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Relevant bibliographies by topics / Assessment of image

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Academic literature on the topic 'Assessment of image'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "Assessment of image"

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Golub, Y. I. "Image quality assessment." «System analysis and applied information science», no. 4 (January 5, 2022): 4–15. http://dx.doi.org/10.21122/2309-4923-2021-4-4-15.

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Quality assessment is an integral stage in the processing and analysis of digital images in various automated systems. With the increase in the number and variety of devices that allow receiving data in various digital formats, as well as the expansion of human activities in which information technology (IT) is used, the need to assess the quality of the data obtained is growing. As well as the bar grows for the requirements for their quality.The article describes the factors that deteriorate the quality of digital images, areas of application of image quality assessment functions, a method fo

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Golub, Yu I., F. V. Starovoitov, and V. V. Starovoitov. "Impact of image size reducing for image quality assesment." «System analysis and applied information science», no. 2 (August 18, 2020): 35–45. http://dx.doi.org/10.21122/2309-4923-2020-2-35-45.

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The article describes studies of the effect of image reduction on the quantitative assessment of their quality. Image reduction refers to the proportional reduction of horizontal and vertical image resolutions in pixels. Within the framework of these studies, correlation analysis between quantitative assessments of image quality and subjective assessments of experts was performed. For the experiments, we used images from the public TID2013 database with a resolution of 512 × 384 pixels and expert estimates of their quality, as well as photographs taken with a Nikon D5000 digital camera with a

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Han, Z., X. Tang, X. Gao, and F. Hu. "IMAGE FUSION AND IMAGE QUALITY ASSESSMENT OF FUSED IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W1 (July 12, 2013): 33–36. http://dx.doi.org/10.5194/isprsarchives-xl-7-w1-33-2013.

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Starovoitov, V. V., Y. I. Golub, and M. M. Lukashevich. "Digital fundus image quality assessment." «System analysis and applied information science», no. 4 (January 5, 2022): 25–38. http://dx.doi.org/10.21122/2309-4923-2021-4-25-38.

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Diabetic retinopathy (DR) is a disease caused by complications of diabetes. It starts asymptomatically and can end in blindness. To detect it, doctors use special fundus cameras that allow them to register images of the retina in the visible range of the spectrum. On these images one can see features, which determine the presence of DR and its grade. Researchers around the world are developing systems for the automated analysis of fundus images. At present, the level of accuracy of classification of diseases caused by DR by systems based on machine learning is comparable to the level of qualif

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Li, Zhipeng, Li Shen, and Linmei Wu. "IMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 11–16. http://dx.doi.org/10.5194/isprs-archives-xli-b7-11-2016.

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The data from remote sensing images are widely used for characterizing land use and land cover at present. With the increasing availability of very high resolution (VHR) remote sensing images, the remote sensing image classification becomes more and more important for information extraction. The VHR remote sensing images are rich in details, but high within-class variance as well as low between-class variance make the classification of ground cover a difficult task. What’s more, some related studies show that the quality of VHR remote sensing images also has a great influence on the ability of

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Li, Zhipeng, Li Shen, and Linmei Wu. "IMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 11–16. http://dx.doi.org/10.5194/isprsarchives-xli-b7-11-2016.

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The data from remote sensing images are widely used for characterizing land use and land cover at present. With the increasing availability of very high resolution (VHR) remote sensing images, the remote sensing image classification becomes more and more important for information extraction. The VHR remote sensing images are rich in details, but high within-class variance as well as low between-class variance make the classification of ground cover a difficult task. What’s more, some related studies show that the quality of VHR remote sensing images also has a great influence on the ability of

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Patil, Varsha, Deepali Sale, and M. A. Joshi. "Image Fusion Methods and Quality Assessment Parameters." Asian Journal of Engineering and Applied Technology 2, no. 1 (2013): 40–45. http://dx.doi.org/10.51983/ajeat-2013.2.1.643.

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Image processing techniques primarily focus upon enhancing the quality of an image or a set of images and to derive the maximum information from them. Image Fusion is such a technique of producing a superior quality image from a set of available images. It is the process of combining relevant information from two or more images into a single image wherein the resulting image will be more informative and complete than any of the input images. A lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection, Image processing, parallel and distribute

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Boushey, C. J., M. Spoden, F. M. Zhu, E. J. Delp, and D. A. Kerr. "New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods." Proceedings of the Nutrition Society 76, no. 3 (2016): 283–94. http://dx.doi.org/10.1017/s0029665116002913.

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For nutrition practitioners and researchers, assessing dietary intake of children and adults with a high level of accuracy continues to be a challenge. Developments in mobile technologies have created a role for images in the assessment of dietary intake. The objective of this review was to examine peer-reviewed published papers covering development, evaluation and/or validation of image-assisted or image-based dietary assessment methods from December 2013 to January 2016. Images taken with handheld devices or wearable cameras have been used to assist traditional dietary assessment methods for

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Jamil, Sonain. "Review of Image Quality Assessment Methods for Compressed Images." Journal of Imaging 10, no. 5 (2024): 113. http://dx.doi.org/10.3390/jimaging10050113.

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The compression of images for efficient storage and transmission is crucial in handling large data volumes. Lossy image compression reduces storage needs but introduces perceptible distortions affected by content, compression levels, and display environments. Each compression method generates specific visual anomalies like blocking, blurring, or color shifts. Standardizing efficient lossy compression necessitates evaluating perceptual quality. Objective measurements offer speed and cost efficiency, while subjective assessments, despite their cost and time implications, remain the gold standard

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Wang, Mei, E. Ye Wang, and Guo Hua Pan. "Image Quality Assessment Based on Invariant Moments Similarity." Advanced Materials Research 546-547 (July 2012): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.565.

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To resolve the problems of the image quality assessment issue and the algorithm adaptability for different image size and deformation, this paper proposes a image quality assessment algorithm based on Invariant Moments Similarity. Firstly, Hu invariant moments values of original image and evaluated image are computed. Secondly the invariant moments distance is completed between original image and evaluated image. At last, the method assess the restoration image quality depend on the invariant moment distance. The experimental result shows that the algorithm result is better than MSE, PSNR, SSI

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Dissertations / Theses on the topic "Assessment of image"

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Verhaaren, Catharine C. "Improving Course Assessments Through a Product Assessment Template." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2338.pdf.

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Šmida, Vladimír. "Fingerprint Image Quality Assessment." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237090.

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Kritickým prvkem biometrického systému pro rozpoznávání otisků prstů je proces snímání. Kvalita snímku totiž ovlivňuje všechny další části systému počínaje zpracováním obrazu, přes extrakci rysů až po samotné rozhodnutí. Přestože bylo navrženo několik metod určování kvality snímku, chybějící formální specifikace kvality otisku nedovoluje ověřit jejich přesnost. Tato diplomová práce se zabývá hodnocením metod určujících kvalitu biometrického signálu otisku prstu. Popisuje jednotlivé faktory ovlivňující kvalitu spolu se současnými přístupy používanými pro její odhad. V práci je rovněž vysvětlena

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Cheng, Wu. "Corrupted Image Quality Assessment." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1335969249.

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Cui, Lei. "Topics in image recovery and image quality assessment /Cui Lei." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/368.

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Image recovery, especially image denoising and deblurring is widely studied during the last decades. Variational models can well preserve edges of images while restoring images from noise and blur. Some variational models are non-convex. For the moment, the methods for non-convex optimization are limited. This thesis finds new non-convex optimizing method called difference of convex algorithm (DCA) for solving different variational models for various kinds of noise removal problems. For imaging system, noise appeared in images can show different kinds of distribution due to the different imagi

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Zhang, Wei. "Visual saliency in image quality assessment." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/100239/.

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Advances in image quality assessment have shown the benefits of modelling functional components of the human visual system in image quality metrics. Visual saliency, a crucial aspect of the human visual system, is increasingly investigated recently. Current applications of visual saliency in image quality metrics are limited by our knowledge on the relation between visual saliency and quality perception. Issues regarding how to simulate and integrate visual saliency in image quality metrics remain. This thesis presents psychophysical experiments and computational models relevant to the percept

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Ivkovic, Goran. "An Algorithm for Image Quality Assessment." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000049.

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CORCHS, SILVIA ELENA. "Image quality assessment for Digital documents." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/50461.

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This thesis focuses on No Reference (NR) methods for Image Quality Assessment (IQA). A review of the IQA field is presented in Chapter 2; where the different IQA methods are described and classified. In particular, the application of IQA methods within a workflow chain is discussed. In Chapter 3 we focus on NR metrics for JPEG-blockiness and noise artifacts. It is in general assumed that subjective methods produce an actual estimate of the perceived quality while objective methods produce values that should be correlated with human perceptions as best as possible. From the analysis of th

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Rousselot, Maxime. "Image quality assessment of High Dynamic Range and Wide Color Gamut images." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S034/document.

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Ces dernières années, les technologies d’écran se sont considérablement améliorées. Par exemple, le contraste des écrans à plage dynamique élevée (HDR) dépasse de loin la capacité d’un écran conventionnel. De plus, un écran à gamut de couleur étendu (WCG) peut couvrir un espace colorimétrique plus grand que jamais. L'évaluation de la qualité de ces nouveaux contenus est devenue un domaine de recherche actif, les métriques de qualité SDR classiques n'étant pas adaptées. Cependant, les études les plus récentes négligent souvent une caractéristique importante: les chrominances. En effet, les base

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Bjurström, Håkan, and Jon Svensson. "Assessment of Grapevine Vigour Using Image Processing." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1342.

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<p>This Master’s thesis studies the possibility of using image processing as a tool to facilitate vine management, in particular shoot counting and assessment of the grapevine canopy. Both are areas where manual inspection is done today. The thesis presents methods of capturing images and segmenting different parts of a vine. It also presents and evaluates different approaches on how shoot counting can be done. Within canopy assessment, the emphasis is on methods to estimate canopy density. Other possible assessment areas are also discussed, such as canopy colour and measurement of canopy gaps

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Sendashonga, Mireille. "Image quality assessment using frequency domain transforms." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99537.

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Measurement of image quality plays a central role in optimization and evaluation of imaging systems. The most straight-forward way to assess image quality is subjective evaluations by human observers, where the mean value of their scores is used as the quality measure. However, objective (quantitative) measures are needed because subjective evaluations are impractical and expensive. The aim of this thesis is to develop simple and low-complexity metrics for quality assessment of digital images.<br>Traditionally, the most widely used quantitative measures are the mean squared error and measures

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Books on the topic "Assessment of image"

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Bigand, André, Julien Dehos, Christophe Renaud, and Joseph Constantin. Image Quality Assessment of Computer-generated Images. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73543-6.

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Wang, Zhou, and Alan C. Bovik. Modern Image Quality Assessment. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-02238-8.

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Ding, Yong, and Guangming Sun. Stereoscopic Image Quality Assessment. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7764-2.

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Tarkowski, Zbigniew. Assessment and self-assessment of stuttering and self-image. University of Marmia and Mazury, 2010.

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Szapiel, Stanisław. Diffraction-based image assessment in optical design. Wydawn. Politechniki Warszawskiej, 1986.

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International Commission on Radiation Units and Measurements., ed. Medical imaging--the assessment of image quality. International Commission on Radiation Units and Measurements, 1995.

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Ding, Yong. Visual Quality Assessment for Natural and Medical Image. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56497-4.

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Centre national d'études spatiales (France)., ed. Spot 1, utilisation des images, bilan, résultats =: Spot 1, image utilization, assessment, results. CEPADUES-Editions, 1988.

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United States. National Telecommunications and Information Administration, ed. Features for automated quality assessment of digitally transmitted video. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1990.

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Zhu, Shu-Yu. Perceptual wavelet coding and quality assessment for still image. National Library of Canada, 2000.

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Book chapters on the topic "Assessment of image"

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Zamzmi, Ghada, Chih-Yun Pai, Dmitry Goldgof, Rangachar Kasturi, Yu Sun, and Terri Ashmeade. "Automated Pain Assessment in Neonates." In Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59129-2_30.

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Langs, Georg, Philipp Peloschek, and Horst Bischof. "ASM Driven Snakes in Rheumatoid Arthritis Assessment." In Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_61.

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Jannin, Pierre, and Werner Korb. "Assessment of Image-Guided Interventions." In Image-Guided Interventions. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-73858-1_18.

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Chaudhuri, Subhasis, and Ketan Kotwal. "Performance Assessment of Fusion Techniques." In Hyperspectral Image Fusion. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7470-8_9.

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Ortiz-Jaramillo, B., L. Platisa, and W. Philips. "iFAS: Image Fidelity Assessment." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56010-6_7.

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Ding, Yong. "Stereoscopic Image Quality Assessment." In Visual Quality Assessment for Natural and Medical Image. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56497-4_7.

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Ding, Yong. "Medical Image Quality Assessment." In Visual Quality Assessment for Natural and Medical Image. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56497-4_8.

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Andrade, Juan. "No-Reference Image Assessment." In A Survey of Blur Detection and Sharpness Assessment Methods. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01529-8_4.

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Tipker-Vos, Corinne, Kim de Crom, Anouk van der Gijp, et al. "Practical Implementation of Innovative Image Testing." In Computer Assisted Assessment. Research into E-Assessment. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08657-6_14.

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Wang, Zhou, and Alan C. Bovik. "No-Reference Image Quality Assessment." In Modern Image Quality Assessment. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-02238-8_4.

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Conference papers on the topic "Assessment of image"

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Sri Haripriya Rai, N., and G. Ranjani. "Image Quality Assessment." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10817020.

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Rifa, Kazi Ramisa, Md Atik Ahamed, Jie Zhang, and Abdullah-Al-Zubaer Imran. "Task-focused knowledge transfer from natural images for CT image quality assessment." In Image Perception, Observer Performance, and Technology Assessment, edited by Jovan G. Brankov and Mark A. Anastasio. SPIE, 2025. https://doi.org/10.1117/12.3047371.

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Suleiman, Mo'ayyad E., Xuetong Tao, Patrick C. Brennan, Jacky Chen, and Ziba Gandomkar. "Enhancing radiological assessment of dust diseases: evaluating the impact of online self-assessment educational modules and feedback interventions." In Image Perception, Observer Performance, and Technology Assessment, edited by Jovan G. Brankov and Mark A. Anastasio. SPIE, 2025. https://doi.org/10.1117/12.3049192.

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Peng, Fei, Huiyuan Fu, Anlong Ming, et al. "AIGC Image Quality Assessment via Image-Prompt Correspondence." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00644.

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Mitra, Sunanda, Vinod Chandran, Thomas F. Krile, and Dah-Jye Lee. "Fundus Image Registration Algorithms for a PC-Based Image Processing System." In Noninvasive Assessment of the Visual System. Optica Publishing Group, 1987. http://dx.doi.org/10.1364/navs.1987.md4.

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Fundus photographs are routinely taken in an ophthalmology clinic to monitor feature changes in the suspected regions of the retina. For quantitative evaluation of subtle changes in the fundus from the photographs taken over a period of time using digital image processing, the fundus images have to be registered to a reference image, usually, the image of the same fundus taken earlier. The problem of registering one digital image with another reduces to that of finding local similarity between two structural data sets.

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Whiteside, Steven, Alice Ling, Sunanda Mitra, Thomas F. Krile, and Zuhair Shihab. "Image Enhancement of Nerve Fiber Layers in Glaucoma." In Noninvasive Assessment of the Visual System. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/navs.1986.wb4.

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Digital image enhancement and restoration techniques have been used in noninvasive diagnostic radiographic imaging for quite some time.1 Application of digital image processing to enhance fundus photographs is relatively recent. Basic problems in standardization, restoration of fundus images and development of an optimal image enhancement procedure for specific applications are being presently investigated by researchers.2,3,6 Enhancement of fundus photographs is of particular significance in diseases such as glaucoma where structural changes in the optic nerve head and retinal nerve fiber lay

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Rajagopal, Heshalini, Norrima Mokhtar, and Anis Salwa Mohd Khairuddin. "Image Quality Assessment for Wood Images." In 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). IEEE, 2022. http://dx.doi.org/10.1109/iicaiet55139.2022.9936864.

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Madhusudana, Pavan C., Neil Birkbeck, Yilin Wang, Balu Adsumilli, and Alan C. Bovik. "Image Quality Assessment using Synthetic Images." In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). IEEE, 2022. http://dx.doi.org/10.1109/wacvw54805.2022.00015.

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Jamson, Hamish. "Image Characteristics and Their Effect on Driving Simulator Validity." In Driving Assessment Conference. University of Iowa, 2001. http://dx.doi.org/10.17077/drivingassessment.1036.

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Rouse, David M., Romuald Pepion, Sheila S. Hemami, and Patrick Le Callet. "Image utility assessment and a relationship with image quality assessment." In IS&T/SPIE Electronic Imaging, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 2009. http://dx.doi.org/10.1117/12.811664.

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Reports on the topic "Assessment of image"

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Schoening, T., and J. Greinert. Quality of image-based manganese nodule abundance assessment. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305928.

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Pelizzari, Charles A>. Image Guidance and Assessment of Radiation Induced Gene Therapy. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada424653.

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Yang, Joyce. Face Analysis Technology Evaluation (FATE) Face Image Quality Vector Assessment:. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.ir.8485.

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Wales, Jesse G., and Peter L. Marasco. Assessment of an Optically Stimulated Infrared Emission From Image Intensifier Tube Photocathodes. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada442754.

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Nelson, Cynthia Lee. Fusion of image data for beyond-the-fence intruder detection and assessment. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/920447.

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Kane, Joshua James, and William Enoch Windes. Initial Assessment of X-Ray Computer Tomography image analysis for material defect microstructure. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1364233.

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Beauchemin, M., K. B. Fung, and X. Geng. A Method Based on Local Variance for Quality Assessment of Multiresolution Image Fusion. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219921.

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Allada, Venkata Bhanu Chowdary. Implementation of image quality assessment algorithms for descriptive statistics and deep learning on StegoAppDB. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-878.

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Guindon, B. Computer-based aerial image understanding: a review and assessment of its application to planimetric information extraction from very high resolution satellite images. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1997. http://dx.doi.org/10.4095/218537.

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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.), 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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