Bibliografie tematiche / Artificial Intelligence and Signal and Image Processing

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Letteratura scientifica selezionata sul tema "Artificial Intelligence and Signal and Image Processing"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 20 febbraio 2023

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Articoli di riviste sul tema "Artificial Intelligence and Signal and Image Processing"

Kim, Byung-Gyu, and Dong-San Jun. "Artificial Intelligence for Multimedia Signal Processing." Applied Sciences 12, no. 15 (July 22, 2022): 7358. http://dx.doi.org/10.3390/app12157358.

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Zhang, Xin, and Wang Dahu. "Application of artificial intelligence algorithms in image processing." Journal of Visual Communication and Image Representation 61 (May 2019): 42–49. http://dx.doi.org/10.1016/j.jvcir.2019.03.004.

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Wei, Hui, Luping Wang, Shanshan Wang, Yuxiang Jiang, and Jingmeng Li. "A Signal-Processing Neural Model Based on Biological Retina." Electronics 9, no. 1 (December 27, 2019): 35. http://dx.doi.org/10.3390/electronics9010035.

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Image signal processing has considerable value in artificial intelligence. However, due to the diverse disturbance (e.g., color, noise), the image signal processing, especially the representation of the signal, remains a big challenge. In the human visual system, it has been justified that simple cells in the primary visual cortex are obviously sensitive to vision signals with partial orientation features. In other words, the image signals are extracted and described along the pathway of visual processing. Inspired by this neural mechanism of the primary visual cortex, it is possible to build

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Raghavendra, U., U. Rajendra Acharya, and Hojjat Adeli. "Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders." European Neurology 82, no. 1-3 (2019): 41–64. http://dx.doi.org/10.1159/000504292.

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Background: Authors have been advocating the research ideology that a computer-aided diagnosis (CAD) system trained using lots of patient data and physiological signals and images based on adroit integration of advanced signal processing and artificial intelligence (AI)/machine learning techniques in an automated fashion can assist neurologists, neurosurgeons, radiologists, and other medical providers to make better clinical decisions. Summary: This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques:

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Yu, Frances, and Haibin Duan. "Meta-heuristic intelligence based image processing." Pattern Recognition Letters 31, no. 13 (October 2010): 1749. http://dx.doi.org/10.1016/j.patrec.2010.06.006.

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Mohammad-Djafari, Ali. "Interaction between Model Based Signal and Image Processing, Machine Learning and Artificial Intelligence." Proceedings 33, no. 1 (November 28, 2019): 16. http://dx.doi.org/10.3390/proceedings2019033016.

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Signale and image processing has always been the main tools in many area and in particular in Medical and Biomedical applications. Nowadays, there are great number of toolboxes, general purpose and very specialized, in which classical techniques are implemented and can be used: all the transformation based methods (Fourier, Wavelets, ...) as well as model based and iterative regularization methods. Statistical methods have also shown their success in some area when parametric models are available. Bayesian inference based methods had great success, in particular, when the data are noisy, uncer

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Shi, Zengfang, and Meizhou Liu. "Moving Vehicle Detection and Recognition Technology based on Artificial Intelligence." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 399–405. http://dx.doi.org/10.46300/9106.2022.16.49.

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The existing target detection and recognition technology has the problem of fuzzy features of moving vehicles, which leads to poor detection effect. A moving car detection and recognition technology based on artificial intelligence is designed. The point operation is adopted to enhance the high frequency information of the image, increase the image contrast, and delineate the video image tracking target. The motion vector similarity is used to predict the moving target area in the next frame of the image. The texture features of the moving car are extracted by artificial intelligence, and the

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Zhao, Qian, and Hong Zhang. "Automatic Color Extraction Algorithm of Graphic Design Image Based on Artificial Intelligence." International Journal of Circuits, Systems and Signal Processing 16 (January 12, 2022): 374–84. http://dx.doi.org/10.46300/9106.2022.16.46.

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The extraction of color features plays an important role in image recognition and image retrieval. In the past, feature extraction mainly depends on manual or supervised learning, which limits the automation of the whole recognition or retrieval process. In order to solve the above problems, an automatic color extraction algorithm based on artificial intelligence is proposed. According to the characteristics of BMP image, the paper makes use of the conversion between image color space and realizes it in the visual C++6.0 environment. The experimental results show that the algorithm realizes th

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Zhao, Ying, and Guocheng Wei. "Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals." Complexity 2021 (June 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/8328532.

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Image recognition is an important field of artificial intelligence. Its basic idea is to use computers to automatically classify different scenes in the acquired images, instead of traditional manual classification tasks. In this paper, through the analysis of rough set theory and artificial intelligence network, as well as the role of the two in image recognition, the rough set theory and artificial intelligence network are organically combined, and a network based on rough set theory and artificial intelligence network is proposed. Using BP artificial intelligence network model, improved BP

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Mon, Yi-Jen. "Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology." Sustainability 14, no. 9 (April 28, 2022): 5335. http://dx.doi.org/10.3390/su14095335.

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The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25 square areas of 5 rows and 5 columns, and then, according to the location of the edge of the image, it determines whether the robot should go straight (represented by S), turn around (represented by A), stop (T), turn left (represented by L), or turn right (represented by R). Then, after sustainable ultrasonic signal acquisition and transformation

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Tesi sul tema "Artificial Intelligence and Signal and Image Processing"

Lambert, T. "Digital Enhancement Techniques for Underwater Video Image Sequences." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/253/1/tristanlthesis.pdf.

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Due to concern about the current state of the world's oceans, several large scale scientific projects have begun to investigate the condition of our oceans. These projects are making use of underwater video sequences to monitor marine species. The move to using underwater video monitoring introduces labour intensive manual processing techniques. This leads to the need for an automated system capable of processing the data at a much greater speed. This project investigated whether the development of suitable image processing techniques could be used for pre-processing underwater images from a f

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Fearn, RC. "The Generalisation Ability of Neural Networks." Thesis, Honours thesis, University of Tasmania, 2004. https://eprints.utas.edu.au/119/1/thesis.pdf.

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Neural Networks (NN) can be trained to perform tasks such as image and handwriting recognition, credit card application approval and the prediction of stock market trends. During the learning process, the outputs of a supervised NN come to approximate the target values given the inputs in the training set. This ability may be good in itself, but often the more important purpose for a NN is to generalise i.e. to have the outputs of the NN approximate target values given inputs that are not in the training set. This project examines the impact a selection of key features has on the general

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Zehmeister, MS. "Development of an Illumination Identification System for the AIBO Robot." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/247/1/mszThesisFinal.pdf.

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The Four Legged League is a division of the RoboCup initiative that uses Sony AIBO robots to further robotics research. Most participants implement vision systems that use the colour of objects to perform identification. Calibration of the colour classification system must be done manually and any changes to the lighting of the environment after calibration reduces the accuracy of the system, often to a point at which the robot is effectively blind. This study investigates the relationships in the colour data of image pixels between different lighting conditions in an effort to identify tre

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Hall, DJ. "Flexible Robot Platform For Autonomous Research." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/249/1/djhThesis.pdf.

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The field of mobile robotics is receiving increasing levels of research. However, the simulation tools which are utilised in the creation of new mobile robot algorithms can produce algorithms which do not work in the real world. In order to try and minimise this problem a flexible robot platform has been created which allows the testing of a variety of algorithms. The platform facilitates the testing of algorithms normally only simulated by allowing algorithms to be easily tested in the real world. Utilising the flexible robot platform for testing algorithms allows higher quality research, as

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D'Alton, S. "A Constructive Neural Network Incorporating Competitive Learning of Locally Tuned Hidden Neurons." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/243/1/D%27Alton05CompetitivelyTrainedRAN.pdf.

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Performance metrics are a driving force in many fields of work today. The field of constructive neural networks is no different. In this field, the popular measurement metrics (resultant network size, test set accuracy) are difficult to maximise, given their dependence on several varied factors, of which the mostimportant is the dataset to be applied. This project set out with the intention to minimise the number of hidden units installed into a resource allocating network (RAN) (Platt 1991), whilst increasing the accuracy by means of application of competitive learning techniques. Three d

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Woolford, E. "Residual Reinforcement Learning using Neural Networks." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/250/1/ewFINAL_with_beginning_pages.pdf.

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A number of reinforcement learning algorithms have been developed that are guaranteed to converge to an optimal solution for look-up tables. However, it has also been shown that these algorithms become unstable when used directly with a function approximation system. A new class of algorithms developed by Baird (1995) were created to handle the problem that direct algorithms have with function approximation systems. This thesis focused on extending Baird's work further by comparing the performance of the residual algorithm against direct application of the Temporal Difference learning algorith

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Kamenetsky, D. "A Comparison of Neural Network Architectures in Reinforcement Learning in the Game of Othello." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/252/1/dkThesis_Final_4.pdf.

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Over the past two decades, Reinforcement Learning has emerged as a promising Machine Learning technique that is capable of solving complex dynamic problems. The benefit of this technique lies in the fact that the agent learns from its experience rather than being told directly. For problems with large state-spaces, Reinforcement Learning algorithms are combined with function approximation techniques, such as neural networks. The architecture of the neural networks plays a significant role in the agent's learning. Past research has demonstrated that networks with a constructive architecture out

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Kelsall, A. "Flexible Shape Models for Marine Animal Detection in Underwater Images." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/248/1/afkThesis_FINAL.pdf.

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Many industries are benefiting from computer automation, however the area of image analysis is still limited. The process of finding a potential object in an image is hard in itself, let alone classifying it. Automating these tasks would significantly reduce the time it takes to complete them thus allowing much more data to be processed. This becomes a problem when data is collect faster than it can be analysed. Images and video sequences are captured for different purposes and need to be manually processed in order to discover their contents. The fishing industry is a perfect example of th

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Rochford, Matthew. "Visual Speech Recognition Using a 3D Convolutional Neural Network." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2109.

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Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN ar

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Knights, MS. "Flexible shape models for image analysis in an automated lobster catch assessment system." Thesis, Honours thesis, University of Tasmania, 2007. https://eprints.utas.edu.au/3013/2/1_front_Knights.pdf.

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Abstract (sommario):

Management of fisheries is an evolving science combining multiple techniques and strategies. The involvement of the computer in industry management and research continues to grow. The area of image analysis is currently limited but continues to grow as computing equipment becomes faster and cheaper. Locating a particular object in an image and processing information about that object is a significant task that requires a great deal of processing power and finesse. The benefits of a functioning automated task that processes data on an object, such as a lobster, simply by processing an image

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Libri sul tema "Artificial Intelligence and Signal and Image Processing"

Paunwala, Chirag, Mita Paunwala, Rahul Kher, Falgun Thakkar, Heena Kher, Mohammed Atiquzzaman, and Norliza Mohd Noor, eds. Biomedical Signal and Image Processing with Artificial Intelligence. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15816-2.

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Kacprzyk, Janusz. Evolutionary Image Analysis and Signal Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Choraś, Ryszard S. Image Processing and Communications Challenges 4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Loménie, Nicolas. Advances in Bio-Imaging: From Physics to Signal Understanding Issues: State-of-the-Art and Challenges. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Vega, Leonardo Rey. A Rapid Introduction to Adaptive Filtering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Holambe, Raghunath S. Advances in Non-Linear Modeling for Speech Processing. Boston, MA: Springer US, 2012.

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David, Hutchison. Image and Signal Processing: 3rd International Conference, ICISP 2008 Cherbourg-Octeville, France, July 1-3, 2008 Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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Petra, Perner, ed. Case-based reasoning on images and signals. Berlin: Springer, 2008.

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Sappa, Angel D. Multimodal Interaction in Image and Video Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (1996 Venice, Italy). Proceedings: International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, Venice, Italy, August 21-23, 1996. Los Alamitos, CA: IEEE Computer Society Press, 1996.

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Capitoli di libri sul tema "Artificial Intelligence and Signal and Image Processing"

Ebrahiminia, Alireza, Mohamad Sadegh Helfroush, Habibollah Danyali, and Shabab Bazrafkan. "Contourlet-Based Levelset SAR Image Segmentation." In Artificial Intelligence and Signal Processing, 51–59. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_6.

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Borhani, Mostafa, and Hassan Ghassemian. "Kernel Grouped Multivariate Discriminant Analysis for Hyperspectral Image Classification." In Artificial Intelligence and Signal Processing, 3–12. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_1.

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Sadeghi, Hamid, and Abolghasem Asadollah Raie. "Semi-dynamic Facial Expression Recognition Based on Masked Displacement Image." In Artificial Intelligence and Signal Processing, 100–108. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_11.

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Nooshyar, Mehdi, Mohammad Abdipour, and Mehdi Khajuee. "Multi-focus Image Fusion for Visual Sensor Networks in Wavelet Domain." In Artificial Intelligence and Signal Processing, 23–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_3.

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Gan, Woon Siong. "Practical Cases of Applications of Artificial Intelligence to Acoustical Imaging." In Signal Processing and Image Processing for Acoustical Imaging, 81–83. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-5550-8_14.

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Farahani, Fatemeh Shahrabi, Mansour Sheikhan, and Ali Farrokhi. "Facial Emotion Recognition Using Gravitational Search Algorithm for Colored Images." In Artificial Intelligence and Signal Processing, 32–40. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_4.

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Sarikhani, Hossein, Ebrahim Abdollahian, Mohsen Shirpour, Alireza Javaheri, and Mohammad Taghi Manzuri. "A Robust and Invariant Keypoint Extraction Algorithm in Brain MR Images." In Artificial Intelligence and Signal Processing, 121–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_13.

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Malekian, Leila, Heidar Ali Talebi, and Farzad Towhidkhah. "Needle Detection in 3D Ultrasound Images Using Anisotropic Diffusion and Robust Fitting." In Artificial Intelligence and Signal Processing, 111–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_12.

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Ehsaee, Shahryar, and Mansour Jamzad. "Robust Zero Watermarking for Still and Similar Images Using a Learning Based Contour Detection." In Artificial Intelligence and Signal Processing, 13–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_2.

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Owjimehr, Mehri, Habibollah Danyali, and Mohammad Sadegh Helfroush. "Diagnosing of Fatty and Heterogeneous Liver Diseases from Ultrasound Images Using Fully Automated Segmentation and Hierarchical Classification." In Artificial Intelligence and Signal Processing, 141–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_15.

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Atti di convegni sul tema "Artificial Intelligence and Signal and Image Processing"

Liao, Jasmin, and Yu Sun. "Artificial Intelligence Designed For Attendance." In 11th International Conference on Signal & Image Processing (SIP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121711.

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Engaging online students is a challenge for many teachers. While I was a student, I saw teachers struggling to take attendance due to the number of students leaving their classes after attendance. Students would be held responsible for their work using facial recognition technology. To simplify the process of applying absences to students in each class, this paper proposes an application that would allow teachers to stay on top of their work. We applied our software to test “students” in the classroom and used various libraries/CSC styles to create a classroom that is easy for both the student

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Aritonang, Sovian, Tatar Bonar, and Jeanne Francoise. "Artificial Intelligence in Simple Command." In ICVISP 2018: The 2nd International Conference on Vision, Image and Signal Processing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3271553.3271568.

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Griffiths, Ieuan. "Automated Condition Monitoring Using Artificial Intelligence." In ICVISP 2020: 2020 4th International Conference on Vision, Image and Signal Processing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3448823.3448863.

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Chew, Kim Mey, Rubita Sudirman, Yu Hang How, and Ching Yee Yong. "Microwave Signal Spatial Domain Transformation Using Signal Processing and Image Reconstruction Method." In 2013 1st International Conference on Artificial Intelligence, Modelling & Simulation (AIMS). IEEE, 2013. http://dx.doi.org/10.1109/aims.2013.23.

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Phalnikar, Rashmi, Subhal Dixit, and Harsha Talele. "Clinical Assessment and Management of Covid-19 Patients using Artificial Intelligence." In 6th International Conference on Signal and Image Processing (SIGI 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.102007.

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Abstract (sommario):

The COVID-19 infection caused by Novel Corona Virus has been declared a pandemic and a public health emergency of international concern. Infections caused by Corona Virus have been previously recognized in people and is known to cause Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Unlike the earlier infections, COVID19 spreads alarmingly and the experience and volume of the scientific knowledge on the virus is small and lacks substantiation. To manage this crisis, Artificial intelligence (AI) promises to play a key role in understanding and addressing the

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Lorch, Benedikt, Nicole Scheler, and Christian Riess. "Compliance Challenges in Forensic Image Analysis Under the Artificial Intelligence Act." In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909723.

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da Fontoura Costa, Luciano. "Systems Biology through complex networks, signal processing, image analysis, and artificial intelligence." In 2009 16th International Conference on Digital Signal Processing (DSP). IEEE, 2009. http://dx.doi.org/10.1109/icdsp.2009.5201155.

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Hirimutugoda, Y. M., and Gamini Wijayarathna. "Artificial Intelligence-Based Approach for Determination of Haematologic Diseases." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5301750.

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Aghajani, Khadijeh, Mohsen Shirpour, and M. T. Manzuri. "Structural image representation for image registration." In 2015 International Symposium on Artificial Intelligence and Signal Processing (AISP). IEEE, 2015. http://dx.doi.org/10.1109/aisp.2015.7123534.

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Kashani, Mahya Mohammadi, and S. Hamid Amiri. "Leveraging deep learning representation for search-based image annotation." In 2017 Artificial Intelligence and Signal Processing Conference (AISP). IEEE, 2017. http://dx.doi.org/10.1109/aisp.2017.8324073.

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Rapporti di organizzazioni sul tema "Artificial Intelligence and Signal and Image Processing"

Elmgren, Karson, Ashwin Acharya, and Will Will Hunt. Superconductor Electronics Research. Center for Security and Emerging Technology, November 2021. http://dx.doi.org/10.51593/20210003.

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Devices based on superconductor electronics can achieve much higher energy efficiency than standard electronics. Research in superconductor electronics could advance a range of commercial and defense priorities, with potential applications for supercomputing, artificial intelligence, sensors, signal processing, and quantum computing. This brief identifies the countries most actively contributing to superconductor electronics research and assesses their relative competitiveness in terms of both research output and funding.

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Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.

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