (纸本)9781665475174

There are many uses for handwritten writing that has been digitally transformed. helping both those working in the field of education and visitors better understand the local culture. The Vedas are the best informational source for understanding Indian culture;the only drawback is that it exists in physical form. The ability to access cultural knowledge and historic heritage is facilitated by the real-time retrieval and translation of Vedic Sanskrit text. The tourist will benefit from this in understanding Indian culture and traditions and its significance. The manual Sanskrit text is translated into an easily editable format using the suggested methods in this study. The language translation and the audio generation in a specific selected language are the features of the methodology used. The proposed method has a four-step process i) image processing, ii) text extraction, iii) text-to-speech conversion, and iv) multi language translation. The OTSUs threshold method is employed in the suggested method to differentiate between the image's foreground and background. Tesseract OCR is used for image processing. The LSTM is utilised in the real-time text extraction process. The outcome demonstrates that the suggested technique was effective in rendering the Sanskrit text into a variety of languages for the purpose of promoting cultural tourism. © 2023 IEEE.

关键词： computer vision

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Deep Autoencoder Multi-Exposure HDR Imaging 12

Deep Autoencoder Multi-Exposure HDR Imaging

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

作者： Omrani, Ali Reza Soheili, Mohammad Reza Kelarestaghi, Manoochehr Kharazmi University Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Recently, in the era of photography, due to capturing images with limited dynamic range by cameras, High Dynamic Range (HDR) imaging has engrossed people's attention because HDR pictures present more details and better luminance than images with Low Dynamic Range (LDR). Moreover, produced HDR images by a single LDR image cannot reconstruct details appropriately, and therefore, in this research, a deep learning method is proposed to generate an HDR picture by multiple LDR pictures with different exposures. The experiments and results illustrate that the proposed algorithm performs better than the other methods in quantitative and visual comparison. © 2022 IEEE.

关键词： Convolutional neural networks

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