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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Wei, Dong Wan, Yi Zhang, Yongjun Liu, Xinyi Zhang, Bin Wang, Xiqi Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Three-dimensional (3D) line segments are helpful for scene reconstruction. Most of the existing 3D-line-segmentreconstruction algorithms deal with two views or dozens of small-size images;while in practice there are usually hundreds or thousands of large-size images. In this paper, we propose an efficient line segment reconstruction method called ELSR1. ELSR exploits scene planes that are commonly seen in city scenes and sparse 3D points that can be acquired easily from the structure-from-motion (SfM) approach. For two views, ELSR efficiently finds the local scene plane to guide the line matching and exploits sparse 3D points to accelerate and constrain the matching. To reconstruct a 3D line segment with multiple views, ELSR utilizes an efficient abstraction approach that selects representative 3D lines based on their spatial consistence. Our experiments demonstrated that ELSR had a higher accuracy and efficiency than the existing methods. Moreover, our results showed that ELSR could reconstruct 3D lines efficiently for large and complex scenes that contain thousands of large-size images.

关键词： image segmentation Computer vision Three-dimensional displays Urban areas Buildings Reconstruction algorithms pattern recognition

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad image processing

Multi-Plant Leaf Disease Detection and Classification using ...

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Recent Advances in Science and Engineering Technology (ICRASET), International Conference on

作者： S S Aishwarya Pushpa Ravikumar I V Chaithra Deeksha Sagar Computer Science and Engineering AIT Adichunchanagiri Institute of Technology Chikmagalur Karnataka INDIA Department of Computer Science and Engineering AIT Adichunchanagiri Institute of Technology Chikmagalur Karnataka INDIA

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Every nation’s basic need is the agricultural products. Disease-ridden leaves have an effect on the nation’s agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are $\mathbf{2 0 6 3 6}$ photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, $\mathbf{1 2}$ are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing; specifically, we achieved $\mathbf{9 8. 2 9 \%}$ accuracy in training and $\mathbf{9 8. 0 2 9 \%}$ accuracy in testing across all utilized data sets. pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision.

关键词： Training Accuracy Neural networks Classification algorithms pattern recognition Convolutional neural networks remote sensing Diseases Testing Principal component analysis

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remote sensing Object Detection Based on Improved Detection Transformer

Remote Sensing Object Detection Based on Improved Detection ...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Yuanzhong Hu Po Hu Haihui Wang Yuzhuo Wang School of Computer Science and Engineering and School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Nowadays, the accuracy of remote sensing image target recognition has greatly improved. It enables humans to efficiently monitor crop growth, track climate changes and natural disasters, and even provide critical strategic intelligence for national defense. However, due to the challenges posed by the large number of objects and the immense size of remote sensing images, there is still considerable room for improvement in recognition accuracy and efficiency. Detection Transformer (DETR) framework utilizes the Transformer architecture, change the object detection task to a set prediction problem, thus simplifying the traditional post-processing workflow. Through its attention mechanisms, DETR effectively captures object features in intricate scenes by modeling global contextual information. Nonetheless, there is still potential for improvement in extracting fine-grained details. In this study, three modifications are proposed to the DETR framework to boost remote sensing object detection accuracy. First, a multi-scale feature extraction approach is employed to capture deeper and more detailed image features. Second, a bidirectional learning path is introduced to enhance the contextual relationships between image features. Experimental results demonstrate that our proposed method has achieved strong competitiveness compared to other approaches

关键词： Accuracy Target tracking Target recognition Object detection Transformers Feature extraction remote sensing Monitoring Software engineering Meteorology

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remote sensing 2021年第3期13卷 1-26页

作者： Malik, Karim Robertson, Colin Wilfrid Laurier Univ Dept Geog & Environm Studies Waterloo ON N2L 3C5 Canada

Convolutional neural networks (CNNs) are known for their ability to learn shape and texture descriptors useful for object detection, pattern recognition, and classification problems. Deeper layer filters of CNN generally learn global image information vital for whole-scene or object discrimination. In landscape pattern comparison, however, dense localized information encoded in shallow layers can contain discriminative information for characterizing changes across image local regions but are often lost in the deeper and non-spatial fully connected layers. Such localized features hold potential for identifying, as well as characterizing, process-pattern change across space and time. In this paper, we propose a simple yet effective texture-based CNN (Tex-CNN) via a feature concatenation framework which results in capturing and learning texture descriptors. The traditional CNN architecture was adopted as a baseline for assessing the performance of Tex-CNN. We utilized 75% and 25% of the image data for model training and validation, respectively. To test the models' generalization, we used a separate set of imagery from the Aerial imagery Dataset (AID) and Sentinel-2 for model development and independent validation. The classical CNN and the Tex-CNN classification accuracies in the AID were 91.67% and 96.33%, respectively. Tex-CNN accuracy was either on par with or outcompeted state-of-the-art methods. Independent validation on Sentinel-2 data had good performance for most scene types but had difficulty discriminating farm scenes, likely due to geometric generalization of discriminative features at the coarser scale. In both datasets, the Tex-CNN outperformed the classical CNN architecture. Using the Tex-CNN, gradient-based spatial attention maps (feature maps) which contain discriminative pattern information are extracted and subsequently employed for mapping landscape similarity. To enhance the discriminative capacity of the feature maps, we further perform spatial fi

关键词： landscape similarity feature maps texture features spatial patterns

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Mid-long-wave infrared broadband absorber based on multi-layer plasma cavities metasurface 12

Mid-long-wave infrared broadband absorber based on multi-lay...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Duan, Shenghua Yang, Guang Zhang, Xinyu National Key Laboratory of Science & Technology on Multispectral Information Processing Huazhong University of Science & Technology Wuhan430074 China School of Artificial Intelligence & Automation Huazhong University of Science & Technology Wuhan430074 China

ISBN: (数字)9781510674929

ISBN: (纸本)9781510674912

Efficient absorption in the mid-long-wave infrared region is crucial for various applications, including thermal imaging and sensing, infrared spectroscopy, and camouflage. A multilayered metal-insulator-metal-based infrared plasmonic metamaterial absorber utilizing a stacked nanocavity meta-surface consisting of a double-layer cylindrical metal-insulator-metal structure (DCMIM) is proposed. This absorber demonstrates wideband absorption capabilities with an average absorption rate exceeding 75% in the 3-14μm range. To understand the spectral absorption mechanisms, a comprehensive analysis is conducted using the Finite Difference Time Domain and Fabry-Perot resonance model. The analysis reveals physical mechanisms such as strong coupling between plasmon resonances and phonon vibrations loss from the dielectric spacer, as well as localized surface plasmon resonance. It is worth noting that the quantities of plasma cavities significantly impacts the performance of the absorber. By adjusting the material type and thickness of the film layer, it becomes possible to effectively compensate for the performance limitations of a single dielectric layer material within specific wavelength bands. Experimental results, combined with theoretical analysis of the DCMIM structure, establish a new approach to the design of broadband infrared absorbers. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Metal insulator boundaries

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Personalized healthcare museum exhibition system design based on VR and deep learning driven multimedia and multimodal sensing

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PERSONAL AND UBIQUITOUS COMPUTING 2022年第3期27卷 973-988页

作者： Zhao, Ling Hubei Inst Fine Arts Dept Ind Design Wuhan Wuhan 430000 Hubei Peoples R China

With the vigorous development of the Internet, multimedia technology, and virtual reality technology, digital museum has been born and gradually entered the stage of rapid development. As a typical application of 3D virtual scene in the field of traditional culture protection and communication, 3D virtual museum has become an important trend in the development of museums in recent years. Three-dimensional virtual museum can maximize the development of museum functions and better play the museum's cultural communication and heritage protection and other functions. Faced with such rich cultural relic resources, how to construct 3D virtual museum quickly becomes an important research problem. Virtual reality technology integrates the latest research and development achievements of digital image graphics processing technology, computer technology, artificial intelligence, multimedia technology, sensor technology, and other information technologies, which provides powerful support and help for us to create and experience virtual world. The combination of virtual reality technology and cultural heritage protection has created a new research field. Based on this, this paper studies personalized healthcare museum exhibition system design based on the VR and deep learning-driven multimedia and multimodal sensing. The novel computational model is designed to improve the systematic performance of the VR framework, and the application scenarios are tested. Through the verification, the performance is tested.

关键词： Virtual reality Human-computer interaction pattern recognition Neural networks Multimedia technology Digital museum Personalized healthcare Multimodal sensing

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Classification of MRI's images using the Fractional Fourier Transform and the Local Binary pattern information by Artificial Neural Network 29

Classification of MRI's images using the Fractional Fourier ...

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Conference on Infrared remote sensing and Instrumentation XXIX

作者： Estrada Marmolejo, Carlos A. Alvarez-Borrego, Josue Garcia-Torales, Guillermo Flores Nunez, Jorge Luis Univ Guadalajara Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico CICESE Carretera Ensenada Tijuana 3918 Zona Playitas Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510644991

ISBN: (纸本)9781510644991;9781510644984

pattern recognition techniques are widely used in computer vision, classification of radio signals, and voice recognition. The fractional Fourier transform is used to recognize patterns using binary rings masks and segment images. This technique has the characteristic of being invariant to position and rotation and finally obtaining a one-dimensional signature. On the other hand, Neural Networks are used for pattern recognition based on a deep neural network algorithm. It has the characteristic of training large datasets with millions of images. Artificial Neural Networks(ANNs) are used for several applications such as pattern recognition and classification of input data. In particular, the ANN has been used to evaluate medical images from the brain to assess if the image corresponds to Alzheimer's disease. One disadvantage of the neural network is a large amount of time to learn depending on the number of patterns to be identified or classified and the ability to adapt and recognize patterns. Besides, the fractional Fourier transform cannot analyze a large amount of information. In this work, a comparison between the Artificial Neural Network and the Fractional Fourier Transform is presented to determine which will be the best for recognizing a batch of selected medical images. We propose a reconstruction method using both techniques for precise image recognition and the evaluation of their respective metrics such as accuracy, precision, sensitivity, and specificity. The medical images regarding Alzheimer's disease are no dementia, very mild dementia, mild dementia presenting the best perfomance regarding the receiver operating characteristics and moderate dementia was the worst classified related to the number of images of the dataset.

关键词： pattern recognition one-dimensional signature fractional Fourier transform binary rings masks Artificial Neural Network computer vision image processing local binary pattern neural network

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Analysis and Application of Multispectral Data for Water Segmentation Using Machine Learning

Analysis and Application of Multispectral Data for Water ...

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International Conference on Computer Vision and Machine Intelligence, CVMI 2022

作者： Gupta, Shubham Uma, D. Hebbar, R. PES University KA Bangaluru560085 India Regional Remote Sensing Centre–South ISRO KA Bengaluru560037 India

ISBN: (纸本)9789811978661

Monitoring water is a complex task due to its dynamic nature, added pollutants, and land build-up. The availability of high-resolution data by Sentinel-2 multispectral products makes implementing remote sensing applications feasible. However, overutilizing or underutilizing multispectral bands of the product can lead to inferior performance. In this work, we compare the performances of ten out of the thirteen bands available in a Sentinel-2 product for water segmentation using eight machine learning algorithms. We find that the shortwave-infrared bands (B11 and B12) are the most superior for segmenting water bodies. B11 achieves an overall accuracy of 71 % while B12 achieves 69 % across all algorithms on the test site. We also find that the Support Vector Machine (SVM) algorithm is the most favorable for single-band water segmentation. The SVM achieves an overall accuracy of 69 % across the tested bands over the given test site. Finally, to demonstrate the effectiveness of choosing the right amount of data, we use only B11 reflectance data to train an artificial neural network, BandNet. Even with a basic architecture, BandNet is proportionate to known architectures for semantic and water segmentation, achieving a 92.47 mIOU on the test site. BandNet requires only a fraction of the time and resources to train and run inference, making it suitable to be deployed on web applications to run and monitor water bodies in localized regions. Our codebase is available at https://***/IamShubhamGupto/BandNet. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Content-based image Retrieval in Cyber-Physical System Modeling of Robots 11th

Content-based Image Retrieval in Cyber-Physical System Model...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Anantha Prabha, P. Subashree, B. Deva Priya, M. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789811988240

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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SAR-Optical image Matching Model Based on Contrastive Learning 20

SAR-Optical Image Matching Model Based on Contrastive Learni...

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20th International Computer Conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Yaoting, Xiong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Due to the strong complementary nature of synthetic aperture radar (SAR) and optical imagery in remote sensing, matching SAR images with optical images enables the complementary extraction of ground surface feature information. This approach is beneficial for addressing tasks such as change detection, target recognition, and land cover classification. With the development of deep learning, most current models for SAR-optical image matching utilize siamese neural networks and have achieved good matching performance. However, in practical applications, test data is often unknown, and there may be differences between training and test data. A matching model that performs well on the training dataset may not generalize well to the test data. Domain generalization research for heterogeneous image matching aims to improve the generalization performance of matching models on unknown test domains, and this task has practical significance and application prospects. In this paper, we propose a contrastive learning-based SAR-optical image matching model specifically designed to enhance the generalization performance of matching models trained on multiple datasets. When dealing with cross-domain scenarios, enforcing feature consistency of matching points preserves more information conducive to matching. We propose a contrastive learning method to limit the feature consistency of matching points, thereby improving the generalization performance of cross-source image matching models. © 2023 IEEE.

关键词： image matching

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