Готові списки джерел за темами / Image analysis – Software

Добірка наукової літератури з теми "Image analysis – Software"

Автор: Grafiati
Опубліковано: 27 липня 2024
Оновлено: 30 липня 2024

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Статті в журналах з теми "Image analysis – Software"

1

Smith, Tony. "Image Measurement Analysis Software." Anti-Corrosion Methods and Materials 41, no. 4 (April 1994): 19. http://dx.doi.org/10.1108/eb007345.

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2

Costa, Mirian Cristina Gomes, Isabela Maria de Lima Cunha, Lúcio André de Castro Jorge, and Isabel Cristina da Silva Araújo. "Public-domain software for root image analysis." Revista Brasileira de Ciência do Solo 38, no. 5 (October 2014): 1359–66. http://dx.doi.org/10.1590/s0100-06832014000500001.

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Анотація:
In the search for high efficiency in root studies, computational systems have been developed to analyze digital images. ImageJ and Safira are public-domain systems that may be used for image analysis of washed roots. However, differences in root properties measured using ImageJ and Safira are supposed. This study compared values of root length and surface area obtained with public-domain systems with values obtained by a reference method. Root samples were collected in a banana plantation in an area of a shallower Typic Carbonatic Haplic Cambisol (CXk), and an area of a deeper Typic Haplic Ta Eutrophic Cambisol (CXve), at six depths in five replications. Root images were digitized and the systems ImageJ and Safira used to determine root length and surface area. The line-intersect method modified by Tennant was used as reference; values of root length and surface area measured with the different systems were analyzed by Pearson's correlation coefficient and compared by the confidence interval and t-test. Both systems ImageJ and Safira had positive correlation coefficients with the reference method for root length and surface area data in CXk and CXve. The correlation coefficient ranged from 0.54 to 0.80, with lowest value observed for ImageJ in the measurement of surface area of roots sampled in CXve. The IC (95 %) revealed that root length measurements with Safira did not differ from that with the reference method in CXk (-77.3 to 244.0 mm). Regarding surface area measurements, Safira did not differ from the reference method for samples collected in CXk (-530.6 to 565.8 mm²) as well as in CXve (-4231 to 612.1 mm²). However, measurements with ImageJ were different from those obtained by the reference method, underestimating length and surface area in samples collected in CXk and CXve. Both ImageJ and Safira allow an identification of increases or decreases in root length and surface area. However, Safira results for root length and surface area are closer to the results obtained with the reference method.
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3

Price, David H. "Software: Image Analysis Is Everything." Analytical Chemistry 68, no. 9 (May 1996): 318A—319A. http://dx.doi.org/10.1021/ac961916k.

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4

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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5

Passoni, Sabrina, Fernando da Silva Borges, Luiz Fernando Pires, Sérgio da Costa Saab, and Miguel Cooper. "Software Image J to study soil pore distribution." Ciência e Agrotecnologia 38, no. 2 (April 2014): 122–28. http://dx.doi.org/10.1590/s1413-70542014000200003.

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In the soil science, a direct method that allows the study of soil pore distribution is the bi-dimensional (2D) digital image analysis. Such technique provides quantitative results of soil pore shape, number and size. The use of specific softwares for the treatment and processing of images allows a fast and efficient method to quantify the soil porous system. However, due to the high cost of commercial softwares, public ones can be an interesting alternative for soil structure analysis. The objective of this work was to evaluate the quality of data provided by the Image J software (public domain) used to characterize the voids of two soils, characterized as Geric Ferralsol and Rhodic Ferralsol, from the southeast region of Brazil. The pore distribution analysis technique from impregnated soil blocks was utilized for this purpose. The 2D image acquisition was carried out by using a CCD camera coupled to a conventional optical microscope. After acquisition and treatment of images, they were processed and analyzed by the software Noesis Visilog 5.4® (chosen as the reference program) and ImageJ. The parameters chosen to characterize the soil voids were: shape, number and pore size distribution. For both soils, the results obtained for the image total porosity (%), the total number of pores and the pore size distribution showed that the Image J is a suitable software to be applied in the characterization of the soil sample voids impregnated with resin.
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6

Brown, Daniel G. "Image and Spatial Analysis Software Tools." Journal of Forestry 98, no. 6 (June 1, 2000): 53–57. http://dx.doi.org/10.1093/jof/98.6.53.

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Abstract To make the most of remotely sensed images and apply them in natural resource management, foresters and other land managers need computer software systems. The ultimate goal is to extract meaningful information from raw images and communicate it to support effective decisionmaking. The following description of the state-of-the-art capabilities available in selected contemporary software systems is a starting point, but readers should compare all available systems that perform a particular function. In many cases there exist low-cost or free alternatives, some of which are mentioned here.
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Lee, Hye-One, Jinsun Kim, and Kibum Kim. "Carbon electrode surface analysis using image analysis software." Journal of Industrial Science and Technology Institute 35, no. 1 (June 30, 2021): 25–29. http://dx.doi.org/10.54726/jisti.35.1.5.

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Vokes, Martha S., and Anne E. Carpenter. "CellProfiler: Open-Source Software to Automatically Quantify Images." Microscopy Today 16, no. 5 (September 2008): 38–39. http://dx.doi.org/10.1017/s1551929500061757.

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Анотація:
Researchers often examine samples by eye on the microscope — qualitatively scoring each sample for a particular feature of interest. This approach, while suitable for many experiments, sacrifices quantitative results and a permanent record of the experiment. By contrast, if digital images are collected of each sample, software can be used to quantify features of interest. For small experiments, quantitative analysis is often done manually using interactive programs like Adobe Photoshop©. For the large number of images that can be easily collected with automated microscopes, this approach is tedious and time-consuming. NIH Image/ImageJ (http://rsb.info.nih.gov/ij) allows users comfortable writing in a macro language to automate quantitative image analysis. We have developed Cell- Profiler, a free, open-source software package, designed to enable scientists without prior programming experience to quantify relevant features of samples in large numbers of images automatically, in a modular system suitable for processing hundreds of thousands of images.
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Tosi, Sébastien, Lídia Bardia, Maria Jose Filgueira, Alexandre Calon, and Julien Colombelli. "LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data." Bioinformatics 36, no. 8 (December 20, 2019): 2634–35. http://dx.doi.org/10.1093/bioinformatics/btz945.

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Abstract Summary Open source software such as ImageJ and CellProfiler greatly simplified the quantitative analysis of microscopy images but their applicability is limited by the size, dimensionality and complexity of the images under study. In contrast, software optimized for the needs of specific research projects can overcome these limitations, but they may be harder to find, set up and customize to different needs. Overall, the analysis of large, complex, microscopy images is hence still a critical bottleneck for many Life Scientists. We introduce LOBSTER (Little Objects Segmentation and Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images exceeding workstation main memory. LOBSTER comes with a starting set of over 75 sample image analysis workflows and associated images stemming from state-of-the-art image-based research projects. Availability and implementation LOBSTER requires MATLAB (version ≥ 2015a), MATLAB Image processing toolbox, and MATLAB statistics and machine learning toolbox. Code source, online tutorials, video demonstrations, documentation and sample images are freely available from: https://sebastients.github.io. Supplementary information Supplementary data are available at Bioinformatics online.
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10

Rueff, M., and K. Melchior. "“BILDLIB:” the image analysis software at IPA." Journal of Robotic Systems 2, no. 2 (1985): 179–98. http://dx.doi.org/10.1002/rob.4620020203.

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Дисертації з теми "Image analysis – Software"

1

Teo, Ching Leong. "Bistatic radar system analysis and software development." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FTeo%5FChing.pdf.

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Анотація:
Thesis (M.S. in Engineering Science)--Naval Postgraduate School, December 2003.
Thesis advisor(s): David C. Jenn, D. Curtis Schleher. Includes bibliographical references (p. 95-96). Also available online.
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2

Francis, Nicholas David. "Parallel architectures for image analysis." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/108844/.

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This thesis is concerned with the problem of designing an architecture specifically for the application of image analysis and object recognition. Image analysis is a complex subject area that remains only partially defined and only partially solved. This makes the task of designing an architecture aimed at efficiently implementing image analysis and recognition algorithms a difficult one. Within this work a massively parallel heterogeneous architecture, the Warwick Pyramid Machine is described. This architecture consists of SIMD, MIMD and MSIMD modes of parallelism each directed at a different part of the problem. The performance of this architecture is analysed with respect to many tasks drawn from very different areas of the image analysis problem. These tasks include an efficient straight line extraction algorithm and a robust and novel geometric model based recognition system. The straight line extraction method is based on the local extraction of line segments using a Hough style algorithm followed by careful global matching and merging. The recognition system avoids quantising the pose space, hence overcoming many of the problems inherent with this class of methods and includes an analytical verification stage. Results and detailed implementations of both of these tasks are given.
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3

Thomas, Mathew. "Semi-Automated Dental Cast Analysis Software." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1310404863.

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4

Self, Joel. "On-the-fly dynamic dead variable analysis /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1791.pdf.

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Minch, Stacy Lynn. "Validity of Seven Syntactic Analyses Performed by the Computerized Profiling Software." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2956.pdf.

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Rungta, Neha Shyam. "Guided Testing for Automatic Error Discovery in Concurrent Software." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3175.pdf.

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7

Brown, Christopher A. "Usability analysis of the channel application programming interface." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FBrown.pdf.

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8

Chen, He. "Microscopic Hyperspectral Image Analysis via Deep Learning." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/396188.

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Анотація:
Hyperspectral imaging (HSI) is a technique that can obtain more spectral information than that in normal color images. Due to this property and strength in material classification, it is widely used in remote sensing, agriculture, and environmental monitoring. In recent years, with the rapid developments of hardware, hyperspectral cameras have become more portable and a ordable. An increasing number of studies are being conducted on HSI systems, and research focuses have expanded from remote sensing to close-range objects. With a proper microscopic kit, a hyperspectral camera can capture images of objects of micrometers in size. In this thesis, an HSI system is introduced which consists of a hyperspectral camera, a microscope, control software, and an image processing workstation. The samples are placed under the microscope which has the camera mounted on the top. The parameters of the camera can be tuned by the control software to have the best image quality. After the setup, the camera takes the HSI image of the samples. Then, the image is transferred to the workstation and saved as a raw HSI image for further process. Two datasets of cells and microplastics are collected and introduced as benchmark datasets for this research. The reason to build these two benchmarks is because of their demands. In the area of cell viability assay, traditional methods use uorescent dyes to distinguish live and dead cells. Although working very reliably, they require physical contact with the cells, which a ects the appearance of the cells and some of the original cell features. As a consequence, there is a demand for the development of non-invasive technology for cell analysis. Our HSI system is capable of using computer vision techniques to classify live and dead cells as a non-invasive and systematic method so that the property of the cells can remain unchanged and the system can be operated without special skills. The microplastics dataset is built to address the needs of environmental protection which is an important research topic with significant social and economic values. The increasing amount of microplastics in the ocean has attracted enormous concern because of its potential to damage the ecosystem and a ect the health of humans and animals. While HSI has shown great potential in analyzing microplastics, studies in this direction are hindered by the lack of public available image data. Therefore, there is an urgent so that there is an urgent demand to build a dataset for microplastics detection. After the datasets have been constructed, we evaluate the support vector machine (SVM) on them for the baseline approach. We apply several feature extraction methods to process the HSI images of the cells before feeding them into the SVM, including extended morphology profile (EMP), tensor morphology profile (TMP), 3D scale-invariant feature transform (SIFT3D), 3D local derivative pattern (3DLDP) and spectral-spatial scaleinvariant feature transform (SS-SIFT). Among them, TMP has the best performance for the cell classification task. Regarding the detection of microplastics, the spectral signature is used to extract the feature and is fed into SVM for detection. Furthermore, we propose a novel attention-based convolutional neural networks (CNN) to classify the cells to take advantage of the development in deep learning. Inspired by the VGG networks, we first build a classification network for our hyperspectral data. Then, a band weighting network and a spatial weighting network are integrated into the backbone. The band weighting network assigns a weight to each band in the hyperspectral images. The weights can suppress redundant bands that do not make an important contribution to the classification task and make the classification network focus on the bands that have more important features for classification. The spatial weighting network assigns a weight to each pixel in the hyperspectral images. The weights can help the classification network focus on important parts of the images and ignore the irrelevant parts. These two weighting networks help to improve the final classification accuracy of the cells. In the experiments on hand-crafted features, SVM with TMP feature extraction method has the best accuracy of 83.72% for the cell classification task. SVM with spectral signature produces 99.13% accuracy on the microplastics detection task. In comparison, the attention-based CNN achieves 98.17% for the cell classification task. These results show that our HSI system and classification methods have great potential for these two classification and detection tasks. The richness of spectral information that is provided by hyperspectral images has a great potential in material recognition tasks, helping to classify di erent materials based on their unique spectral signatures of each material. Because of this, our research can contribute to a wider range of biomedical and environmental domains.
Thesis (Masters)
Master of Philosophy (MPhil)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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9

Caffall, Dale Scott. "Conceptual framework approach for system-of-systems software developments." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FCaffall.pdf.

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Анотація:
Thesis (M.S. in Software Engineering)--Naval Postgraduate School, March 2003.
Thesis advisor(s): James Bret Michael, Man-Tak Shing. Includes bibliographical references (p. 83-84). Also available online.
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10

Rexilius, Jan [Verfasser], and Klaus-Dietz [Akademischer Betreuer] Tönnies. "Software phantoms in medical image analysis / Jan Rexilius. Betreuer: Klaus-Dietz Tönnies." Magdeburg : Universitätsbibliothek, 2015. http://d-nb.info/1072685531/34.

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Книги з теми "Image analysis – Software"

1

Center, Langley Research, and United States. National Aeronautics and Space Administration., eds. PIV/HPIV film analysis software package. Hampton, Va: National Aeronautics and Space Administration, Lewis Research Center, 1997.

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2

J, Hanisch R., Brissenden R. J. V, Barnes Jeannette V, and Analysis Software and Systems (2nd : 1992 : Boston, Mass.), eds. Astronomical Data Analysis Software and Systems II. San Francisco, Calif: Astronomical Society of the Pacific, 1993.

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3

Gore, Neil M. An interface and analysis software tool for image sequence data. Manchester: UMIST, 1997.

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4

Center, Langley Research, ed. Digital PIV (DPIV) software analysis system: Under contract NAS1-19505. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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5

Ahmad, Anees. Development of software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1996.

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6

Hockgraver, Valerie Ruth. Implementation of ImageActionplus software for improved image analysis of solid propellant combustion holograms. Monterey, Calif: Naval Postgraduate School, 1989.

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7

Skifstad, Kurt D. High-speed range estimation based on intensity gradient analysis. New York: Springer-Verlag, 1991.

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8

Skifstad, Kurt D. High-speed range estimation based on intensity gradient analysis. New York: Springer-Verlag, 1991.

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9

Ahmad, Anees. Development of software to model AXAF-I image quality: Final report, contract no. NAS8-38609. [Washington, DC: National Aeronautics and Space Administration, 1996.

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10

Joseph, Geary, and United States. National Aeronautics and Space Administration., eds. Development of software to model AXAF-I image quality: Final report : contract no. NAS8-38609 ... [Washington, DC: National Aeronautics and Space Administration, 1997.

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Частини книг з теми "Image analysis – Software"

1

Mukherjee, Dipti Prasad, and Dwijesh Dutta Majumder. "Tongue Image Analysis Software." In Intelligent Systems Design and Applications, 403–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44999-7_39.

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2

Lewis, Robert H. "Image Analysis: Identification of Objects via Polynomial Systems." In Mathematical Software – ICMS 2018, 305–9. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96418-8_36.

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3

Roberston, D., J. Shotton, and T. Sharp. "The Sherwood Software Library." In Decision Forests for Computer Vision and Medical Image Analysis, 333–42. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4929-3_22.

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4

Pooja Sri, Devineni, and K. Kiran Kumar. "Image and Video Processing-Based Traffic Analysis Using OpenCV." In Communication, Software and Networks, 459–65. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4990-6_42.

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Wolf, Ivo. "Toolkits and Software for Developing Biomedical Image Processing and Analysis Applications." In Biomedical Image Processing, 521–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15816-2_21.

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Rasoul Banaeeyan, K. A., Y. K. Chiam, Z. H. Azizul, T. K. Chiew, S. H. Ab Hamid, and T. Thasaratharajah. "Classification of Image Processing Software Tools for Cardiovascular Image Analysis." In IFMBE Proceedings, 72–75. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0266-3_15.

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Pintor, J. M., P. Carrión, E. González-Rufino, A. Formella, M. Fernández-Delgado, E. Cernadas, R. Domínguez-Petit, and S. Rábade-Uberos. "A Multi-platform Graphical Software for Determining Reproductive Parameters in Fishes Using Histological Image Analysis." In Pattern Recognition and Image Analysis, 743–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_83.

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Pitarma, Rui, João Crisóstomo, and Luís Jorge. "Analysis of Materials Emissivity Based on Image Software." In New Advances in Information Systems and Technologies, 749–57. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31232-3_70.

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Arafat, Youssef, and Constantino Carlos Reyes-Aldasoro. "Computational Image Analysis Techniques, Programming Languages and Software Platforms Used in Cancer Research: A Scoping Review." In Medical Image Understanding and Analysis, 833–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12053-4_61.

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Dobesova, Zdena. "The Similarity of European Cities Based on Image Analysis." In Intelligent Systems Applications in Software Engineering, 341–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30329-7_31.

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Тези доповідей конференцій з теми "Image analysis – Software"

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Liang, J., T. McInerney, and D. Terzopoulos. "United Snakes [image analysis software]." In Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, 1999. http://dx.doi.org/10.1109/iccv.1999.790348.

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Tiwari, Richa. "Comparison of microarray image analysis software." In the 46th Annual Southeast Regional Conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1593105.1593125.

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Ramer, Daniel J., and Paul K. Little. "Precision software-based image analysis system." In Aerospace Sensing, edited by Firooz A. Sadjadi. SPIE, 1992. http://dx.doi.org/10.1117/12.138290.

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Varnasuthan, S., W. T. M. Fernando, D. S. Dahanayaka, A. B. N. Dassanayake, M. A. D. M. G. Wickrama, and I. M. T. N. Illankoon. "Image analysis approach to determine the porosity of rocks." In International Symposium on Earth Resources Management & Environment - ISERME 2023. Department of Earth Resources Engineering, 2023. http://dx.doi.org/10.31705/iserme.2023.7.

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Accurate characterisation of rock porosity is essential for assessing its strength and durability. This study explores both conventional and image analysis methods for determining rock porosity of two types of rocks, Bibai sandstone, a hard clastic rock and limestone, a soft rock. Conventional methods for determining rock porosity involve physical measurements and laboratory analysis, while image analysis methods utilize advanced imaging techniques such as CT scans or SEM to assess porosity based on visual information extracted from rock images. While various image analysis approaches exist to determine rock porosity, questions arise as to which approach is applicable and whether the results are comparable to current conventional methods. Hence, this study focuses on comparing the accuracy of alternative image analysis approaches. Representative rock chips from each core sample were examined using SEM, and 2D porosity was evaluated through image processing with ImageJ software. The Avizo visualisation software was employed to assess Bibai sandstone samples' porosity from CT images. The research offers insights into the pros and cons of each approach, contributing to the enhancement of accuracy and efficiency in rock porosity evaluation, particularly in geology, mining, and civil engineering applications.
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Fang, Zhenman, Donglei Yang, Weihua Zhang, Haibo Chen, and Binyu Zang. "A comprehensive analysis and parallelization of an image retrieval algorithm." In Software (ISPASS). IEEE, 2011. http://dx.doi.org/10.1109/ispass.2011.5762732.

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Roth, Karl n., Knut Wenzelides, Guenter Wolf, and Peter Hufnagl. "Image analysis software and sample preparation demands." In 5th Congres of the Brazilian Soc., Brazil -p.o., edited by Volkmar Miszalok. SPIE, 1990. http://dx.doi.org/10.1117/12.23909.

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Rudowicz-Nawrocka, J., R. J. Tomczak, K. Nowakowski, W. Mueller, and S. Kujawa. "Development of software for airborne photos analysis." In Sixth International Conference on Digital Image Processing, edited by Charles M. Falco, Chin-Chen Chang, and Xudong Jiang. SPIE, 2014. http://dx.doi.org/10.1117/12.2064836.

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D'Amato, Juan P., Rafael Namias, and Mariana Del Fresno. "Open-source software platform for medical image segmentation applications." In 13th International Symposium on Medical Information Processing and Analysis, edited by Jorge Brieva, Juan David García, Natasha Lepore, and Eduardo Romero. SPIE, 2017. http://dx.doi.org/10.1117/12.2283487.

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Cha, SeungJu, Uijeong Kang, and EunJung Choi. "The Image Forensics Analysis of JPEG Image Manipulation (Lightning Talk)." In 2018 International Conference on Software Security and Assurance (ICSSA). IEEE, 2018. http://dx.doi.org/10.1109/icssa45270.2018.00029.

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Hye-Jin Jeong, Tae-Yun Kim, Hae-Gil Hwang, Hyun-Ju Choi, Byeong-Il Lee, Jung-Joon Min, and Heung-Kook Choi. "Development of image processing software for quantitative analysis of bioluminescence image." In HEALTHCOM 2006 8th International Conference on e-Health Networking, Applications and Services. IEEE, 2006. http://dx.doi.org/10.1109/health.2006.246434.

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Звіти організацій з теми "Image analysis – Software"

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Huang, Haohang, Jiayi Luo, Kelin Ding, Erol Tutumluer, John Hart, and Issam Qamhia. I-RIPRAP 3D Image Analysis Software: User Manual. Illinois Center for Transportation, June 2023. http://dx.doi.org/10.36501/0197-9191/23-008.

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Riprap rock and aggregates are commonly used in various engineering applications such as structural, transportation, geotechnical, and hydraulic engineering. To ensure the quality of the aggregate materials selected for these applications, it is important to determine their morphological properties such as size and shape. There have been many imaging approaches developed to characterize the size and shape of individual aggregates, but obtaining 3D characterization of aggregates in stockpiles at production or construction sites can be a challenging task. This research study introduces a new approach based on deep learning techniques that combines three developed research components: field 3D reconstruction procedures, 3D stockpiles instance segmentation, and 3D shape completion. The approach is designed to reconstruct aggregate stockpiles from multiple images, segment the stockpile into individual instances, and predict the unseen sides of each instance (particle) based on the partially visible shapes. The approach was validated using ground-truth measurements and demonstrated satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. For better user experience, the integrated approach has been implemented into a software application named “I-RIPRAP 3D,” with a user-friendly graphical user interface (GUI). This stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site quality assurance and quality control tasks of riprap rock and aggregate stockpiles. This document provides information for users of the I-RIPRAP 3D software to make the best use of the software’s capabilities.
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Wendelberger, James G. SRS 2020 Annual Meeting Image Analysis Software Development and Testing Update. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1597312.

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Samson, Scott, Dmitry Goldgol, Thomas Hopkins, and Lawrence Hall. Development of Automated Image Analysis Software for Suspended Marine Particle Classification. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada620262.

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Samson, Scott, Dmitry Goldgof, Thomas Hopkins, and Lawrence Hall. Development of Automated Image Analysis Software for Suspended Marine Particle Classification. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada627430.

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Wendelberger, James G. SRS 2020 Annual Meeting: Corrosion Working Group Discussion of LANL Image Analysis Software Development and Testing Update. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1597318.

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CT Tumor Volume Change for Advanced Disease, Clinically Feasible Profile. Chair Ritu Gill, Rudresh Jarecha, and Ehsan Samei. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), July 2022. http://dx.doi.org/10.1148/qiba/20220721.

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A QIBA Profile is an implementation guide to generate a biomarker with an effective level of performance, mostly by reducing variability and bias in the measurement. The expected performance is expressed as Claims (Section 1.2). To achieve those claims, Actors (Scanners, Technologists, Physicists, Radiologists, Reconstruction Software, and Image Analysis Tools) must meet the Checklist Requirements (Section 3) covering Periodic QA, Subject Handling, Image Data Acquisition, Image Data Reconstruction, Image QA, and Image Analysis. This Profile is at the Clinically Feasible stage (qibawiki.rsna.org/index.php/QIBA_Profile_Stages) so, *The requirements have been performed and found to be practical by multiple sites *The claim is a hypothesis based on committee assessment of literature and QIBA groundwork CT Tumor Volume Change is used as a biomarker of disease risk, characterization, progression, and response to treatment. This involves measuring tumor volumes and assessing longitudinal changes within subjects, based on image processing of CT scans acquired at different timepoints. See Appendix B for a discussion of usage of this biomarker in practice. QIBA Profiles for other CT, MRI, PET, and Ultrasound biomarkers can be found at qibawiki.rsna.org
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Hoch, Brendon, and Samantha Cook. A 10-Year monthly climatology of wind direction : case-study assessment. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46912.

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A 10-year monthly climatology of wind direction in compass degrees is developed utilizing datasets from the National Oceanic Atmospheric Administration, Climate Forecast System. Data retrieval methodologies, numerical techniques, and scientific analysis packages to develop the climatology are explored. The report describes the transformation of input data in Gridded Binary format to the Geographic Tagged Image File Format to support geospatial analyses. The specific data sources, software tools, and data-verification techniques are outlined.
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Huang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-017.

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Анотація:
Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry production process and at construction sites, particularly for aggregates in stockpile form. This research study presents a 3D reconstruction-segmentation-completion approach based on deep learning techniques by combining three developed research components: field 3D reconstruction procedures, 3D stockpile instance segmentation, and 3D shape completion. The approach was designed to reconstruct aggregate stockpiles from multi-view images, segment the stockpile into individual instances, and predict the unseen side of each instance (particle) based on the partial visible shapes. Based on the dataset constructed from individual aggregate models, a state-of-the-art 3D instance segmentation network and a 3D shape completion network were implemented and trained, respectively. The application of the integrated approach was demonstrated on re-engineered stockpiles and field stockpiles. The validation of results using ground-truth measurements showed satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. The algorithms are integrated into a software application with a user-friendly graphical user interface. Based on the findings of this study, this stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site QA/QC tasks of riprap rock and aggregate stockpiles.
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Burbano Figueroa, Oscar, Milena Moreno Moran, Lorena Osorio Almanza, Karen Montes Mercado, Enrique Vergara, Maria Del Valle Rodriguez Pinto, Keyra Salazar, and Everto Mosquera. Identification of resistance to ramulosis caused by Colletotrichum gossypii var. cephalosporioides in cotton advanced breeding lines and monitoring of ramulosis disease by RGB-image analysis. Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, 2016. http://dx.doi.org/10.21930/agrosavia.informe.2016.2.

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Identification of resistance to ramulosis caused by Colletotrichum gossypii var. cephalosporioides in cotton advanced breeding lines and monitoring of ramulosis disease by RGB-image analysis Abstract Cotton growing regions in South America are affected by Colletotrichum gossypii var. cephalosporioides (Cgc). The most severe epidemics provokes considerable yield reductions linked to meristem necrosis, oversprouting, excessive branching and stunting (Figure 1). The Sinu Valley is a major cotton producer in Colombia and is heavily affected by this disease. Rainfall was identified as the main driver of ramulosis development in the Sinu Valley prevailing in crops planted at the beginning of the main rainy season (Figure 2). Fifty five advanced breeding lines (ABLs) were assessed by ramulosis field resistance. Nine ABLs exhibited high levels of partial resistance (< 10% of plants exhibiting oversprouting). With the aim to optimize disease assessing accuracy and breeding efforts for ramulosis resistance, we had evaluated the use of red-green-blue (RGB) based indices for automated assessment of ramulosis disease. Eleven cultivars exhibiting contrasting level of ramulosis resistance were grown and photographed at different phenological stages. RGB indices extracted by Breedpix software from these plot images were compared with visual assessment of plant disease severity. The RGB indices Hue, Saturation, b, and v measured ten weeks after planting (boll opening) were correlated with accumulated disease severity and oversprouting (estimated as the area under the disease progress stairs). Oversprouting exhibited the higher correlation coefficients (r = 0.60,-0.65,-0.65,-0.60 and 0.54, P < 0.001). Additionally, destructive sampling across phenological development showed that green area (GA) has a positive correlation with total fresh biomass, leaf area index, leaf fresh biomass and green cover (GC) (r = 0.65, 0.60, 0.70 and 0.95, P < 0.001). RGB-based indices are appropriate predictors of cotton growth and ramulosis severity and a cost effective tool for cotton phenotyping based on automation of RGB-images assessment and affordable cost of RGB-cameras
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Pepin, Kay, ed. MR Elastography of the Liver, Clinically Feasible Profile. Chair Richard Ehman and Patricia Cole. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), November 2023. http://dx.doi.org/10.1148/qiba/20231107.

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Анотація:
The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance. The Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed. This QIBA Profile (Magnetic Resonance Elastography of the Liver) addresses the application of Magnetic Resonance Elastography (MRE) for the quantification of liver stiffness, which is often used as a biomarker of liver fibrosis. It places requirements on Acquisition Devices, Technologists, Radiologists, Reconstruction Software and Image Analysis Tools involved in Subject Handling, Image Data Acquisition, Image Data Reconstruction, Image QA and Image Analysis. The requirements are focused on achieving sufficient accuracy and avoiding unnecessary variability of the measurement of hepatic stiffness. The clinical performance target is to achieve a 95% confidence interval for a true change in stiffness has occurred when there is a measured change in hepatic stiffness of 19% or larger. This document is intended to help clinicians basing decisions on this biomarker, imaging staff generating this biomarker, vendor staff developing related products, purchasers of such products and investigators designing trials with imaging endpoints. Note that this document only states requirements to achieve the claim, not “requirements on standard of care.” Conformance to this Profile is secondary to properly caring for the patient. QIBA Profiles addressing other imaging biomarkers using CT, MRI, PET and Ultrasound can be found at https://qibawiki.rsna.org/index.php/Profiles
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