Relevant bibliographies by topics / Spectral geometry processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spectral geometry processing'

Author: Grafiati
Published: 11 March 2023

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Journal articles on the topic "Spectral geometry processing"

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Vallet, B., and B. Lévy. "Spectral Geometry Processing with Manifold Harmonics." Computer Graphics Forum 27, no. 2 (2008): 251–60. http://dx.doi.org/10.1111/j.1467-8659.2008.01122.x.

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Fumero, Marco, Michael Möller, and Emanuele Rodolà. "Nonlinear spectral geometry processing via the TV transform." ACM Transactions on Graphics 39, no. 6 (2020): 1–16. http://dx.doi.org/10.1145/3414685.3417849.

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Clouet, Axel, Jérôme Vaillant, and David Alleysson. "The Geometry of Noise in Color and Spectral Image Sensors." Sensors 20, no. 16 (2020): 4487. http://dx.doi.org/10.3390/s20164487.

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Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC).
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Kleinert, A., F. Friedl-Vallon, T. Guggenmoser, et al. "Level 0 to 1 processing of the imaging Fourier transform spectrometer GLORIA: generation of radiometrically and spectrally calibrated spectra." Atmospheric Measurement Techniques 7, no. 12 (2014): 4167–84. http://dx.doi.org/10.5194/amt-7-4167-2014.

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Abstract. The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) is an imaging Fourier transform spectrometer that is capable of operating on various high-altitude research aircraft. It measures the atmospheric emission in the thermal infrared spectral region in limb and nadir geometry. GLORIA consists of a classical Michelson interferometer combined with an infrared camera. The infrared detector has a usable area of 128 × 128 pixels, measuring up to 16 384 interferograms simultaneously. Imaging Fourier transform spectrometers impose a number of challenges with respect to instrument calibration and algorithm development. The optical setup with extremely high optical throughput requires the development of new methods and algorithms for spectral and radiometric calibration. Due to the vast amount of data there is a high demand for scientifically intelligent optimisation of the data processing. This paper outlines the characterisation and processing steps required for the generation of radiometrically and spectrally calibrated spectra. Methods for performance optimisation of the processing algorithm are presented. The performance of the data processing and the quality of the calibrated spectra are demonstrated for measurements collected during the first deployments of GLORIA on aircraft.
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Kleinert, A., F. Friedl-Vallon, T. Guggenmoser, et al. "Level 0 to 1 processing of the imaging Fourier transform spectrometer GLORIA: generation of radiometrically and spectrally calibrated spectra." Atmospheric Measurement Techniques Discussions 7, no. 3 (2014): 2827–78. http://dx.doi.org/10.5194/amtd-7-2827-2014.

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Abstract. The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) is an imaging Fourier transform spectrometer that is capable of operating on various high altitude research aircraft. It measures the atmospheric emission in the thermal infrared spectral region in limb and nadir geometry. GLORIA consists of a classical Michelson interferometer combined with an infrared camera. The infrared detector has a usable range of 128 × 128 pixels, measuring up to 16 384 interferograms simultaneously. Imaging Fourier transform spectrometers impose a number of challenges with respect to instrument calibration and algorithm development. The innovative optical setup with extremely high optical throughput requires the development of new methods and algorithms for spectral and radiometric calibration. Due to the vast amount of data there is a high demand for scientifically intelligent optimisation of the data processing. This paper outlines the characterisation and processing steps required for the generation of radiometrically and spectrally calibrated spectra. Methods for performance optimisation of the processing algorithm are presented. The performance of the data processing and the quality of the calibrated spectra are demonstrated for measurements collected during the first deployments of GLORIA on aircraft.
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Patané, Giuseppe. "STAR - Laplacian Spectral Kernels and Distances for Geometry Processing and Shape Analysis." Computer Graphics Forum 35, no. 2 (2016): 599–624. http://dx.doi.org/10.1111/cgf.12866.

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Litvinovich, Hl S., and I. I. Bruchkouski. "Algorithm for preliminary processing of charge coupled devices array data based on the adaptive Wiener filter." Informatics 18, no. 1 (2021): 72–83. http://dx.doi.org/10.37661/1816-0301-2021-18-1-72-83.

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The researcher should choose the modes of recording spectra which allow to achieve the highest accuracy of spectral measurements in remote sensing systems. When registering a signal from aircraft which provide maximum coverage of the studied area, it is important to obtain a signal with the maximum signal-to- noise ratio in a minimum time, since the accumulation of spectra samples for averaging is impossible. The paper presents the experimental results of determining the noise components (readout noise, photon, electronic shot, pattern noise) for a monochrome uncooled CCD-line detector Toshiba TCD1304DG (CCD – charge-coupled devices) with various conditions of spectrum registration: detector temperature, exposition. Obtained dependences of the noise components make it possible to estimate the noise level for well-known conditions of spectra registration. The algorithm for processing CCD data based on an adaptive Wiener filter is proposed to increase the signal-to-noise ratio by using a priori information about the statistical parameters of the noise components. Such approach has allowed to increase the signal-to-noise ratio of sky spectral brightness by 4–9 dB for exposure times. The practical application of the algorithm has reduced the uncertainty in the vegetation index NDVI by 1.5 times when recording the reflection spectra of vegetation from the aircraft in the nadir measurement geometry.
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Jiang, Yonghua, Jingyin Wang, Li Zhang, Guo Zhang, Xin Li, and Jiaqi Wu. "Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites." Remote Sensing 11, no. 9 (2019): 996. http://dx.doi.org/10.3390/rs11090996.

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The second batch of Zhuhai-1 microsatellites was successfully launched on 26 April 2018. The batch included four Orbita hyperspectral satellites (referred to as OHS-A, OHS-B, OHS-C, and OHS-D) and one video satellite (OVS-2A), which have excellent hyperspectral data acquisition abilities. For the first time in China, a number of hyperspectral satellite networks have been realized. To ensure the application of hyperspectral remote sensing data, a series of on-orbit geometry processing and accuracy verification studies has been carried out on the “Zhuhai-1” hyperspectral camera since the satellite was launched. This paper presents the geometric processing methods involved in the production of Zhuhai-1 hyperspectral satellite basic products, including geometric calibration and basic product production algorithms. The OHS images were used to perform on-orbit geometric calibration, and the calibration accuracy was better than 0.5 pixels. The registration accuracy of the image spectrum of the basic product after calibration, the single orientation accuracy, and the accuracy of the regional network adjustment were evaluated. The spectral registration accuracy of the OHS basic products is 0.3–0.5 pixels, which is equivalent to the spectral band calibration accuracy. The single orientation accuracy is better than 1.5 pixels and the regional network adjustment accuracy is better than 1.2 pixels. The generated area orthoimages meet the seamless edge requirements, which verifies that the OHS basic product image has good regional mapping capabilities and can meet the application requirements.
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Feng, Sheng, Xiaoqiang Hua, and Xiaoqian Zhu. "Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction." Entropy 22, no. 9 (2020): 914. http://dx.doi.org/10.3390/e22090914.

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In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract abundant features, then exploit the advantages of matrix information geometry technique by constructing the high-dimensional features as symmetric positive definite (SPD) matrices. In this case, our task for signal detection becomes a binary classification problem lying on an SPD manifold. Promoting the discrimination of heterogeneous samples through information geometric DR technique that is dedicated to SPD manifold, our proposed detector achieves satisfactory signal detection performance in low SNR cases using the K distribution simulation and the real-life sea clutter data, which can be widely used in the field of signal detection.
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Zolotov, Denis, Alexey Buzmakov, Maxim Grigoriev, and Igor Schelokov. "Dual-energy crystal-analyzer scheme for spectral tomography." Journal of Applied Crystallography 53, no. 3 (2020): 781–88. http://dx.doi.org/10.1107/s1600576720005439.

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In the present work, a method for adjusting a crystal analyzer to separate two characteristic lines from the spectrum of a conventional X-ray tube for simultaneous registration of tomographic projections is proposed. The experimental implementation of this method using radiation of a molybdenum anode (Kα1, Kβ lines) and a silicon Si(111) crystal analyzer in Laue geometry is presented. Projection images at different wavelengths are separated in space and can be recorded independently for further processing. Potential uses of this scheme are briefly discussed.
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Dissertations / Theses on the topic "Spectral geometry processing"

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Yu, Wang S. M. Massachusetts Institute of Technology. "Steklov geometry processing : an extrinsic approach to spectral shape analysis." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118033.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 75-80).<br>We propose using the Dirichlet-to-Neumann operator as an extrinsic alternative to the Laplacian for spectral geometry processing and shape analysis. Intrinsic approaches, usually based on the Laplace-Beltrami operator, cannot capture the spatial embedding of a shape up to rigid motion, and many previous extrinsic methods lack theoretical justification. Instead, we consider the Steklov eigenvalue problem, computing the spectrum of the Dirichlet-to-Neumann operator of a surface bounding a volume. A remarkable property of this operator is that it completely encodes volumetric geometry. We use the boundary element method (BEM) to discretize the operator, accelerated by hierarchical numerical schemes and preconditioning; this pipeline allows us to solve eigenvalue and linear problems on large-scale meshes despite the density of the Dirichlet-to-Neumann discretization. We further demonstrate that our operators naturally fit into existing frameworks for geometry processing, making a shift from intrinsic to extrinsic geometry as simple as substituting the Laplace-Beltrami operator with the Dirichlet-to-Neumann operator.<br>by Yu Wang.<br>S.M.
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Rabiei, Hamed. "Spectral analysis of the cerebral cortex complexity." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0289/document.

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La complexité de la forme de la surface est une caractéristique morphologique des surfaces pliées. Dans cette thèse, nous visons à développer des méthodes spectrales pour quantifier cette caractéristique du cortex cérébral humain reconstruit à partir d'images MR structurales. Tout d'abord, nous suggérons certaines propriétés qu'une mesure standard de la complexité de surface devrait posséder. Ensuite, nous proposons deux définitions claires de la complexité de la surface en fonction des propriétés de flexion de surface. Pour quantifier ces définitions, nous avons étendu la transformée de Fourier à fenêtres illustrée récemment pour transformer en maillage des surfaces. Grâce à certaines expériences sur les surfaces synthétiques, nous montrons que nos mesures basées sur la courbure permettent de surmonter les surfaces classiques basées sur la surface, ce qui ne distingue pas les plis profonds des oscillants ayant une surface égale. La méthode proposée est appliquée à une base de données de 124 sujets adultes en bonne santé. Nous définissons également la complexité de la surface par la régularité de Hölder des mouvements browniens fractionnés définis sur les collecteurs. Ensuite, pour la première fois, nous développons un algorithme de régression spectrale pour quantifier la régularité de Hölder d'une surface brownienne fractionnée donnée en estimant son paramètre Hurst H. La méthode proposée est évaluée sur un ensemble de sphères browniennes fractionnées simulées. En outre, en supposant que le cortex cérébral est une surface brownienne fractionnée, l'algorithme proposé est appliqué pour estimer les paramètres Hurst d'un ensemble de 14 corticus cérébraux fœtaux<br>Surface shape complexity is a morphological characteristic of folded surfaces. In this thesis, we aim at developing some spectral methods to quantify this feature of the human cerebral cortex reconstructed from structural MR images. First, we suggest some properties that a standard measure of surface complexity should possess. Then, we propose two clear definitions of surface complexity based on surface bending properties. To quantify these definitions, we extended the recently introduced graph windowed Fourier transform to mesh model of surfaces. Through some experiments on synthetic surfaces, we show that our curvature-based measurements overcome the classic surface area-based ones which may not distinguish deep folds from oscillating ones with equal area. The proposed method is applied to a database of 124 healthy adult subjects. We also define the surface complexity by the Hölder regularity of fractional Brownian motions defined on manifolds. Then, for the first time, we develop a spectral-regression algorithm to quantify the Hölder regularity of a given fractional Brownian surface by estimating its Hurst parameter H. The proposed method is evaluated on a set of simulated fractional Brownian spheres. Moreover, assuming the cerebral cortex is a fractional Brownian surface, the proposed algorithm is applied to estimate the Hurst parameters of a set of 14 fetal cerebral cortices
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Zhou, Bingxin. "Geometric Signal Processing with Graph Neural Networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28617.

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One of the most predominant techniques that have achieved phenomenal success in many modern applications is deep learning. The obsession with massive data analysis in image recognition, speech processing, and text understanding spawns remarkable advances in deep learning of diverse research areas. The alliance of deep learning technologies yields mighty graph neural networks (GNNs), an emerging type of deep neural networks that encodes internal structural relationships of inputs. The mainstream of GNNs finds an adequate numerical representation of graphs, which is vital to the prediction performance of machine learning models. Graph representation learning has many real-world applications, such as drug repurposing, protein classification, epidemic spread controlling, and social networks analysis. The rapid development of GNNs in the last five years has witnessed a couple of design flaws, such as over-smoothing, vulnerability to perturbation, lack of expressivity, and missing explainability. Meanwhile, the persistent enthusiasm in this research area allows for cumulative experience in solving complicated problems, such as size-variant graph compression and time-variant graph dynamic capturing. The ambition of this thesis is to shed some light of mathematics on a few outlined issues. The permutation-invariant design of graph compression is supported by manifold learning, the robust graph smoothing relies heavily on the principles of convex optimization, and the efficient dynamic graph embedding leverages global spectral transforms and power method singular value decomposition. The author believes that the effectiveness of deep learning designs should not be oriented solely by performance over particular datasets. Modifications on a black-box model should operate beyond fine-tuning tricks. The reliability of deep learning looks forward to designing models with rigorous mathematics so that the `computer science' becomes actual science one day.
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Sirugue, Léa. "Conception et développement d’une méthode de comparaison de surfaces appliquée aux protéines." Thesis, Paris, HESAM, 2020. http://www.theses.fr/2020HESAC042.

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Les interactions entre protéines jouent un rôle crucial dans les processus du vivant comme la communication cellulaire, l’immunité, la croissance, prolifération et la mort cellulaires. Ces interactions se font via leur surface et la perturbation des interactions entre protéines est à la base de nombreux processus pathologiques. Il est donc nécessaire de bien comprendre et caractériser la surface des protéines et leurs interactions mutuelles de manière à mieux comprendre les processus du vivant. Différentes méthodes de comparaison de la surface des protéines ont été développées ces dernières années mais aucune n’est assez puissante pour traiter l’ensemble des structures disponibles dans les différentes bases de données. Le projet de thèse est donc de développer des méthodes rapides de comparaison de surface et de les appliquer à la surface des macromolécules<br>Protein interactions play a crucial role in the living processes such as cell communication, immunity, cell growth, proliferation and death. These interactions occur through the surface of proteins and the disruption of their interactions is the start of many disease processes. It is therefore necessary to understand and characterize the surface of proteins and their interactions to better understand living processes. Different methods of protein surfaces comparison have been developed in the recent years but none are powerful enough to handle all the structures currently available in databases. The PhD project is to develop rapid methods of surface comparison and apply them to the surface of macromolecules
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Goes, Fernando Ferrari de. "Analise espectral de superficies e aplicações em computação grafica." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275916.

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Orientador: Siome Klein Goldenstein<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-14T02:23:56Z (GMT). No. of bitstreams: 1 Goes_FernandoFerraride_M.pdf: 31957234 bytes, checksum: c369081bcbbb5f360184a1f8467839ea (MD5) Previous issue date: 2009<br>Resumo: Em computação gráfica, diversos problemas consistem na análise e manipulação da geometria de superfícies. O operador Laplace-Beltrami apresenta autovalores e autofunções que caracterizam a geometria de variedades, proporcionando poderosas ferramentas para o processamento geométrico. Nesta dissertação, revisamos as propriedades espectrais do operador Laplace-Beltrami e propomos sua aplicação em computação gráfica. Em especial, introduzimos novas abordagens para os problemas de segmentação semântica e geração de atlas em superfícies<br>Abstract: Many applications in computer graphics consist of the analysis and manipulation of the geometry of surfaces. The Laplace-Beltrami operator presents eigenvalues and eigenfuncitons which caracterize the geometry of manifolds, supporting powerful tools for geometry processing. In this dissertation, we revisit the spectral properties of the Laplace-Beltrami operator and apply them in computer graphics. In particular, we introduce new approaches for the problems of semantic segmentation and atlas generation on surfaces<br>Mestrado<br>Computação Grafica<br>Mestre em Ciência da Computação
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Batard, Thomas. "Géométrie différentielle des fibrés vectoriels et algèbres de Clifford appliquées au traitement d'images multicanaux." Phd thesis, Université de La Rochelle, 2009. http://tel.archives-ouvertes.fr/tel-00684250.

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Le sujet de cette thèse est l'apport d'applications du formalisme des algèbres de Clifford au traitement d'images multicanaux. Nous y introduisons également l'utilisation du cadre des fibrés vectoriels en traitement d'image. La Partie 1 est consacrée à la segmentation d'images multicanaux. Nous généralisons l'approche de Di Zenzo pour la détection de contours en construisant des tenseurs métriques adaptés au choix de la segmentation. En utilisant le cadre des fibrés en algèbres de Clifford, nous montrons que le choix d'une segmentation d'une image est directement lié au choix d'une métrique, d'une connexion et d'une section sur un tel fibré. La Partie 2 est consacrée à la régularisation. Nous utilisons le cadre des équations de la chaleur associées à des Laplaciens généralisés sur des fibrés vectoriels. Le résultat principal que nous obtenons est qu'en considérant l'équation de la chaleur associée à l'opérateur de Hodge sur le fibré de Clifford d'une variété Riemannienne bien choisie, nous obtenons un cadre global pour régulariser de manière anisotrope des images (vidéos) multicanaux, et des champs s'y rapportant tels des champs de vecteurs ou des champs de repères orthonormés. Enfin, dans la Partie 3, nous nous intéressons à l'analyse spectrale via la définition d'une transformée de Fourier d'une image multicanaux. Cette définition repose sur une théorie abstraite de la transformée de Fourier basée sur la notion de représentation de groupe. De ce point de vue, la transformée de Fourier usuelle pour les images en niveau de gris est basée sur les représentations irréductibles du groupe des translations du plan. Nous l'étendons aux images multicanaux en lui associant les représentations réductibles de ce groupe.
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Books on the topic "Spectral geometry processing"

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Fischer, Paul. On the optimal number of subdomains for hyperbolic problems on parallel computers. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1996.

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Fischer, P. F. On the optimal number of subdomains for hyperbolic problems on parallel computers. National Aeronautics and Space Administration, Langley Research Center, 1997.

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Bluston, H. S. Geometric and computer graphics applications of the spectrum / spectrum plus computers. Energy Consultancy, 1986.

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Jørgensen, Palle E. T., 1947-, ed. Wavelets through a looking glass: The world of the spectrum. Birkhäuser, 2002.

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Patane, Giuseppe. Introduction to Laplacian Spectral Distances and Kernels: Theory, Computation, and Applications. Morgan & Claypool Publishers, 2017.

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Bratteli, Ola, Palle Jorgensen, and B. Treadway. Wavelets Through a Looking Glass: The World of the Spectrum. Birkhäuser, 2013.

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Bratteli, Ola, Palle Jorgensen, and B. Treadway. Wavelets Through a Looking Glass: The World of the Spectrum. Springer, 2007.

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Bratteli, Ola, and Palle Jorgensen. Wavelets through a Looking Glass (Applied and Numerical Harmonic Analysis). Birkhäuser Boston, 2002.

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Book chapters on the topic "Spectral geometry processing"

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Kim, Sung-Yeol, Seung-Uk Yoon, and Yo-Sung Ho. "Spectral Coding of Three-Dimensional Mesh Geometry Information Using Dual Graph." In Advances in Multimedia Information Processing - PCM 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30541-5_51.

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Liu, Rong, Hao Zhang, and Oliver van Kaick. "Spectral Sequencing Based on Graph Distance." In Geometric Modeling and Processing - GMP 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802914_50.

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Jain, Varun, and Hao Zhang. "Shape-Based Retrieval of Articulated 3D Models Using Spectral Embedding." In Geometric Modeling and Processing - GMP 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802914_21.

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"Spectral Transform." In A Sampler of Useful Computational Tools for Applied Geometry, Computer Graphics, and Image Processing. A K Peters/CRC Press, 2015. http://dx.doi.org/10.1201/b18472-7.

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Conference papers on the topic "Spectral geometry processing"

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Pauly, Mark, and Markus Gross. "Spectral processing of point-sampled geometry." In the 28th annual conference. ACM Press, 2001. http://dx.doi.org/10.1145/383259.383301.

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Heylen, Rob, and Paul Scheunders. "Spectral unmixing using distance geometry." In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2011. http://dx.doi.org/10.1109/whispers.2011.6080889.

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Li, Yili, and K. M. Wong. "Signal classification by power spectral density: An approach via Riemannian geometry." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319854.

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Konur, Umut, Ulug Bayazit, Hasan F. Ates, and Fikret S. Gurgen. "Spectral Coding of Mesh Geometry with a Hierarchical Set Partitioning Algorithm." In 2007 IEEE 15th Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298692.

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Forster, P., and T. Aste. "Rectification of cross spectral matrices for arrays of arbitrary geometry." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.761351.

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Cui, Kangning, Ruoning Li, Sam L. Polk, James M. Murphy, Robert J. Plemmons, and Raymond H. Chan. "Unsupervised Spatial-Spectral Hyperspectral Image Reconstruction And Clustering With Diffusion Geometry." In 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2022. http://dx.doi.org/10.1109/whispers56178.2022.9955069.

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Cai, Baolai, Chentao Yue, Jiamin Li, and Pengcheng Zhu. "Downlink spectral efficiency of multi-user distributed antenna systems under a stochastic geometry model." In 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2017. http://dx.doi.org/10.1109/wcsp.2017.8171033.

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Pazi, Idan, Dvir Ginzburg, and Dan Raviv. "Unsupervised Scale-Invariant Multispectral Shape Matching." In 24th Irish Machine Vision and Image Processing Conference. Irish Pattern Recognition and Classification Society, 2022. http://dx.doi.org/10.56541/vhmq4826.

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Alignment between non-rigid stretchable structures is one of the most challenging tasks in computer vision, as the invariant properties are hard to define, and there is no labeled data for real datasets. We present unsupervised neural network architecture based upon the spectral domain of scale-invariant geometry. We build on top of the functional maps architecture, but show that learning local features, as done until now, is not enough once the isometry assumption breaks. We demonstrate the use of multiple scale-invariant geometries for solving this problem. Our method is agnostic to local-scale deformations and shows superior performance for matching shapes from different domains when compared to existing spectral state-of-the-art solutions.
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Chang, Hanpeng, Yue Wen, Siu Lung Lee, et al. "Light Induced Autofluorescence for Detection of Nasopharyngeal Carcinoma in vivo." In European Conference on Biomedical Optics. Optica Publishing Group, 2001. http://dx.doi.org/10.1364/ecbo.2001.4432_186.

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To improve the accuracy of conventional white light endoscopy in detecting the small lesion and identifying the margin of observable tumors, in vivo, the potential of light-induced fluorescence (LIF) spectroscopic imaging, using a genera multivariate spectral classification algorithm, was evaluated. A conventional endoscopic system with a multiple channel spectrometer was used to measure the autofluorescence of nasopharyngeal tissue in vivo. Classification was based on the spectral difference between the carcinoma and normal tissue. A sophisticated algorithm based on Principal Component Analysis (PCA) was developed to differentiate between the nasopharyngeal carcinoma (NPC) from the normal tissue. Firstly, preprocessing was done to reduce noise and to calibrate the different measurement distances an geometry. Secondly, processing by PCA was done to effectively reduce the variable dimensions while maintaining useful information for analysis. Thirdly, various post-processing techniques were investigated and the classification performance was compared. Algorithms based on ratio of autofluorescence at two-wavelength and three-wavelength bands were used for comparison. The PCA based method shows a significant improvement over the two-wavelength and three-wavelength algorithm. Based on the entire spectra, the sensitivity of 92% and specificity of 96% were achieved using the PCA based algorithm for the detection of nasopharyngeal carcinomas. In conclusion, the PCA based statistical algorithm is efficient to achieve high spectral classification performance of NPC.
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Tootooni, M. Samie, Ashley Dsouza, Ryan Donovan, Prahalad K. Rao, Zhenyu (James) Kong, and Peter Borgesen. "Assessing the Geometric Integrity of Additive Manufactured Parts From Point Cloud Data Using Spectral Graph Theoretic Sparse Representation-Based Classification." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2794.

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This work proposes a novel approach for geometric integrity assessment of additive manufactured (AM, 3D printed) components, exemplified by acrylonitrile butadiene styrene (ABS) polymer parts made using fused filament fabrication (FFF) process. The following two research questions are addressed in this paper: (1) what is the effect of FFF process parameters, specifically, infill percentage (If) and extrusion temperature (Te) on geometric integrity of ABS parts?; and (2) what approach is required to differentiate AM parts with respect to their geometric integrity based on sparse sampling from a large (∼ 2 million data points) laser-scanned point cloud dataset? To answer the first question, ABS parts are produced by varying two FFF parameters, namely, infill percentage (If) and extrusion temperature (Te) through design of experiments. The part geometric integrity is assessed with respect to key geometric dimensioning and tolerancing (GD&amp;T) features, such as flatness, circularity, cylindricity, root mean square deviation, and in-tolerance percentage. These GD&amp;T parameters are obtained by laser scanning of the FFF parts. Concurrently, coordinate measurements of the part geometry in the form of 3D point cloud data is also acquired. Through response surface statistical analysis of this experimental data it was found that discrimination of geometric integrity between FFF parts based on GD&amp;T parameters and process inputs alone was unsatisfactory (regression R2 &lt; 50%). This directly motivates the second question. Accordingly, a data-driven analytical approach is proposed to classify the geometric integrity of FFF parts using minimal number (&lt; 2% of total) of laser-scanned 3D point cloud data. The approach uses spectral graph theoretic Laplacian eigenvalues extracted from the 3D point cloud data in conjunction with a modeling framework called sparse representation to classify FFF part quality contingent on the geometric integrity. The practical outcome of this work is a method that can quickly classify the part geometric integrity with minimal point cloud data and high classification fidelity (F-score &gt; 95%), which bypasses tedious coordinate measurement.
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Reports on the topic "Spectral geometry processing"

1

Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.
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