Готові списки джерел за темами / Intelligent imaging

Добірка наукової літератури з теми "Intelligent imaging"

Автор: Grafiati
Опубліковано: 7 липня 2024

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Intelligent imaging".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Статті в журналах з теми "Intelligent imaging":

1

Currie, Geoffrey M. "Intelligent Imaging: Artificial Intelligence Augmented Nuclear Medicine." Journal of Nuclear Medicine Technology 47, no. 3 (August 10, 2019): 217–22. http://dx.doi.org/10.2967/jnmt.119.232462.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Li, Xiang, and Donggang He. "Image Processing and Recognition Algorithm Design in Intelligent Imaging Device System." Security and Communication Networks 2022 (May 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/9669903.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system’s endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage.
3

Fyfe, Ian. "Intelligent, mobile stroke imaging." Nature Reviews Neurology 18, no. 2 (December 21, 2021): 66. http://dx.doi.org/10.1038/s41582-021-00614-5.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Griffiths, J., G. Royle, C. Esbrand, G. Hall, R. Turchetta, and R. Speller. "I-ImaS: intelligent imaging sensors." Journal of Instrumentation 5, no. 08 (August 3, 2010): C08001. http://dx.doi.org/10.1088/1748-0221/5/08/c08001.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Kotera, Hiroaki. "Overview Paper: Intelligent color imaging." Journal of the Society for Information Display 14, no. 9 (2006): 745. http://dx.doi.org/10.1889/1.2358568.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Ying, Kui, Xinyu Yu, Jiana Shen, Shilu Zhang, and Yuanyue Guo. "Intelligent Microwave Staring Correlated Imaging." Progress In Electromagnetics Research 176 (2023): 109–28. http://dx.doi.org/10.2528/pier22091907.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Alexander, Alan, Adam Jiang, Cara Ferreira, and Delphine Zurkiya. "An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging." Journal of the American College of Radiology 17, no. 1 (January 2020): 165–70. http://dx.doi.org/10.1016/j.jacr.2019.07.019.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

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.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
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.
9

Wang, Qianqian. "Imaging-Guided Micromachines: Towards Intelligent Systems." Micromachines 13, no. 11 (November 18, 2022): 2016. http://dx.doi.org/10.3390/mi13112016.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Micromachines with controllable motion, deformation, and collective behaviors provide advanced methods for performing tasks that traditional machines have difficulty completing thanks to the development of small-scale robotics, nanotechnology, biocompatible materials, and imaging techniques [...]
10

Ren, Hongfei. "Optimization of Lung CT Image Processing and Recognition Based on E-SRG Segmentation Algorithm." BIO Web of Conferences 59 (2023): 03002. http://dx.doi.org/10.1051/bioconf/20235903002.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Intelligent algorithms such as deep learning and parallel processing technologies such as mobile clouds are constantly evolving, heralding a new era of intelligence. In the new historical period, the development of intelligent medicine is facing great challenges and opportunities. In traditional medicine, medical imaging includes medical imaging and pathological imaging, which is an important reference for doctors in disease diagnosis. Image processing and recognition, as one of the key technologies of computer vision, must be improved under the premise of meeting the needs in practical applications. Therefore, according to the unique pathological characteristics of medical images, combined with the real-time and accuracy of images, the auxiliary diagnosis of images is the need of the development of intelligent medicine. The preprocessing technique and E-SRG algorithm used in this paper can improve the quality of images without being limited by the size of the dataset, and realize the complete segmentation of organs and tissues.
Більше джерел
Також вас можуть зацікавити розширені добірки на тему "Intelligent imaging" для конкретних типів джерел:
Статті в журналах Дисертації Книги

Дисертації з теми "Intelligent imaging":

1

Москаленко, Альона Сергіївна, Алена Сергеевна Москаленко, and Alona Serhiivna Moskalenko. "Intelligent decision support system for renal radionuclide imaging." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46806.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Radionuclide imaging of kidneys has a special place in nuclear medicine. It allows to register functional changes, far earlier than the structural and anatomical changes. Therefore, it is indispensable at early diagnosis. The reliability of data interpretation of renal scintigraphy studies depends on the level of doctor-diagnostician’s professional qualification and on the presence of their practical experience.
2

Fukuda, Toshio, Naoyuki Kubota, Baiqing Sun, Fei Chen, Tomoya Fukukawa, and Hironobu Sasaki. "ACTIVE SENSING FOR INTELLIGENT ROBOT VISION WITH RANGE IMAGING SENSOR." IEEE, 2010. http://hdl.handle.net/2237/14442.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Amza, Catalin Gheorghe. "Intelligent X-ray imaging inspection system for the food industry." Thesis, De Montfort University, 2002. http://hdl.handle.net/2086/10731.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine.
4

Dong, Leng. "Intelligent computing applications to assist perceptual training in medical imaging." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/22333.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
The research presented in this thesis represents a body of work which addresses issues in medical imaging, primarily as it applies to breast cancer screening and laparoscopic surgery. The concern here is how computer based methods can aid medical practitioners in these tasks. Thus, research is presented which develops both new techniques of analysing radiologists performance data and also new approaches of examining surgeons visual behaviour when they are undertaking laparoscopic training. Initially a new chest X-Ray self-assessment application is described which has been developed to assess and improve radiologists performance in detecting lung cancer. Then, in breast cancer screening, a method of identifying potential poor performance outliers at an early stage in a national self-assessment scheme is demonstrated. Additionally, a method is presented to optimize whether a radiologist, in using this scheme, has correctly localised and identified an abnormality or made an error. One issue in appropriately measuring radiological performance in breast screening is that both the size of clinical monitors used and the difficulty in linking the medical image to the observer s line of sight hinders suitable eye tracking. Consequently, a new method is presented which links these two items. Laparoscopic surgeons have similar issues to radiologists in interpreting a medical display but with the added complications of hand-eye co-ordination. Work is presented which examines whether visual search feedback of surgeons operations can be useful training aids.
5

Sasaki, Hironobu, Toshio Fukuda, Masashi Satomi, and Naoyuki Kubota. "Growing neural gas for intelligent robot vision with range imaging camera." IEEE, 2009. http://hdl.handle.net/2237/13913.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Scott-Jackson, William. "Marker-less respiratory gating for PET imaging with intelligent gate optimisation." Thesis, University of Surrey, 2018. http://epubs.surrey.ac.uk/849418/.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
PET image degradation imposed by patient respiratory motion is a well-established problem in clinical oncology; strategies exist to study and correct this. Some attempt to minimise or arrest patient motion through restraining hardware; their effectiveness is subject to the comfort and compliance. Another practice is to gate PET data based on signals acquired from an external device. This thesis presents several contributions to the field of respiratory motion correction research in PET imaging. First and foremost, this thesis presents a framework which allows a researcher to process list mode data from a Siemens Biograph mCT scanner and reconstruct sinograms of which in the open source image reconstruction package STIR. Secondly, it demonstrates the viability of a depth camera for respiratory monitoring and gating in a clinical environment. It was demonstrated that it was an effective device to capture anterior surface motion. Similarly, it has been shown that it can be used to perform respiratory gating. The third contribution is the design, implementation and validation of a novel respiring phantom. It has individually programmable degrees of freedom and was able to reproduce realistic respiration motion derived from real volunteers. The final contribution is a new gating algorithm which optimises the number and width of gates based on respiratory motion data and the distribution of radioactive counts. This new gating algorithm iterates on amplitude based gating, where gates as positioned based on respiratory pose at a given instant. The key improvement is that it considers the distribution of counts as a consequence of the distribution of motion in a typical PET study. The results show that different studies can be optimised with a unique number of gates based on the maximum extent of motion present and can take into account shifts in baseline position due to patient perturbation.
7

Fukuda, Toshio, Baiqing Sun, Fei Chen, Tomoya Fukukawa, and Hironobu Sasaki. "Active Sensing and Information Structuring for Intelligent Robot Vision with Range Imaging Sensor." IEEE, 2010. http://hdl.handle.net/2237/14441.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Sharif, Mhd Saeed. "An artificial intelligent system for oncological volumetric medical PET classification." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/13095.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Positron emission tomography (PET) imaging is an emerging medical imaging modality. Due to its high sensitivity and ability to model physiological function, it is effective in identifying active regions that may be associated with different types of tumour. Increasing numbers of patient scans have led to an urgent need for the development of new efficient data analysis system to aid clinicians in the diagnosis of disease and save decent amount of the processing time, as well as the automatic detection of small lesions. In this research, an automated intelligent system for oncological PET volume analysis has been developed. Experimental NEMA (national electrical manufacturers association) IEC (International Electrotechnical Commission) body phantom data set, Zubal anthropomorphic phantom data set with simulated tumours, clinical data set from patient with histologically proven non-small cell lung cancer, and clinical data sets from seven patients with laryngeal squamous cell carcinoma have been utilised in this research. The initial stage of the developed system involves different thresholding approaches, and transforming the processed volumes into the wavelet domain at different levels of decomposition by deploying Haar wavelet transform. K-means approach is also deployed to classify the processed volume into a distinct number of classes. The optimal number of classes for each processed data set has been obtained automatically based on Bayesian information criterion. The second stage of the system involves artificial intelligence approaches including feedforward neural network, adaptive neuro-fuzzy inference system, self organising map, and fuzzy C-means. The best neural network design for PET application has been thoroughly investigated. All the proposed classifiers have been evaluated and tested on the experimental, simulated and clinical data sets. The final stage of the developed system includes the development of new optimised committee machine for PET application and tumour classification. Objective and subjective evaluations have been carried out for all the systems outputs, they show promising results for classifying patient lesions. The new approach results have been compared with all of the results obtained from the investigated classifiers and the developed committee machines. Superior results have been achieved using the new approach. An accuracy of 99.95% is achieved for clinical data set of patient with histologically proven lung tumour, and an average accuracy of 98.11% is achieved for clinical data set of seven patients with laryngeal tumour.
9

關福延 and Folk-year Kwan. "An intelligent approach to automatic medical model reconstruction fromserial planar CT images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243216.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Yang, Kun. "An Intelligent Analysis Framework for Clinical-Translational MRI Research." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592254585828664.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Більше джерел

Книги з теми "Intelligent imaging":

1

Dede, Christopher. Designing a training tool for imaging mental models. [Houston, Tex.?]: Research Institute for Computing and Information Systems, University of Houston, Clear Lake, 1990.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Loce, Robert P., Raja Bala, and Mohan Trivedi, eds. Computer Vision and Imaging in Intelligent Transportation Systems. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118971666.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Xu, Yangsheng. Intelligent wearable interfaces. Hoboken, N.J: John Wiley, 2008.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Kwaśnicka, Halina. Innovations in Intelligent Image Analysis. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Mann, Steve. Intelligent image processing. New York: IEEE, 2002.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Amza, Catalin Gheorghe. Intelligent X-ray imaging inspection system for the food industry. Leicester: De Montfort University, 2002.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Fehlman, William L. Mobile robot navigation with intelligent infrared image interpretation. Dordrecht: Springer, 2009.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

A, Velastin Sergio, Remagnino Paolo 1963-, and Institution of Electrical Engineers, eds. Intelligent distributed video surveillance systems. London: Institution of Electrical Engineers, 2006.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Santhi, V., D. P. Acharjya, and M. Ezhilarasan. Emerging technologies in intelligent applications for image and video processing. Hershey PA: Information Science Reference, 2016.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Tian, Jing, and Li Chen. Intelligent image and video interpretation: Algorithms and applications. Hershey, PA: Information Science Reference, 2013.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Більше джерел

Частини книг з теми "Intelligent imaging":

1

Dymetman, Marc, and Max Copperman. "Intelligent paper." In Electronic Publishing, Artistic Imaging, and Digital Typography, 392–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053286.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Qian, Jian-Zhong. "Intelligent Diagnostic Imaging and Analysis." In Frontiers in Biomedical Engineering, 315–25. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-8967-3_20.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Fidler, Valentin, Milan Prepadnik, Peter Rakovec, Jurij Fettich, Sergej Hojker, Ciril Grošelj, and Miran Porenta. "Intelligent Triggering of Multigated Studies." In Information Processing in Medical Imaging, 531–38. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7263-3_35.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Song, Zhengxi, Yanning Zhang, Tao Yang, and Xiaoqiang Zhang. "High-Quality Synthetic Aperture Auto-imaging under Occlusion." In Intelligent Science and Intelligent Data Engineering, 407–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36669-7_50.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Bleck, J. S., M. Gebel, R. H. Hebel, S. Wagner, K. J. Schmitt, S. T. Kruip, M. Westhoff-Bleck, and M. Wolf. "Intelligent Adaptive Filter in the Diagnosis of Diffuse and Focal Liver Disease." In Acoustical Imaging, 375–80. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3370-2_59.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Shah, Devansh, Purav Nisar, and Pankaj Sonawane. "Chest Pathology Detection Using Medical Imaging." In Algorithms for Intelligent Systems, 177–86. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_18.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Saraf, Vaibhav, Pallavi Chavan, and Ashish Jadhav. "Deep Learning Challenges in Medical Imaging." In Algorithms for Intelligent Systems, 293–301. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_28.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Jat, Dharm Singh, and Chucknorris Garikayi Madamombe. "Deep Learning Techniques to Classify and Analyze Medical Imaging Data." In Intelligent Systems, 57–70. Includes bibliographical references and index.: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429265020-4.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Peng, Faxiang, Hongwei Li, Bin Cai, Donghu Deng, and Ying Liang. "Study on Transmitting Mode and Imaging Algorithm of MIMO-SAR." In Intelligent Science and Intelligent Data Engineering, 745–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31919-8_95.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Qian, Yu, and Tao Hong. "2D ISAR Imaging Using SFFT." In Advances in Intelligent Systems and Computing, 643–50. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8944-2_74.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "Intelligent imaging":

1

Denyer, Peter B., David Renshaw, and Stewart Smith. "Intelligent CMOS imaging." In IS&T/SPIE's Symposium on Electronic Imaging: Science & Technology, edited by Morley M. Blouke. SPIE, 1995. http://dx.doi.org/10.1117/12.206525.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Theisen, Bernard L. "The 16th annual intelligent ground vehicle competition: intelligent students creating intelligent vehicles." In IS&T/SPIE Electronic Imaging, edited by David P. Casasent, Ernest L. Hall, and Juha Röning. SPIE, 2009. http://dx.doi.org/10.1117/12.805883.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Theisen, Bernard L. "The 17th Annual Intelligent Ground Vehicle Competition: intelligent robots built by intelligent students." In IS&T/SPIE Electronic Imaging, edited by David P. Casasent, Ernest L. Hall, and Juha Röning. SPIE, 2010. http://dx.doi.org/10.1117/12.846780.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Shen, Hong, and Ernst Bartsch. "Intelligent data splitting for volume data." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.653997.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Garcia, Hong-Mei C., and David Y. Yun. "Intelligent distributed medical image management." In Medical Imaging 1995, edited by R. Gilbert Jost and Samuel J. Dwyer III. SPIE, 1995. http://dx.doi.org/10.1117/12.208762.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Saffih, Faycal. "Artificially-Intelligent Imaging (AI2) sensors: How intelligent CMOS imaging devices can benefit Photovoltaics?" In 2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO). IEEE, 2017. http://dx.doi.org/10.1109/icmsao.2017.7934899.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

McLeod, Alastair, Tord Braband, Steinar Smastuen, Roy Nicklasson, Arne Sommerfelt, Bjorn Lillekjendlie, and Eivind Strom. "Applications of intelligent cameras." In Electronic Imaging Device Engineering, edited by Donald W. Braggins. SPIE, 1993. http://dx.doi.org/10.1117/12.164852.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Liu, Xiaoyuan, Mu Ku Chen, Yubin Fan, Jin Yao, Yao Liang, Jingcheng Zhang, Linshan Sun, and Din Ping Tsai. "Meta-optic for Intelligent Imaging and Sensing." In Conference on Lasers and Electro-Optics/Pacific Rim. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleopr.2022.cmp16a_03.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
We developed a meta-lens array based light field imaging system for full-color imaging, depth perception, edge detection and intelligent sensing. We reported the design, fabrication, and applications of the intelligent meta-lens.
9

Lin, Xiaofan. "Intelligent content fitting for digital publishing." In Electronic Imaging 2006, edited by Jan P. Allebach and Hui Chao. SPIE, 2006. http://dx.doi.org/10.1117/12.644285.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Rodríguez-Fajardo, Valeria, Jonathan Pinell, Christopher Ayala, and Andrew Forbes. "Intelligent ghost imaging (Conference Presentation)." In Quantum Communications and Quantum Imaging XVII, edited by Keith S. Deacon. SPIE, 2019. http://dx.doi.org/10.1117/12.2528757.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Звіти організацій з теми "Intelligent imaging":

1

Summa, Deborah A. Intelligent Integrated Machining: Ultrasonic Imaging for Assessment of Al-6061 Cladding Thickness in Monolithic U-10Mo Fuel Plates. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1091812.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Brunet, Luc. Elements by artificial Intelligence. Rd mediation, September 2022. http://dx.doi.org/10.17601/rdmediation.2022.9.1.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Jones, Nicole S., and John Grassel, eds. 2022 Firearm and Toolmarks Policy and Practice Forum. RTI Press, May 2022. http://dx.doi.org/10.3768/rtipress.2022.cp.0014.2204.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
The National Institute of Justice (NIJ) and the Forensic Technology Center of Excellence, an NIJ program hosted a four-day symposium, January 11–14, 2022. The symposium included presentations and panel discussions on topics relevant to recent advances in firearm and toolmark examination with a focus on the future. The symposium brought together 685 criminal justice processionals to explore implementation of three-dimensional (3D) imaging technologies, best practices for forensic examination of firearm and toolmark evidence, federal initiatives, gun crime intelligence, black box studies on firearm and toolmark examination, legal challenges to the admissibility of current examination of firearm and toolmark evidence and engineering solutions that will be used in court in the future, implementation of Organization of Scientific Area Committee (OSAC) standards and reporting, uniform language in testimony and conclusion scales. The panel discussions and presentations and provided examples of how agencies implement new imaging technologies for firearms and toolmark examination, incorporate statistics to add weight to forensic comparisons, address legal issues, and operationalize forensic intelligence to improve public safety and share information with the justice community. The symposium also provided a platform to discuss a series of considerations for the forensic, law enforcement, and greater criminal justice community that could help support a successful national transition to incorporate statistics in forensic testimony and accelerate the adoption of imaging technologies for firearm and toolmark examination.
4

Bentz, Brian, Jeremy Wright, John VanderLaan, Andres Sanchez, Christian Pattyn, John Zenker, Brian Redman, et al. Project final report : computational imaging for intelligence in highly scattering aerosols. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/2204416.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Abernathy, M. F., and M. G. Puccetti. Optical simulation for imaging reconnaissance and intelligence sensors OSIRIS: High fidelity sensor simulation test bed; Modified user`s manual. Office of Scientific and Technical Information (OSTI), January 1988. http://dx.doi.org/10.2172/143980.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Roschelle, Jeremy, James Lester, and Judi Fusco. AI and the Future of Learning: Expert Panel Report. Digital Promise, November 2020. http://dx.doi.org/10.51388/20.500.12265/106.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
This report is based on the discussion that emerged from a convening of a panel of 22 experts in artificial intelligence (AI) and in learning. It introduces three layers that can frame the meaning of AI for educators. First, AI can be seen as “computational intelligence” and capability can be brought to bear on educational challenges as an additional resource to an educator’s abilities and strengths. Second, AI brings specific, exciting new capabilities to computing, including sensing, recognizing patterns, representing knowledge, making and acting on plans, and supporting naturalistic interactions with people. Third, AI can be used as a toolkit to enable us to imagine, study, and discuss futures for learning that don’t exist today. Experts voiced the opinion that the most impactful uses of AI in education have not yet been invented. The report enumerates important strengths and weaknesses of AI, as well as the respective opportunities and barriers to applying AI to learning. Through discussions among experts about these layers, we observed new design concepts for using AI in learning. The panel also made seven recommendations for future research priorities.
7

WANG, MIN, Sheng Chen, Changqing Zhong, Tao Zhang, Yongxing Xu, Hongyuan Guo, Xiaoying Wang, Shuai Zhang, Yan Chen, and Lianyong Li. Diagnosis using artificial intelligence based on the endocytoscopic observation of the gastrointestinal tumours: a systematic review and meta-analysis. InPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0096.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Review question / Objective: With the development of endoscopic techniques, several diagnostic endoscopy methods are available for the diagnosis of malignant lesions, including magnified pigmented endoscopy and narrow band imaging (NBI).The main goal of endoscopy is to achieve the real-time diagnostic evaluation of the tissue, allowing an accurate assessment comparable to histopathological diagnosis based on structural and cellular heterogeneity to significantly improve the diagnostic rate for cancerous tissues. Endocytoscopy (ECS) is based on ultrahigh magnification endoscopy and has been applied to endoscopy to achieve microscopic observation of gastrointestinal (GI) cells through tissue staining, thus allowing the differentiation of cancerous and noncancerous tissues in real time.To date, ECS observation has been applied to the diagnosis of oesophageal, gastric and colorectal tumours and has shown high sensitivity and specificity.Despite the highly accurate diagnostic capability of this method, the interpretation of the results is highly dependent on the operator's skill level, and it is difficult to train all endoscopists to master all methods quickly. Artificial intelligence (AI)-assisted diagnostic systems have been widely recognized for their high sensitivity and specificity in the diagnosis of GI tumours under general endoscopy. Few studies have explored on ECS for endoscopic tumour identification, and even fewer have explored ECS-based AI in the endoscopic identification of GI tumours, all of which have reached different conclusions. Therefore, we aimed to investigate the value of ECS-based AI in detecting GI tumour to provide evidence for its clinical application.
8

Fearns, Joshua, and Lydia Harriss. Data science skills in the UK workforce. Parliamentary Office of Science and Technology, June 2023. http://dx.doi.org/10.58248/pn697.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
This POSTnote looks at specialist data skills in the UK, including for artificial intelligence. It considers demand and supply, workforce demographics, challenges, and initiatives to increase supply. Key points: • Collecting and analysing data offers potential economic and social benefits. Analysis by the McKinsey Global Institute estimated that, by 2030, UK GDP could increase by up to 22% as a result of AI. • Potential societal benefits could range from climate change mitigation, to improving early detection and diagnosis of cancers by using AI to identify patterns from imaging (MRI) scans that are not readily detected by humans. • Evidence suggests that the availability of people with specialist data skills in the UK is not sufficient to meet demand. • A 2021 study estimated that the supply of data scientists from UK universities was unlikely to exceed 10,000 per year, yet there were potentially at least 178,000 data specialist roles vacant in the UK. • Research finds that certain groups (such as women, those from minority ethnic backgrounds and people with disabilities) are underrepresented in the data workforce. A lack of workforce diversity has the potential to amplify existing inequalities and prejudices. • Initiatives to increase the number of people with data skills include degree conversion courses, doctoral training centres for PhD students, online up-skilling platforms, apprenticeships, and visas to attract international talent. • Efforts to reduce the skills gap can be hindered by the inconsistent definition of data skills, organisational culture, the availability of specialist primary and secondary school teachers, and barriers to people moving between sectors. • A 2022 inquiry by the Lords Science and Technology Committee concluded that a mismatch exists between the scale of the UK’s STEM skills gap and the solutions proposed by the UK Government, “especially given the UK’s ambition to be a science and technology superpower”. It described the Government’s policies as “inadequate and piecemeal”.
9

de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
10

de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.

До бібліографії