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Journal articles on the topic 'Reconstruction of the self'

Author: Grafiati

Published: 10 September 2021

Last updated: 29 July 2025

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1

Hu, Yu, and Hongmin Cai. "Hypergraph-Supervised Deep Subspace Clustering." Mathematics 9, no. 24 (2021): 3259. http://dx.doi.org/10.3390/math9243259.

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Auto-encoder (AE)-based deep subspace clustering (DSC) methods aim to partition high-dimensional data into underlying clusters, where each cluster corresponds to a subspace. As a standard module in current AE-based DSC, the self-reconstruction cost plays an essential role in regularizing the feature learning. However, the self-reconstruction adversely affects the discriminative feature learning of AE, thereby hampering the downstream subspace clustering. To address this issue, we propose a hypergraph-supervised reconstruction to replace the self-reconstruction. Specifically, instead of enforci

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Yu, Thomas, Tom Hilbert, Gian Franco Piredda, et al. "Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI." Machine Learning for Biomedical Imaging 1, September 2022 (2022): 1–31. http://dx.doi.org/10.59275/j.melba.2022-6g33.

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Deep learning methods have become the state of the art for undersampled MR reconstruction. Particularly for cases where it is infeasible or impossible for ground truth, fully sampled data to be acquired, self-supervised machine learning methods for reconstruction are becoming increasingly used. However potential issues in the validation of such methods, as well as their generalizability, remain underexplored. In this paper, we investigate important aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective recons

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Yang, Xiaogang, Maik Kahnt, Dennis Brückner, et al. "Tomographic reconstruction with a generative adversarial network." Journal of Synchrotron Radiation 27, no. 2 (2020): 486–93. http://dx.doi.org/10.1107/s1600577520000831.

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This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorith

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Mendona Ernesto Rego, R. L., A. F. R. Araujo, and F. B. de Lima Neto. "Growing Self-Reconstruction Maps." IEEE Transactions on Neural Networks 21, no. 2 (2010): 211–23. http://dx.doi.org/10.1109/tnn.2009.2035312.

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Riccio, Michele, Angelica Aquinati, Mario Fordellone, Nicola Carboni, Andrea Marchesini, and Francesco De Francesco. "Innovations in Nipple-areolar Complex Reconstruction: Evaluation of a New Prosthesis." Plastic and Reconstructive Surgery - Global Open 13, no. 1 (2025): e6410. https://doi.org/10.1097/gox.0000000000006410.

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Background: The reconstruction of the nipple-areola complex (NAC) is a crucial step for completing breast restoration with patient satisfaction. Surgical reconstruction or tattooing of the NAC may not be preferable or feasible for some patients. There is no universal method for NAC that is ideal for every patient or clinical situation. Various approaches often do not maintain projection over time. Over time, several techniques have been perfected, including the C-V flap, to improve and support projection for patients with bilateral implant-based reconstructions. Methods: We used, for the first

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Palines, Patrick A., Sarah Y. Park, Rory J. Loo, et al. "Facial Reconstruction Following Self-Inflicted Gunshot Wounds: Predictors, Complications, and Acceptable Outcomes." Trauma Care 2, no. 2 (2022): 211–25. http://dx.doi.org/10.3390/traumacare2020018.

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Background: Self-inflicted gunshot wounds (SIGSWs) produce devastating facial defects that are challenging to reconstruct, but are rarely reported in large cohorts in the literature. This study sought to characterize these injuries, and identify parameters influencing complications and outcomes among survivors following facial reconstruction. Methods: A retrospective cohort study was performed identifying 22 patients with SIGSWs to the face reconstructed at our center from 2009 to 2019. Charts were reviewed for patient, injury, and reconstructive details and course. Outcomes were statistically

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Palines, Patrick A., Sarah Y. Park, Rory J. Loo, et al. "Facial Reconstruction Following Self-Inflicted Gunshot Wounds: Predictors, Complications, and Acceptable Outcomes." Trauma Care 2, no. 2 (2022): 211–25. http://dx.doi.org/10.3390/traumacare2020018.

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Background: Self-inflicted gunshot wounds (SIGSWs) produce devastating facial defects that are challenging to reconstruct, but are rarely reported in large cohorts in the literature. This study sought to characterize these injuries, and identify parameters influencing complications and outcomes among survivors following facial reconstruction. Methods: A retrospective cohort study was performed identifying 22 patients with SIGSWs to the face reconstructed at our center from 2009 to 2019. Charts were reviewed for patient, injury, and reconstructive details and course. Outcomes were statistically

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Gardella, Rebecca, Alyssa D. Reese, Jhobani Torres-Gomez, et al. "Presentation and Management of Self-Inflicted Gunshot Wounds to the Face." Seminars in Plastic Surgery 39, no. 01 (2025): 019–27. https://doi.org/10.1055/s-0044-1801409.

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AbstractSelf-inflicted gunshot wounds (SI-GSW) to the head and face are associated with significant morbidity and mortality. Patients who do survive their injuries often face life-altering disfigurement and disability. To effectively care for these patients, providers must not only detect and address the immediate life-threatening injuries, but also plan for long-term definitive reconstruction. This review provides an overview of presentation and management of SI-GSW to the face, including initial stabilization and workup, assembly of a multidisciplinary care team, surgical reconstruction of s

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TAKATA, Yoshinori. "Bereavement and Self-narrative Reconstruction." Annual review of sociology 2003, no. 16 (2003): 175–86. http://dx.doi.org/10.5690/kantoh.2003.175.

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10

Dye, Simon, and Andy Taylor. "Self-consistent gravitational lens reconstruction." Monthly Notices of the Royal Astronomical Society 300, no. 2 (1998): L23—L28. http://dx.doi.org/10.1046/j.1365-8711.1998.02056.x.

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11

Dubietis, A., E. Kučinskas, G. Tamošauskas, E. Gaižauskas, M. A. Porras, and P. Di Trapani. "Self-reconstruction of light filaments." Optics Letters 29, no. 24 (2004): 2893. http://dx.doi.org/10.1364/ol.29.002893.

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12

Garrison, Jim. "Recovery, Reconstruction, and Self-Renewal." Philosophy of Education 58 (2002): 282–84. http://dx.doi.org/10.47925/2002.282.

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13

Li, Zinuo, Zhen Zhang, Shenghong Luo, Yuxing Cai, and Shuna Guo. "An Improved Matting-SfM Algorithm for 3D Reconstruction of Self-Rotating Objects." Mathematics 10, no. 16 (2022): 2892. http://dx.doi.org/10.3390/math10162892.

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The 3D reconstruction experiment can be performed accurately in most cases based on the structure from motion (SfM) algorithm with the combination of the multi-view stereo (MVS) framework through a video recorded around the object. However, we need to artificially hold the camera and stabilize the recording process as much as possible to obtain better accuracy. To eliminate the inaccurate recording caused by shaking during the recording process, we tried to fix the camera on a camera stand and placed the object on a motorized turntable to record. However, in this case, the background did not c

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Ai, Gangzheng, Jihao Yin, and Linyan Cui. "Learning Gravity Fields of Small Bodies: Self-adaptive Physics-informed Neural Networks." Astronomical Journal 168, no. 6 (2024): 242. http://dx.doi.org/10.3847/1538-3881/ad7951.

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Abstract The reconstruction of the gravity field within the surface region of small bodies is crucial for the surface proximity operations of a probe. However, the irregular shape, uneven mass distribution, and sparse gravitational data of small bodies pose challenges in the reconstruction. We propose a self-adaptive physics-informed neural network (PINN) for the reconstruction of the gravity field within the surface region of irregular and heterogeneous small bodies. First, we introduce an auxiliary-point-based data augmentation strategy to reduce the model’s dependency on the quantity of dat

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Tian, Xuanyu, Lixuan Chen, Qing Wu, et al. "Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 7383–91. https://doi.org/10.1609/aaai.v39i7.32794.

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Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential in addressing sparse-view computed tomography (SVCT) inverse problems. While these INR-based methods perform well on relatively dense SVCT reconstructions, they struggle to achieve comparable performance with supervised methods in sparser SVCT scenarios and are prone to being affected by noise, limiting their applicability in real clinical settings. Additionally, current methods have not fully explored the use of image domain priors for solving SVCT inverse problems. In

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Emm, Toby A., and Yu Zhang. "Self-Adaptive Evolutionary Info Variational Autoencoder." Computers 13, no. 8 (2024): 214. http://dx.doi.org/10.3390/computers13080214.

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With the advent of increasingly powerful machine learning algorithms and the ability to rapidly obtain accurate aerodynamic performance data, there has been a steady rise in the use of algorithms for automated aerodynamic design optimisation. However, long training times, high-dimensional design spaces and rapid geometry alteration pose barriers to this becoming an efficient and worthwhile process. The variational autoencoder (VAE) is a probabilistic generative model capable of learning a low-dimensional representation of high-dimensional input data. Despite their impressive power, VAEs suffer

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Kulikajevas, Audrius, Rytis Maskeliunas, Robertas Damasevicius, and Rafal Scherer. "HUMANNET—A Two-Tiered Deep Neural Network Architecture for Self-Occluding Humanoid Pose Reconstruction." Sensors 21, no. 12 (2021): 3945. http://dx.doi.org/10.3390/s21123945.

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Majority of current research focuses on a single static object reconstruction from a given pointcloud. However, the existing approaches are not applicable to real world applications such as dynamic and morphing scene reconstruction. To solve this, we propose a novel two-tiered deep neural network architecture, which is capable of reconstructing self-obstructed human-like morphing shapes from a depth frame in conjunction with cameras intrinsic parameters. The tests were performed using on custom dataset generated using a combination of AMASS and MoVi datasets. The proposed network achieved Jacc

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Vasnetsov, M. V., I. G. Marienko, and M. S. Soskin. "Self-reconstruction of an optical vortex." Journal of Experimental and Theoretical Physics Letters 71, no. 4 (2000): 130–33. http://dx.doi.org/10.1134/1.568297.

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19

Walter, Garry, and Jeffrey Streimer. "Genital Self-mutilation: Attempted Foreskin Reconstruction." British Journal of Psychiatry 156, no. 1 (1990): 125–27. http://dx.doi.org/10.1192/bjp.156.1.125.

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20

Chen, Hanbo, Hang Xiao, Tianming Liu, and Hanchuan Peng. "SmartTracing: self-learning-based Neuron reconstruction." Brain Informatics 2, no. 3 (2015): 135–44. http://dx.doi.org/10.1007/s40708-015-0018-y.

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21

Park, Suhyung, and Jaeseok Park. "Adaptive self-calibrating iterative GRAPPA reconstruction." Magnetic Resonance in Medicine 67, no. 6 (2011): 1721–29. http://dx.doi.org/10.1002/mrm.23188.

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22

Bauer, Alexander, Shinichi Nakajima, and Klaus-Robert Müller. "Self-Supervised Autoencoders for Visual Anomaly Detection." Mathematics 12, no. 24 (2024): 3988. https://doi.org/10.3390/math12243988.

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We focus on detecting anomalies in images where the data distribution is supported by a lower-dimensional embedded manifold. Approaches based on autoencoders have aimed to control their capacity either by reducing the size of the bottleneck layer or by imposing sparsity constraints on their activations. However, none of these techniques explicitly penalize the reconstruction of anomalous regions, often resulting in poor detection. We tackle this problem by adapting a self-supervised learning regime that essentially implements a denoising autoencoder with structured non-i.i.d. noise. Informally

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23

Song, Yi-zhong, Bin Zhang, Lin Fu, and An-zhi He. "Reconstructing non-ideal-bordered field by simple self-correlative algebraic reconstruction technique." Optoelectronics Letters 2, no. 1 (2006): 63–67. http://dx.doi.org/10.1007/bf03033596.

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24

Konopka, Agnieszka, Robert A. Neimeyer, and Jason Jacobs-Lentz. "Composing the Self: Toward the Dialogical Reconstruction of Self-Identity." Journal of Constructivist Psychology 31, no. 3 (2017): 308–20. http://dx.doi.org/10.1080/10720537.2017.1350609.

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25

Wang, Xiaoli, Yan Piao, Yuanshang Jin, et al. "Fourier Ptychographic Reconstruction Method of Self-Training Physical Model." Applied Sciences 13, no. 6 (2023): 3590. http://dx.doi.org/10.3390/app13063590.

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Fourier ptychographic microscopy is a new microscopic computational imaging technology. A series of low-resolution intensity images are collected by a Fourier ptychographic microscopy system, and high-resolution intensity and phase images are reconstructed from the collected low-resolution images by a reconstruction algorithm. It is a kind of microscopy that can achieve both a large field of view and high resolution. Here in this article, a Fourier ptychographic reconstruction method applied to a self-training physical model is proposed. The SwinIR network in the field of super-resolution is i

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Wang, Xiufang, Lina Feng, Peng Chen, Zhen Huang, and Ye Yuan. "Micro displacement reconstruction of self-mixing grating interferometer based on Littrow structure." Chinese Optics Letters 19, no. 10 (2021): 101402. http://dx.doi.org/10.3788/col202119.101402.

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27

Liu, Xiuwen, Xinghua Lei, Xin Li, and Sirui Chen. "Self-Interested Coalitional Crowdsensing for Multi-Agent Interactive Environment Monitoring." Sensors 24, no. 2 (2024): 509. http://dx.doi.org/10.3390/s24020509.

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As a promising paradigm, mobile crowdsensing (MCS) takes advantage of sensing abilities and cooperates with multi-agent reinforcement learning technologies to provide services for users in large sensing areas, such as smart transportation, environment monitoring, etc. In most cases, strategy training for multi-agent reinforcement learning requires substantial interaction with the sensing environment, which results in unaffordable costs. Thus, environment reconstruction via extraction of the causal effect model from past data is an effective way to smoothly accomplish environment monitoring. Ho

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Harahap, Elly Warnisyah, and Zulkarnain Zulkarnain. "Government Policy in the National Strategy for Prevent and Eradicate Corruption in State Islamic Religious Universities." Al-Hayat: Journal of Islamic Education 8, no. 1 (2024): 350. http://dx.doi.org/10.35723/ajie.v8i1.517.

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This research focuses on self-reconstruction in corruption prevention policies at State Islamic Religious Universities (PTKIN). The problem of this research is how self-reconstruction becomes a solution in corruption prevention policies to achieve justice and social welfare at PTKIN. This research proposes a self-reconstruction mechanism to prevent corruption and execute justice and social welfare at PTKIN. This research design uses a qualitative approach with case studies as the primary method. The research locations are UIN Sunan Kalijaga, UIN Syarifhidayatullah, and UIN North Sumatra Medan.

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Mu, Shao-Shuo, Ye Zhang, Ping Jia, Xun Yang, and Xiao-Feng Qiu. "Multiframe Superresolution Reconstruction Based on Self-Learning Method." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/181864.

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One category of the superresolution algorithms widely used in practical applications is dictionary-based superresolution algorithms, which constructs a single high-resolution (HR) and high-clarity image from multiple low-resolution (LR) images. Despite the fact that general dictionary-based superresolution algorithms obtain redundant dictionaries from numerous HR-LR images, HR image distortion is unavoidable. To solve this problem, this paper proposes a multiframe superresolution reconstruction based on self-learning methods. First, multiple images from the same scene are selected to be both i

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Xu, Yifeng. "The Necklace View of the Self." Stance: An International Undergraduate Philosophy Journal 12 (2019): 96–104. http://dx.doi.org/10.5840/stance2019129.

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In this paper, I provide a framework for accounting for the self, based on a reconstruction of Galen Strawson’s “theory of SESMETs,” or the Pearl view, with Barry Dainton’s continuous consciousness thesis. I argue that the framework I provide adequately accounts for the self and is preferable to solely adopting either Strawson’s or Dainton’s theory. I call my reconstruction the “Necklace” view of the self.

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Xu, Yifeng. "The Necklace View of the Self." Stance: an international undergraduate philosophy journal 12, no. 1 (2019): 96–105. http://dx.doi.org/10.33043/s.12.1.96-105.

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In this paper, I provide a framework for accounting for the self, based on a reconstruction of Galen Strawson’s “theory of SESMETs,” or the Pearl view, with Barry Dainton’s continuous consciousness thesis. I argue that the framework I provide adequately accounts for the self and is preferable to solely adopting either Strawson’s or Dainton’s theory. I call my reconstruction the “Necklace” view of the self.

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Zhao, Jianping, Yong Cheng, Gen Cai, et al. "A Calibration Method for a Self-Rotating, Linear-Structured-Light Scanning, Three-Dimensional Reconstruction System Based on Plane Constraints." Sensors 21, no. 24 (2021): 8359. http://dx.doi.org/10.3390/s21248359.

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This paper proposes a calibration method for a self-rotating, linear-structured-light (LSL) scanning, three-dimensional reconstruction system based on plane constraints. The point cloud of plane target collected by the self-rotating, LSL scanning, 3D reconstruction system should be constrained to the basic principle of the plane equation; it can quickly and accurately calibrate the position parameters between the coordinate system of the LSL module and the coordinate system of the self-rotating, LSL scanning, 3D reconstruction system. Additionally, the transformation equation could be establis

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Zeng, Ying, and Yimin Wang. "Complete Neuron Reconstruction Based on Branch Confidence." Brain Sciences 14, no. 4 (2024): 396. http://dx.doi.org/10.3390/brainsci14040396.

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In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they of

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Luo, Fulin, Xi Chen, Xiuwen Gong, Weiwen Wu, and Tan Guo. "Dual-Window Multiscale Transformer for Hyperspectral Snapshot Compressive Imaging." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3972–80. http://dx.doi.org/10.1609/aaai.v38i4.28190.

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Coded aperture snapshot spectral imaging (CASSI) system is an effective manner for hyperspectral snapshot compressive imaging. The core issue of CASSI is to solve the inverse problem for the reconstruction of hyperspectral image (HSI). In recent years, Transformer-based methods achieve promising performance in HSI reconstruction. However, capturing both long-range dependencies and local information while ensuring reasonable computational costs remains a challenging problem. In this paper, we propose a Transformer-based HSI reconstruction method called dual-window multiscale Transformer (DWMT),

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Hämmann, M., H. R. Maurer, A. G. Green, and H. Horstmeyer. "Self‐Potential Image Reconstruction: Capabilities and Limitations." Journal of Environmental and Engineering Geophysics 2, no. 1 (1997): 21–35. http://dx.doi.org/10.4133/jeeg2.1.21.

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Leaver, Rachel, and Donna Pressland. "Intermittent self-catheterisation in urinary tract reconstruction." British Journal of Community Nursing 6, no. 5 (2001): 253–58. http://dx.doi.org/10.12968/bjcn.2001.6.5.7086.

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Litvin, Igor A., Liesl Burger, and Andrew Forbes. "Angular self-reconstruction of petal-like beams." Optics Letters 38, no. 17 (2013): 3363. http://dx.doi.org/10.1364/ol.38.003363.

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Butkus, R., R. Gadonas, J. Janušonis, et al. "Nonlinear self-reconstruction of truncated Bessel beam." Optics Communications 206, no. 1-3 (2002): 201–9. http://dx.doi.org/10.1016/s0030-4018(02)01397-4.

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Bouchal, Z., J. Wagner, and M. Chlup. "Self-reconstruction of a distorted nondiffracting beam." Optics Communications 151, no. 4-6 (1998): 207–11. http://dx.doi.org/10.1016/s0030-4018(98)00085-6.

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Amy Millstone. "Gyp and the Reconstruction of (Self-)Consciousness." Biography 10, no. 3 (1987): 189–207. http://dx.doi.org/10.1353/bio.2010.0648.

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Tao, Shao Hua, and Xiaocong Yuan. "Self-reconstruction property of fractional Bessel beams." Journal of the Optical Society of America A 21, no. 7 (2004): 1192. http://dx.doi.org/10.1364/josaa.21.001192.

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Jiang, Hongliang, Qun He, Youkui Zhang, and Li Song. "Structural Self-Reconstruction of Catalysts in Electrocatalysis." Accounts of Chemical Research 51, no. 11 (2018): 2968–77. http://dx.doi.org/10.1021/acs.accounts.8b00449.

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Rusinek, Rafal, and Jerzy Warminski. "Attractor reconstruction of self-excited mechanical systems." Chaos, Solitons & Fractals 40, no. 1 (2009): 172–82. http://dx.doi.org/10.1016/j.chaos.2007.07.040.

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Kyvalský, Jiří, and Zdeněk Bouchal. "Realistic description of the self-reconstruction effect." Optik 112, no. 6 (2001): 225–31. http://dx.doi.org/10.1078/0030-4026-00046.

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Unal, Mehmet Ozan, Metin Ertas, and Isa Yildirim. "Proj2Proj: self-supervised low-dose CT reconstruction." PeerJ Computer Science 10 (February 29, 2024): e1849. http://dx.doi.org/10.7717/peerj-cs.1849.

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In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On

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Charmaz, Kathy. "The Self as Habit: The Reconstruction of Self in Chronic Illness." OTJR: Occupation, Participation and Health 22, no. 1_suppl (2002): 31S—41S. http://dx.doi.org/10.1177/15394492020220s105.

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Chuttur, Y., R. Kaudeerally, and A. Nazurally. "Lane Reconstruction for Self-Driving Vehicles on Dynamic Road Networks." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 15, no. 2 (2023): 29–36. http://dx.doi.org/10.54554/jtec.2023.15.02.004.

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Lane detection is a crucial task that involves identifying lane lines and proper markings on road structures. The field of lane detection has gained significant attention due to the increasing use of autonomous vehicles and ADAS systems, as they require robust lane detection systems to navigate safely in complex environments. While previous studies have focused on lane detection in various scenarios, lane line reconstruction for dynamic road networks has been largely overlooked. To address this gap, we propose a novel approach to reconstruct lane lines for dynamic road networks using roadblock

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Liu, Qingli, Jiaxu Sun, Shaoming Qiu, Yana Lv, and Xiuli Du. "A Convolutional Self-Attention Network for CSI Reconstruction in MIMO System." Wireless Communications and Mobile Computing 2023 (January 27, 2023): 1–10. http://dx.doi.org/10.1155/2023/2922232.

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A convolutional self-attention network-based channel state information reconstruction method is presented to address the issue of low reconstruction accuracy of channel state information in Multiple-Input Multiple-Output (MIMO) at a high compression rate. First, an encoder-decoder structure-based channel state information reconstruction model is built. The feature is extracted by the encoder’s convolutional network, and the information is compressed by adding an attention block. At the same time, the compressed information is nonuniform quantized to prevent the transmission process from using

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Xu, Mengyao. "A Study of the Construction of Tony Loneman’s Self in There There from the Perspective of Mirror Image." Pacific International Journal 8, no. 2 (2025): 8–11. https://doi.org/10.55014/pij.v8i2.776.

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There There, the debut novel by Tommy Orange, breaks away from the traditional “homing in plots” often associated with Native American narratives. This work vividly portrays the struggles of urban Native Americans as they navigate cultural heritage and societal prejudice to achieve self-identity and identity reconstruction. From the perspective of Lacan's mirror theory, this paper analyzes the process of self-construction of Tony Loneman in Tommy Orange's novel There There. Tony, grappling with fetal alcohol syndrome, undergoes an identity crisis, moving through the chaotic pre-mirror stage, t

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Liu, Zitian, Zhao Chen, Xiaoli Zhang, and Shihan Cheng. "CDP-MVS: Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation." Remote Sensing 16, no. 20 (2024): 3845. http://dx.doi.org/10.3390/rs16203845.

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Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view re

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