Relevant bibliographies by topics / Image processing analysis

Contents

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

Academic literature on the topic 'Image processing analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Image processing analysis"

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Desai, Miss Shivpriya, and Dr A. P. Rao. "Seed Quality Analysis Using Image Processing and ANN." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (June 30, 2017): 705–9. http://dx.doi.org/10.31142/ijtsrd137.

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Alsiya, S., C. Jeya Lekshmi, B. P. Jishna Priya, and R. C. Mehta. "Image Processing Algorithm for Fringe Analysis in Photoelasticity." Scholars Journal of Engineering and Technology 4, no. 7 (July 2016): 325–28. http://dx.doi.org/10.21276/sjet.2016.4.7.5.

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V, Srujana, Chaithanya P, Ramesh B, Manoranjan S, and Mahesh V. "Crop Analysis Using Image Processing." International Journal of Engineering Technology and Management Sciences 4, no. 3 (May 28, 2020): 9–15. http://dx.doi.org/10.46647/ijetms.2020.v04i03.002.

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To detect the uniqueness and quantities of agriculture product images a new method is proposed using MATLAB software .In this paper we propose a method to increase the contrast level of a image with exponential low pass filter and histogram equalization technique. Next by using region props function we extract the binary features of the image, and then we calculated the number of targets in gray level image. This method can be easily applied in modern agriculture.
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Oxford Instruments (UK) Ltd. "Image processing and analysis." NDT & E International 27, no. 3 (June 1994): 174–75. http://dx.doi.org/10.1016/0963-8695(94)90753-6.

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Sumners, DeWitt. "Image Analysis and Processing." Computers & Chemistry 14, no. 1 (January 1990): 106. http://dx.doi.org/10.1016/0097-8485(90)80014-s.

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Legland, David, and Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab." F1000Research 10 (April 30, 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.
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Bele, Petra, Ulrich Stimming, Hiroshi Yano, Hiroyuki Uchida, and Masahiro Watanabe. "STEM Image Analysis Using LAT Image Processing." Imaging & Microscopy 11, no. 3 (August 2009): 34–38. http://dx.doi.org/10.1002/imic.200990059.

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Walker, James S. "Wavelet-based image processing." Applicable Analysis 85, no. 4 (April 2006): 439–58. http://dx.doi.org/10.1080/00036810500358874.

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Kotropoulos, Constantine, Ioannis Pitas, and Athina Petropulu. "Ultrasonic Image Processing and Analysis." Pattern Recognition Letters 24, no. 4-5 (February 2003): iii. http://dx.doi.org/10.1016/s0167-8655(02)00170-8.

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Boyce, J. F. "Seismic processing and image analysis." Journal of Physics D: Applied Physics 19, no. 3 (March 14, 1986): 397–415. http://dx.doi.org/10.1088/0022-3727/19/3/010.

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Dissertations / Theses on the topic "Image processing analysis"

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Hamid, Muhammed Hamed. "Hyperspectral Image Generation, Processing and Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5905.

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May, Heather. "Wavelet-based Image Processing." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448037498.

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Gavin, John. "Subpixel image analysis." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307131.

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Munechika, Curtis K. "Merging panchromatic and multispectral images for enhanced image analysis /." Online version of thesis, 1990. http://hdl.handle.net/1850/11366.

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Cevik, Alper. "A Medical Image Processing And Analysis Framework." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612965/index.pdf.

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Medical image analysis is one of the most critical studies in field of medicine, since results gained by the analysis guide radiologists for diagnosis, treatment planning, and verification of administered treatment. Therefore, accuracy in analysis of medical images is at least as important as accuracy in data acquisition processes. Medical images require sequential application of several image post-processing techniques in order to be used for quantification and analysis of intended features. Main objective of this thesis study is to build up an application framework, which enables analysis and quantification of several features in medical images with minimized input-dependency over results. Intended application targets to present a software environment, which enables sequential application of medical image processing routines and provides support for radiologists in diagnosis, treatment planning and treatment verification phases of neurodegenerative diseases and brain tumors
thus, reducing the divergence in results of operations applied on medical images. In scope of this thesis study, a comprehensive literature review is performed, and a new medical image processing and analysis framework - including modules responsible for automation of separate processes and for several types of measurements such as real tumor volume and real lesion area - is implemented. Performance of the fully-automated segmentation module is evaluated with standards introduced by Neuro Imaging Laboratory, UCLA
and the fully-automated registration module with Normalized Cross-Correlation metric. Results have shown a success rate above 90 percent for both of the modules. Additionally, a number of experiments have been designed and performed using the implemented application. It is expected for an accurate, flexible, and robust software application to be accomplished on the basis of this thesis study, and to be used in field of medicine as a contributor by even non-engineer professionals.
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Elder, John Kenneth. "Image processing in nucleic acid sequence analysis." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358635.

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Baradez, Marc-Olivier M. P. "Image processing analysis of stem cell antigens." Thesis, Kingston University, 2005. http://eprints.kingston.ac.uk/20292/.

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This thesis aims to investigate the automation of an image processing driven analysis of antigen distributions in the membrane of early human Haematopoietic StemIProgenitor Cells (HSPCs ) imaged by Laser Scanning Confocal Microscopy (LSCM). LSCM experiments generated a vast amount of images of both single and dual labelled HSPCs. Special focus was given to the analysis of colocalised antigen distributions, as colocalisation may involve functional relationships. However, quantitative methods are also investigated to characterise both single and dual labelled antigen distributions. Firstly, novel segmentation algorithms are developed and assessed for their performances in automatically achieving fast fluorescence signal identification. Special attention is given to global histogram-based thresholding methods due to their potential use in real time applications. A new approach to fluorescence quantification is proposed and tested. Secondly, visualisation techniques are developed in order to further assist the analysis of the antigen distributions in cell membranes. They include 3D reconstruction of the fluorescence, newly proposed 2D Antigen Density Maps (ADMs) and new 3D graphs of the spatial distributions (sphere models). Thirdly, original methods to quantitatively characterise the fluorescence distributions are developed. They are applied to both single and dual/colocalised distributions. For the latest, specific approaches are investigated and applied to colocalised CD34/CD164 distributions and to colocalised CD34[sup]class I CD34[sup]class II and CD34[sup]c1ass I CD34[sup]class III epitopes distributions (two combinations of the three known different isoforms of the CD34 molecule, a major clinical marker for HSPCs). The visualisation tools revealed that HSPC membrane antigens are often clustered within membrane domains. Three main types of clusters were identified: small clusters, large patch-like clusters and newly identified meridian-shaped crest-like (MSCL) clusters. Quantitative analysis of antigen distributions showed heterogeneous distributions of the various measured features (such as polarity or colocalisation patterns) within the HSPC populations analysed. Finally, the proposed methodology to characterise membrane antigen distributions is discussed, and its potential application to other biomedical studies is commented. The potential extensions of the innovative linear diffusion-based MultiScale Analysis (MSA) algorithm to other applications are outlined. Visual and quantitative analyses of antigen membrane distributions are eventually used to generate hypotheses on the potential, yet unknown roles of these early antigens and are discussed in the context of haematopoietic theories.
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Thomson, Malcolm S. "Real-time image processing for traffic analysis." Thesis, Edinburgh Napier University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260986.

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Pollak, Ilya. "Nonlinear scale space analysis in image processing." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9334.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 129-133) and index.
The objective of this work is to develop and analyze robust and fast image segmentation algorithms. They must be robust to pervasive, large-amplitude noise, which cannot be well characterized in terms of probabilistic distributions. This is because the applications of interest include synthetic aperture radar (SAR) segmentation in which speckle noise is a well-known problem that has defeated many algorithms. The methods must also be robust to blur, because many imaging techniques result in smoothed images. For example, SAR image formation has a natural blur associated with it, due to the finite aperture used in forming the image. We introduce a family of first-order multi-dimensional ordinary differential equations with discontinuous right-hand sides and demonstrate their applicability to segmenting both scalar-valued and vector-valued images, as well as images taking values on a circle. An equation belonging to this family is an inverse diffusion everywhere except at local extrema, where some stabilization is introduced. For this reason, we call these equations "stabilized inverse diffusion equations" ( "SIDEs" ). Existence and uniqueness of solutions, as well as stability, are proven for SIDEs. A SIDE in one spatial dimension may be interpreted as a limiting case of a semi-discretized Perona-Malik equation [49,50], which, in turn, was proposed in order to overcome certain shortcomings of Gaussian scale spaces [72]. These existing techniques are reviewed in a background chapter. SIDEs are then described and experimentally shown to suppress noise while sharpening edges present in the input image. Their application to the detection of abrupt changes in 1-D signals is also demonstrated. It is shown that a version of the SIDEs optimally solves certain change detection problems. Its relations to the Mumford-Shah functional [44] and to linear programming are discussed. Theoretical performance analysis is carried out, and a fast implementation of the algorithm is described.
by Ilya Pollak.
Ph.D.
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Suriyal, Shorav Singh. "Quantitative Analysis of Strabismus Using Image Processing." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10841398.

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A technique to calculate the deviation of an iris along the horizontal and vertical axis is implemented on the images of people’s faces, downloaded from google images, as well as performed on five healthy subjects. Strabismus analysis is a quantitative analysis of finding the deviation of an iris in people with strabismus or crossed eyes.

There are three primary techniques involved in developing this method, each of which will be used in this project: Hough transform, Histogram of oriented gradients, and Haar features. These techniques are widely used and implemented in Matlab 2016b software. The final value of deviation is calculated in pixels and then compared to both eyes to get an estimate of deviation and error calculation in the final result.

This experiment must be performed under a set of conditions which limit the capability of the developed algorithm. This thesis makes three contributions. Firstly, we propose two graphical user interfaces; these interfaces have a live as well as local image processing capability. Secondly, we recommended a bounding box approach to make the face of person align to minimize the error in calculating the vertical deviation. Thirdly, we propose a bottom-up method to find the horizontal and vertical variation in pixels as its measuring unit. Since, in case of a normal eye, this variation will be close to zero, gives us the probability of a person being non-strabismic while a higher value of difference makes a person probable to strabismus.

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Books on the topic "Image processing analysis"

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International Conference on Image Analysis and Processing (3rd 1985 Rapallo, Italy). Image analysis and processing. New York: Plenum Press, 1986.

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Del Bimbo, Alberto, ed. Image Analysis and Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6.

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Del Bimbo, Alberto, ed. Image Analysis and Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4.

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Braccini, Carlo, Leila DeFloriani, and Gianni Vernazza, eds. Image Analysis and Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4.

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Cantoni, V., S. Levialdi, and G. Musso, eds. Image Analysis and Processing. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2239-9.

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Cantoni, V. Image Analysis and Processing. Boston, MA: Springer US, 1986.

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Collet, Christophe. Multivariate image processing. London, UK: ISTE, 2009.

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Collet, Christophe. Multivariate image processing. London, UK: ISTE, 2009.

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Jocelyn, Chanussot, and Chehdi Kacem, eds. Multivariate image processing. London, UK: ISTE, 2009.

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Shevlin, F. Image processing for pattern analysis. Dublin: Trinity College, Department of Computer Science, 1992.

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Book chapters on the topic "Image processing analysis"

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Olson, Tim. "Image Processing." In Applied Fourier Analysis, 227–53. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7393-4_8.

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Cree, Michael J., and Herbert F. Jelinek. "Image Analysis of Retinal Images." In Medical Image Processing, 249–68. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1_11.

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Rosin, Paul L. "Training Cellular Automata for Image Processing." In Image Analysis, 195–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_22.

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Jähne, Bernd. "Shape Analysis." In Digital Image Processing, 489–519. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03477-4_15.

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Sundararajan, D. "Fourier Analysis." In Digital Image Processing, 65–107. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_3.

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de Luis-García, Rodrigo, Rachid Deriche, Mikael Rousson, and Carlos Alberola-López. "Tensor Processing for Texture and Colour Segmentation." In Image Analysis, 1117–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_113.

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Tankyevych, Olena, Hugues Talbot, Nicolas Passat, Mariano Musacchio, and Michel Lagneau. "Angiographic Image Analysis." In Medical Image Processing, 115–44. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1_6.

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Heilbronner, Renée, and Steve Barrett. "Digital Image Processing." In Image Analysis in Earth Sciences, 31–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-10343-8_3.

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Duff, M. J. B. "Image Processing Architectures." In Image Analysis and Processing II, 19–30. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1007-5_2.

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Koprowski, Robert. "Image Pre-processing." In Image Analysis for Ophthalmological Diagnosis, 19–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29546-6_2.

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Conference papers on the topic "Image processing analysis"

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Ergin, F. Go¨khan, Bo Beltoft Watz, Kaspars Erglis, and Andrejs Cebers. "Poor-Contrast Particle Image Processing in Microscale Mixing." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-24900.

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Particle image velocimetry (PIV) often employs the cross-correlation function to identify average particle displacement in an interrogation window. The quality of correlation peak has a strong dependence on the signal-to-noise ratio (SNR), or contrast of the particle images. In fact, variable-contrast particle images are not uncommon in the PIV community: Strong light sheet intensity variations, wall reflections, multiple scattering in densely-seeded regions and two-phase flow applications are likely sources of local contrast variations. In this paper, we choose an image pair obtained in a micro-scale mixing experiment with severe local contrast gradients. In regions where image contrast is sufficiently poor, the noise peaks cast a shadow on the true correlation peak, producing erroneous velocity vectors. This work aims to demonstrate that two image pre-processing techniques — local contrast normalization and Difference of Gaussian (DoG) filter — improve the correlation results significantly in poor-contrast regions.
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Hirata, Nina S. T., Igor S. Montagner, and Roberto Hirata. "Comics image processing." In MANPU '16: First International Workshop on coMics ANalysis, Processing and Understanding. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3011549.3011560.

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Duan, ZongTao, and XingShe Zhou. "A parallel processing system of images." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.655210.

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Quan, Kin, Rebecca J. Shipley, Ryutaro Tanno, Graeme McPhillips, Vasileios Vavourakis, David Edwards, Joseph Jacob, John R. Hurst, and David J. Hawkes. "Tapering analysis of airways with bronchiectasis." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2292306.

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Sakai, Kaoru, Osamu Kikuchi, Masafumi Takada, Natsuki Sugaya, and Shigeru Ohno. "Image improvement using image processing for scanning acoustic tomograph images." In 2015 IEEE 22nd International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA). IEEE, 2015. http://dx.doi.org/10.1109/ipfa.2015.7224357.

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Elbakary, Mohamed I., and Khan Iftekharuddin. "COVID-19 detection using image analysis methods on CT images." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2581667.

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Piestun, Rafael. "What Can Digital Processing Do for 3-D Super-Resolution Microscopy?" In Digital Image Processing and Analysis. Washington, D.C.: OSA, 2010. http://dx.doi.org/10.1364/dipa.2010.dtua1.

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Mao, Hai-cen, Tian-xu Zhang, Wei-dong Yang, and Meng Li. "A novel reconfigurable image-processing system using multi-processor." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.654548.

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Kolluru, Pavan Kumar, K. Sri Vijaya, and Meka Sowjanya. "Programmed image processing based dental image analysis." In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). IEEE, 2017. http://dx.doi.org/10.1109/icpcsi.2017.8392215.

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Yuhong Zhang. "Image processing using spatial transform." In 2009 International Conference on Image Analysis and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/iasp.2009.5054663.

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Reports on the topic "Image processing analysis"

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Cheng, Qiuming. Spatially and geographically weighted multivariate analysis methods for mineral image processing. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0169.

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Xu, Bohou, Xingxing Wu, Zhong-Ping Jiang, and Daniel W. Repperger. Theoretical Analysis of Image Processing Using Parameter-Tuning Stochastic Resonance Technique. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada472486.

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
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Jackson, Michael A. Collaborative Research and Development (CR&D) III Task Order 0090: Image Processing Framework: From Acquisition and Analysis to Archival Storage. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada589223.

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Processing and analysis of commercial satellite image data of the nuclear accident near Chernobyl, U.S.S.R. US Geological Survey, 1987. http://dx.doi.org/10.3133/b1785.

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