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Статті в журналах з теми "Aesthetics Assessment"
Elwazani, Salim. "Purposing aesthetics in historic preservation: advocating, signifying, and interpreting aesthetics." Virtual Archaeology Review 12, no. 24 (January 19, 2021): 66. http://dx.doi.org/10.4995/var.2021.13812.
Повний текст джерелаLeong, S. C. L., M. Abdelkader, and P. S. White. "Changes in nasal aesthetics following nasal bone manipulation." Journal of Laryngology & Otology 122, no. 1 (May 14, 2007): 38–41. http://dx.doi.org/10.1017/s0022215107008225.
Повний текст джерелаPan, Bowen, Shangfei Wang, and Qisheng Jiang. "Image Aesthetic Assessment Assisted by Attributes through Adversarial Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 679–86. http://dx.doi.org/10.1609/aaai.v33i01.3301679.
Повний текст джерелаZhu, Hancheng, Yong Zhou, Zhiwen Shao, Wenliang Du, Guangcheng Wang, and Qiaoyue Li. "Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning." Mathematics 10, no. 22 (November 9, 2022): 4181. http://dx.doi.org/10.3390/math10224181.
Повний текст джерелаGrazuleviciute-Vileniske, Indre, Gediminas Viliunas, and Aurelija Daugelaite. "The role of aesthetics in building sustainability assessment." Spatium, no. 45 (2021): 79–89. http://dx.doi.org/10.2298/spat2145079g.
Повний текст джерелаTribot, Anne-Sophie, Julie Deter, and Nicolas Mouquet. "Integrating the aesthetic value of landscapes and biological diversity." Proceedings of the Royal Society B: Biological Sciences 285, no. 1886 (September 5, 2018): 20180971. http://dx.doi.org/10.1098/rspb.2018.0971.
Повний текст джерелаKim, Min Soon, Juliano C. Sbalchiero, Gregory P. Reece, Michael J. Miller, Elisabeth K. Beahm, and Mia K. Markey. "Assessment of Breast Aesthetics." Plastic and Reconstructive Surgery 121, no. 4 (April 2008): 186e—194e. http://dx.doi.org/10.1097/01.prs.0000304593.74672.b8.
Повний текст джерелаYan, Gang, Rongjia Bi, Yingchun Guo, and Weifeng Peng. "Image Aesthetic Assessment Based on Latent Semantic Features." Information 11, no. 4 (April 17, 2020): 223. http://dx.doi.org/10.3390/info11040223.
Повний текст джерелаSchwirtz, Roderic M. F., Frans J. Mulder, David G. M. Mosmuller, Robin A. Tan, Thomas J. Maal, Charlotte Prahl, Henrica C. W. de Vet, and J. Peter W. Don Griot. "Rating Nasolabial Aesthetics in Unilateral Cleft Lip and Palate Patients." Cleft Palate-Craniofacial Journal 55, no. 5 (January 19, 2018): 747–52. http://dx.doi.org/10.1177/1055665617747702.
Повний текст джерелаSULTANOV, M. SH, and O. A. SOCHAEV. "EVALUATION OF THE AESTHETIC RESULT OF A SINGLE DENTAL IMPLANT PROSTHETICS." AVICENNA BULLETIN 25, no. 2 (2023): 201–14. http://dx.doi.org/10.25005/2074-0581-2023-25-2-201-214.
Повний текст джерелаДисертації з теми "Aesthetics Assessment"
Fourie, René. "Applying GIS in the evaluation of landscape aesthetics /." Link to the online version, 2005. http://hdl.handle.net/10019/1644.
Повний текст джерелаKuzovkin, Dmitry. "Assessment of photos in albums based on aesthetics and context." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S032/document.
Повний текст джерелаAn automatic photo assessment can significantly aid the process of photo selection within photo collections. However, existing computational methods approach this problem in an independent manner, by evaluating each image apart from other images in a photo album. In this thesis, we explore the modeling of photo context via a clustering approach for photo collections and the possibility of applying such context information in photo assessment. To better understand user actions within photo albums, we conduct experimental user studies, where we study how users cluster and select photos in photo collections. We estimate the level of agreement between users and investigate how the context, defined by similar photos in corresponding clusters, influences their decisions. After studying the nature of user decisions, we propose a computational approach to model user behavior. First, we introduce a hierarchical clustering method, which allows to group similar photos according to a multi-level similarity structure, based on visual descriptors. Then, the photo context information is extracted from the obtained cluster data and used to adapt a pre-computed independent photo score, using the statistics-based data and a machine learning approach. In addition, as the majority of recent methods for photo assessment are based on convolutional neural networks, we explore and visualize the aesthetic characteristics learned by such methods
Chambe, Mathieu. "Improving image quality using high dynamic range and aesthetics assessment." Electronic Thesis or Diss., Université de Rennes (2023-....), 2023. http://www.theses.fr/2023URENS015.
Повний текст джерелаTo cope with the increasing amount of visual content available, it is important to devise automatic processes that can sort, improve, compress or store images and videos. In this thesis, we propose two different approaches to software-based image improvement. First, we propose a study on existing aesthetics assessment algorithms. These algorithms are based on supervised neural networks. We have collected several datasets of images, and we have tested different models using these images. We report here the performances of such networks, as well as an idea to improve the already trained networks. Our study shows that the features needed to accurately predict the aesthetics of competitive and professional are different but can be learned simultaneously by a single network. In a second time, we propose to work with High Dynamic Range (HDR) images. We present here a new operator to increase the dynamic range of images called HDR-LFNet, that merges the output of existing operators and therefore, consists in far fewer parameters. Besides, we evaluate our method through objective metrics and a user study. We show that our method is on-par with the state-of-the-art according to objective metrics, but is preferred by observers during the user study, while using less resources overall
Fourie, Rene. "Applying GIS in the evaluation of landscape aesthetics." Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/1813.
Повний текст джерелаScenic beauty, or landscape aesthetics, should be regarded as a valuable resource, to be protected and enhanced in order to generate income. Current environmental impact assessment (EIA) studies do not include the evaluation of scenic beauty as a resource properly, due to the lack of effective evaluation methods. A general dilemma lies in objectively evaluating beauty. If scenic preferences can be associated consistently with the physical landscape features, the latter can be used as predictors of the former. Analysis of aesthetics can therefore be done with a degree of objectivity, based on these general preferences. A large number of these preferences are morphologically measurable. In other words, these preferences can be mapped in a Geographical Information System (GIS), rated, and evaluated quantitatively. The first step in objectively evaluating landscape aesthetics entailed identification and compilation from the literature of conceptual components in a landscape, i.e. the units defining a landscape. Four components were identified: landform, vegetation, water features and man-made features. Each of the four components can be subdivided into several elements. Secondly, scenic preferences that can be consistently associated with landscape features were identified. It was found that any subjective experience of landscape aesthetics would be either one of calmness or one of excitement. The presence or absence of the landscape elements, and specific combinations of elements and element variables within the context of an individual landscape, will determine the type and extent of the aesthetic experience of the viewer. Finally, this theory was put into practice. Coverages were created of a test region, with landscape elements as the features of the coverages, and element variables or characteristics as feature attributes. These landscape elements, as they enhance either calmness or excitement, were quantified by assigning value ratings to the elements according to the extent of the influence of the elements on the aesthetic value of the landscape. ArcInfo GRID functionality was used to convert the coverages to raster (or grid) overlays, using the element variables enhancing both calmness and excitement. A simple cumulative summing function was used to derive an aggregate Calm Aesthetic Experience map by adding grids enhancing calmness. An aggregate Exciting Aesthetic Experience map was constructed by adding grids enhancing excitement. Finally, these two grids were summed in order to construct a Total Aesthetic Experience map, which is an indication of the total aesthetic value of the test region. The outcome of this research was a method for analysis and objective evaluation of a landscape, using a GIS for data creation, analysis and map construction. The resultant map is an indication of aesthetic value, showing the test region graded according to intrinsic aesthetic value.
Lothian, Andrew. "Landscape quality assessment of South Australia." Title page, table of contents, abstract and detailed contents only, 2000. http://hdl.handle.net/2440/37804.
Повний текст джерелаThesis (Ph.D.)--School of Social Sciences, 2000.
Baraheem, Samah Saeed. "Text to Image Synthesis via Mask Anchor Points and Aesthetic Assessment." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton158800567702413.
Повний текст джерелаTan, Joseph Tsun Daw. "Hypothetical Etiology and Competitive Assessment of Terahertz Light Induced Rhytide Improvement." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1327511578.
Повний текст джерелаKang, Chen. "Image Aesthetic Quality Assessment Based on Deep Neural Networks." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG004.
Повний текст джерелаWith the development of capture devices and the Internet, people access to an increasing amount of images. Assessing visual aesthetics has important applications in several domains, from image retrieval and recommendation to enhancement. Image aesthetic quality assessment aims at determining how beautiful an image looks to human observers. Many problems in this field are not studied well, including the subjectivity of aesthetic quality assessment, explanation of aesthetics and the human-annotated data collection. Conventional image aesthetic quality prediction aims at predicting the average score or aesthetic class of a picture. However, the aesthetic prediction is intrinsically subjective, and images with similar mean aesthetic scores/class might display very different levels of consensus by human raters. Recent work has dealt with aesthetic subjectivity by predicting the distribution of human scores, but predicting the distribution is not directly interpretable in terms of subjectivity, and might be sub-optimal compared to directly estimating subjectivity descriptors computed from ground-truth scores. Furthermore, labels in existing datasets are often noisy, incomplete or they do not allow more sophisticated tasks such as understanding why an image looks beautiful or not to a human observer. In this thesis, we first propose several measures of subjectivity, ranging from simple statistical measures such as the standard deviation of the scores, to newly proposed descriptors inspired by information theory. We evaluate the prediction performance of these measures when they are computed from predicted score distributions and when they are directly learned from ground-truth data. We find that the latter strategy provides in general better results. We also use the subjectivity to improve predicting aesthetic scores, showing that information theory inspired subjectivity measures perform better than statistical measures. Then, we propose an Explainable Visual Aesthetics (EVA) dataset, which contains 4070 images with at least 30 votes per image. EVA has been crowd-sourced using a more disciplined approach inspired by quality assessment best practices. It also offers additional features, such as the degree of difficulty in assessing the aesthetic score, rating for 4 complementary aesthetic attributes, as well as the relative importance of each attribute to form aesthetic opinions. The publicly available dataset is expected to contribute to future research on understanding and predicting visual quality aesthetics. Additionally, we studied the explainability of image aesthetic quality assessment. A statistical analysis on EVA demonstrates that the collected attributes and relative importance can be linearly combined to explain effectively the overall aesthetic mean opinion scores. We found subjectivity has a limited correlation to average personal difficulty in aesthetic assessment, and the subject's region, photographic level and age affect the user's aesthetic assessment significantly
Greene, Lawrence D. "Prediction of aesthetic response: a comparison of different philosophical paradigms' predictive utilities of aesthetic response towards natural landscape scenes." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/49784.
Повний текст джерелаPembegul, Tugba. "Assessment Of Convention Centers From Users." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610581/index.pdf.
Повний текст джерелаpriorities and evaluate what extent these were provided by the convention centre. Data has been collected using self-administered questionnaires from three group of users
attendees, employees and meeting planners. The study has been conducted in istanbul Lü
tfi Kirdar Convention and Exhibition Center as a case, because of being the most remarkable convention center of Turkey. Each participant will be required to assess this convention center in terms of their priorities of expectations and features provided. The results have been evaluated statistically, and significant differences between the level of importance and performance of the facility features have been presented. This research is expected to be useful for constitution of design criteria of convention centers and effective management of the facilities, in terms of both identifying the features of convention centers and providing a method evaluating the performance of the facilities from the users&rsquo
perspective.
Книги з теми "Aesthetics Assessment"
The aesthetics of environment. Philadelphia: Temple University Press, 1992.
Знайти повний текст джерелаThe aesthetics of landscape. London: Belhaven Press, 1991.
Знайти повний текст джерелаLandscapes beyond land: Routes, aesthetics, narratives. New York: Berghahn Books, 2012.
Знайти повний текст джерела1944-, Klein Evelin E., Klein Hans-Dieter, and Birkhan Ines 1974-, eds. Technikkritik und Ästhetik. Frankfurt am Main: P. Lang, 2007.
Знайти повний текст джерелаInterpretare il paesaggio: Qualità territoriale e valorizzazione delle identità locali. Roma: Aracne, 2011.
Знайти повний текст джерелаNature and landscape: An introduction to environmental aesthetics. New York: Columbia University Press, 2009.
Знайти повний текст джерелаRobert, Schäfer, ed. Was heisst denn schon Natur?: Ein Essaywettbewerb. München: Callwey, 1993.
Знайти повний текст джерелаLiving in the landscape: Toward an aesthetics of environment. Lawrence: University Press of Kansas, 1997.
Знайти повний текст джерелаHội thảo thả̂m mỹ môi trường (1999 Hanoi, Vietnam). Kỹ yé̂u Hội thảo thả̂m mỹ môi trường. [Hà Nội]: Nhà xuá̂t bản Mỹ thuật, 2002.
Знайти повний текст джерелаAndrews, Malcolm. The search for the picturesque: Landscape aesthetics and tourism in Britain, 1760-1800. Aldershot: Scolar, 1989.
Знайти повний текст джерелаЧастини книг з теми "Aesthetics Assessment"
Cooper, Lyndon F., and Homayoun H. Zadeh. "Recognition of Risk Factors and Patient Assessment." In Implant Aesthetics, 3–17. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50706-4_1.
Повний текст джерелаHsu, Yung-Ting, and Hom-Lay Wang. "Clinical Assessment of the Gingiva and Alveolus." In Implant Aesthetics, 103–16. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50706-4_7.
Повний текст джерелаSanford, Robert M., and Donald G. Holtgrieve. "Aesthetics and visual impact analysis." In Environmental Impact Assessment in the United States, 161–70. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003030713-12.
Повний текст джерелаZou, Ruoyu, Jiangbo Xu, and Ziyu Xue. "Technological Development of Image Aesthetics Assessment." In Lecture Notes in Computer Science, 341–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87361-5_28.
Повний текст джерелаApostolidis, Konstantinos, and Vasileios Mezaris. "Image Aesthetics Assessment Using Fully Convolutional Neural Networks." In MultiMedia Modeling, 361–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05710-7_30.
Повний текст джерелаLiu, Huihui, Chaoran Cui, Yuling Ma, Cheng Shi, Yongchao Xu, and Yilong Yin. "Image Aesthetics Assessment Based on User Social Behavior." In Advances in Multimedia Information Processing – PCM 2018, 755–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00776-8_69.
Повний текст джерелаDoshi, Nishi, Gitam Shikkenawis, and Suman K. Mitra. "Image Aesthetics Assessment Using Multi Channel Convolutional Neural Networks." In Communications in Computer and Information Science, 15–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4018-9_2.
Повний текст джерелаLu, Peng, Zhijie Kuang, Xujun Peng, and Ruifan Li. "Discovering Harmony: A Hierarchical Colour Harmony Model for Aesthetics Assessment." In Computer Vision -- ACCV 2014, 452–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16811-1_30.
Повний текст джерелаGragson, Ted L., and Andrew G. Keeler. "Aesthetics and Practice of Maintaining the Ideal Lawn in Peachtree City, GA." In Turf Grass: Pesticide Exposure Assessment and Predictive Modeling Tools, 11–22. Washington DC: American Chemical Society, 2009. http://dx.doi.org/10.1021/bk-2009-1028.ch002.
Повний текст джерелаOyedele, Adesegun, Soonkwan Hong, and Michael S. Minor. "Individual Assessment of Humanlike Consumer Robots: An Extended Tam with Aesthetics." In Revolution in Marketing: Market Driving Changes, 67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11761-4_32.
Повний текст джерелаТези доповідей конференцій з теми "Aesthetics Assessment"
Li, Yaohui, Yuzhe Yang, Huaxiong Li, Haoxing Chen, Liwu Xu, Leida Li, Yaqian Li, and Yandong Guo. "Transductive Aesthetic Preference Propagation for Personalized Image Aesthetics Assessment." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548244.
Повний текст джерелаDeng, Xiang, Chaoran Cui, Huidi Fang, Xiushan Nie, and Yilong Yin. "Personalized Image Aesthetics Assessment." In CIKM '17: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132847.3133052.
Повний текст джерелаLiu, Dong, Rohit Puri, Nagendra Kamath, and Subhabrata Bhattacharya. "Composition-Aware Image Aesthetics Assessment." In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093412.
Повний текст джерелаMai, Long, Hailin Jin, and Feng Liu. "Composition-Preserving Deep Photo Aesthetics Assessment." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.60.
Повний текст джерелаKang, Chen, Giuseppe Valenzise, and Frederic Dufaux. "Predicting Subjectivity in Image Aesthetics Assessment." In 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2019. http://dx.doi.org/10.1109/mmsp.2019.8901716.
Повний текст джерелаDuan, Jiachen, Pengfei Chen, Leida Li, Jinjian Wu, and Guangming Shi. "Semantic Attribute Guided Image Aesthetics Assessment." In 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2022. http://dx.doi.org/10.1109/vcip56404.2022.10008896.
Повний текст джерелаBianco, Simone, Luigi Celona, and Raimondo Schettini. "Aesthetics Assessment of Images Containing Faces." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451368.
Повний текст джерелаIranfar, Maryam, and Hourakhsh Ahmad Nia. "The Synthesis of Ethics and Aesthetics in Modern Movement of Architecture: ‘Truth’ Theory as an Assessment Tool." In 4th International Conference of Contemporary Affairs in Architecture and Urbanism – Full book proceedings of ICCAUA2020, 20-21 May 2021. Alanya Hamdullah Emin Paşa University, 2021. http://dx.doi.org/10.38027/iccaua2021235n17.
Повний текст джерелаWang, Zhangyang, Ding Liu, Shiyu Chang, Florin Dolcos, Diane Beck, and Thomas Huang. "Image aesthetics assessment using Deep Chatterjee's machine." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965953.
Повний текст джерелаCui, Chaoran, Huidi Fang, Xiang Deng, Xiushan Nie, Hongshuai Dai, and Yilong Yin. "Distribution-oriented Aesthetics Assessment for Image Search." In SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3077136.3080704.
Повний текст джерелаЗвіти організацій з теми "Aesthetics Assessment"
McCoy-Sulentic, Miles, Diane Menuz, and Rebecca Lee. Central Basin and Range Ecoregion Wetland Assessment and Landscape Analysis. Utah Geological Survey, November 2021. http://dx.doi.org/10.34191/ofr-738.
Повний текст джерелаNELYUBINA, E., and L. PANFILOVA. ASSESSMENT OF THE QUALITY OF EDUCATIONAL ELECTRONIC PUBLICATIONS AND RESOURCES. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-4-2-85-97.
Повний текст джерела