Journal articles: 'Imagerie intelligente'

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Saigre-Tardif, Chloé, Rashid Faqiri, Hanting Zhao, Lianlin Li, and Philipp del Hougne. "Intelligent meta-imagers: From compressed to learned sensing." Applied Physics Reviews 9, no. 1 (March 2022): 011314. http://dx.doi.org/10.1063/5.0076022.

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Computational meta-imagers synergize metamaterial hardware with advanced signal processing approaches such as compressed sensing. Recent advances in artificial intelligence (AI) are gradually reshaping the landscape of meta-imaging. Most recent works use AI for data analysis, but some also use it to program the physical meta-hardware. The role of “intelligence” in the measurement process and its implications for critical metrics like latency are often not immediately clear. Here, we comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-imagers. We introduce a clear taxonomy in terms of the flow of task-relevant information that has direct links to information theory: compressive meta-imagers indiscriminately acquire all scene information in a task-agnostic measurement process that aims at a near-isometric embedding; intelligent meta-imagers highlight task-relevant information in a task-aware measurement process that is purposefully non-isometric. The measurement process of intelligent meta-imagers is, thus, simultaneously an analog wave processor that implements a first task-specific inference step “over-the-air.” We provide explicit design tutorials for the integration of programmable meta-atoms as trainable physical weights into an intelligent end-to-end sensing pipeline. This merging of the physical world of metamaterial engineering and the digital world of AI enables the remarkable latency gains of intelligent meta-imagers. We further outline emerging opportunities for cognitive meta-imagers with reverberation-enhanced resolution, and we point out how the meta-imaging community can reap recent advances in the vibrant field of metamaterial wave processors to reach the holy grail of low-energy ultra-fast all-analog intelligent meta-sensors.

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Sun, Roger, Eric Deutsch, and Laure Fournier. "Intelligence artificielle et imagerie médicale." Bulletin du Cancer 109, no. 1 (January 2022): 83–88. http://dx.doi.org/10.1016/j.bulcan.2021.09.009.

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Caicedo Caicedo, Julio César, and José Nelson Pérez Castillo. "An intelligent web service for classifying digital imagery by using rough sets." Ingeniería e Investigación 30, no. 1 (January 1, 2010): 45–51. http://dx.doi.org/10.15446/ing.investig.v30n1.15206.

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Integrating recent developments in service-orientated computing, Web technologies and computational intelligence has facilitated the development of applications for solving complex problems in several fields of scientific and technological research. Rough sets theory provides a solid theoretical background within the computational intelligence (CI) field for the qualitative reasoning required for analysing datasets loaded with uncertainties due to the vagueness and lack of precision associated with them. This paper describes the development of an intelligent Web service to process digital imagery, demonstrating the benefits of rough sets theory in dealing with the flexible supervised classification of the pixels associated with them.

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Kapilaratne, R. G. C. J., and S. Kakuta. "TOWARDS HIGH RESOLUTION FEATURE MAPPNG WITH SENTINEL-2 IMAGES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 137–44. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-137-2023.

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Abstract. High resolution feature mapping from medium resolution imageries gained special attention among remote sensing user community with the launch of Copernicus’ Sentinel-2 mission due to its capability to provide global coverage with relatively high revisit time at no cost. In this paper, we have examined and evaluated the potential of high resolution (2.5m) feature mapping from Sentinel-2 imageries with the aid of artificial intelligence. Generative adversarial network (GAN) is used as single image super resolution (SISR) technology in this study. And SPOT satellite imageries are used as corresponding high-resolution images. From qualitative and quantitative analysis of the experimental results found that spectral quality of the generated images is adequate for remote sensing applications. In conclusion, high resolution feature mapping from Sentinel-2 images found to be feasible to a greater extent for remote sensing applications.

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Foucart, Jean-Michel, Augustin Chavanne, and Jérôme Bourriau. "Intelligence artificielle : le futur de l’Orthodontie ?" Revue d'Orthopédie Dento-Faciale 53, no. 3 (September 2019): 281–94. http://dx.doi.org/10.1051/odf/2019026.

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Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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Makendran, C., M. Karthik, S. M. Jakir Hasan, M. Harivignesh, and G. Varun Raahul. "Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence." MATEC Web of Conferences 393 (2024): 02005. http://dx.doi.org/10.1051/matecconf/202439302005.

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This paper presents the development of an innovative pavement maintenance and management system leveraging advanced drone imagery and Convolutional Neural Network (CNN) image classification. Our system is designed to perform 2D modelling of road surfaces using high-resolution images captured by drones. These images are then analysed by a CNN model specifically trained to detect and classify pavement damages in accordance with the IRC:82 'Code of Practice for Maintenance of Bituminous Surfaces of Highways'. The classification process identifies various types of road distresses such as cracks, potholes, and surface wear. Each identified distress is documented in a comprehensive report detailing the nature of the damage and recommending specific remedies as per IRC guidelines. Furthermore, the system categorizes the severity of the damages, facilitating the dispatch of these results to maintenance authorities for immediate action. This ensures that repair efforts are prioritized effectively, contributing to the maintenance of safer and higher quality roadways. By automating the detection and classification of road damages, this system not only accelerates the repair process but also plays a crucial role in reducing road accidents by maintaining better road conditions. This approach showcases the potential of integrating artificial intelligence and drone technology in the field of road maintenance, marking a significant step towards smarter and safer road infrastructure.

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Wang, Hui, Hong Chang Ke, and Li Juan Zhang. "Calculus Method Research of Imagery Conceptual Network Based on Revision Calculus." Advanced Materials Research 317-319 (August 2011): 937–41. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.937.

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Through developing intelligent system of human-like thinking, national defense, economic, education, culture and so on will be impact. Constructing intelligence system of human-like intelligent mainly lies in how to effectively imitate the approach with which human use imagery to cognize. From psychological point of view, the process of cognitive is the transformation process of objects and attributes. Based on this thought, imagery of conceptual network is constructed based on imagery concept and attribute to store the overall planning information of imagery object conceptual. According to imagery conceptual network we build, calculus method imagery of conceptual network based on revision calculus is proposed to guide filling and revision of the nodes of imagery of conceptual network. According to the instance calculus given in the paper can be seen, the calculus method imagery conceptual network can fill and revise the nodes of imagery conceptual network, which can effectively imitate storing imagery and induction imagery of the human brain.

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Labazanova, Saida K., Timur G. Aygumov, and Marat Kh Mursaliev. "Issues with generative artificial intelligence tools." ITM Web of Conferences 59 (2024): 04007. http://dx.doi.org/10.1051/itmconf/20245904007.

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Technological progress thrives any industry with its correct implementation. Technological improvement further levels up implemented fields. Although, the results not always pleasing to the eye and pockets. The idea is that technology is and will have some issues, varying from mild to severe. In the era of refined technology, one shines out from the most, and it is artificial intelligence. Its capabilities are enormous and application thus vast. The innovation branched into the different tools, that it is hard for one to follow and use the new ones, that emerges almost daily. This article will be focusing on the generative tools that are capable of mimicking almost anything, starting from text and ending with long sequences of imageries. Above all on its issues. As an outcome of the last, the possible solutions will also be discussed.

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DAVIES, PHILIP H. J. "Imagery in the UK: Britain's troubled imagery intelligence architecture." Review of International Studies 35, no. 4 (October 2009): 957–69. http://dx.doi.org/10.1017/s0260210509990386.

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AbstractThis article examines the status, role and development of imagery intelligence in the UK government. It is argued that imagery intelligence occupies a subordinate and marginalised position compared to other forms of intelligence, chiefly from human sources and the interception of communications. The origins of that position are recounted, and the problems arising from internal struggles over control of imagery examined. It is concluded that the existing approach to imagery represents a serious problem and that a substantial restructuring and upgrading of imagery intelligence is essential if UK foreign policy decision-making is to be properly informed in the 21st Century.

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Kunda, Maithilee. "AI, visual imagery, and a case study on the challenges posed by human intelligence tests." Proceedings of the National Academy of Sciences 117, no. 47 (November 23, 2020): 29390–97. http://dx.doi.org/10.1073/pnas.1912335117.

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Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone’s mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven’s Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place.

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Blanc-Durand, P. "Instrumentation « CZT grand champ et intelligence artificielle en imagerie médicale »." Médecine Nucléaire 43, no. 5-6 (October 2019): 409–10. http://dx.doi.org/10.1016/j.mednuc.2019.07.009.

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Wang, Jiachen, Yun-Hsuan Chen, Jie Yang, and Mohamad Sawan. "Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern." Biosensors 12, no. 6 (June 2, 2022): 384. http://dx.doi.org/10.3390/bios12060384.

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To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.

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Li, Zhixian, Feng Zheng, Shihao Wang, and Zitong Zhao. "Research on the Intelligent Modeling Design of a Truck Front Face Driven by User Imagery." Applied Sciences 13, no. 20 (October 18, 2023): 11438. http://dx.doi.org/10.3390/app132011438.

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The design of the front face of a truck can directly affect the user’s sensory evaluation of the vehicle. Therefore, based on Kansei Engineering theory and deep learning technology, this paper proposes an intelligent design method for the rapid generation of truck front face modeling solutions driven by user images. First, through Kansei Engineering’s relevant experimental methods and scientific data analysis process, the emotional image of the truck’s front face is deeply excavated and positioned, and the corresponding relationship between the characteristics of the truck’s front face and the user’s emotional image cognition is explored. Then, we used the generative confrontation network to integrate the user’s emotional image of the front face of the truck into the intelligent and rapid generation process of the new design scheme of the front face of the truck. Finally, the physiological data of the Electroencephalogram (EEG) experiment are used to evaluate the degree of objective matching between the generated modeling design scheme and the expected image. The purpose of this research is to improve the efficiency, reliability, and intelligence level of truck front face design, and to achieve a more personalized, precise, and high-quality design. This helps to improve the conformity of the modeling design scheme under specific image semantics.

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Fareed, Nadeem. "Intelligent High Resolution Satellite/Aerial Imagery." Advances in Remote Sensing 03, no. 01 (2014): 1–9. http://dx.doi.org/10.4236/ars.2014.31001.

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Brunelle, F., and P. Brunelle. "Intelligence artificielle et imagerie médicale : définition, état des lieux et perspectives." Bulletin de l'Académie Nationale de Médecine 203, no. 8-9 (November 2019): 683–87. http://dx.doi.org/10.1016/j.banm.2019.06.016.

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Cremer, Stefan, and Claudia Loebbecke. "Artificial Intelligence Imagery Analysis Fostering Big Data Analytics." Future Internet 11, no. 8 (August 15, 2019): 178. http://dx.doi.org/10.3390/fi11080178.

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In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

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Qian, Chenqi, and Philipp del Hougne. "Noise-Adaptive Intelligent Programmable Meta-Imager." Intelligent Computing 2022 (December 2, 2022): 1–13. http://dx.doi.org/10.34133/2022/9825738.

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We present an intelligent programmable computational meta-imager that tailors its sequence of coherent scene illuminations not only to a specific information-extraction task (e.g., object recognition) but also adapts to different types and levels of noise. We systematically study how the learned illumination patterns depend on the noise, and we discover that trends in intensity and overlap of the learned illumination patterns can be understood intuitively. We conduct our analysis based on an analytical coupled-dipole forward model of a microwave dynamic metasurface antenna (DMA); we formulate a differentiable end-to-end information-flow pipeline comprising the programmable physical measurement process including noise as well as the subsequent digital processing layers. This pipeline allows us to jointly inverse-design the programmable physical weights (DMA configurations that determine the coherent scene illuminations) and the trainable digital weights. Our noise-adaptive intelligent meta-imager outperforms the conventional use of pseudo-random illumination patterns most clearly under conditions that make the extraction of sufficient task-relevant information challenging: latency constraints (limiting the number of allowed measurements) and strong noise. Programmable microwave meta-imagers in indoor surveillance and earth observation will be confronted with these conditions.

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Kusumaningrum, Evy, Dedy Hariyadi, and Sumarsono Sumarsono. "Kajian Geospatial Intelligence dan Imagery Intelligence Menggunakan Analisis Bibliometrika Berdasarkan Indexing Scopus." JURNAL GEOGRAFI Geografi dan Pengajarannya 20, no. 1 (August 3, 2022): 9–18. http://dx.doi.org/10.26740/jggp.v20n1.p9-18.

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Amerika Serikat mengkategorikan kajian intelijen berdasarkan pengumpulan informasi seperti Open Source Intelligence, Measurements and Signatures Intelligence, Human Intelligence, Signals Intelligence, Geospatial Intelligence, dan Imagery Intelligence. Geospatial Intelligence atau GEOINT memiliki keterkaitan dengan Imagery Intelligence atau IMINT dalam pengumpulan dan pengolahan informasi yang berupa citra dan pemetaan. Dengan menggunakan analisis bibliometrika dapat melakukan kajian terkait GEOINT dan IMINT seperti tren penelitian, analisi sub-bidang kajian, analisis peneliti, dan analisi jejaring kolaborasi antar negara. Sumber data untuk melakukan analisis ini menggunakan basis data indeks Scopus maka hasil analisisnya dapat bersifat global. Berdasarkan analisi bibliometrika tidak ditemukan kajian yang berasal dari Indonesia walaupun pengumpulan dan pengolahan informasi berbasis GEOINT dan IMINT sudah dilakukan oleh lembaga militer maupun non-militer. Negara yang paling mendominasi dalam penelitian GEOINT dan IMINT adalah Amerika Serikat.

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Bartoli, A., E. Quarello, I. Voznyuk, and G. Gorincour. "Intelligence artificielle et imagerie en médecine fœtale : de quoi parle-t-on ?" Gynécologie Obstétrique Fertilité & Sénologie 47, no. 11 (November 2019): 765–68. http://dx.doi.org/10.1016/j.gofs.2019.09.012.

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Yang, Yuan. "Visual Abstract Reasoning in Computational Imagery." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23431–32. http://dx.doi.org/10.1609/aaai.v38i21.30416.

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Despite current AI’s human-like behavior, super efficiency, and unbelievable ability to handle complex games, we still complain that it shows no sign of creativity, originality, or novelty outside its training set, and that it fails to develop new insights into old experience or establish understanding of new experience. In short, it generates content from its training set, but does not invent content. A fundamental reason for this is that current AI is incapable of abstraction and reasoning in an abstract, generalizable, and systematic way. Think, for instance, of what AI systems we can build if we have a base system that can answer this simple question—when two things are the same. Instead of studying these high-level questions, I put my thesis in the context of visual abstract reasoning (VAR), a task widely used in human intelligence tests. A classical example of this task is Raven’s Progressive Matrices (RPM, see Figure 1), a family of intelligence tests that was designed to measure eductive ability, i.e., the ability to make meaning out of confusion and generate high-level, usually nonverbal, schemata which make it easy to handle complexity. A similar concept to eductive ability is fluid intelligence, or the ability to discriminate and perceive complex relationships when no recourse to answers is stored in memory. Whether eductive ability or fluid intelligence, RPM points to the qualities that have been lacking in AI. To explore these qualities in AI, I propose the following research questions.

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Hosseiny, Benyamin, Heidar Rastiveis, and Saeid Homayouni. "An Automated Framework for Plant Detection Based on Deep Simulated Learning from Drone Imagery." Remote Sensing 12, no. 21 (October 27, 2020): 3521. http://dx.doi.org/10.3390/rs12213521.

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Traditional mapping and monitoring of agricultural fields are expensive, laborious, and may contain human errors. Technological advances in platforms and sensors, followed by artificial intelligence (AI) and deep learning (DL) breakthroughs in intelligent data processing, led to improving the remote sensing applications for precision agriculture (PA). Therefore, technological advances in platforms and sensors and intelligent data processing methods, such as machine learning and DL, and geospatial and remote sensing technologies, have improved the quality of agricultural land monitoring for PA needs. However, providing ground truth data for model training is a time-consuming and tedious task and may contain multiple human errors. This paper proposes an automated and fully unsupervised framework based on image processing and DL methods for plant detection in agricultural lands from very high-resolution drone remote sensing imagery. The proposed framework’s main idea is to automatically generate an unlimited amount of simulated training data from the input image. This capability is advantageous for DL methods and can solve their biggest drawback, i.e., requiring a considerable amount of training data. This framework’s core is based on the faster regional convolutional neural network (R-CNN) with the backbone of ResNet-101 for object detection. The proposed framework’s efficiency was evaluated by two different image sets from two cornfields, acquired by an RGB camera mounted on a drone. The results show that the proposed method leads to an average counting accuracy of 90.9%. Furthermore, based on the average Hausdorff distance (AHD), an average object detection localization error of 11 pixels was obtained. Additionally, by evaluating the object detection metrics, the resulting mean precision, recall, and F1 for plant detection were 0.868, 0.849, and 0.855, respectively, which seem to be promising for an unsupervised plant detection method.

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Kunda, Maithilee. "Visual mental imagery: A view from artificial intelligence." Cortex 105 (August 2018): 155–72. http://dx.doi.org/10.1016/j.cortex.2018.01.022.

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Wysocki, Krzysztof, and Martyna Niewińska. "Counteracting imagery (IMINT), optoelectronic (EOIMINT) and radar (SAR) intelligence." Scientific Journal of the Military University of Land Forces 204, no. 2 (June 15, 2022): 222–44. http://dx.doi.org/10.5604/01.3001.0015.8975.

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The development of military technique and technology forces necessary changes in military reconnaissance using advanced methods of contemporary battlefield imaging. This paper addresses the topic of imagery intelligence as an essential source for gaining information about the deployment and quantity of means and forces of a potential enemy. Currently, armies of the world are equipped with modern imagery intelligence systems that make it possible to collect, process and analyse the collected data on enemy’s troops and the environment in which the enemy operates. The purpose of the study is to present the proper role of camouflage undertakings that make it possible to counteract imagery, optoelectronic and radar intelligence. The increasing capabilities in this problem area mean that in the near future intelligence tasks will be carried out not only by ground, space or naval systems, but primarily by reconnaissance aircraft and unmanned aerial systems. In accordance with the problem indicated in the topic, the paper brings closer the possibilities of counteracting imagery intelligence from the theoretical and practical perspective. In addition, it presents the latest camouflage solutions employed both in the Polish Armed Forces and other selected armies. At the end of the paper, the authors formulate the most important conclusions that constitute a generalisation of the results of studies presented in different parts of the publication.

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Vannucci, Manila, Giuliana Mazzoni, Carlo Chiorri, and Lavinia Cioli. "Object imagery and object identification: object imagers are better at identifying spatially-filtered visual objects." Cognitive Processing 9, no. 2 (January 24, 2008): 137–43. http://dx.doi.org/10.1007/s10339-008-0203-5.

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Slezak, Peter. "ARTIFICIAL IMAGERY?" Computational Intelligence 9, no. 4 (April 2, 2007): 349–52. http://dx.doi.org/10.1111/j.1467-8640.1993.tb00230.x.

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Thagard, Paul. "REPRESENTING IMAGERY." Computational Intelligence 9, no. 4 (April 2, 2007): 360–61. http://dx.doi.org/10.1111/j.1467-8640.1993.tb00233.x.

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Miller, A. Eve, David L. Strayer, and Julie L. Marble. "Neurotechnology for Imagery Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 19 (October 2007): 1363–67. http://dx.doi.org/10.1177/154193120705101919.

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This research combined image alignment techniques with techniques for processing neurophysiological signals to find consistent classifiers of changed and unchanged aerial reconnaissance images. These physiological markers were independent of explicit responses, and in fact, can compensate for errors made by analysts. In addition, we found that measures of eye movements have significant potential for use in image triage. This research has applications not only to the Intelligence community but also in commercial applications, such as analysis of medical imagery, geological and environmental studies, and building construction and inspection.

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Burk, Roger C., Carolina Deschapelles, Karl Doty, Jonathan E. Gayek, and Thomas Gurlitz. "Performance Analysis in the Selection of Imagery Intelligence Satellites." Military Operations Research 7, no. 2 (March 1, 2002): 45–60. http://dx.doi.org/10.5711/morj.7.2.45.

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Diamond, John M. "Re-examining Problems and Prospects in U.S. Imagery Intelligence." International Journal of Intelligence and CounterIntelligence 14, no. 1 (January 2001): 1–24. http://dx.doi.org/10.1080/08850600150501308.

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Sloan, Sean, Raiyan R. Talkhani, Tao Huang, Jayden Engert, and William F. Laurance. "Mapping Remote Roads Using Artificial Intelligence and Satellite Imagery." Remote Sensing 16, no. 5 (February 28, 2024): 839. http://dx.doi.org/10.3390/rs16050839.

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Road building has long been under-mapped globally, arguably more than any other human activity threatening environmental integrity. Millions of kilometers of unmapped roads have challenged environmental governance and conservation in remote frontiers. Prior attempts to map roads at large scales have proven inefficient, incomplete, and unamenable to continuous road monitoring. Recent developments in automated road detection using artificial intelligence have been promising but have neglected the relatively irregular, sparse, rustic roadways characteristic of remote semi-natural areas. In response, we tested the accuracy of automated approaches to large-scale road mapping across remote rural and semi-forested areas of equatorial Asia-Pacific. Three machine learning models based on convolutional neural networks (UNet and two ResNet variants) were trained on road data derived from visual interpretations of freely available high-resolution satellite imagery. The models mapped roads with appreciable accuracies, with F1 scores of 72–81% and intersection over union scores of 43–58%. These results, as well as the purposeful simplicity and availability of our input data, support the possibility of concerted program of exhaustive, automated road mapping and monitoring across large, remote, tropical areas threatened by human encroachment.

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Majidi, S., M. Omidalizarandi, and M. A. Sharifi. "INTELLIGENT 3D CRACK RECONSTRUCTION USING CLOSE RANGE PHOTOGRAMMETRY IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 14, 2023): 443–50. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-443-2023.

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Abstract. Civil infrastructure Structural Health Monitoring (SHM) and its preservation from deterioration is a crucial task. In general, natural disasters like severe earthquakes, extreme landslides, subsidence or intensive floods directly influence the health of civil structures such as buildings, bridges, roads, and dams. Evaluation and inspection of defects and damages of the aforementioned structures help to preserve them from destruction by accelerating rehabilitation and reconstruction. An automatic and precise crack detection framework is required for periodic assessment and inspection due to the large number of the structures. In this study, a two-step crack segmentation and its 3D reconstruction procedure is proposed. The crack segmentation is carried out by using Deeplabv3+ architecture and Xception as the backbone. Next, Squeeze-and-Excitation is added as an attention module to achieve higher accuracy. Integration of predicted masks and original images into a structure-from-motion procedure is additionally taken into account. In the last step, ground control points and scale bars are considered to overcome the problem of datum rank deficiency in absolute orientation through the bundle adjustment procedure in aerial triangulation. The most probable segmented cracks are overlaid on the 3D point clouds in the global coordinate system with true scales. Our network is trained based on 8000 images and their corresponding masks, leading to 69% in Intersection over Union (IoU) index. Sub-millimetre accuracy of crack reconstruction using the proposed methodology is validated with a scale bar.

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Komaraasih, Risa Intan, Imas Sukaesih Sitanggang, Annisa Annisa, and Muhammad Asyhar Agmalaro. "Sentinel-1A image classification for identification of garlic plants using decision tree and convolutional neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (December 1, 2022): 1323. http://dx.doi.org/10.11591/ijai.v11.i4.pp1323-1332.

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The Indonesian government launched a garlic self-sufficiency program by 2033 to reduce imports by monitoring garlic lands in several central garlic areas. Remote sensing using satellite imageries can assist the monitoring program by mapping the garlic lands. A previous study has classified Sentinel-1A satellite imageries to identify garlic lands in Sembalun Lombok Indonesia using the decision tree C5.0 algorithm with three scenarios data input and produced a model with an accuracy of 78.45% using scenarios with two attributes vertical-vertical (VV) and vertical-horizontal (VH) bands. Therefore, this study aims to improve the accuracy of the classification model from the previous study. This study applied two classification algorithms, decision tree C5.0 and convolutional neural network (CNN), with two new scenarios which used two new combinations of attributes). The results show that the use of new data scenarios as input for C5.0 can not increase the previous model's accuracy. While the use of the CNN algorithm shows that it can improve the previous study's accuracy by 7.91% because it produced a model with an accuracy of 86.36%. This study is expected to help garlic land identification in the Sembalun area to support government programs in monitoring garlic lands.

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Vannucci, Manila, Lavinia Cioli, Carlo Chiorri, Amanda Grazi, and Maria Kozhevnikov. "Individual differences in visuo-spatial imagery: further evidence for the distinction between object and spatial imagers." Cognitive Processing 7, S1 (August 8, 2006): 144–45. http://dx.doi.org/10.1007/s10339-006-0108-0.

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Nasim, S. F., S. Fatimah, and A. Amin. "ARTIFICIAL INTELLIGENCE IN MOTOR IMAGERY-BASED BCI SYSTEMS: A NARRATIVE." Asian Journal Of Medical Technology 2, no. 2 (August 5, 2022): 55–64. http://dx.doi.org/10.32896/ajmedtech.v2n2.55-64.

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Artificial intelligence concepts using machine learning models are implemented in medicines to examine medical data and gain insights to improve decision-making. This paper provides a narrative review of “Motor Imagery based brain-computer interface systems”. The essential techniques of machine learning and deep learning are reviewed and compared based on computation and test data accuracy. Various preprocessing and feature extraction techniques are highlighted in this paper, which include FFT-LDA, Wavelet Packet Decomposition (WPD), CSP Algorithm, Fisher ratio algorithm, Discrete Wavelet Transform, and Filter Bank Common Spatial Pattern (FBCSP). This method collects outcomes with multiple perspectives of the MI-BCI and optimizes it. Necessary details of Algorithms applied are also compared to give an insight into Ml techniques.

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ZHANG, Fan, and Yu LIU. "Street view imagery： Methods and applications based on artificial intelligence." National Remote Sensing Bulletin 25, no. 5 (2021): 1043–54. http://dx.doi.org/10.11834/jrs.20219341.

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A, Atijosan, Badru R, Babalogbon A, and Alaga T. "Classification of Medium Resolution Satellite Imageries using Artificial Neural Network and Swarm Intelligence." International Journal of Hybrid Information Technology 9, no. 11 (November 30, 2016): 215–28. http://dx.doi.org/10.14257/ijhit.2016.9.11.19.

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Shaw, Geraldine A. "The Use of Imagery by Intelligent and by Creative Schoolchildren." Journal of General Psychology 112, no. 2 (April 1985): 153–71. http://dx.doi.org/10.1080/00221309.1985.9711000.

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Pinheiro, O. R., L. R. G. Alves, and J. R. D. Souza. "EEG Signals Classification: Motor Imagery for Driving an Intelligent Wheelchair." IEEE Latin America Transactions 16, no. 1 (January 2018): 254–59. http://dx.doi.org/10.1109/tla.2018.8291481.

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Timilsina, Shirisa, Jagannath Aryal, and Jamie B. Kirkpatrick. "Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)." Remote Sensing 12, no. 18 (September 16, 2020): 3017. http://dx.doi.org/10.3390/rs12183017.

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Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.

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Argyrou, Argyro, Athos Agapiou, Apostolos Papakonstantinou, and Dimitrios D. Alexakis. "Comparison of Machine Learning Pixel-Based Classifiers for Detecting Archaeological Ceramics." Drones 7, no. 9 (September 13, 2023): 578. http://dx.doi.org/10.3390/drones7090578.

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Recent improvements in low-altitude remote sensors and image processing analysis can be utilised to support archaeological research. Over the last decade, the increased use of remote sensing sensors and their products for archaeological science and cultural heritage studies has been reported in the literature. Therefore, different spatial and spectral analysis datasets have been applied to recognise archaeological remains or map environmental changes over time. Recently, more thorough object detection approaches have been adopted by researchers for the automated detection of surface ceramics. In this study, we applied several supervised machine learning classifiers using red-green-blue (RGB) and multispectral high-resolution drone imageries over a simulated archaeological area to evaluate their performance towards semi-automatic surface ceramic detection. The overall results indicated that low-altitude remote sensing sensors and advanced image processing techniques can be innovative in archaeological research. Nevertheless, the study results also pointed out existing research limitations in the detection of surface ceramics, which affect the detection accuracy. The development of a novel, robust methodology aimed to address the “accuracy paradox” of imbalanced data samples for optimising archaeological surface ceramic detection. At the same time, this study attempted to fill a gap in the literature by blending AI methodologies for non-uniformly distributed classes. Indeed, detecting surface ceramics using RGB or multi-spectral drone imageries should be reconsidered as an ‘imbalanced data distribution’ problem. To address this paradox, novel approaches need to be developed.

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Asif, Qurat Ul Ain. "Cancer Pharmacology Advances: Fundamentals, Combination, Imagery, Shipment and Therapeutic Applications." Indian Journal of Pure & Applied Biosciences 11, no. 6 (December 30, 2023): 1–9. http://dx.doi.org/10.18782/2582-2845.9030.

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Cancer continues to be a major health issue worldwide, necessitating creative solutions to medication production and therapy techniques. It delves into the concepts of cancer drug activity, mixtures of drugs, imaging of molecules, delivery of medications, the interaction between medications, clinical utility and use, and the place of cancer medications across the procedure for discovering and developing drugs. Cancer therapy assumptions serve as the cornerstone for treatment methods. This understanding contributes directly to the intelligent planning of focused medicines, which aim to deliberately interrupt these channels while minimizing outside-target consequences and adverse reactions. Genome and transcriptomic studies have supplied a lot of understanding to aid in inventing personalized treatment techniques in cancer treatment. Cancer pharmaco has been transformed by molecular imaging tools that provide immediate visualization of medication action in the tumour ecosystem. Investigators can monitor medication transportation, pharmaceutical kinetics, and therapeutic using PET and MRI, as well as other approaches, supporting personalized regimens for therapy. Cancer medication means of distribution have emerged in order to increase drug absorption, decrease poisoning, or enhance directed drug administration. Creative ideas for drug delivery techniques that have shown interest in clinical studies include micron-sized particles of liposomes and antibody-drug conjugates. In cancer survivors, extensive toxicological research and computer modelling are critical in discovering likely interactions and guiding choosing therapy and administration. Cancer pharmacology's clinical significance and applications span from test to bed.

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Kong, Wanyue, Teng Zhong, Xin Mai, Shuliang Zhang, Min Chen, and Guonian Lv. "Automatic Detection and Assessment of Pavement Marking Defects with Street View Imagery at the City Scale." Remote Sensing 14, no. 16 (August 18, 2022): 4037. http://dx.doi.org/10.3390/rs14164037.

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Pavement markings could wear out before their expected service life expires, causing traffic safety hazards. However, assessing pavement-marking conditions at the city scale was a great challenge in previous studies. In this article, we advance the method of detecting and evaluating pavement-marking defects at the city scale with Baidu Street View (BSV) images, using a case study in Nanjing. Specifically, we employ inverse perspective mapping (IPM) and a deep learning-based approach to pavement-marking extraction to make efficient use of street-view imageries. In addition, we propose an evaluation system to assess three types of pavement-marking defects, with quantitative and qualitative results provided for each image. Factors causing pavement-marking defects are discussed by mapping the spatial distribution of pavement-marking defects at the city scale. Our proposed methods are conducive to pavement-marking repair operations. Beyond this, this article can contribute to smart urbanism development by creating a new road maintenance solution and ensuring the large-scale realization of intelligent decision-making in urban infrastructure management.

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Kothari, Arati, and Dr B. Indira. "A Unique Six Sigma Based Segmentation Technique for Brain Tumor Detection and Classification using Hybrid CNN-SVM Model." International Journal of Recent Technology and Engineering (IJRTE) 8, no. 2 (July 30, 2019): 35–40. http://dx.doi.org/10.35940/ijrte.a1239.078219.

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An intelligent organizing scheme to detect and classify normal, abnormal MRI brain sequences has been illustrated here. At present, handling of brain tumors disease and decision is based on radiological appearance and its symptoms. Magnetic-Resonance-Imaging (MRI) is a powerful substantial precise instrument for functional conclusion of brain tumorous. In existing study, broad range of methods is used for brain cancer detection and classification. Under this methods viz., image pre-processing, enhancement, segmentation, feature mining and resulting classification is efficiently conducted. Furthermore, when various machine learning algorithms like: Six Sigma, Convolutional Neural Network (CNN), Support Vector Machine (SVM), are employed to detect and extract the tumor region and classify numerous sequence of imageries, it is witnessed from our results that this Hybrid CNN-SVM model gives maximum classification accuracy rate of 99.33% compared to previous models. The foremost aim of this research is to get an effective result for detecting type of brain tumor using six sigma based segmentation technique, and to achieve efficient classification rate, using hybrid CNN-SVM model.

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Song, Haoyang. "Street view imagery: AI-based analysis method and application." Applied and Computational Engineering 40, no. 1 (February 21, 2024): 54–62. http://dx.doi.org/10.54254/2755-2721/40/20230627.

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Street view imagery is an emerging form of geographic big data. It presents urban visual environments from the perspective of urban residents and also contains non-visual environment of cities, such as urban human activities and socio-economic development. However, traditional digital image processing has its limitations, and the continuous development of artificial intelligence, especially computer vision and deep learning, provides strong technical support for exploring the rich semantic information in street view imagery. This paper reviews the related research on street view imagery and its artificial intelligence analysis methods and applications. It outlines the acquisition, storage, and common data sources of street view imagery. Then it introduces computer vision, deep learning, and commonly used open-source datasets in street view imagery analysis. It also detailed three aspects of AI-based street view imagery applications, namely quantification of the physical space, urban perception, and spatial semantic speculation. Finally, issues like data acquisition, domain adaption and deep learning black box are discussed. The hotspots and prospects for the development of this research topic are also prospected.

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Ułanowicz, Leszek, and Ryszard Sabak. "Unmanned aerial vehicles supporting imagery intelligence using the structured light technology." Archives of Transport 58, no. 2 (June 30, 2021): 35–45. http://dx.doi.org/10.5604/01.3001.0014.8796.

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One of the possible tasks for unmanned aerial vehicles (UAVs) is field capturing of object images. The field capturing of object images (scenes) is possible owing to the UAV equipped with photographic cameras, TV cameras, infrared camer-as or synthetic aperture radars (SAR). The result of the recognition is a metric mapping of space, i.e. 2D flat images. In order to increase the quality of image recognition, it is necessary to search for and develop stereoscopic visualization with the possibility of its mobile use. A pioneering approach presented in the research paper is using a UAV with an imagery intelligence system based on structured light technology for air reconnaissance of object over a selected area or in a given direction in the field. The outcome of imagery intelligence is a three-dimensional (3D imaging) information on the geometry of an observed scene. The visualization with a stereoscopic interface proposed in the work allows for a natural perception of the depth of the scene and mutual spatial relationships, as well as seeing which objects are closer and which are further. The essence of the article is to present the application of three-dimensional vision measurement technology on UAVs. The paper presents an analysis of the possibilities of using UAVs for image recognition and a method of image recognition based on the technology of structural lighting using the method of projection of Gray’a fringes and codes. The designed image recognition system based on the structural lighting technology is described. It also discusses task modules forming a measuring head, i.e., projection, detection and calculation modules, and the exchange of control or measurement data between imaging system components. It presents the results of tests on the possibility of rapidly acquiring images using a UAV. The test results and the analyses indicate that using a UAV with an imaging technology based on structural light can contribute to improving the abilities to detect, identify, locate and monitor objects at close range, within a selected direction outdoors or indoors.

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Ghosh, Rajdeep, Vikas Kumar, Nidul Sinha, and Saroj Kumar Biswas. "Motor imagery task classification using intelligent algorithm with prominent trial selection." Journal of Intelligent & Fuzzy Systems 35, no. 2 (August 26, 2018): 1501–10. http://dx.doi.org/10.3233/jifs-169690.

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Arun, P. V., and S. K. Katiyar. "Intelligent adaptive resampling technique for the processing of remotely sensed imagery." Annals of GIS 20, no. 1 (January 2, 2014): 53–60. http://dx.doi.org/10.1080/19475683.2013.862298.

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Khare , Smith K., Nikhil Gaikwad , and Neeraj Dhanraj Bokde . "An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets." Sensors 22, no. 21 (October 24, 2022): 8128. http://dx.doi.org/10.3390/s22218128.

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Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.

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Hikuwai, Mia, Nicholas Patorniti, Abel Vieira, Georgia Frangioudakis Khatib, and Rodney Stewart. "Artificial Intelligence for the Detection of Asbestos Cement Roofing: An Investigation of Multi-Spectral Satellite Imagery and High-Resolution Aerial Imagery." Sustainability 15, no. 5 (February 27, 2023): 4276. http://dx.doi.org/10.3390/su15054276.

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Artificial Intelligence (AI) is providing the technology for large-scale, cost-effective and current asbestos-containing material (ACM) roofing detection. AI models can provide additional data to monitor, manage and plan for ACM in situ and its safe removal and disposal, compared with traditional approaches alone. Advances are being made in AI algorithms and imagery applied to ACM detection. This study applies mask region-based convolution neural networks (Mask R-CNN) to multi-spectral satellite imagery (MSSI) and high-resolution aerial imagery (HRAI) to detect the presence of ACM roofing on residential buildings across an Australian case study area. The results provide insights into the challenges and benefits of using AI and different imageries for ACM detection, providing future directions for its practical application. The study found model 1, using HRAI and 460 training samples, was the more reliable model of the three with a precision of 94%. These findings confirm the efficacy of combining advanced AI techniques and remote sensing imagery, specifically Mask R-CNN with HRAI, for ACM roofing detection. Such combinations can provide efficient methods for the large-scale detection of ACM roofing, improving the coverage and currency of data for the implementation of coordinated management policies for ACM in the built environment.

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Glasgow, Janice, and Dimitri Papadias. "Computational Imagery." Cognitive Science 16, no. 3 (July 1992): 355–94. http://dx.doi.org/10.1207/s15516709cog1603_2.

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Journal articles on the topic 'Imagerie intelligente'

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