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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是101-110 订阅

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E²(GO)MOTION: Motion Augmented Event Stream for Egocentric Action recognition

E<SUP>2</SUP>(GO)MOTION: Motion Augmented Event Stream for E...

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

作者： Plizzari, Chiara Planamente, Mirco Goletto, Gabriele Cannici, Marco Gusso, Emanuele Matteucci, Matteo Caputo, Barbara Politecn Torino Turin Italy CINI Consortium Rome Italy Politecn Milan Milan Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Due to their sensing mechanism, event cameras have little to no motion blur, a very high temporal resolution and require significantly less power and memory than traditional frame-based cameras. These characteristics make them a perfect fit to several real-world applications such as egocentric action recognition on wearable devices, where fast camera motion and limited power challenge traditional vision sensors. However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications. In this paper, we show that event data is a very valuable modality for egocentric action recognition. To do so, we introduce N-EPIC-Kitchens, the first event-based camera extension of the large-scale EPIC-Kitchens dataset. In this context, we propose two strategies: (i) directly processing eventcamera data with traditional video processing architectures (E-2(GO)) and (ii) using event-data to distill optical flow information (E-2(GO)MO). On our proposed benchmark, we show that event data provides a comparable performance to RGB and optical flow, yet without any additional flow computation at deploy time, and an improved performance of up to 49' with respect to RGB only information. The NEPIC-Kitchens dataset is available at https:/EgocentricVision/N-EPIC-Kitchens.

关键词： Computer vision image motion analysis Wearable computers Memory management Vision sensors Cameras Robustness

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Adaptive remote sensing image Attribute Learning for Active Object Detection 25

Adaptive Remote Sensing Image Attribute Learning for Active ...

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25th International conference on pattern recognition (ICPR)

作者： Xu, Nuo Huo, Chunlei Guo, Jiacheng Liu, Yiwei Wang, Jian Pan, Chunhong Chinese Acad Sci Univ Chinese Acad Sci Sch Artificial Intelligence NLPRInst Automat Beijing Peoples R China Beijing Informat Sci & Technol Univ Beijing Peoples R China Beijing Univ Civil Engn & Architecture Beijing Peoples R China Beijing Union Univ Coll Robot Beijing Peoples R China

ISBN: (纸本)9781728188089

In recent years, deep learning methods bring incredible progress to the field of object detection. However, in the field of remote sensing image processing, existing methods neglect the relationship between imaging configuration and detection performance, and do not take into account the importance of detection performance feedback for improving image quality. Therefore, detection performance is limited by the passive nature of the conventional object detection framework. In order to solve the above limitations, this paper takes adaptive brightness adjustment and scale adjustment as examples, and proposes an active object detection method based on deep reinforcement learning. The goal of adaptive image attribute learning is to maximize the detection performance. With the help of active object detection and image attribute adjustment strategies, low-quality images can be converted into high-quality images, and the overall performance is improved without retraining the detector.

关键词： Object detection

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Dual Stream Fusion Network for Multi-spectral High Resolution remote sensing image Segmentation 4th

Dual Stream Fusion Network for Multi-spectral High Resolutio...

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4th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Cao, Yong Shi, Yiwen Liu, Yiwei Huo, Chunlei Xiang, Shiming Pan, Chunhong Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Beijing City Univ Intelligent Elect Mfg Res Ctr Beijing 100191 Peoples R China Beijing Univ Civil Engn & Architecture Beijing 102627 Peoples R China

ISBN: (纸本)9783030880071;9783030880064

Semantic segmentation is in-demand in High Resolution remote sensing (HRRS) image processing. Unlike natural images, HRRS images usually provide channels such as Near Infrared (NIR) in addition to RGB channels. However, in order to make use of the pre-trained model, the current semantic segmentation methods in remote sensing field usually only use the RGB channel and discard the information of other channels. In this paper, to make full use of the HRRS image information, a dual-stream fusion network is proposed to fuse the information of different channel combinations through a Feature Pyramid Network (FPN), then a Stage Pyramid Pooling (SPP) module is used to integrate the features of different scales and produce the final segmentation results. Experiments on the RSCUP competition dataset show that the proposed approach can effectively improve the segmentation performance.

关键词： Semantic segmentation remote sensing Stream fusion

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Leveraging Segment-Anything model for automated zero-shot road width extraction from aerial imagery

Leveraging Segment-Anything model for automated zero-shot ro...

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2023 International conference on Digital image Computing: Techniques and Applications, DICTA 2023

作者： Xu, Nan Nice, Kerry Seneviratne, Sachith Stevenson, Mark Faculty of Engineering and It Department of Infrastructure Engineering Australia Melbourne School of Design Transport Health and Urban Systems Research Lab Australia The University of Melbourne Optimization and Pattern Recognition Group Faculty of Engineering and It ParkvilleVIC Australia

ISBN: (纸本)9798350382204

Segment-Anything model (SAM) is a foundation segmentation model published in April 2023. Trained on an unprecedented 11 million annotated images, the model can generate segmented masks bearing clear-cut contours by integrating user-provided prompts. It is zero-shot transferable, requiring no task-specific training. Nevertheless, its applicability for geographic vision tasks has not been fully evaluated. There is no automated prompt-feeding method incorporating with SAM that can work efficiently for purposeful batch processing as well. To fill these gaps, we developed a process that can be executed automatically from visual-prompts extraction to road width measurement, utilizing OpenStreetMap (OSM) and SAM. By examining the quality of segmentation in various image contexts, we evaluated the capacity and limitations of SAM working on aerial imagery. Through comparing measured widths to VicRoads records, we validated the specially designed width-measuring algorithm for high precision and accuracy. After this process, prompt-indicated zero-shot approach in solving basic geographic vision tasks is to be shaped synchronously on both theory and application ends. © 2023 IEEE.

关键词： remote sensing

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Application of Band Combination in Landslide Identification of remote sensing images Driven by Deep Learning

Application of Band Combination in Landslide Identification ...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Tianyu Yang Weijie Xu Yu Hua College of Computer Science and Cyber Security (Oxford Brookes College) Chengdu University of Technology Chengdu China

With the frequent occurrence of geological disasters, landslide identification has become an important research problem. In recent years, with the application and research of deep learning in the fields of computer vision and remote sensing image analysis, deep learning has become a popular method for landslide identification research because of its excellent feature extraction and pattern recognition capabilities. In this paper, we add new bands reflecting vegetation and water of landslide factors to the original bands of remote sensing image samples by linear combination of bands, and conduct two sets of comparison experiments on Unet and Swin-Unet to verify that adding band features can help improve landslide identification accuracy. The experimental results show that the experimental group with additional band features has a numerical improvement in landslide identification accuracy, with F1-score as the evaluation index, the Unet experimental group has a 2.29% improvement and the Swin-Unet experimental group has a 1.78% improvement. The results of this paper have implications for the subsequent application of bands combination of landslide factor features in remote sensing landslide identification driven by deep learning methods.

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Diffusion posterior sampling for remote sensing image fusion

Diffusion posterior sampling for remote sensing image fusion

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7th International conference on Vision, image and Signal processing (ICVISP 2023)

作者： C. Zhang Z. Wang J. Han College of science National University of Defense Technology Changsha People's Republic of China

remote sensing image fusion, i.e., fusing remote sensing images from different sensors or different time into a comprehensive image, can integrate image information for all kinds of image tasks, such as object detection, pattern recognition, and so on. In this paper, we focus on the image fusion of optical image and synthetic aperture radar (SAR) image, where the traditional methods, such as sparse representation and image decomposition, usually fail to improve the image quality and enhance object features' level. Inspired by the powerful generation model named diffusion denoising probabilistic model (DDPM), we propose a novel method based on diffusion posterior sampling for image fusion of optical image and SAR image. Starting from the variational model of image fusion and the total variation constraint, we approximate the posterior sampling process of image fusion by a closed-form analytic solution. After that, DDPM can be used to generate the fused image with high image quality and enhanced object features. The feasibility and the superiority of the proposed method is validated in numerical experiments.

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A Convolutional Fully Connected Structure with Hard Sample Memory Pool for Land Use Classification 32

A Convolutional Fully Connected Structure with Hard Sample M...

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conference on pattern recognition and Tracking XXXii

作者： Xie, Dong Depoian, Arthur C., ii Jaques, Lorenzo E. Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (数字)9781510643086

ISBN: (纸本)9781510643086

As one of the classic fields of computer vision, image classification has been booming with the improvement of chip performance and algorithm efficiency. With the rapid progress of deep learning in recent decades, remote sensing land cover and land use image classification has ushered in a golden period of development. This paper presents a new deep learning classifier to classify remote sensing land cover and land use images. The approach first uses multi-layer convolutional neural networks to extract the image features, attached through a fully-connected neural network to generate the sample loss. Then, a hard sample memory pool is created to collect the samples with large losses during the training. A batch of hard samples is randomly extracted from the memory pool to participate in the training of the convolutional fully connected model so that the model becomes more robust. Our method is validated by testing the classic remote sensing land cover and land use dataset. Compared with the previous popular classification algorithm, our algorithm can classify images more accurately with a shorter training iteration.

关键词： computer vision remote sensing deep learning neural networks land use classification

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A Fast Line and Ellipse Detection on High Resolution images 17th

A Fast Line and Ellipse Detection on High Resolution Image...

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17th Chinese conference on image and Graphics Technologies and Applications, IGTA 2022

作者： Liu, Limin Li, Dingzhe Li, Zhaoxi Meng, Cai The Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Image Processing Center Beijing University of Aeronautics and Astronautics Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100083 China

ISBN: (纸本)9789811950957

Line and ellipse are important image features in pattern recognition and computer vision. Many methods have been developed to extract line or ellipse in images separately but few try to detect them simultaneously. In this paper, a novel fast line and ellipse detection (FLED) method is proposed to detect line and ellipse simultaneously, even in high resolution images. At first, a detection framework (Pre-SGV) for high detection speed is proposed, which explicitly decomposes the detection into precalculate, segment, grouping, validation phases. Secondly, a simple but efficient algorithm is designed to segment the edges into line or arc candidates. Thirdly, the grouping constraints and fitting methods are further improved. Finally, validation are conducted to exclude erroneous detection. Experiments on synthetic images and real image dataset show that the proposed method, FLED, can robustly detect lines and ellipses fast and efficiently, especially for high resolution image (e.g. remote sensing image, the scanning image). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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Watershed-Based Attribute Profiles for Pixel Classification of remote sensing Data 1st

Watershed-Based Attribute Profiles for Pixel Classification ...

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1st IAPR International Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Maia, Deise Santana Minh-Tan Pham Lefevre, Sebastien Univ Bretagne Sud IRISA UMR 6074 F-56000 Vannes France

ISBN: (纸本)9783030766566;9783030766573

We combine two well-established mathematical morphology notions: watershed segmentation and morphological attribute profile (AP), a multilevel feature extraction method commonly applied to the analysis of remote sensing images. To convey spatial-spectral features of remote sensing images, APs were initially defined as sequences of filtering operators on the max- and min-trees computed from the original data. Since its appearance, the notion of APs has been extended to other hierarchical representations including tree-of-shapes and partition trees such as a-tree and.-tree. In this article, we propose a novel extension of APs to hierarchical watersheds. Furthermore, we extend the proposed approach to consider prior knowledge from training samples, leading to a more meaningful hierarchy. More precisely, in the construction of hierarchical watersheds, we combine the original data with the semantic knowledge provided by labeled training pixels. We illustrate the relevance of the proposed method with an application in land cover classification using optical remote sensing images, showing that the new profiles outperform various existing features.

关键词： Mathematical morphology

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image segmentation technology and its application in digital image processing 2

Image segmentation technology and its application in digital...

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2nd IEEE Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Cheng, Yang Li, Bingjie Information Engineering University Zhengzhou Henan450000 China

ISBN: (纸本)9781728190181

In recent years, the level of science and technology in China has been significantly improved. Digital image processing technology is the product of the times with the rapid development of science and technology, and has been applied in many fields. Digital image processing technology includes many practical technologies, and image segmentation technology is one of them. At present, the widely used pattern recognition technology is realized by image segmentation technology. Based on this, this paper mainly studies the application of image segmentation technology in digital image processing. This paper first introduces the methods of image segmentation, including threshold segmentation, clustering segmentation and edge detection segmentation. Secondly, according to a need to extract a road image for image segmentation, and achieved very good results. Finally, this paper mainly analyzes the application of image segmentation in digital image processing. It is found that image segmentation has good application in automatic license plate recognition, biomedical engineering, remote sensing engineering and fire prevention and detection. © 2021 IEEE.

关键词： remote sensing

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