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Journal articles on the topic 'Blue-noise Sampling'

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Author: Grafiati

Published: 9 March 2023

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1

Kailkhura, Bhavya, Jayaraman J. Thiagarajan, Peer-Timo Bremer, and Pramod K. Varshney. "Stair blue noise sampling." ACM Transactions on Graphics 35, no. 6 (November 11, 2016): 1–10. http://dx.doi.org/10.1145/2980179.2982435.

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2

Chen, Zhonggui, Zhan Yuan, Yi-King Choi, Ligang Liu, and Wenping Wang. "Variational Blue Noise Sampling." IEEE Transactions on Visualization and Computer Graphics 18, no. 10 (October 2012): 1784–96. http://dx.doi.org/10.1109/tvcg.2012.94.

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3

Qin, Hongxing, Yi Chen, Jinlong He, and Baoquan Chen. "Wasserstein Blue Noise Sampling." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1. http://dx.doi.org/10.1145/3072959.3119910.

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4

Qin, Hongxing, Yi Chen, Jinlong He, and Baoquan Chen. "Wasserstein blue noise sampling." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1. http://dx.doi.org/10.1145/3072959.3126841.

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5

Li, Hongwei, Li-Yi Wei, Pedro V. Sander, and Chi-Wing Fu. "Anisotropic blue noise sampling." ACM Transactions on Graphics 29, no. 6 (December 2010): 1–12. http://dx.doi.org/10.1145/1882261.1866189.

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6

Reinert, Bernhard, Tobias Ritschel, Hans-Peter Seidel, and Iliyan Georgiev. "Projective Blue-Noise Sampling." Computer Graphics Forum 35, no. 1 (August 20, 2015): 285–95. http://dx.doi.org/10.1111/cgf.12725.

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7

Qin, Hongxing, Yi Chen, Jinlong He, and Baoquan Chen. "Wasserstein Blue Noise Sampling." ACM Transactions on Graphics 36, no. 5 (October 17, 2017): 1–13. http://dx.doi.org/10.1145/3119910.

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8

Chen, Jiating, Xiaoyin Ge, Li-Yi Wei, Bin Wang, Yusu Wang, Huamin Wang, Yun Fei, Kang-Lai Qian, Jun-Hai Yong, and Wenping Wang. "Bilateral blue noise sampling." ACM Transactions on Graphics 32, no. 6 (November 2013): 1–11. http://dx.doi.org/10.1145/2508363.2508375.

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9

Ahmed, Abdalla G. M., Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, Jianwei Guo, Dong-Ming Yan, Hui Huang, and Oliver Deussen. "Low-discrepancy blue noise sampling." ACM Transactions on Graphics 35, no. 6 (November 11, 2016): 1–13. http://dx.doi.org/10.1145/2980179.2980218.

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10

Wei, Li-Yi. "Multi-class blue noise sampling." ACM Transactions on Graphics 29, no. 4 (July 26, 2010): 1–8. http://dx.doi.org/10.1145/1778765.1778816.

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11

Xu, Yin, Ruizhen Hu, Craig Gotsman, and Ligang Liu. "Blue noise sampling of surfaces." Computers & Graphics 36, no. 4 (June 2012): 232–40. http://dx.doi.org/10.1016/j.cag.2012.02.005.

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12

Parada-Mayorga, Alejandro, Daniel L. Lau, Jhony H. Giraldo, and Gonzalo R. Arce. "Blue-Noise Sampling on Graphs." IEEE Transactions on Signal and Information Processing over Networks 5, no. 3 (September 2019): 554–69. http://dx.doi.org/10.1109/tsipn.2019.2922852.

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13

Heck, Daniel, Thomas Schlömer, and Oliver Deussen. "Blue noise sampling with controlled aliasing." ACM Transactions on Graphics 32, no. 3 (June 2013): 1–12. http://dx.doi.org/10.1145/2487228.2487233.

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14

Sun, Xin, Kun Zhou, Jie Guo, Guofu Xie, Jingui Pan, Wencheng Wang, and Baining Guo. "Line segment sampling with blue-noise properties." ACM Transactions on Graphics 32, no. 4 (July 21, 2013): 1–14. http://dx.doi.org/10.1145/2461912.2462023.

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15

Zhang, Sen, Jianwei Guo, Hui Zhang, Xiaohong Jia, Dong-Ming Yan, Junhai Yong, and Peter Wonka. "Capacity constrained blue-noise sampling on surfaces." Computers & Graphics 55 (April 2016): 44–54. http://dx.doi.org/10.1016/j.cag.2015.11.002.

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16

Ahmed, Abdalla G. M., Jing Ren, and Peter Wonka. "Gaussian Blue Noise." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–15. http://dx.doi.org/10.1145/3550454.3555519.

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Among the various approaches for producing point distributions with blue noise spectrum, we argue for an optimization framework using Gaussian kernels. We show that with a wise selection of optimization parameters, this approach attains unprecedented quality, provably surpassing the current state of the art attained by the optimal transport (BNOT) approach. Further, we show that our algorithm scales smoothly and feasibly to high dimensions while maintaining the same quality, realizing unprecedented high-quality high-dimensional blue noise sets. Finally, we show an extension to adaptive sampling.

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17

Li, Tianyu, Wenyou Wang, Daqi Lin, and Cem Yuksel. "Virtual Blue Noise Lighting." Proceedings of the ACM on Computer Graphics and Interactive Techniques 5, no. 3 (July 25, 2022): 1–26. http://dx.doi.org/10.1145/3543872.

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We introduce virtual blue noise lighting, a rendering pipeline for estimating indirect illumination with a blue noise distribution of virtual lights. Our pipeline is designed for virtual lights with non-uniform emission profiles that are more expensive to store, but required for properly and efficiently handling specular transport. Unlike the typical virtual light placement approaches that traverse light paths from the original light sources, we generate them starting from the camera. This avoids two important problems: wasted memory and computation with fully-occluded virtual lights, and excessive virtual light density around high-probability light paths. In addition, we introduce a parallel and adaptive sample elimination strategy to achieve a blue noise distribution of virtual lights with varying density. This addresses the third problem of virtual light placement by ensuring that they are not placed too close to each other, providing better coverage of the (indirectly) visible surfaces and further improving the quality of the final lighting estimation. For computing the virtual light emission profiles, we present a photon splitting technique that allows efficiently using a large number of photons, as it does not require storing them. During lighting estimation, our method allows using both global power-based and local BSDF important sampling techniques, combined via multiple importance sampling. In addition, we present an adaptive path extension method that avoids sampling nearby virtual lights for reducing the estimation error. We show that our method significantly outperforms path tracing and prior work in virtual lights in terms of both performance and image quality, producing a fast but biased estimate of global illumination.

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18

Payan, Frédéric, Jean-Luc Peyrot, and Marc Antonini. "Blue noise Sampling of surfaces from stereoscopic images." Electronic Imaging 2016, no. 21 (February 14, 2016): 1–8. http://dx.doi.org/10.2352/issn.2470-1173.2016.21.3dipm-037.

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19

Ostromoukhov, Victor, Charles Donohue, and Pierre-Marc Jodoin. "Fast hierarchical importance sampling with blue noise properties." ACM Transactions on Graphics 23, no. 3 (August 2004): 488–95. http://dx.doi.org/10.1145/1015706.1015750.

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20

Jiang, Min, Yahan Zhou, Rui Wang, Richard Southern, and Jian Jun Zhang. "Blue noise sampling using an SPH-based method." ACM Transactions on Graphics 34, no. 6 (November 4, 2015): 1–11. http://dx.doi.org/10.1145/2816795.2818102.

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21

Fattal, Raanan. "Blue-noise point sampling using kernel density model." ACM Transactions on Graphics 30, no. 4 (July 2011): 1–12. http://dx.doi.org/10.1145/2010324.1964943.

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22

Qin, Hongxing, XiaoYang Hong, Bin Xiao, Shaoting Zhang, and Guoyin Wang. "Blue noise sampling method based on mixture distance." Journal of Electronic Imaging 23, no. 6 (December 16, 2014): 063015. http://dx.doi.org/10.1117/1.jei.23.6.063015.

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23

Mitchell, Scott A., Mohamed S. Ebeida, Muhammad A. Awad, Chonhyon Park, Anjul Patney, Ahmad A. Rushdi, Laura P. Swiler, Dinesh Manocha, and Li-Yi Wei. "Spoke-Darts for High-Dimensional Blue-Noise Sampling." ACM Transactions on Graphics 37, no. 2 (July 3, 2018): 1–20. http://dx.doi.org/10.1145/3194657.

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24

Qi, Ruotong, Yanyang Xiao, Juan Cao, and Zhonggui Chen. "Blue-Noise Point Sampling Based on Centroidal Delaunay Triangulation." Journal of Computer-Aided Design & Computer Graphics 30, no. 7 (2018): 1205. http://dx.doi.org/10.3724/sp.j.1089.2018.16760.

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25

Ahmed, Abdalla G. M., Jianwei Guo, Dong-Ming Yan, Jean-Yves Franceschia, Xiaopeng Zhang, and Oliver Deussen. "A Simple Push-Pull Algorithm for Blue-Noise Sampling." IEEE Transactions on Visualization and Computer Graphics 23, no. 12 (December 1, 2017): 2496–508. http://dx.doi.org/10.1109/tvcg.2016.2641963.

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26

Chen, Renjie, and Craig Gotsman. "Parallel Blue-noise Sampling by Constrained Farthest Point Optimization." Computer Graphics Forum 31, no. 5 (August 2012): 1775–85. http://dx.doi.org/10.1111/j.1467-8659.2012.03182.x.

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27

Yan, Dong-Ming, Jian-Wei Guo, Bin Wang, Xiao-Peng Zhang, and Peter Wonka. "A Survey of Blue-Noise Sampling and Its Applications." Journal of Computer Science and Technology 30, no. 3 (May 2015): 439–52. http://dx.doi.org/10.1007/s11390-015-1535-0.

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28

Kalantari, Nima Khademi, and Pradeep Sen. "Efficient Computation of Blue Noise Point Sets through Importance Sampling." Computer Graphics Forum 30, no. 4 (June 2011): 1215–21. http://dx.doi.org/10.1111/j.1467-8659.2011.01980.x.

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29

Lanaro, Matteo Paolo, Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, and Alessandro Rizzi. "Blue-noise sampling for human retinal cone spatial distribution modeling." Journal of Physics Communications 4, no. 3 (March 31, 2020): 035013. http://dx.doi.org/10.1088/2399-6528/ab8064.

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30

Wong, Kin-Ming, and Tien-Tsin Wong. "Blue noise sampling using an N-body simulation-based method." Visual Computer 33, no. 6-8 (May 3, 2017): 823–32. http://dx.doi.org/10.1007/s00371-017-1382-9.

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31

Corsini, M., P. Cignoni, and R. Scopigno. "Efficient and Flexible Sampling with Blue Noise Properties of Triangular Meshes." IEEE Transactions on Visualization and Computer Graphics 18, no. 6 (June 2012): 914–24. http://dx.doi.org/10.1109/tvcg.2012.34.

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32

Cornel, Daniel, Robert F. Tobler, Hiroyuki Sakai, Christian Luksch, and Michael Wimmer. "Forced Random Sampling: fast generation of importance-guided blue-noise samples." Visual Computer 33, no. 6-8 (May 10, 2017): 833–43. http://dx.doi.org/10.1007/s00371-017-1392-7.

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33

Kita, Naoki, and Kazunori Miyata. "Multi-class anisotropic blue noise sampling for discrete element pattern generation." Visual Computer 32, no. 6-8 (May 18, 2016): 1035–44. http://dx.doi.org/10.1007/s00371-016-1248-6.

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34

Zhan, Aiyun, Yong Hu, Meng Yu, and Yuejin Zhang. "A blue noise pattern sampling method based on cloud computing to prevent aliasing." International Journal of Innovative Computing and Applications 9, no. 3 (2018): 173. http://dx.doi.org/10.1504/ijica.2018.093735.

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35

Zhang, Yuejin, Meng Yu, Aiyun Zhan, and Yong Hu. "A blue noise pattern sampling method based on cloud computing to prevent aliasing." International Journal of Innovative Computing and Applications 9, no. 3 (2018): 173. http://dx.doi.org/10.1504/ijica.2018.10014862.

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36

Lau, Daniel L., Gonzalo R. Arce, Alejandro Parada-Mayorga, Daniela Dapena, and Karelia Pena-Pena. "Blue-Noise Sampling of Graph and Multigraph Signals: Dithering on Non-Euclidean Domains." IEEE Signal Processing Magazine 37, no. 6 (November 2020): 31–42. http://dx.doi.org/10.1109/msp.2020.3014070.

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37

Majumdar, Angshul, and Rabab K. Ward. "Increasing energy efficiency in sensor networks: blue noise sampling and non-convex matrix completion." International Journal of Sensor Networks 9, no. 3/4 (2011): 158. http://dx.doi.org/10.1504/ijsnet.2011.040237.

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38

Schulz, Christoph, Kin Chung Kwan, Michael Becher, Daniel Baumgartner, Guido Reina, Oliver Deussen, and Daniel Weiskopf. "Multi-class inverted stippling." ACM Transactions on Graphics 40, no. 6 (December 2021): 1–12. http://dx.doi.org/10.1145/3478513.3480534.

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We introduce inverted stippling , a method to mimic an inversion technique used by artists when performing stippling. To this end, we extend Linde-Buzo-Gray (LBG) stippling to multi-class LBG (MLBG) stippling with multiple layers. MLBG stippling couples the layers stochastically to optimize for per-layer and overall blue-noise properties. We propose a stipple-based filling method to generate solid color backgrounds for inverting areas. Our experiments demonstrate the effectiveness of MLBG in terms of reducing overlapping and intensity accuracy. In addition, we showcase MLBG with color stippling and dynamic multi-class blue-noise sampling, which is possible due to its support for temporal coherence.

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39

Ahmed, Abdalla G. M., and Peter Wonka. "Screen-space blue-noise diffusion of monte carlo sampling error via hierarchical ordering of pixels." ACM Transactions on Graphics 39, no. 6 (November 26, 2020): 1–15. http://dx.doi.org/10.1145/3414685.3417881.

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40

Tan, Xin, Shiming Lai, Yu Liu, and Maojun Zhang. "Green Channel Guiding Denoising on Bayer Image." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/979081.

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Denoising is an indispensable function for digital cameras. In respect that noise is diffused during the demosaicking, the denoising ought to work directly on bayer data. The difficulty of denoising on bayer image is the interlaced mosaic pattern of red, green, and blue. Guided filter is a novel time efficient explicit filter kernel which can incorporate additional information from the guidance image, but it is still not applied for bayer image. In this work, we observe that the green channel of bayer mode is higher in both sampling rate and Signal-to-Noise Ratio (SNR) than the red and blue ones. Therefore the green channel can be used to guide denoising. This kind of guidance integrates the different color channels together. Experiments on both actual and simulated bayer images indicate that green channel acts well as the guidance signal, and the proposed method is competitive with other popular filter kernel denoising methods.

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41

Kasprzak, Paweł, Mateusz Urbańczyk, and Krzysztof Kazimierczuk. "Clustered sparsity and Poisson-gap sampling." Journal of Biomolecular NMR 75, no. 10-12 (November 5, 2021): 401–16. http://dx.doi.org/10.1007/s10858-021-00385-7.

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AbstractNon-uniform sampling (NUS) is a popular way of reducing the amount of time taken by multidimensional NMR experiments. Among the various non-uniform sampling schemes that exist, the Poisson-gap (PG) schedules are particularly popular, especially when combined with compressed-sensing (CS) reconstruction of missing data points. However, the use of PG is based mainly on practical experience and has not, as yet, been explained in terms of CS theory. Moreover, an apparent contradiction exists between the reported effectiveness of PG and CS theory, which states that a “flat” pseudo-random generator is the best way to generate sampling schedules in order to reconstruct sparse spectra. In this paper we explain how, and in what situations, PG reveals its superior features in NMR spectroscopy. We support our theoretical considerations with simulations and analyses of experimental data from the Biological Magnetic Resonance Bank (BMRB). Our analyses reveal a previously unnoticed feature of many NMR spectra that explains the success of ”blue-noise” schedules, such as PG. We call this feature “clustered sparsity”. This refers to the fact that the peaks in NMR spectra are not just sparse but often form clusters in the indirect dimension, and PG is particularly suited to deal with such situations. Additionally, we discuss why denser sampling in the initial and final parts of the clustered signal may be useful.

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42

Esdaile, James, Ivo Labbé, Karl Glazebrook, Jacqueline Antwi-Danso, Casey Papovich, Edward Taylor, Z. Cemile Marsan, et al. "Introducing the FLAMINGOS-2 Split-K Medium-band Filters: The Impact on Photometric Selection of High-z Galaxies in the FENIKS-pilot survey." Astronomical Journal 162, no. 6 (November 3, 2021): 225. http://dx.doi.org/10.3847/1538-3881/ac2148.

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Abstract Deep near-infrared photometric surveys are efficient in identifying high-redshift galaxies, however, they can be prone to systematic errors in photometric redshift. This is particularly salient when there is limited sampling of key spectral features of a galaxy’s spectral energy distribution (SED), such as for quiescent galaxies where the expected age-sensitive Balmer/4000 Å break enters the K-band at z > 4. With single-filter sampling of this spectral feature, degeneracies between SED models and redshift emerge. A potential solution to this comes from splitting the K band into multiple filters. We use simulations to show an optimal solution is to add two medium-band filters, K blue (λ cen = 2.06 μm, Δλ = 0.25 μm) and K red (λ cen = 2.31 μm, Δλ = 0.27 μm), that are complementary to the existing K s filter. We test the impact of the K-band filters with simulated catalogs comprised of galaxies with varying ages and signal-to-noise. The results suggest that the K-band filters do improve photometric redshift constraints on z > 4 quiescent galaxies, increasing precision and reducing outliers by up to 90%. We find that the impact from the K-band filters depends on the signal-to-noise, the redshift, and the SED of the galaxy. The filters we designed were built and used to conduct a pilot of the FLAMINGOS-2 Extragalactic Near-Infrared K-band Split survey. While no new z > 4 quiescent galaxies are identified in the limited area pilot, the K blue and K red filters indicate strong Balmer/4000 Å breaks in existing candidates. Additionally, we identify galaxies with strong nebular emission lines, for which the K-band filters increase photometric redshift precision and in some cases indicate extreme star formation.

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43

Rickard, L. J., and Patrick Palmer. "Comments on the distribution of molecules in spiral galaxies." Symposium - International Astronomical Union 106 (1985): 193–94. http://dx.doi.org/10.1017/s0074180900242393.

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Young and Scoville (1982) have argued that the CO distribution, and by inference the H2 distribution, follows the shape of the blue luminosity of the disk, and is thus exponential. However, full maps of bright-CO galaxies (Rickard and Palmer 1981) show considerable structure, with real peaks and depressions on scales as small as the telescope beam. This means that the noise in the determination of the underlying structure is dominated not by the instrumental contribution but by the intrinsic noise of the structure itself. A correct analysis of the axisymmetric structure requires the use of statistical tests comparing the observations with different hypothetical distributions. One finds that equally good fits can be obtained with exponential distributions, r-1 distributions, or even flat disks with central nuclei. However, profiles of the full map data averaged over azimuth, with “error” bars determined by the structural variation with azimuth, show a clear deviation from the optical luminosity profile in the outer disk of NGC 6946. Furthermore, if the profiles for NGC 6946 and IC 342 are fit with exponentials, the scale lengths are rather larger than previously suggested (8.9 and 6.6 kpc, respectively). By sampling only a few radii, one can miss much of the emission of the outer parts of the galaxies, and also underestimate the intrinsic noise of the structure.

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44

Morsy, Mostafa Morsy Abdelkader, Alan Brunton, and Philipp Urban. "Shape dithering for 3D printing." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–12. http://dx.doi.org/10.1145/3528223.3530129.

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We present an efficient, purely geometric, algorithmic, and parameter free approach to improve surface quality and accuracy in voxel-controlled 3D printing by counteracting quantization artifacts. Such artifacts arise due to the discrete voxel sampling of the continuous shape used to control the 3D printer, and are characterized by low-frequency geometric patterns on surfaces of any orientation. They are visually disturbing, particularly on small prints or smooth surfaces, and adversely affect the fatigue behavior of printed parts. We use implicit shape dithering, displacing the part's signed distance field with a high-frequent signal whose amplitude is adapted to the (anisotropic) print resolution. We expand the reverse generalized Fourier slice theorem by shear transforms, which we leverage to optimize a 3D blue-noise mask to generate the anisotropic dither signal. As a point process it is efficient and does not adversely affect 3D halftoning. We evaluate our approach for efficiency, geometric accuracy and show its advantages over the state of the art.

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45

Nash, Maliha S., and Deborah J. Chaloud. "Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin." ISRN Ecology 2011 (June 18, 2011): 1–11. http://dx.doi.org/10.5402/2011/571749.

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Ecologists are often faced with problem of small sample size, correlated and large number of predictors, and high noise-to-signal relationships. This necessitates excluding important variables from the model when applying standard multiple or multivariate regression analyses. In this paper, we present the results of applying PLS to explore relationships among biotic indicators of surface water quality and landscape conditions accounting for the above problems. Available field sampling and remotely sensed data sets for the Savannah Basin are used. We were able to develop models and compare results for the whole basin and for each ecoregion (Blue Ridge, Piedmont, and Coastal Plain) in spite of the data constraints. The amount of variability in surface water biota explained by each model reflects the scale, spatial location, and the composition of contributing landscape metrics. The landscape-biota model developed for the whole basin using PLS explains 43% and 80% of the variation in water biota and landscape data sets, respectively. Models developed for each of the three ecoregions indicate dominance of landscape variables which reflect the geophysical characteristics of that ecoregion.

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46

Parsons, Miles J. G., Karen J. Miller, Michele Thums, James P. Gilmour, Luciana C. Ferreira, Robert D. McCauley, and Mark G. Meekan. "Innovation and technology in marine science: AIMS' North West Shoals to Shore Research Program – an update." APPEA Journal 59, no. 2 (2019): 679. http://dx.doi.org/10.1071/aj18043.

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In 2017, the Australian Institute of Marine Science and its partners commenced the North West Shoals to Shore Research Program. The program is designed to address significant scientific and environmental knowledge gaps pertinent to the management of the offshore petroleum industry, a key stakeholder in this ecologically and commercially important region of Australia. The program comprises four themes. 1. Marine noise monitoring and impacts: includes two seismic source (2600 cubic inch air-gun array) exposure experiments have been conducted to investigate selected responses by demersal fishes and pearl oysters across different spatial and temporal scales. 2. Seabed habitats and demersal biodiversity: seeks to understand the physical and biological characteristics of the ancient coastline key ecological feature around the 125 m depth contour and pearl oyster habitats offshore from Eighty Mile Beach. The work examines the ecological processes that maintain benthic communities on both ancient and contemporary coastlines 3. Protected and iconic species movement, distribution and threats: uses innovative sampling techniques to confirm biologically important areas for pygmy blue whales, hawksbill and green turtles. This will assist the quantification and mitigation of the risks vessel movements, industrial infrastructure and activities pose to marine megafauna on the Northwest Shelf. 4. Spatial dynamics of isolated coral reef atolls: develops a habitat model and adaptive monitoring program that informs the future condition of these remote coral reef atolls. Significant progress has been made by the program in 2018, including the development of innovative and technical approaches to sampling.

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47

Zhou, Chengquan, Hongbao Ye, Zhifu Xu, Jun Hu, Xiaoyan Shi, Shan Hua, Jibo Yue, and Guijun Yang. "Estimating Maize-Leaf Coverage in Field Conditions by Applying a Machine Learning Algorithm to UAV Remote Sensing Images." Applied Sciences 9, no. 11 (June 11, 2019): 2389. http://dx.doi.org/10.3390/app9112389.

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Leaf coverage is an indicator of plant growth rate and predicted yield, and thus it is crucial to plant-breeding research. Robust image segmentation of leaf coverage from remote-sensing images acquired by unmanned aerial vehicles (UAVs) in varying environments can be directly used for large-scale coverage estimation, and is a key component of high-throughput field phenotyping. We thus propose an image-segmentation method based on machine learning to extract relatively accurate coverage information from the orthophoto generated after preprocessing. The image analysis pipeline, including dataset augmenting, removing background, classifier training and noise reduction, generates a set of binary masks to obtain leaf coverage from the image. We compare the proposed method with three conventional methods (Hue-Saturation-Value, edge-detection-based algorithm, random forest) and a frontier deep-learning method called DeepLabv3+. The proposed method improves indicators such as Qseg, Sr, Es and mIOU by 15% to 30%. The experimental results show that this approach is less limited by radiation conditions, and that the protocol can easily be implemented for extensive sampling at low cost. As a result, with the proposed method, we recommend using red-green-blue (RGB)-based technology in addition to conventional equipment for acquiring the leaf coverage of agricultural crops.

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48

Bacca, Jorge Luis, and Henry Arguello. "Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels." TecnoLógicas 22, no. 46 (September 20, 2019): 1–14. http://dx.doi.org/10.22430/22565337.1205.

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Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces. Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

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Helbich, Marco. "Dynamic UrbanEnvironmentalExposures onDepression andSuicide (NEEDS) in the Netherlands: a protocol for a cross-sectional smartphone tracking study and a longitudinal population register study." BMJ Open 9, no. 8 (August 2019): e030075. http://dx.doi.org/10.1136/bmjopen-2019-030075.

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IntroductionEnvironmental exposures are intertwined with mental health outcomes. People are exposed to the environments in which they currently live, and to a multitude of environments along their daily movements and through their residential relocations. However, most research assumes that people are immobile, disregarding that such dynamic exposures also serve as stressors or buffers potentially associated with depression and suicide risk. The aim of the Dynamic Urban Environmental Exposures on Depression and Suicide (NEEDS) study is to examine how dynamic environmental exposures along people’s daily movements and over their residential histories affect depression and suicide mortality in the Netherlands.Methods and analysisThe research design comprises two studies emphasising the temporality of exposures. First, a cross-sectional study is assessing how daily exposures correlate with depression. A nationally representative survey was administered to participants recruited through stratified random sampling of the population aged 18–65 years. Survey data were enriched with smartphone-based data (eg, Global Positioning System tracking, Bluetooth sensing, social media usage, communication patterns) and environmental exposures (eg, green and blue spaces, noise, air pollution). Second, a longitudinal population register study is addressing the extent to which past environmental exposures over people’s residential history affect suicide risk later in life. Statistical and machine learning-based models are being developed to quantify environment–health relations.Ethics and disseminationEthical approval (FETC17-060) was granted by the Ethics Review Board of Utrecht University, The Netherlands. Project-related findings will be disseminated at conferences and in peer-reviewed journal papers. Other project outcomes will be made available through the project’s web page,http://www.needs.sites.uu.nl.

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Xu, Sizhe, Xingang Xu, Clive Blacker, Rachel Gaulton, Qingzhen Zhu, Meng Yang, Guijun Yang, et al. "Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV." Remote Sensing 15, no. 3 (February 3, 2023): 854. http://dx.doi.org/10.3390/rs15030854.

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LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram–Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R2 of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields.

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