Relevant bibliographies by topics / Artificial Intelligence and Signal and Image Processing

Contents

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

Academic literature on the topic 'Artificial Intelligence and Signal and Image Processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Artificial Intelligence and Signal and Image Processing"

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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 an image signal-processing model as the neural architecture. In this paper, we presented a method to process the image signal involving a multitude of disturbance. For image signals, we first extracted 4 rivalry pathways via the projection of color. Secondly, we designed an algorithm in which the computing process of the stimulus with partial orientation features can be altered into a process of analytical geometry, resulting in that the signals with orientation features can be extracted and characterized. Finally, through the integration of characterizations from the 4 different rivalry pathways, the image signals can be effectively interpreted and reconstructed. Instead of data-driven methods, the presented approach requires no prior training. With the use of geometric inferences, the method tends to be interpreted and applied in the signal processor. The extraction and integration of rivalry pathways of different colors allow the method to be effective and robust to the signals with the image noise and disturbance of colors. Experimental results showed that the approach can extract and describing the image signal with diverse disturbance. Based on the characterization of the image signal, it is possible to reconstruct signal features which can effectively represent the important information from the original image signal.
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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: epilepsy, Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, and ischemic brain stroke using physiological signals and images. Recent research articles on different feature extraction methods, dimensionality reduction techniques, feature selection, and classification techniques are reviewed. Key Message: CAD systems using AI and advanced signal processing techniques can assist clinicians in analyzing and interpreting physiological signals and images more effectively.
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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, uncertain, incomplete (missing values) or with outliers and where there is a need to quantify uncertainties. In some applications, nowadays, we have more and more data. To use these “Big Data” to extract more knowledge, the Machine Learning and Artificial Intelligence tools have shown success and became mandatory. However, even if in many domains of Machine Learning such as classification and clustering these methods have shown success, their use in real scientific problems are limited. The main reasons are twofold: First, the users of these tools cannot explain the reasons when the are successful and when they are not. The second is that, in general, these tools can not quantify the remaining uncertainties. Model based and Bayesian inference approach have been very successful in linear inverse problems. However, adjusting the hyper parameters is complex and the cost of the computation is high. The Convolutional Neural Networks (CNN) and Deep Learning (DL) tools can be useful for pushing farther these limits. At the other side, the Model based methods can be helpful for the selection of the structure of CNN and DL which are crucial in ML success. In this work, I first provide an overview and then a survey of the aforementioned methods and explore the possible interactions between them.
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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 center moment is calculated by the gray histogram distribution curve, the edge feature extraction algorithm is used to set the detection and recognition mode. Experimental results: under complex conditions, this design technology, compared with the other two kinds of moving vehicle detection and recognition technology, detected three more moving vehicles, which proved that the application prospect of the moving vehicle detection and recognition technology integrated with artificial intelligence is broader.
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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 the basic operation of image preprocessing, and realizes the automatic extraction of image color features by proper data clustering algorithm.
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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 artificial intelligence network model, and improved PSO-SVM model to identify and classify the extracted characteristic signals and compare the results, all reached 85% correct rate. The PCA and SVM are combined and applied to the MNIST handwritten digit collection for recognition and classification. At the data level, dimensionality reduction is performed on high-dimensional image data to compress the data. This greatly improves the performance of the algorithm, the recognition accuracy rate is as high as 98%, and the running time is shortened by about 90%. The model first preprocesses the original image data and then uses rough set theory to select features, which reduces the input dimension of the artificial intelligence network, improves the learning and recognition speed of the artificial intelligence network, and further improves the accuracy of recognition. The paper applies the model to handwritten digital image recognition, and the experimental results show that the model is effective and feasible. The system has the characteristics of easy deployment and easy maintenance and integration. Experiments show that the system has good time characteristics in the process of multialgorithm parallel image fusion processing.
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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 into digital signals are completed, the sustainable supervised neural network named SGAFNN (Supervised Gaussian adaptive fuzzy neural network) will perform an optimal path control that can accurately control the traveling speed and turning of the robot to avoid hitting walls or obstacles. Based on the above, this paper proposes the use of the ART operation after image processing to judge the rough direction, followed by the use of the ultrasonic signal to carry out the sustainable development of artificial intelligence and to carry out accurate speed and direction SGAFNN control to avoid obstacles. After simulation and practical evaluations, the proposed method is proved to be feasible and to exhibit good performance.
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Dissertations / Theses on the topic "Artificial Intelligence and Signal and Image Processing"

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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 fish farm and locating fish within these images using computer vision techniques. Using underwater images leads to some serious problems when compared to images from a clearer environment. Visibility in an underwater environment is poor, even when using state of the art equipment. After reviewing the broad field of computer vision and current underwater projects, an image pre-processing system was developed in MATLAB using suitable image processing and analysis techniques. The application developed was able to successfully locate an acceptable number of fish within the underwater images. The project demonstrated that automated analysis of underwater video images is needed and is possible. Automatic processing of large quantities of video image sequences will be of great benefit in the future. It will allow scientific researchers to study the ocean environment and its species more effectively. Pre-processing is an essential component of the overall process that will lead to automation of underwater video data analysis for marine science applications.
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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 generalisation ability of NNs. This is achieved through a critical analysis of the following aspects; inputs to the network, selection of training data, size of training data, prior knowledge and the smoothness of the function. Techniques devised to measure the effects these factors have on generalisation are implemented. The results of testing are discussed in detail and are used to form the basis of further work, directed at continuing to refine the processes involved during the training and testing of NNs.
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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 trends that can be used as the basis of a rule-based system. The aim of the system is to identify the current lighting level as one of a set of known conditions. The proposed system uses the colour data of image pixels and information about the AIBO's location and orientation to identify lighting levels, allowing a vision system to switch to an appropriate pre-cofigured calibration.
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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 algorithms can be assessed with physical evidence.
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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 datasets were used for evaluation of the hypothesis, one being a time-series set, and the other two being more general regression sets. Many trials were conducted during the period of this work, in order to be able to prove conclusively the discovered results. Each trial was different in only one respect from another in an effort to maximise the comparability of the results found. Four metrics were recorded for each trial- network size (per training epoch, and final), test and training set accuracy (again, per training epoch and final), and overall trial runtime. The results indicate that the application of competitive learning algorithms to the RAN results in a considerable reduction in network size (and therefore the associated reduction in processing time) across the vast majority of the trials run. Inspection of the accuracy related metrics indicated that using this method offered no real difference to that of the originalimplementation of the RAN. As such, the positive network-size results found are only half of the bigger picture, meaning there is scope for future work to be done to increase the test set accuracy.
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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 algorithm. Four benchmark experiments were used to test each algorithm with various values of lambda and alpha over a period of twenty trials. Overall it was shown that the residual algorithm outperformed direct application of the TD learning algorithm on all four experiments.
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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 outperform those with a fixed architecture on some benchmark problems. This study compares the performance of these two architectures in Othello - a complex deterministic board game. Three networks are used in the comparison: two with constructive architecture - Cascade and Resource Allocating Network, and one with fixed architecture - Multilayer Perceptron. Investigation is also made with respect to input representation, number of hidden nodes and other parameters used by the networks. Training is performed with both on-policy (Sarsa) and off-policy (Q-Learning) algorithms. Results show that agents were able to learn the positional strategy (novice strategy in Othello) and could beat each of the three built-in opponents. Agents trained with Multilayer Perceptron perform better, but converge slower than those trained with Cascade.
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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 this. A fish farm needs to know the average size of the fish in a ring. At present, this involves either manually taking a sample of fish from the ring and measuring them, or taking a series of stereoscopic images and manually tracing a sample of fish. By using active shape models, the process of tracing a fish sample can be automated. The Active Shape Model (ASM) Toolkit is an implementation of active appearance models, an advanced type of active shape model. The wrapper application that was written as part of this research allows a more streamlined process to input region data into the ASM Toolkit for searching. Once a sample has been matched, it is possible to use the key points around it to base further calculations on such as its size and weight. The ASM Toolkit and the wrapper program demonstrate how the process of identifying a fish in an image can be automated and that it is possible to calculate the size and weight of fish. In an ideal manual test, the most effective model matched 68% of samples, and in the automated test matched 50% of the samples. If the program can run over several days collecting appropriate samples, the model will be able to match enough fish to estimate the average size and weight within a ring. It is shown that the types of samples used in training the model affects the performance more than the number of samples used.
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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 architecture achieves a testing accuracy of 64%, comparable with recent works, but using an input data size that is up to 75% smaller. Overall, our research shows that 3D-CNNs can be successful in finding spatial-temporal features using unsupervised feature extraction and are a suitable choice for VSR-based systems.
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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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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 of that object would greatly enhance the ability to manage a fishery with accurate, up to date data. The Tasmanian Aquaculture and Fisheries Institute (TAFI) intend to create a lobster-sorting tray, which can be used on lobster fishing vessels as standard equipment. This tray would include functionality to take an image of the current lobster and estimate its sex and weight from pertinent measurements on the lobster. This research demonstrates that through the use of the Active Shape Modeller (ASM) these details can be identified and processed from an image of the lobster. The ASM is used within an image analysis process, which can be fully automated, to draw out the required salient details of a lobster from an area of interest in the images. A series of experiments showed that the ASM was able to draw out and fully identify 77.3% images in a test set of 216 images. These images then had pertinent lengths and a sex estimated based on these measurements where 90% of the matched lobsters were sexed correctly.
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Books on the topic "Artificial Intelligence and Signal and Image Processing"

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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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Book chapters on the topic "Artificial Intelligence and Signal and Image Processing"

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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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Conference papers on the topic "Artificial Intelligence and Signal and Image Processing"

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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 and the teacher to read. Our designs are built upon OpenCV and PIL which are used as geometric classifiers to determine if the student is present. We tested several faces to see if the algorithm was suitable for the program. After conducting a qualitative evaluation of the approach, we’ve begun to implement registration, create new classrooms with different databases, and apply verification. With the addition of HTML code, we wereable to create a classroom that is safe, engaging, and easy to use.
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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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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 COVID-19 crisis. It tends to be valuable to identify the infection, analyse it, treat it and also predict the stages of infection. Artificial intelligence algorithms can be applied to make diagnosis of COVID-19 and stepping up research and therapy. The paper explains a detailed flowchart of COVID-19 patient and discusses the use of AI at various stages. The preliminary contribution of the paper is in identifying the stages where the use of Artificial Intelligence and its allied fields can help in managing COVID-19 patient and paves a road for systematic research in future.
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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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Reports on the topic "Artificial Intelligence and Signal and Image Processing"

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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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