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共 707 条记录，以下是141-150 订阅

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Estimation of Varietal Classification and Quality Analysis in Rice Seeds Using Digital image processing – A Survey

Estimation of Varietal Classification and Quality Analysis i...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International conference on

作者： M. Sandhiya S. Christy Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Agriculture plays a major role in today’s world. 70% of rice production is cultivated in south India. Thailand, the US, India, Vietnam, etc., are exporting rice all over the world. But they are facing one of the most difficult tasks: assuring the rice seed quality. Good seeds can increase production, so selecting seeds is very important. A maximum of the work was done physically or by using huge, expensive machines. For choosing good seeds, agronomists be conscious of two factors, such as the variety which is determined by some quality criteria, and next by the skilled one who is going to analyze the rice seed physically. Both tasks take a long time and the result of accuracy is also not as expected. Nowadays computer vision technology with Digital image processing plays a major role in many fields, namely agronomy, healthcare, machine vision, pattern recognition, remote sensing, Video processing, etc. Our research work mainly focuses on the agricultural arena, going to provide good quality seeds and identify dissimilar varieties of rice seeds by digital image processing techniques such as image capturing, segmenting the rice seeds, extracting the features from the rice seeds and finally feeding into the machine learning algorithm to find out the accuracy too.

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GGRNet: Global Graph Reasoning Network for Salient Object Detection in Optical remote sensing images 4th

GGRNet: Global Graph Reasoning Network for Salient Object De...

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

作者： Liu, Xuan Zhang, Yumo Cong, Runmin Zhang, Chen Yang, Ning Zhang, Chunjie Zhao, Yao Beijing Jiaotong Univ Sch Software Beijing Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China

ISBN: (纸本)9783030880071;9783030880064

The task of salient object detection (SOD) in optical remote sensing images (RSIs) is more challenging than the SOD in natural sensing images (NSIs) because of the unique characteristics of remote sensing images such as various object scales and background context redundancy. However, the existing methods ignore the global relationship modeling between different salient objects or different parts in one salient object. To this end, we design a Global Graph Reasoning Module (GGRM) in a lightweight and effective form, and propose a novel Global Graph Reasoning Network (GGRNet) for SOD in optical RSIs. During the graph reasoning, the GGRM considers the role of the global information. Specifically, we explore two ways to utilize the global information, including the global features and global nodes, which are ingeniously added to the interaction of graph nodes and fully integrated through iteration. Besides, we stabilize the projection channel between coordinate space and interactive space through an attention mechanism. The GGRNet outperforms the existing state-of-the-art SOD algorithms on two publicly available datasets, and the number of parameters is only 25.01 Mb.

关键词： Salient object detection Optical remote sensing images Graph reasoning Global information

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Fast Calculation of the Local Entropy of a Digital image Using Machine Learning 15th

Fast Calculation of the Local Entropy of a Digital Image Usi...

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15th International conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bavrina, Alina Sergeyev, Vladislav IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardeyskaya 151 Samara443001 Russia Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia

ISBN: (纸本)9783031810824

This paper proposes a method for fast construction of the entropy field calculated in a sliding local window. The method is based on the representation of the local histogram by a truncated series using cosine basis functions and approximation of the entropy value using regression models. Experiments show a gain in time compared to the direct calculation of the entropy field with a good enough quality of approximation. The method can be applied for other histogram features calculation (e.g., rank statistics). It is reasonable to use the proposed method for obtaining additional features when analyzing large size remote sensing images, where the processing speed is critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linear regression

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Medical image Denoising in MRI Reconstruction Procedure 21st

Medical Image Denoising in MRI Reconstruction Procedure

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21st International conference on Computational Science and Its Applications (ICCSA)

作者： Han, Dong Velastegui, Ronny Norwegian Univ Sci & Technol Gjovik Norway

ISBN: (纸本)9783030869601;9783030869595

With the rapid development of computer technology, deep learning can be used in nearly every field, and it always has the potential to achieve a high efficiency performance. Specifically, in the field of medical images, it makes doctors possible to distinguish and diagnose diseases in a more accurate way. Medical images like any other form of imaging techniques are affected by noise and artifacts. There are many types of noise, such as quantum, random, electric, and gaussian noise, etc. The presence of noise affects image clarity and may obstruct the recognition and analysis of diseases. The traditional image denoising method has much more limitation when came to medical images, and the results cannot meet some specific medical image standards. Hence, denoising of medical images in deep learning can be an important technique for further medical image processing. In this work, we conducted a deep learning method, which is a sparse dilated convolution neural network based on compressed sensing, for medical image denoising.

关键词： Medical image denoising Medical resonance imaging Compressed sensing Dilated convolution Deep learning

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Unsupervised Cross-Domain Network Based on Bi-classifier for PolSAR image Classification 23

Unsupervised Cross-Domain Network Based on Bi-classifier for...

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Proceedings of the 2023 6th International conference on Artificial Intelligence and pattern recognition

作者： Wenqiang Hua Lin Liu Nan Sun Chen Wang Department of Computer Science Xi'an University of Posts & Telecommunications China

ISBN: (纸本)9798400707674

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is an important research content in the field of remote sensing. However, the lack of effective labeled data and the serious class distribution shift problem between different datasets hinder the application of deep learning networks in PolSAR image classification. Therefore, this paper proposes a Bi-classifier adversarial network based on covariance difference matrix and Convolutional Block Attention Module (CBAM) to solve the cross-domain problem in PolSAR images. Firstly, since PolSAR image processing generally uses the covariance matrix or coherence matrix of a single target pixel to construct the data set, which cannot effectively use the local information of PolSAR data,Therefore, the PolSAR coherence difference matrix P is proposed as the description of the PolSAR image difference information. Secondly, due to the different importance of features between neighboring pixels, the CBAM mechanism is introduced in order to exploit the spatial information of PolSAR data. Finally, in order to solve the cross-domain problem of PolSAR images more effectively, a bi-classifier adversarial network based on covariance difference matrix and CBAM is proposed. In this paper, we conduct experiments on public datasets, using images of San FranciscoⅠ acquired in 2008 as the source domain, and images of San Francisco Ⅱ acquired in 1992 as the target domain data. The experimental results show that the proposed method can effectively solve the cross-domain problem of PolSAR images.

关键词： Convolutional Block Attention Module (CBAM) Polarimetric Synthetic Aperture Radar (PolSAR) image classification coherence difference matrix unspervised cross domain

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Object Detection Algorithm Based on Coordinate Attention and Context Feature Enhancement 22

Object Detection Algorithm Based on Coordinate Attention and...

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Proceedings of the 2022 11th International conference on Computing and pattern recognition

作者： Lingzhi Liu Baohua Qiang Yuanchun Wang Xianyi Yang Jubo Tian Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

In recent years, object detection has been widely used in various fields such as face detection, remote sensing image detection and pedestrian detection. Due to the complex environment in the actual scene, we need to fully obtain the feature information in the image to improve the accuracy of object detection. This paper proposes an object detection algorithm based on coordinate attention and contextual feature enhancement. We design a multi-scale attention feature pyramid network, which first uses multi-branch atrous convolution to capture multi-scale context information, and then fuses the coordinate attention mechanism to embed location information into channel attention, and finally uses a bidirectional feature pyramid structure to effectively fuse high-level features and low-level features. We also adopt the GIoU loss function to further improve the accuracy of object detection. The experimental results show that the proposed method has certain advantages compared with other detection algorithms in the PASCAL VOC datasets.

关键词： Feature pyramid network Object detection Coordinate attention Atrous convolution

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Optimizing low-cost uav aerial image mosaicing for crop growth monitoring

Optimizing low-cost uav aerial image mosaicing for crop grow...

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American Society for Photogrammetry and remote sensing, ASPRS 2021 Annual conference

作者： Bupathy, Pranavan Sivanpillai, Ramesh Sajithvariyar, V.V. Sowmya, V. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham TN Coimbatore641112 India Wyoming GIS Center University of Wyoming LaramieWY82072 United States

High spatial resolution images acquired with drones can provide useful information to farmers for devising suitable management practices and increase crop yield. Data collected as individual frames or images have to be mosaiced using pattern recognition and matching process. Most flight missions collect hundreds of photos with high overlap and side overlap in order to generate mosaic without data gaps or distortion. These frames are aligned using the location information associated with each image. The same features are identified in multiple frames for generating the mosaic. In this process, it is common to use all or most of the images which requires a lot of resources. Uploading and processing hundreds of images could take several hours to days. Many farmers and crop consultants in developing countries may not have the necessary resources to upload hundreds of images. This study assessed the optimal number of images required to generate an image mosaic for a crop field without any data gaps or distortion. images were collected at two different heights and directions. First, the mosaic was generated using all (100%) frames followed by subsets containing 90%, through 50% of images. Results obtained will assist us to plan the settings in future flight missions for acquiring optimal number of images required for generating image mosaic. © 2021 International Society for Photogrammetry and remote sensing. All rights reserved.

关键词： Developing countries

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Ice Floe Tracking with Sentinel-2

Ice Floe Tracking with Sentinel-2

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conference on remote sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions

作者： Koenig, Marcel Wagner, Matthias P. Oppelt, Natascha Univ Kiel Dept Geog Earth Observat & Modelling Ludewig Meyn Str 14 D-24098 Kiel Germany

ISBN: (数字)9781510638723

ISBN: (纸本)9781510638716;9781510638723

Sentinel-2 offers the capabilities to observe Arctic sea ice features with high spatial and temporal resolution. Arctic sea ice drift, however, exacerbates observing the temporal evolution of floes by means of time series analysis. We therefore developed a novel rotation-invariant ice floe descriptor based on ice floe geometry and an image-processing workflow consisting of three main steps: (i) ice floe extraction, (ii) ice floe description and (iii) ice floe matching. We tested the methodology on Sentinel-2A images from 10 June and 3 July, 2017, and selected five floes present in both images. We further added ten "false samples" in the second image. All floes from the first image were correctly identified and matched with the floes from the second image. The methodology enables the identification of individual ice floes and determination of their relative rotation from multiple Sentinel-2 images.

关键词： Sea ice Sentinel-2 Arctic feature matching image processing pattern recognition

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Web Based GIS Village Information System: A Review 3rd

Web Based GIS Village Information System: A Review

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3rd International conference on Recent Trends in image processing and pattern recognition, RTIP2R 2020

作者： Chaudhari, Reena H. Ishaq, Bidoor Noori Gawali, Bharti W. Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad431004 India Basra Technical Institute Southern Technical University Basra Iraq

ISBN: (纸本)9789811605062

This review gives a recapitulation of the Village Information System (VIS). Village information system is a research at the very small scale, which may useful to villagers, government sectors and administrators in planning and making decisions about the development of Village. A village is a legal group of individuals residing in a particular geographical region, characterized by kind consciousness, prevalent lifestyles and numerous extensive social interactions. Village should be at the core in the nation’s preparation and growth process. It is acknowledged that the planner scheduling may fail due to insufficient and unavailability of village-related data. VIS helps to provide adequate information. This study reveals VIS growth with numerous tools including different parameters. remote sensing (RS) and Geographical Information System (GIS), these are the two robust tools through which VIS can be developed. The advent of remote sensing and Geographic Information System as a strong tool for spatial analysis and memory board has effectively alleviated the issue through spatial data computerization. Web-based GIS is a computer network-wide GIS that integrates, disseminates and communicates geographic information on the Web. © 2021, Springer Nature Singapore Pte Ltd.

关键词： remote sensing

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Prompt–RSVQA: Prompting visual context to a language model for remote sensing Visual Question Answering

Prompt–RSVQA: Prompting visual context to a language model ...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Christel Chappuis Valé rie Zermatten Sylvain Lobry Bertrand Le Saux Devis Tuia EPFL Sion Switzerland Universit&#x00E9 Paris Cit&#x00E9 Paris France European Space Agency &#x03A6 lab Frascati Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

remote sensing visual question answering (RQA) was recently proposed with the aim of interfacing natural language and vision to ease the access of information contained in Earth Observation data for a wide audience, which is granted by simple questions in natural language. The traditional vision/language interface is an embedding obtained by fusing features from two deep models, one processing the image and another the question. Despite the success of early VQA models, it remains difficult to control the adequacy of the visual information extracted by its deep model, which should act as a context regularizing the work of the language model. We propose to extract this context information with a visual model, convert it to text and inject it, i.e. prompt it, into a language model. The language model is therefore responsible to process the question with the visual context, and extract features, which are useful to find the answer. We study the effect of prompting with respect to a black-box visual extractor and discuss the importance of training a visual model producing accurate context.

关键词： Training Visualization Natural languages Feature extraction Transformers Question answering (information retrieval) Data mining

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