Relevant bibliographies by topics / Iterative projection algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Iterative projection algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Iterative projection algorithm"

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Zhang, Ruiqiu, Chen Li, and Wenhua Gao. "Projection Classification Based Iterative Algorithm." IOP Conference Series: Materials Science and Engineering 83 (May 29, 2015): 012022. http://dx.doi.org/10.1088/1757-899x/83/1/012022.

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Khan, Safeer Hussain, Timilehin Opeyemi Alakoya, and Oluwatosin Temitope Mewomo. "Relaxed Projection Methods with Self-Adaptive Step Size for Solving Variational Inequality and Fixed Point Problems for an Infinite Family of Multivalued Relatively Nonexpansive Mappings in Banach Spaces." Mathematical and Computational Applications 25, no. 3 (2020): 54. http://dx.doi.org/10.3390/mca25030054.

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In each iteration, the projection methods require computing at least one projection onto the closed convex set. However, projections onto a general closed convex set are not easily executed, a fact that might affect the efficiency and applicability of the projection methods. To overcome this drawback, we propose two iterative methods with self-adaptive step size that combines the Halpern method with a relaxed projection method for approximating a common solution of variational inequality and fixed point problems for an infinite family of multivalued relatively nonexpansive mappings in the sett
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Gomi, Tsutomu, and Yukio Koibuchi. "Use of a Total Variation Minimization Iterative Reconstruction Algorithm to Evaluate Reduced Projections during Digital Breast Tomosynthesis." BioMed Research International 2018 (June 19, 2018): 1–14. http://dx.doi.org/10.1155/2018/5239082.

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Purpose. We evaluated the efficacies of the adaptive steepest descent projection onto convex sets (ASD-POCS), simultaneous algebraic reconstruction technique (SART), filtered back projection (FBP), and maximum likelihood expectation maximization (MLEM) total variation minimization iterative algorithms for reducing exposure doses during digital breast tomosynthesis for reduced projections. Methods. Reconstructions were evaluated using normal (15 projections) and half (i.e., thinned-out normal) projections (seven projections). The algorithms were assessed by determining the full width at half-ma
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ZHAO, JIAN, and JIAN AN. "AN ITERATIVE CONVEX HULL APPROACH FOR IMAGE SEGMENTATION AND CONTOUR EXTRACTION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 07 (2012): 1255013. http://dx.doi.org/10.1142/s0218001412550130.

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The contours and segments of objects in digital images have many important applications. Contour extractions of gray images can be converted into contour extractions of binary images. This paper presents a novel contour-extraction algorithm for binary images and provides a deduction theory for this algorithm. First, we discuss the method used to construct convex hulls of regions of objects. The contour of an object evolves from a convex polygon until the exact boundary is obtained. Second, the projection methods from lines to objects are studied, in which, a polygon iteration method is present
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Dang, Yazheng, and Wenwen Liu. "A Nonmonotone Projection Method for Constrained System of Nonlinear Equations." Mathematical Problems in Engineering 2017 (2017): 1–5. http://dx.doi.org/10.1155/2017/8270649.

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This paper deals with the nonmonotone projection algorithm for constrained nonlinear equations. For some starting points, the previous projection algorithms for the problem may encounter slow convergence which is related to the monotone behavior of the iterative sequence as well as the iterative direction. To circumvent this situation, we adopt the nonmonotone technique introduced by Dang to develop a nonmonotone projection algorithm. After constructing the nonmonotone projection algorithm, we show its convergence under some suitable condition. Preliminary numerical experiment is reported at t
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Li, Xia, Zhanqiang Song, Guiping Shen, Ying Huang, and Junyu Chen. "Diagnostic Value of Chest CT Images Based on Full Model Iterative Reconstruction Algorithm for Lung Cancer Patients." Scientific Programming 2021 (September 10, 2021): 1–7. http://dx.doi.org/10.1155/2021/5257682.

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Objective. To evaluate the value of low-dose CT scanning and full model iteration recombinant technology peripheral lung cancer in the paper using whole model iterative reconstruction algorithm and compare iterative model-wide restructuring, reorganization part of an iterative algorithm, affecting filtered back projection image quality. Method. Fifty-two patients with peripheral lung cancer, all of whom were diagnosed by pathological biopsy, were selected for the study. All patients received three scans of low-dose chest CT, next-low-dose, and low-dose, after which the raw data of three differ
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de Lima, Camila, and Elias Salomão Helou. "Fast projection/backprojection and incremental methods applied to synchrotron light tomographic reconstruction." Journal of Synchrotron Radiation 25, no. 1 (2018): 248–56. http://dx.doi.org/10.1107/s1600577517015715.

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Iterative methods for tomographic image reconstruction have the computational cost of each iteration dominated by the computation of the (back)projection operator, which take roughlyO(N3) floating point operations (flops) forN×Npixels images. Furthermore, classical iterative algorithms may take too many iterations in order to achieve acceptable images, thereby making the use of these techniques unpractical for high-resolution images. Techniques have been developed in the literature in order to reduce the computational cost of the (back)projection operator toO(N2logN) flops. Also, incremental a
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Wallace, Tim, and Ali Sekmen. "Kaczmarz Iterative Projection and Nonuniform Sampling with Complexity Estimates." Journal of Medical Engineering 2014 (December 15, 2014): 1–15. http://dx.doi.org/10.1155/2014/908984.

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Kaczmarz’s alternating projection method has been widely used for solving mostly over-determined linear system of equations Ax=b in various fields of engineering, medical imaging, and computational science. Because of its simple iterative nature with light computation, this method was successfully applied in computerized tomography. Since tomography generates a matrix A with highly coherent rows, randomized Kaczmarz algorithm is expected to provide faster convergence as it picks a row for each iteration at random, based on a certain probability distribution. Since Kaczmarz’s method is a subspa
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Jing, Mingli, Xueqin Zhou, and Chun Qi. "Quasi-Newton Iterative Projection Algorithm for Sparse Recovery." Neurocomputing 144 (November 2014): 169–73. http://dx.doi.org/10.1016/j.neucom.2014.04.055.

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Reznik, A. M. "Iterative projection algorithm for learning in neural networks." Cybernetics and Systems Analysis 29, no. 6 (1994): 897–906. http://dx.doi.org/10.1007/bf01122743.

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Dissertations / Theses on the topic "Iterative projection algorithm"

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Pokhrel, Damodar. "Brachytherapy Seed and Applicator Localization via Iterative Forward Projection Matching Algorithm using Digital X-ray Projections." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2283.

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Interstitial and intracavitary brachytherapy plays an essential role in management of several malignancies. However, the achievable accuracy of brachytherapy treatment for prostate and cervical cancer is limited due to the lack of intraoperative planning and adaptive replanning. A major problem in implementing TRUS-based intraoperative planning is an inability of TRUS to accurately localize individual seed poses (positions and orientations) relative to the prostate volume during or after the implantation. For the locally advanced cervical cancer patient, manual drawing of the source positions
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Pesolillo, Daniele. "Image quality and dose evaluation of filtered back projection versus iterative reconstruction algorithm in multislice computed tomography." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8315/.

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Il presente lavoro di tesi è stato svolto presso il servizio di Fisica Sanitaria del Policlinico Sant'Orsola-Malpighi di Bologna. Lo studio si è concentrato sul confronto tra le tecniche di ricostruzione standard (Filtered Back Projection, FBP) e quelle iterative in Tomografia Computerizzata. Il lavoro è stato diviso in due parti: nella prima è stata analizzata la qualità delle immagini acquisite con una CT multislice (iCT 128, sistema Philips) utilizzando sia l'algoritmo FBP sia quello iterativo (nel nostro caso iDose4). Per valutare la qualità delle immagini sono stati analizzati i seguent
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Nikazad, Touraj. "The Use of Landweber Algorithm in Image Reconstruction." Licentiate thesis, Linköping University, Linköping University, Scientific Computing, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10030.

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<p>Ill-posed sets of linear equations typically arise when discretizing certain types of integral transforms. A well known example is image reconstruction, which can be modelled using the Radon transform. After expanding the solution into a finite series of basis functions a large, sparse and ill-conditioned linear system arises. We consider the solution of such systems. In particular we study a new class of iteration methods named DROP (for Diagonal Relaxed Orthogonal Projections) constructed for solving both linear equations and linear inequalities. This class can also be viewed, when applie
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Chen, Joe. "Image Reconstruction in Serial Femtosecond Nanocrystallography." Thesis, University of Canterbury. Electrical and Computer Engineering, 2015. http://hdl.handle.net/10092/10661.

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X-ray crystallography is a form of microscopy that allows the three-dimensional arrangement of atoms belonging to molecules within crystals to be determined. In this method, a crystal is illuminated with a beam of X-rays and the diffracted amplitudes resulting from the illumination are measured and computationally processed to enable the electron density of the unit molecule, or the unit cell, constituting the crystal to be calculated. The recent development of the X-ray free-electron laser (XFEL) provides new routes for determining molecular structures via its ability to generate intense but
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Lo, Victor Lai-Xin. "Iterative projection algorithms and applications in x-ray crystallography." Thesis, University of Canterbury. Electrical and Computer Engineering, 2011. http://hdl.handle.net/10092/5476.

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X-ray crystallography is a technique for determining the structure (positions of atoms in space) of molecules. It is a well developed technique, and is applied routinely to both small inorganic and large organic molecules. However, the determination of the structures of large biological molecules by x-ray crystallography can still be an experimentally and computationally expensive task. The data in an x-ray experiment are the amplitudes of the Fourier transform of the electron density in the crystalline specimen. The structure determination problem in x-ray crystallography is therefore identic
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Kelly, Shaun Innes. "Iterative synthetic aperture radar imaging algorithms." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9368.

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Synthetic aperture radar is an important tool in a wide range of civilian and military imaging applications. This is primarily due to its ability to image in all weather conditions, during both the day and the night, unlike optical imaging systems. A synthetic aperture radar system contains a step which is not present in an optical imaging system, this is image formation. This is required because the acquired data from the radar sensor does not directly correspond to the image. Instead, to form an image, the system must solve an inverse problem. In conventional scenarios, this inverse problem
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Davis, Carlos Clifford Jr. "Iterative algorithms for the reconstruction of multidimensional signals from their projections." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/15636.

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Banjak, Hussein. "X-ray computed tomography reconstruction on non-standard trajectories for robotized inspection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI113/document.

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La tomographie par rayons X ou CT pour "Computed Tomography" est un outil puissant pour caractériser et localiser les défauts internes et pour vérifier la conformité géométrique d’un objet. Contrairement au cas des applications médicales, l’objet inspecté en Contrôle Non Destructif (CND) peut être très grand et composé de matériaux de haute atténuation, auquel cas l’utilisation d’une trajectoire circulaire pour l’inspection est impossible à cause de contraintes dans l’espace. Pour cette raison, l’utilisation de bras robotisés est l’une des nouvelles tendances reconnues dans la CT, car elle aut
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"Phasing Two-Dimensional Crystal Diffraction Pattern with Iterative Projection Algorithms." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.40706.

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abstract: Phase problem has been long-standing in x-ray diffractive imaging. It is originated from the fact that only the amplitude of the scattered wave can be recorded by the detector, losing the phase information. The measurement of amplitude alone is insufficient to solve the structure. Therefore, phase retrieval is essential to structure determination with X-ray diffractive imaging. So far, many experimental as well as algorithmic approaches have been developed to address the phase problem. The experimental phasing methods, such as MAD, SAD etc, exploit the phase relation in vector space.
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Yan, Jie. "Methods for ℓp/TVp Regularized Optimization and Their Applications in Sparse Signal Processing". Thesis, 2014. http://hdl.handle.net/1828/5728.

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Exploiting signal sparsity has recently received considerable attention in a variety of areas including signal and image processing, compressive sensing, machine learning and so on. Many of these applications involve optimization models that are regularized by certain sparsity-promoting metrics. Two most popular regularizers are based on the l1 norm that approximates sparsity of vectorized signals and the total variation (TV) norm that serves as a measure of gradient sparsity of an image. Nevertheless, the l1 and TV terms are merely two representative measures of sparsity. To explore the ma
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Book chapters on the topic "Iterative projection algorithm"

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Zaslavski, Alexander J. "Iterative Subgradient Projection Algorithm." In Approximate Solutions of Common Fixed-Point Problems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33255-0_11.

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Zaslavski, Alexander J. "Iterative Subgradient Projection Algorithm." In Springer Optimization and Its Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78849-0_5.

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Schimek, Michael G. "An Iterative Projection Algorithm and Some Simulation Results." In COMPSTAT. Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-46992-3_62.

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Liu, Qingshan, Jiang Xiong, and Shaofu Yang. "Mixed-Norm Projection-Based Iterative Algorithm for Face Recognition." In Advances in Neural Networks – ISNN 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22808-8_33.

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Zhang, Zhongcheng. "Alternating Iterative Projection Algorithm of Multivariate Time Series Mixed Models." In Advances in Neural Networks – ISNN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01507-6_73.

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Pokhrel, D., M. J. Murphy, D. A. Todor, et al. "Brachytherapy seed localization via iterative forward projection matching (IFPM) algorithm using intraoperative cone-beam-CT sinogram projections." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03474-9_87.

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Gubareny, N., and A. Katkov. "Parallel chaotic iterative algorithms for image reconstruction with limited projection data." In Parallel Computation. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61695-0_17.

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Gilyazov, S. F., and N. L. Gol’dman. "Descriptive Regularization Algorithms on the basis of the Conjugate Gradient Projection method." In Regularization of Ill-Posed Problems by Iteration Methods. Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9482-0_7.

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Mersereau, Russell M. "Iterative Algorithms for Deconvolution and Reconstruction of Multidimensional Signals from Their Projections." In Adaptive Methods in Underwater Acoustics. Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5361-1_50.

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Backenköhler, Michael, Luca Bortolussi, Gerrit Großmann, and Verena Wolf. "Analysis of Markov Jump Processes under Terminal Constraints." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_12.

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AbstractMany probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We propose a solution to this bridging problem for the widely used class of population-structured Markov jump processes. The method is based on a state-space lumping scheme that aggregates states in a grid structure. The resulting approximate bridging distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way, the algorithm learns a well-justified finite-state projection yielding guaranteed lower bounds for the system behavior under endpoint constraints. We demonstrate the method’s applicability to a wide range of problems such as Bayesian inference and the analysis of rare events.
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Conference papers on the topic "Iterative projection algorithm"

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Kang, Jingli, and Yaoguo Li. "Iterative learning control algorithm based on newton-projection method." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053412.

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Gensheimer, Florian, Tobias Dietz, Stefan Ruzika, Kira Kraft, and Norbert Wehn. "A Low-Complexity Projection Algorithm for ADMM-Based LP Decoding." In 2018 IEEE 10th International Symposium on Turbo Codes & Iterative Information Processing (ISTC). IEEE, 2018. http://dx.doi.org/10.1109/istc.2018.8625295.

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Wang, Zeying, Shihong Yue, and Xiaoyuan Liu. "An Iterative Linear Back-projection Algorithm for Electrical Impedance Tomography*." In 2018 13th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2018. http://dx.doi.org/10.1109/wcica.2018.8630576.

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Baikun Wan, Lin Meng, Dong Ming, Hongzhi Qi, Yong Hu, and K. D. K. Luk. "Video image super-resolution restoration based on iterative back-projection algorithm." In 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA). IEEE, 2009. http://dx.doi.org/10.1109/cimsa.2009.5069916.

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Song, Haiying, Xiaohai He, Weilong Chen, and Yanyue Sun. "An Improved Iterative Back-Projection Algorithm for Video Super-Resolution Reconstruction." In 2010 Symposium on Photonics and Optoelectronics (SOPO 2010). IEEE, 2010. http://dx.doi.org/10.1109/sopo.2010.5504209.

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Luo, Lan, Hongxia Gao, Yinghao Luo, and Yongfei Chen. "A stochastic iterative evolution CT reconstruction algorithm for limited-angle sparse projection data." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217747.

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Liu, Baobin, and Wei Zhou. "Projection algorithm based adaptive iterative learning control for a class of discrete-time systems." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162434.

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Wu, Danyang, Jin Xu, Xia Dong, et al. "GSPL: A Succinct Kernel Model for Group-Sparse Projections Learning of Multiview Data." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/438.

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This paper explores a succinct kernel model for Group-Sparse Projections Learning (GSPL), to handle multiview feature selection task completely. Compared to previous works, our model has the following useful properties: 1) Strictness: GSPL innovatively learns group-sparse projections strictly on multiview data via ‘2;0-norm constraint, which is different with previous works that encourage group-sparse projections softly. 2) Adaptivity: In GSPL model, when the total number of selected features is given, the numbers of selected features of different views can be determined adaptively, which avoi
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Wei Hong, Xuanqin Mou, Ying Yang, Tao Luo, Shaojie Tang, and Lingqiu Jiang. "Reconstruction of Vessel Axis Based on the Matching of iterative Projection Algorithm and Analysis of Corresponding Points." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1617186.

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Lin, Jeng-Wen, Chih-Wei Huang, and Hao-Ping Wen. "Repetitive Control Based Disturbance Cancellation Using Iterative Basis Function Feedback With Wavelet Filtering." In ASME 2008 Pressure Vessels and Piping Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/pvp2008-61105.

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This paper presents repetitive control laws in real time using matched basis functions. These laws adjust the command given a feedback control system in order to eliminate tracking errors, resulting from in general a periodic disturbance and a non-periodic disturbance. The periodic error can be reduced by linear basis functions while the non-periodic error by the projection algorithm along with the wavelet filtering. The control laws do not use a system model, but instead the control action is chosen to be a linear combination of chosen input basis functions, and the corresponding output basis
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