Relevant bibliographies by topics / Computational intelligence. Image analysis

Contents

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

Academic literature on the topic 'Computational intelligence. Image analysis'

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

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Journal articles on the topic "Computational intelligence. Image analysis"

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Rymarczyk, Tomasz. "LEVEL SETS AND COMPUTATIONAL INTELLIGENCE ALGORITHMS TO MEDICAL IMAGE ANALYSIS IN E-MEDICUS SYSTEM." Informatics Control Measurement in Economy and Environment Protection 7, no. 1 (2017): 63–67. http://dx.doi.org/10.5604/01.3001.0010.4585.

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In this work, there were implemented methods to analyze and segmentation medical images by using topological, statistical algorithms and artificial intelligence techniques. The solution shows the architecture of the system collecting and analyzing data. There was tried to develop an algorithm for level set method (LSM) applied to piecewise constant image segmentation. These algorithms are needed to identify arbitrary number of phases for the segmentation problem. The image segmentation refers to the process of partitioning a digital image into multiple regions. There is typically used to locat
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CHUBB, CHARLIE, YOSHI INAGAKI, C. COTMAN, B. CUMMINGS, and P. C. Y. SHEU. "SEMANTIC BIOLOGICAL IMAGE MANAGEMENT AND ANALYSIS." International Journal on Artificial Intelligence Tools 13, no. 04 (2004): 881–96. http://dx.doi.org/10.1142/s0218213004001879.

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Most commercially available image retrieval systems are so generic that they are not specialized to handle biological images and the feature domains associated with them. In molecular biology, neurobiology and cellular biology, for example, the recognition, classification and retrieval of distinct cellular features is a critically needed tool representing a computational problem that embodies the central challenges facing biological image database research. It often requires the consideration of expert/conceptual knowledge of images and the objects contained within such images. This paper disc
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Et. al., Akshay Kapoor,. "Disease Identification System using Image Analysis." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 115–23. http://dx.doi.org/10.17762/turcomat.v12i1s.1582.

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Artificial Intelligence (AI) and Sensor Technologies making a point-of-care delivery possible which was considered impossible. The human body shows early indicators of disease manifestation before actual clinical symptoms appear. These indicators can be picked up by analyzing the optical face image, which can be used for rapid and cost-effective screening of life-threatening health conditions. This article focuses on how health status can be tracked using optical image analytics. Early identification of disease plays a vital role in therapeutics of a patient. In accordance with review of secon
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Hirschberg, Cosima, Magnus Edinger, Else Holmfred, Jukka Rantanen, and Johan Boetker. "Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality." Pharmaceutics 12, no. 9 (2020): 877. http://dx.doi.org/10.3390/pharmaceutics12090877.

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Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The ima
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Bhowmik, Anirban, Joydeep Dey, Arindam Sarkar, and Sunil Karforma. "Computational Intelligence based Lossless Regeneration (CILR) of Blocked Gingivitis Intraoral Image Transportation." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 3 (2019): 197. http://dx.doi.org/10.11591/ijai.v8.i3.pp197-204.

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<p>This paper presented that an intraoral image has been wrapped during wireless transportation with an encryption tool with an added essence of lossless regeneration property. Threshold based cryptographic transportation has provided the construction of reliable and robust medical data communication system. The accumulation of threshold shares only would result to the formation of the intraoral gingivitis image at the receivers’ end. The proposed technique dealt with the generation of n number of partial shares by creating a unique frame structure by the dentist / physician. Additional
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Luther, Kurt. "Human Computation for Image and Video Analysis." AI Magazine 39, no. 4 (2018): 67–68. http://dx.doi.org/10.1609/aimag.v39i4.2825.

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The second Workshop on Human Computation for Image and Video Analysis (GroupSight) took place in Quebec City, Canada on October 24, 2017, as part of the fifth AAAI Conference on Human Computation and Crowdsourcing (HCOMP). The aim of this workshop was to promote greater interaction between researchers and practitioners who examine how to mix human and computer efforts to convert visual data into discoveries and innovations that benefit society.
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Liu, Jiming, and Y. Y. Tang. "Distributed Autonomous Agents for Chinese Document Image Segmentation." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 01 (1998): 97–118. http://dx.doi.org/10.1142/s0218001498000087.

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In Chinese document image processing, text and/or graphical block detection serves as an essential step in document layout analysis that in turn permits the effective reasoning about the logical relationships among various text paragraphs and graphical entities for the purpose of document understanding. This paper presents a novel computational paradigm for extracting text/graphic blocks from Chinese document images, which is based on a notion of distributed autonomous agents. The primary features of the agents lie in that they are (1) adaptive to the locality of given images and hence efficie
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Chavat, Juan, Sergio Nesmachnow, Andrei Tchernykh, and Vladimir Shepelev. "Active Safety System for Urban Environments with Detecting Harmful Pedestrian Movement Patterns Using Computational Intelligence." Applied Sciences 10, no. 24 (2020): 9021. http://dx.doi.org/10.3390/app10249021.

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This article presents a system for detecting pedestrian movement patterns in urban environments, by applying computational intelligence methods for image processing and pattern detection. The proposed system is capable of processing multiple images and video sources in real-time. Furthermore, it has a flexible design, as it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques to address the subproblems involved in each stage of the process. Two main stages are implemented in the proposed system: the first stage is in charge
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Chunduru, Pranathi, Joanna Phillips, and Annette Molinaro. "PATH-31. ARTIFICIAL INTELLIGENCE IN GLIOMA PATHOLOGY IMAGE ANALYSIS FOR RISK PREDICTION." Neuro-Oncology 22, Supplement_2 (2020): ii171. http://dx.doi.org/10.1093/neuonc/noaa215.712.

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Abstract Pathological evaluation of tumor tissue images stained with hematoxylin and eosin (H&E) is pivotal in diagnosis and predictive of outcome, yet only a small fraction of the rich phenotypic information on the slide is currently used for clinical care. In this study, we developed a computational approach based on deep learning to predict overall survival within distinct molecular subtypes of glioma patients and to extract prognostic biomarkers from microscopic images of tissue biopsies. Whole-slide images from 766 unique patients [IDH: 336 IDH-wildtype, 364 IDH-mutant, 1p/19q: 142 1p
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Nandhini Abirami, R., P. M. Durai Raj Vincent, Kathiravan Srinivasan, Usman Tariq, and Chuan-Yu Chang. "Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis." Complexity 2021 (April 15, 2021): 1–30. http://dx.doi.org/10.1155/2021/5541134.

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Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of de
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Dissertations / Theses on the topic "Computational intelligence. Image analysis"

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Sulistyo, Susanto Budi. "Computational intelligence image processing for precision farming on-site nitrogen analysis in plants." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/4048.

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Nitrogen is one of the macronutrients which is essentially required by plants. To support the precision farming, it is important to analyse nitrogen status in plants in order to prevent excessive fertilisation as well as to reduce production costs. Image-based analysis has been widely utilised to estimate nitrogen content in plants. Such research, however, is commonly conducted in a controlled environment with artificial lighting systems. This thesis proposes three novel computational intelligence systems to evaluate nitrogen status in wheat plants by analysing plant images captured on field a
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Dufour, Jean-Yves. "Contribution algorithmique a la conception d'un systeme integre d'analyse temps-reel de scenes dynamiques : reconnaissance d'objets et analyse de mouvement dans une sequence d'images." Caen, 1988. http://www.theses.fr/1988CAEN2034.

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Deux problemes de l'analyse de scene ont ete etudies: la reconnaissance automatique d'objets et l'analyse temporelle des images fournies par le senseur. En ce qui concerne la reconnaissance d'objets, l'interet a ete porte sur l'aspect "decision" du probleme. Pour la partie analyse temporelle les travaux ont ete consacres a la conception d'un module de recalage d'images
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Serguieva, Antoaneta. "Computational intelligence techniques in asset risk analysis." Thesis, Brunel University, 2004. http://bura.brunel.ac.uk/handle/2438/5424.

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The problem of asset risk analysis is positioned within the computational intelligence paradigm. We suggest an algorithm for reformulating asset pricing, which involves incorporating imprecise information into the pricing factors through fuzzy variables as well as a calibration procedure for their possibility distributions. Then fuzzy mathematics is used to process the imprecise factors and obtain an asset evaluation. This evaluation is further automated using neural networks with sign restrictions on their weights. While such type of networks has been only used for up to two network inputs an
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Riera, Sardà Alexandre. "Computational Intelligence Techniques for Electro-Physiological Data Analysis." Doctoral thesis, Universitat de Barcelona, 2012. http://hdl.handle.net/10803/107818.

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This work contains the efforts I have made in the last years in the field of Electrophysiological data analysis. Most of the work has been done at Starlab Barcelona S.L. and part of it at the Neurodynamics Laboratory of the Department of Psychiatry and Clinical Psychobiology of the University of Barcelona. The main work deals with the analysis of electroencephalography (EEG) signals, although other signals, such as electrocardiography (ECG), electroculography (EOG) and electromiography (EMG) have also been used. Several data sets have been collected and analysed applying advanced Signal Proces
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Che, Fidelis Ndeh. "Object-oriented analysis and design of computational intelligence systems." Thesis, City University London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245861.

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Slade, Jr Wayne Homer. "Computational Intelligence Approaches to Ocean Color Inversion." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/SladeWH2004.pdf.

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Lee, Kang Qi Ian. "Computational tools for enabling longitudinal skin image analysis." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107060.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 165-174).<br>We present a set of computational tools that enable quantitative analysis of longitudinally acquired skin images: the assessment and characterization of the evolution of skin features over time. A framework for time-lapsed skin imaging is proposed. A nonrigid registration algorithm based on multiple plane detection for landmark identification accurately aligns pairs of longitudinal skin im
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Alzu'Bi, Hamzah. "Analysis of human activities and animal behaviours based on computational intelligence." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2037039/.

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The study of behaviour is vital for animal welfare assessment in animal husbandry systems, exploring mechanisms of underlying diverse forms of behaviours and animal physiological and ecological interaction. Understanding animal behaviour is used in a systematic way to unlock and explore underlying functionalities of the brain which is one of the biggest challenges to science. This thesis introduces four novel applications for computational intelligence in human and animal behaviours. The four applications are: horse transport stress prediction system, human activity recognition, fish behaviour
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Harrison, Kyle Robert. "An Analysis of Parameter Control Mechanisms for the Particle Swarm Optimization Algorithm." Thesis, University of Pretoria, 2018. http://hdl.handle.net/2263/66103.

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The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization technique influenced by social dynamics. It has been shown that the performance of the PSO algorithm can be greatly improved if the control parameters are appropriately tuned. However, the tuning of control parameter values has traditionally been a time-consuming, empirical process followed by statistical analysis. Furthermore, ideal values for the control parameters may be time-dependent; parameter values that lead to good performance in an exploratory phase may not be ideal for an exploitative phas
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Stenerlöw, Oskar. "Artefact detection in microstructures using image analysis." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417342.

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Gyros Protein Technologies AB produce instruments designed to perform automated immunoassaying on plastic CDs with microstructures. While generally being a very robust process, the company had noticed that some runs on the instruments encountered problems. They hypothesised it had to do with the chamber on the CD in which the sample is added to. It was believed that the chamber was not being filled properly, leaving it completely empty or contained with a small amount of air, rather than liquid. This project aimed to investigate this hypothesis and to develop an image analysis solution that co
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Books on the topic "Computational intelligence. Image analysis"

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Zhi neng mu biao shi bie yu fen lei. Ke xue chu ban she, 2010.

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Kamila, Narendra Kumar. Handbook of research on emerging perspectives in intelligent pattern recognition, analysis, and image processing. Information Science Reference, 2015.

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Lee, Wang Fu, Deng Hepu, Miao Duoqian, and SpringerLink (Online service), eds. Artificial Intelligence and Computational Intelligence: 4th International Conference, AICI 2012, Chengdu, China, October 26-28, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Kountchev, Roumen. Advances in Intelligent Analysis of Medical Data and Decision Support Systems. Springer International Publishing, 2013.

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R, Martin Ralph, and SpringerLink (Online service), eds. Computational Visual Media: First International Conference, CVM 2012, Beijing, China, November 8-10, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Peterson, Leif E. Computational Intelligence Methods for Bioinformatics and Biostatistics: 9th International Meeting, CIBB 2012, Houston, TX, USA, July 12-14, 2012 Revised Selected Papers. Springer Berlin Heidelberg, 2013.

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Siarry, Patrick, and Amitava Chatterjee. Computational intelligence in image processing. Springer, 2013.

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Chatterjee, Amitava, and Patrick Siarry, eds. Computational Intelligence in Image Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30621-1.

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Anastassiou, George A. Intelligent Mathematics: Computational Analysis. Springer Berlin Heidelberg, 2011.

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Hauta-Kasari, Markku. Computational techniques for spectral image analysis. Lappeenranta University of Technology, 1999.

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Book chapters on the topic "Computational intelligence. Image analysis"

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Huang, Po-Whei, Cheng-Hsiung Lee, and Phen-Lan Lin. "Classifying Pathological Prostate Images by Fractal Analysis." In Computational Intelligence in Image Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30621-1_13.

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Li, Wei, and Lin-Hua Jiang. "Fast Iris Segmentation by Rotation Average Analysis of Intensity-Inversed Image." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05253-8_38.

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Manviya, Monica, and Jyoti Bharti. "Image Fusion Survey: A Comprehensive and Detailed Analysis of Image Fusion Techniques." In Social Networking and Computational Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2071-6_53.

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Verma, Nishant, Matthew C. Cowperthwaite, Mark G. Burnett, and Mia K. Markey. "Image Analysis Techniques for the Quantification of Brain Tumors on MR Images." In Computational Intelligence in Biomedical Imaging. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7245-2_11.

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Hayashi, T., H. Chen, K. Miyamoto, X. Zhou, T. Hara, and H. Fujita. "Computer-Aided Image Analysis for Vertebral Anatomy on X-Ray CT Images." In Computational Intelligence in Biomedical Imaging. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7245-2_7.

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B.D., Parameshachari, and K. M. S. Soyjaudah. "Analysis and Comparison of Fully Layered Image Encryption Techniques and Partial Image Encryption Techniques." In Wireless Networks and Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31686-9_70.

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Aradhya, V. N. Manjunath, G. Hemantha Kumar, and S. Noushath. "Fisher Linear Discriminant Analysis and Connectionist Model for Efficient Image Recognition." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75398-8_12.

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Aggarwal, Sakshi, Navjot Singh, and K. K. Mishra. "Digital Image Analysis Is a Silver Bullet to COVID-19 Pandemic." In Studies in Computational Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8534-0_21.

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Tong, Hengjian, Man Zhao, and Xiang Li. "Applications of Computational Intelligence in Remote Sensing Image Analysis." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04962-0_20.

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Callejas Ramos, Alejandro I., Edgardo M. Felipe-Riveron, Pablo Manrique Ramirez, and Oleksiy Pogrebnyak. "Image Filter Based on Block Matching, Discrete Cosine Transform and Principal Component Analysis." In Advances in Computational Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62434-1_34.

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Conference papers on the topic "Computational intelligence. Image analysis"

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Alpana and Subrajeet Mohapatra. "Automated coal characterization using computational intelligence and image analysis techniques." In 2015 Communication, Control and Intelligent Systems (CCIS). IEEE, 2015. http://dx.doi.org/10.1109/ccintels.2015.7437903.

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Hiltner, Jens, Madjid Fathi, and Bernd Reusch. "New model for medical image analysis based on computational intelligence." In Medical Imaging 2000, edited by Kenneth M. Hanson. SPIE, 2000. http://dx.doi.org/10.1117/12.387638.

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Yang, Feng, Zheng Ma, and Mei Xie. "Image Parsing with Superpixels and Gray Image Analysis." In 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2018. http://dx.doi.org/10.1109/iscid.2018.00028.

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Caro Gutierrez, Jesus, Felix Fernando Gonzalez Navarro, Benjamin Valdez Salas, and Mario Alberto Curiel Alvarez. "Poster Paper: Image Analysis for Automatic Characterization of Nanomaterials." In 2015 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2015. http://dx.doi.org/10.1109/csci.2015.150.

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Kuvychko, Igor. "Analysis and coding technique based on computational intelligence methods and image-understanding architecture." In Visual Communications and Image Processing 2000, edited by King N. Ngan, Thomas Sikora, and Ming-Ting Sun. SPIE, 2000. http://dx.doi.org/10.1117/12.386592.

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Wang, Weixing, Wang Luo, and Jun Tang. "Particle Measurement by Image Processing and Analysis." In 2006 International Conference on Computational Intelligence and Security. IEEE, 2006. http://dx.doi.org/10.1109/iccias.2006.295388.

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"Session details: CIVIA - computational intelligence and video & image analysis track." In SAC 2017: Symposium on Applied Computing, edited by Agostinho Rosa and Yin-Fu Huang. ACM, 2017. http://dx.doi.org/10.1145/3243943.

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Silva, Wanessa da, Matheus Habermann, Elcio Hideiti Shiguemori, Leidiane do Livramento Andrade, and Ruy Morgado de Castro. "Multispectral Image Classification Using Multilayer Perceptron and Principal Components Analysis." In 2013 BRICS Congress on Computational Intelligence & 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC). IEEE, 2013. http://dx.doi.org/10.1109/brics-cci-cbic.2013.98.

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Matsuda, Nobuo, Hideaki Sato, and Fumiaki Tajima. "Image Analysis of Fundus Using Filter Bank and Subspace Classifier." In 2016 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2016. http://dx.doi.org/10.1109/csci.2016.0146.

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Deligiannidis, Leonidas. "An Image Analysis of Sky-Localization Maps of Gravitational-Wave Transients." In 2016 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2016. http://dx.doi.org/10.1109/csci.2016.0137.

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Reports on the topic "Computational intelligence. Image analysis"

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Keyvan, Shahla. Diagnostics and Control of Natural Gas-Fired furnaces via Flame Image Analysis using Machine Vision & Artificial Intelligence Techniques. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/862201.

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Chornodon, Myroslava. FEAUTURES OF GENDER IN MODERN MASS MEDIA. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.49.11064.

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The article clarifies of gender identity stereotypes in modern media. The main gender stereotypes covered in modern mass media are analyzed and refuted. The model of gender relations in the media is reflected mainly in the stereotypical images of men and woman. The features of the use of gender concepts in modern periodicals for women and men were determined. The most frequently used derivatives of these macroconcepts were identified and analyzed in detail. It has been found that publications for women and men are full of various gender concepts that are used in different contexts. Ingeneral,
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