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

作者： Liu, Zeping Tang, Hong Huang, Wei Beijing Normal Univ Fac Geog Sci State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China

ISBN: (纸本)9783030880071;9783030880064

Accurately and automatically delineating the rooftops of buildings from remote sensing images is very essential to many fields. Recently, several Sequent Prediction Models have been proposed to deal with this task. These models use CNN encoder to recognize the boundary fragments by edge and vertex probability maps in order to guide RNN decoder find a set of sequent vertexes linking the boundaries into object regions. However, the recognition result of edge looks like large buffers around the real edges or vertexes of an object instance, which significantly influences the accuracy of predicted polygons. In this paper, we present a novel framework named AFM-RNN, in which a dual representation of lines called Attraction Field Map (AFM) is embedded by Neural Discriminative Dimensionality Reduction Layer (NDDR Layer). Consequently, the problem of over-smooth edge recognition can be effectively solved by directly modeling line segments. Experimental results over three datasets show that the proposed method outperforms other closely related methods. Code is available at https://***/Zeping-Liu/AFMRNN-pytorch.

关键词： Rooftop delineation Sequent Prediction Model CNN RNN

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remote sensing image Super-Resolution via Efficient Non-Local Feature Extraction Strategy 23

Remote Sensing Image Super-Resolution via Efficient Non-Loca...

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

作者： Jinpeng Shi Dong Liang Shizhuang Weng Anhui University School of Electronic and Information Engineering China

ISBN: (纸本)9798400707674

remote sensing (RS) images typically exhibit com plex spatial distributions, making non-local features critical for achieving high-quality super-resolution (SR). Most existing SR networks extract local and non-local features alternately, making the non-local feature to be explored at high spatial resolution. This leads to substantial computational costs and limits the performance of these networks. In this paper, we propose an efficient non-local feature extraction strategy to solve this problem. Specifically, we propose a dual branch super-resolution network (DBSRN) with different branches focusing on local and non-local feature extraction. For the local feature extraction branch (LFEBranch), we design an adaptive feature enhancement block (AFEB) to optimize its processing of local features. For the non-local feature extraction branch (NFEBranch), we propose a non-local feature aggregation block (NFAB) to extract non-local features more efficiently by continuously reducing the spatial resolution of the input. Extensive experiments have demonstrated that the proposed DBSRN can effectively leverage the non-local features of RS images, resulting in superior SR performance compared to state-of-the-art networks.

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Applying Computational Topology for Enhanced image recognition and Computer Vision

Applying Computational Topology for Enhanced Image Recogniti...

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International conference on Advances in Information Technology (ICAIT)

作者： Saravanan Siddhan A. Moorthy S. Christy C. Sivasankar K. Vijaya Bhaskar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Saveetha School of Engineering SIMATS Saveetha University Thandalam Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Computational topology has consequently shorten the time taken for image recognition with good accuracy and therefore has boosted the performance of computer vision. This paper uses computational topology in different domains like society, livestock, medicine, and remote-sensing. Two primary outcomes have been revealed in the experiment: tightly-spaced pictures, thanks to topology, have increased accuracy by up to 5.0 percent compared to conventional facilitation. Considering the topology-enhanced method edge processing time, the calculation time will increase slightly, but it will have a significant performance advantage. Additionally, the edge, texture, shape, and pattern detection are contributed to the overall classifier output, and the performed method reaches the rate of accuracy of entities detection up to the prior methods. Primarily, we develop and compare the two methods with each other on the basis of accuracy, computational cost, scalability, noise resistance. The grading results show that the improved technique influences picture identification and computer vision system, making the technologies more mature and precise to be used on the real-world tasks.

关键词： Training Computer vision Accuracy Shape image edge detection Noise Wildlife

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Manmade structure segmentation based on morphological operation from high resolution PAN image 2

Manmade structure segmentation based on morphological operat...

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2nd International conference on Range Technology, ICORT 2021

作者： Singh, Manish Pratap Gayathri, V. Chaudhuri, D. DRDO Young Scientist Laboratory - CT IIT-M Research Park Tamilnadu Chennai600113 India DRDO Integration Centrre Panagarh Base West Bengal Muraripur713419 India

ISBN: (纸本)9781665449564

Manmade structures are important objects or targets for both civilian and defence applications. Isolation of manmade structures from background are very critical task due to noise, non-uniform intensity distribution and class overlapping of remote sensing satellite image. image segmentation is a classic subject in the field of image processing and pattern recognition and also is a hotspot and focus of image processing techniques. The paper presents only the technique of manmade structures isolation from the background by morphological enhancement technique and local threshold method. The results demonstrate that the proposed algorithm is segmenting the manmade structures accurately and efficiently. © 2021 IEEE.

关键词： Morphology

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Anisotropic Weighted Total Variation Feature Fusion Network for remote sensing image Denoising

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remote sensing 2022年第24期14卷 6300-6300页

作者： Qi, Huiqing Tan, Shengli Li, Zhichao East China Normal Univ Sch Math Sci Shanghai 200241 Peoples R China East China Normal Univ Shanghai Inst AI Educ Shanghai 200062 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Land Surface Pattern & Simulat Beijing 100101 Peoples R China

remote sensing images are widely applied in instance segmentation and objetive recognition;however, they often suffer from noise, influencing the performance of subsequent applications. Previous image denoising works have only obtained restored images without preserving detailed texture. To address this issue, we proposed a novel model for remote sensing image denoising, called the anisotropic weighted total variation feature fusion network (AWTVF(2)Net), consisting of four novel modules (WTV-Net, SOSB, AuEncoder, and FB). AWTVF(2)Net combines traditional total variation with a deep neural network, improving the denoising ability of the proposed approach. Our proposed method is evaluated by PSNR and SSIM metrics on three benchmark datasets (NWPU, patternNet, UCL), and the experimental results show that AWTVF(2)Net can obtain 0.12 similar to 19.39 dB/0.0237 similar to 0.5362 higher on PSNR/SSIM values in the Gaussian noise removal and mixed noise removal tasks than State-of-The-Art (SoTA) algorithms. Meanwhile, our model can preserve more detailed texture features. The SSEQ, BLiiNDS-ii, and BRISQUE values of AWTVF(2)Net on the three real-world datasets (AVRIS Indian Pines, ROSIS University of Pavia, HYDICE Urban) are 3.94 similar to 12.92 higher, 8.33 similar to 27.5 higher, and 2.2 similar to 5.55 lower than those of the compared methods, respectively. The proposed framework can guide subsequent remote sensing image applications, regarding the pre-processing of input images.

关键词： remote sensing image noise removal weighted total variation feature fusion convolutional neural network

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Applications of pattern recognition for Hyper Spectral Time Series Algorithm Detection

Applications of Pattern Recognition for Hyper Spectral Time ...

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), International conference on

作者： Manish Srivastava Jamuna K. V Arvind Kumar Pandey Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Forensic Science School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Sample recognition is the system of changing complicated, multidimensional data into significant patterns that may be used for numerous analytical or predictive purposes. It's used to discover, classify, and discover styles within huge units of facts and is specifically beneficial while handling excessive-dimensional and hyper-spectral time series information. The application of pattern reputation for hyper spectral time collection set of rules detection is a rising area which has been used in a number of applications. This approach has been applied within the subject of remote sensing and environmental monitoring to detect various target gadgets or capabilities. It has also been used within the healthcare industry to discover unusual tissue inside medical snapshots. Moreover, this method is utilized in bioinformatics and system learning packages to locate subtle styles in massive datasets of medical or organic samples. In conclusion, the sample reputation for hyper spectral time collection algorithm detection is a precious device for its capacity to extract styles from excessive-dimensional information, allowing researchers to recognize the underlying relationships between variables in order to benefit new insights and broaden greater accurate fashions…

关键词： Training Industries image processing Time series analysis Medical services Machine learning Internet of Things

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2nd International conference on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning, 3DIT-MSPandDL 2020

2nd International Conference on 3D Imaging Technologies-Mult...

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2nd International conference on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning, 3DIT-MSPandDL 2020

ISBN: (纸本)9789811633904

The proceedings contain 39 papers. The special focus in this conference is on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning. The topics include: Appearance Defect Detection of Injection Parts Based on Deep Learning;thresholding Method for the Sonar image of a Seabed Target Based on Two-Dimensional Renyi's Entropy;noise Reduction in Sonar images of Seabed Targets Based on a Variational Method;sonar image Segmentation of Seabed Targets Using a Variational Approach;balance the Robustness and Invisibility of Digital Watermarking by image Entropy in Multiple Domains;research Progress of Electrical Resistance Tomography;research on Low-Resolution image Matching and recognition Algorithm Based on Unified Feature Space;research on Realistic Representation Technology in Virtual Environment;intelligent Customer Service Operation Management System Solution Based on Intelligent Voice Analysis;Research Status of Motor imagery EEG Signal Based on Deep Learning;research on Car Seat Comfort Evaluation System Based on Chinese Population;research on Building Extraction from High-Resolution remote sensing image Based on Improved U-Net;a Survey of image Classification Algorithms Based on Graph Neural Networks;an Improved Method of Non-local Means Denoising Based on Histogram;short-Term Traffic Volume Prediction Based on Gray Wolf Optimization Algorithm;Biological Particle recognition Based on BP Neural Network Algorithm;imageCube: A Low-Cost 6D Controller with Smooth Tracking for Mobile Augmented Reality;A Clinical Decision-Making System for COVID-19;License Plate recognition System Based on OpenCV;LUCC and Landscape pattern Variation in the Lower Tarim River by remote sensing;image recognition Based on Super-Resolution Wasserstein Generative Adversarial Nets with Gradient Penalty.

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Object Detection Algorithm Based on Coordinate Attention and Context Feature Enhancement 11

Object Detection Algorithm Based on Coordinate Attention and...

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11th International conference on Computing and pattern recognition, ICCPR 2022

作者： Liu, Lingzhi Qiang, Baohua Wang, Yuanchun Yang, Xianyi Tian, Jubo Zhang, Shihao Guilin University of Electronic Technology Guilin China The 54th Research Institute of Cetc Shijiazhuang China Hebei Key Laboratory of Intelligent Information Perception and Processing Shijiazhuang 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. © 2022 ACM.

关键词： Object detection

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CDCEO'21-First Workshop on Complex Data Challenges in Earth Observation 21

CDCEO'21-First Workshop on Complex Data Challenges in Earth ...

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30th ACM International conference on Information and Knowledge Management (CIKM)

作者： Gruca, Aleksandra Herruzo, Pedro Ripodas, Pilar Kucik, Andrzej Briese, Christian Kopp, Michael K. Hochreiter, Sepp Ghamisi, Pedram Kreil, David P. Silesian Tech Univ Gliwice Poland Inst Adv Res Artificial Intelligence Vienna Austria Agencia Estatal Meteorol AEMET Madrid Spain European Space Agcy ESRIN Frascati Italy EODC Earth Observat Data Ctr Water Resources Moni Vienna Austria Helmholtz Zentrum Dresden Rossendorf Dresden Germany

ISBN: (纸本)9781450384469

High-resolution remote sensing technology for Earth Observation (EO) has radically changed how we monitor the state of our planet around the clock. An effective interpretation of the resulting complex large-scale time series adopts the best machine learning techniques from signal processing, computer vision, pattern recognition, and artificial intelligence. The First Workshop on Complex Data Challenges in Earth Observation was open to both method development and advanced applications in a wide range of related topics, including image and signal processing, gap-filling, data fusion, feature extraction, prediction of spatio-temporal features, and the detection of rules underlying the observed state transitions and causal relationships. The full agenda, featuring keynotes and a selection of high quality contributed talks is available online at ***/cdceo21.

关键词： earth observation machine learning remote sensing weather

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An entropy-based membership approach on type-ii fuzzy set (EMT2FCM) for biomedical image segmentation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期127卷

作者： Bose, Ananya Maulik, Ujjwal Sarkar, Anasua St Thomas Coll Engn & Technol Dept Elect Engn Kolkata India Jadavpur Univ Dept Comp Sci & Engn Kolkata India

Medical image segmentation is a challenging task owing to its aliasing artifacts, existence of mixed pixels, interference of noise and blurring effect, etc. The brain and the spinal cord together constitute the Central Nervous System (CNS), with the brain being the most complex component. Brain segmentation is crucial for surgical planning, disease detection, and therapeutic interventions. A unique entropy based soft clustering approach (EMT2FCM) for the brain MRI imaging on both real and simulated datasets is proposed in this paper. This paper presents a refined version of the type-2 Fuzzy C-Means (FCM) technique (EMT2FCM) for isolating diverse tissues in Magnetic Resonance (MR) brain images, achieved through an enhanced entropy-based mem-bership function. Difficulty in edge detection occurs due to the vagueness caused by mixed pixels. To overcome the problem with mixed pixels a new membership function proposal has been given. EMT2FCM, i.e., fuzzy membership function with Shannon's entropy-based variation, is able to handle uncertainty and vagueness in images offered by mixed pixels, by implementing new distance criteria modification for the calculation of op-timum cluster. The approach has been validated using datasets from BRAINWEB (simulated) and IBSR (real-life). Following the deployment of our new algorithm, we assessed its performance using six observation criteria. The results demonstrated significant improvements in clustering performance, as evidenced by enhanced Mean Squared Error (MSE), Dice Index, and Jaccard Index, Accuracy, Sensitivity and Partition Indices validation measures. The evaluation of EMT2FCM in comparison to other state-of-the-art methods is established to reduce segmentation errors significantly.

关键词： Medical imaging Type-2 FCM Gaussian membership Entropy BRAINWEB IBSR Segmentation

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