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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Hung, Kuo-Ching, Meng-Chun Lin, and Sheng-Fuu Lin. "A Novel Power-Saving Reversing Camera System with Artificial Intelligence Object Detection." Electronics 11, no. 2 (January 17, 2022): 282. http://dx.doi.org/10.3390/electronics11020282.
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According to a study by the Insurance Institute for Highway Safety (IIHS), the driving collision rate of using only the reversing camera system is lower than that of using both the reversing camera system and the reversing radar. In this article, we implemented a reversing camera system with artificial intelligence object detection to increase the information of the reversing image. Our system consists of an image processing chip (IPC) with wide-angle image distortion correction and an image buffer controller, a low-power KL520 chip and an optimized artificial intelligence model MobileNetV2-YOLOV3-Optimized (MNYLO). The results of the experiment show the three advantages of our system. Firstly, through the image distortion correction of IPC, we can restore the distorted reversing image. Secondly, by using a public dataset and collected images of various weathers for artificial intelligence model training, our system does not need to use image algorithms that eliminate bad weathers such as rain, fog, and snow to restore polluted images. Objects can still be detected by our system in images contaminated by weather. Thirdly, compared with the AI model Tiny_YOLOV3, not only the parameters of our MNYLO have been reduced by 72.3%, the amount of calculation has been reduced by 86.4%, but the object detection rate has also been maintained and avoided sharp drops.
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Love, P. L., and M. Simaan. "Segmentation of a seismic section using image processing and artificial intelligence techniques." Pattern Recognition 18, no. 6 (January 1985): 409–19. http://dx.doi.org/10.1016/0031-3203(85)90011-1.
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Rajan, K., KS Sangunni, and J. Ramakrishna. "Dual-DSP system for signal and image processing." Microprocessors and Microsystems 17, no. 9 (November 1993): 556–60. http://dx.doi.org/10.1016/s0141-9331(09)91007-9.
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Yang, Yue, Wei Liu, Zuoxun Zeng, and Wei Xue. "Signal Processing on Precursory “Fingerprint” Image Pattern Feature of Yushu Earthquake." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 7 (December 20, 2016): 1165–69. http://dx.doi.org/10.20965/jaciii.2016.p1165.
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Precursory earthquake data are linked closely to the earthquake processes. Taking the Tibetan Autonomous Region’s Yushu County earthquake as an example, we analyzed three types of earthquake signals and studied a modeling method for self-adaptative matching warning data on precursory data’s fingerprint features. We calculated different timescale features of precursory fingerprint pattern images based on statistical physics and image matching. We also developed corresponding fuzzy discriminant rules and established a database of warning-image fingerprint pattern features for the Yushu County region and studied evolutionary laws for the data feature patterns under different time scales during abnormal development in front of and behind of abnormal development. Result were similar to the general “fingerprint” pattern feature among precursory earthquake data for different signal channels, but the details of these characteristics are completely different. This special “fingerprint” image pattern feature is useful as on early warning of possible geological follow-up activity. Our method could improve the limitations on and low efficiency of manual handling and could also heighten observational accuracy and work efficiency.
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Zheng, Xianwei, Yuan Yan Tang, Jiantao Zhou, Jianjia Pan, Shouzhi Yang, Youfa Li, and Patrick S. P. Wang. "Multi-Level Downsampling of Graph Signals via Improved Maximum Spanning Trees." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 03 (February 19, 2019): 1958005. http://dx.doi.org/10.1142/s0218001419580059.
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Graph signal processing (GSP) is an emerging field in the signal processing community. Novel GSP-based transforms, such as graph Fourier transform and graph wavelet filter banks, have been successfully utilized in image processing and pattern recognition. As a rapidly developing research area, graph signal processing aims to extend classical signal processing techniques to signals with irregular underlying structures. One of the hot topics in GSP is to develop multi-scale transforms such that novel GSP-based techniques can be applied in image processing or other related areas. For designing graph signal multi-scale frameworks, downsampling operations that ensuring multi-level downsampling should be specifically constructed. Among the existing downsampling methods in graph signal processing, the state-of-the-art method was constructed based on the maximum spanning tree (MST). However, when using this method for multi-level downsampling of graph signals defined on unweighted densely connected graphs, such as social network data, the sampling rates are not close to [Formula: see text]. This phenomenon is summarized as a new problem and called downsampling unbalance problem in this paper. Due to the unbalance, MST-based downsampling method cannot be applied to construct graph signal multi-scale transforms. In this paper, we propose a novel and efficient method to detect and reduce the downsampling unbalance generated by the MST-based method. For any given graph signal, we apply the graph density to construct a measurement of the downsampling unbalance generated by the MST-based method. If a graph signal has large unbalance possibility, the multi-level downsampling is conducted after the MST is improved. The experimental results on synthetic and real-world social network data show that downsampling unbalance can be efficiently detected and then reduced by our method.
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Sahith, Jai Krishna. "RECOGNITION OF AIRCRAFT IN REMOTE SENSING IMAGES USING CONVOLUTIONAL NEURAL NETWORK." Journal of Airline Operations and Aviation Management 1, no. 1 (July 25, 2022): 63–70. http://dx.doi.org/10.56801/jaoam.v1i1.8.
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Picture processing is a stand-alone, non-human image comprehension system that is one of the major breakthroughs (IUS). When one tries to explain what comprehension is, the effort of comprehending images becomes massive. In addition to classical signal processing, pattern recognition and artificial intelligence are applied. The early phases of the picture comprehension process might be called scene analysis methods that use edge and texture segmentation. As a result, putting a man in a signal processing loop at particular sensors, such as remotely piloted vehicles, satellites, and spacecraft, is not acceptable. Smart sensors and semi- automated procedures are being created as a result. Another major use of image processing is land remote sensing. With the debut of shows like Star Wars, this application has taken on a new level of relevance in the military's eyes. This study gives an overview of digital image processing and investigates the reach of remote sensing and IUS technology from the military's perspective. To demonstrate the significance of IUSs, a detailed description of a current autonomous car project in the United States is provided.
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Allende, Héctor, Jorge Galbiati, and Ronny Vallejos. "Robust image modeling on image processing." Pattern Recognition Letters 22, no. 11 (September 2001): 1219–31. http://dx.doi.org/10.1016/s0167-8655(01)00054-x.
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Owoeye, Oluwatobi M., and Olusola Abayomi Ojo-Omoniyi. "Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy." International Journal of Current Microbiology and Applied Sciences 11, no. 6 (June 10, 2022): 119–33. http://dx.doi.org/10.20546/ijcmas.2022.1106.014.
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Digital image processing involves the usage of a functional algorithm to process images with special regions of interest. In most case scenarios, this is termed as an active aspect of digital signal processing; image processing comes with several rewards over analog image processing. Its relevance and application spans Autonomous Vehicles, Biometric fingerprint technologies as well as Face recognition applications. Reliable statistics through feature engineering from the image can be extracted and in turn serve as focus points of deep learning insights. Besides, its application in monitoring Climatic changes, Agricultural crop yields, security measures, industrial manufacturing as well as medical fields exponentially advances each day. Meanwhile, deep learning being a feature of Artificial Intelligence has brought forward several useful models that is being used as transfer base for further model accuracies and baselines. In this study, we make use of a certain Microscopy datasets, sampling one of the images for digital processing, in order to gain useful insights through Cropped Quantizing, Laplace Edge Detection and Gaussian noise with sigma methods respectively. The statistical results of the extracted image features through Support Vector Method (SVM) give accuracy of up to 75%.
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Mishra, Ishani, and Sanjay Jain. "Soft computing based compressive sensing techniques in signal processing: A comprehensive review." Journal of Intelligent Systems 30, no. 1 (September 11, 2020): 312–26. http://dx.doi.org/10.1515/jisys-2019-0215.
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Abstract In this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals included can be viewed as light in a few spaces. The compressive sensing theory could be an appropriate contender to manage these limitations. “Compressive Sensing theory” preserves extremely helpful while signals are sparse or compressible. It very well may be utilized to recoup light or compressive signals with less estimation than customary strategies. Two issues must be addressed by CS: plan of the estimation framework and advancement of a proficient sparse recovery calculation. The essential intention of this work expects to audit a few ideas and utilizations of compressive sensing and to give an overview of the most significant sparse recovery calculations from every class. The exhibition of acquisition and reconstruction strategies is examined regarding the Compression Ratio, Reconstruction Accuracy, Mean Square Error, and so on.
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Alcalde, Cristina, Ana Burusco, and Ramón Fuentes-González. "Application of the L-fuzzy concept analysis in the morphological image and signal processing." Annals of Mathematics and Artificial Intelligence 72, no. 1-2 (January 18, 2014): 115–28. http://dx.doi.org/10.1007/s10472-014-9397-7.
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Abbod, Maysam, and Jiann-Shing Shieh. "Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”." Sensors 19, no. 21 (October 31, 2019): 4727. http://dx.doi.org/10.3390/s19214727.
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Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.
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Xue, Bin, and Zhisheng Wu. "Key Technologies of Steel Plate Surface Defect Detection System Based on Artificial Intelligence Machine Vision." Wireless Communications and Mobile Computing 2021 (April 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/5553470.
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With the rapid development of visual inspection technology, computer technology, and image processing technology, machine vision technology has become more and more mature, and the role of quality inspection and control in the steel industry is becoming more and more obvious and important. Defects on the surface of the strip are a key factor affecting the quality inspection process. Its inspection plays an extremely important role in improving the final quality. For a long time, traditional manual inspection methods cannot meet actual production needs, so in-depth research on steel surface defect inspection systems has become the consensus of today’s steel companies. The accuracy and low performance of traditional detection methods can no longer meet the needs of people and society. The surface defect detection method based on machine vision has the characteristics of high accuracy, fast processing speed, and intelligent processing, which is the main trend of surface defect detection. We select a steel plate; take the invariant moment features of the cracks, holes, scratches, oil stains, and other images on it; extract the data results; and analyze them. Then, we read the texture features of these defect images again, extract the data results, and analyze them. The experimental results prove that after the mean value filter and Gaussian filter process the image, the mean variance value MSE is relatively large ( 46.276 > 31.2271 ), and as the concentration of salt and pepper noise increases, the rate of increase of MSE increases obviously, and as the peak signal-to-noise ratio and the mean variance value MSE increase continuously ( 32.2271 < 33.3695 ), the image distortion is more serious. The method designed in this paper is extremely effective. Improving the surface quality of steel is of great significance to improving market competitiveness.
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Vigneron, Vincent, and Sylvie Lelandais. "Statistics for Image Processing." Pattern Recognition Letters 31, no. 14 (October 2010): 2191. http://dx.doi.org/10.1016/j.patrec.2010.07.003.
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Zhang, Na, Hongsen Xie, Jinjie Li, and Hong Chen. "Application of radar signal processing and image display algorithm based on computer hardware system in intelligence processing." Microprocessors and Microsystems 81 (March 2021): 103747. http://dx.doi.org/10.1016/j.micpro.2020.103747.
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Küçükdemirci, Melda, and Apostolos Sarris. "GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection." Remote Sensing 14, no. 14 (July 13, 2022): 3377. http://dx.doi.org/10.3390/rs14143377.
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Ground penetrating radar (GPR) is a well-established technique used in archaeological prospection and it requires a number of specialized routines for signal and image processing to enhance the data acquired and lead towards a better interpretation of them. Computer-aided techniques have advanced the interpretation of GPR data, dealing with a wide range of operations aiming towards locating, imaging, and diagnosis/interpretation. This article will discuss the novel and recent applications of machine learning (ML) and deep learning (DL) techniques, under the artificial intelligence umbrella, for processing GPR measurements within archaeological contexts, and their potential, limitations, and possible future prospects.
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Gong, Suning, Rakesh Kumar, and D. Kumutha. "Design of Lighting Intelligent Control System Based on OpenCV Image Processing Technology." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, Supp01 (March 26, 2021): 119–39. http://dx.doi.org/10.1142/s0218488521400079.
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The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic.
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Kumar, Subash. "Abstract PO-012: The concept of artificial intelligence against pancreatic cancer." Cancer Research 81, no. 22_Supplement (November 15, 2021): PO—012—PO—012. http://dx.doi.org/10.1158/1538-7445.panca21-po-012.
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Abstract Pancreatic cancer (PC) remains the fourth leading cause of cancer-related death in both men and women in the United States. Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Pancreatic ductal adenocarcinoma (PDAC) is on track to become the number 2 cancer killer in the United States within the next decade unless there is a major improvement in outcomes. Surgical resection remains the only reasonable hope for a cure from PDAC. The potential for early detection of asymptomatic pancreatic neoplasms in high-risk individuals using an endoscopic approach, but this approach is operator dependent and at the same time, these existing techniques are favored once patients reach the age of 75 years. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. Machine learning refers to the study of algorithms that learn their behavior from data. To see why such algorithms are important, consider the following basic task, building a program to predict if an image contains a dog or a cat. Although it is exceedingly difficult for us to manually specify the exact rules to determine that a dog is a dog, it is comparatively straightforward to prepare a reference set of images and labels. This setting, where knowledge is more easily encoded in data rather than as a descriptive set of rules, is the focus of ML algorithms. One of the most promising areas of innovation in medical imaging in the past decade has been the application of deep learning. Deep learning has the potential to impact the entire medical imaging workflow from image acquisition, image registration, to interpretation. Traditional image processing is dominated by algorithms that are based on statistical models. These statistical model-based processing algorithms carry out inference based on a complete knowledge of the underlying statistical model relating the observations at hand and the desired information and do not require data to learn their mapping. In practice, accurate knowledge of the statistical model relating the observations and the desired information is typically unavailable. The past decade has witnessed a deep learning revolution. Deep learning methods have surpassed the state of the art for many problems in signal processing, imaging, and vision with unprecedented performance gains. Citation Format: Subash Kumar. The concept of artificial intelligence against pancreatic cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-012.
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Arya, Vivek. "Robust Image Compression Algorithm using Discrete Fractional Cosine Transform." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 17 (January 5, 2022): 25–33. http://dx.doi.org/10.37394/23203.2022.17.3.
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The discrete fractional Fourier transform become paradigm in signal processing. This transform process the signal in joint time-frequency domain. The attractive and very important feature of DFrCT is an availability of extra degree of one free parameter that is provided by fractional orders and due to which optimization is possible. Less execution time and easy implementation are main advantages of proposed algorithm. The merit of effectiveness of proposed technique over existing technique is superior due to application of discrete fractional cosine transform by which higher compression ratio and PSNR are obtained without any artifacts in compressed images. The novelty of the proposed algorithm is no artifacts in compressed image along with good CR and PSNR. Compression ratio (CR) and peak signal to noise ratio (PSNR) are quality parameters for image compression with optimum fractional order.
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Viergever, Max A. "Volume image processing (VIP'93)." Pattern Recognition Letters 15, no. 5 (May 1994): 437–38. http://dx.doi.org/10.1016/0167-8655(94)90133-3.
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Suneetha, Akula, and E. Srinivasa Reddy. "Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter." Journal of Intelligent Systems 30, no. 1 (August 15, 2020): 240–57. http://dx.doi.org/10.1515/jisys-2019-0211.
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Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.
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Ru, Lei, Bin Zhang, Jing Duan, and Guo Ru. "ANALYSIS OF BIOLO ARTIFICIAL NEURAL NETWORK IN PREDICTION OF AEROBIC EXERCISE INDEX BASED ON ALGORITHM." Revista Brasileira de Medicina do Esporte 27, no. 4 (August 2021): 367–71. http://dx.doi.org/10.1590/1517-8692202127042021_0126.
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ABSTRACT Objective: To study the relationship between aerobic activity and cardiac autonomic nerve activity by artificial neural network algorithm and biological image fusion; because of the artificial neural network model (ANN) problems, biological image processing technology is introduced based on ANN. Methods: An Ann under biological image intelligence algorithm is proposed, a classifier suitable for electrocardiograph (ECG) screening is designed, and an ECG signal screening system is successfully established. Moreover, the data set of normal recovered ECG signals of the subjects during the experimental period is constructed, and a classifier is used to extract the characteristic data of a normal ECG signal during the experimental period. Results: The changes in resting heart rate and other physical health indicators are analyzed by combining resting physiological indicators, namely heart rate, body weight, body mass index and body fat rate. The results show that the self-designed classifier can efficiently process the ECG images, and long-term regular activities can improve the physical conditions of most people. Most subjects’ body weight and body fat rate decrease with the extension of experiment time, and the resting heart rate decreases relatively. Conclusions: Certain indicators can be used to predict a person's dynamic physical health, which indicates that the experimental research of index prediction in this research has a good effect, which not only extends the application of artificial neural network but also lays a foundation for the research and implementation of ECG intelligent testing wearable devices. Level of evidence II; Therapeutic studies - investigation of treatment results.
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Yang, Wenxian, Radoslaw Zimroz, and Mayorkinos Papaelias. "Advances in Machine Condition Monitoring and Fault Diagnosis." Electronics 11, no. 10 (May 13, 2022): 1563. http://dx.doi.org/10.3390/electronics11101563.
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In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...]
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Iakovidis, Dimitris K., Melanie Ooi, Ye Chow Kuang, Serge Demidenko, Alexandr Shestakov, Vladimir Sinitsin, Manus Henry, et al. "Roadmap on signal processing for next generation measurement systems." Measurement Science and Technology 33, no. 1 (November 16, 2021): 012002. http://dx.doi.org/10.1088/1361-6501/ac2dbd.
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Abstract Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.
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Jia, Tao. "The Realization of FPGA + ARM-Based Image Matching." Applied Mechanics and Materials 220-223 (November 2012): 2799–802. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2799.
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The intelligent monitoring refers to the use of pattern recognition technology, digital signal processing, artificial intelligence theory and automatic control technology, and integrating video surveillance into the monitoring system, to enable the system become intelligent. The core technology for the system is the identification and matching of moving targets; and the fast & exact match detection of a moving target is the key step and technical basis for subsequent identification. Image-based matching has a wide range of applications in the field of industrial product testing, identification of aerial targets, the identification of ground targets. In this paper, I’d discuss over the program of utilizing 1K50 + LPC2106 processor to achieve the target detection, together with the specific algorithm. Experimental results show that the processing system is absolutely capable of real-time detection and matching of the target image.
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Fukuda, Toshio, Yasuhisa Hasegawa, Yasuhiro Kawai, Shinsuke Sato, Zakarya Zyada, and Takayuki Matsuno. "GPR Signal Processing with Geography Adaptive Scanning using Vector Radar for Antipersonal Landmine Detection." International Journal of Advanced Robotic Systems 4, no. 2 (June 1, 2007): 22. http://dx.doi.org/10.5772/5696.
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Ground Penetrating Radar (GPR) is a promising sensor for landmine detection, however there are two major problems to overcome. One is the rough ground surface. The other problem is the distance between the antennas of GPR. It remains irremovable clutters on a sub-surface image output from GPR by first problem. Geography adaptive scanning is useful to image objects beneath rough ground surface. Second problem makes larger the nonlinearity of the relationship between the time for propagation and the depth of a buried object, imaging the small objects such as an antipersonnel landmine closer to the antennas. In this paper, we modify Kirchhoff migration so as to account for not only the variation of position of the sensor head, but also the antennas alignment of the vector radar. The validity of this method is discussed through application to the signals acquired in experiments.
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LI, YUN, BAO-LIANG LU, and TENG-FEI ZHANG. "COMBINING FEATURE SELECTION WITH EXTRACTION: UNSUPERVISED FEATURE SELECTION BASED ON PRINCIPAL COMPONENT ANALYSIS." International Journal on Artificial Intelligence Tools 18, no. 06 (December 2009): 883–904. http://dx.doi.org/10.1142/s0218213009000445.
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Principal components analysis (PCA) is a popular linear feature extractor, and widely used in signal processing, face recognition, etc. However, axes of the lower-dimensional space, i.e., principal components, are a set of new variables carrying no clear physical meanings. Thus we propose unsupervised feature selection algorithms based on eigenvectors analysis to identify critical original features for principal component. The presented algorithms are based on k-nearest neighbor rule to find the predominant row components and eight new measures are proposed to compute the correlation between row components in transformation matrix. Experiments are conducted on benchmark data sets and facial image data sets for gender classification to show their superiorities.
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Basha, D. Khalandar, and T. Venkateswarlu. "Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration." Journal of Intelligent Systems 29, no. 1 (July 13, 2019): 1480–95. http://dx.doi.org/10.1515/jisys-2018-0492.
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Abstract The image restoration (IR) technique is a part of image processing to improve the quality of an image that is affected by noise and blur. Thus, IR is required to attain a better quality of image. In this paper, IR is performed using linear regression-based support vector machine (LR-SVM). This LR-SVM has two steps: training and testing. The training and testing stages have a distinct windowing process for extracting blocks from the images. The LR-SVM is trained through a block-by-block training sequence. The extracted block-by-block values of images are used to enhance the classification process of IR. In training, the imperfections on the image are easily identified by setting the target vectors as the original images. Then, the noisy image is given at LR-SVM testing, based on the original image restored from the dictionary. Finally, the image block from the testing stage is enhanced using the hybrid Laplacian of Gaussian (HLOG) filter. The denoising of the HLOG filter provides enhanced results by using block-by-block values. This proposed approach is named as LR-SVM-HLOG. A dataset used in this LR-SVM-HLOG method is the Berkeley Segmentation Database. The performance of LR-SVM-HLOG was analyzed as peak signal-to-noise ratio (PSNR) and structural similarity index. The PSNR values of the house and pepper image (color image) are 40.82 and 36.56 dB, respectively, which are higher compared to the inter- and intra-block sparse estimation method and block matching and three-dimensional filtering for color images at 20% noise.
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Wang, Yuqing, and Yong Wang. "An Improved Biologically-Inspired Image Fusion Method." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 08 (April 8, 2018): 1857004. http://dx.doi.org/10.1142/s0218001418570045.
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A biologically inspired image fusion mechanism is analyzed in this paper. A pseudo-color image fusion method is proposed based on the improvement of a traditional method. The proposed model describes the fusion process using several abstract definitions which correspond to the detailed behaviors of neurons. Firstly, the infrared image and visible image are respectively ON against enhanced and OFF against enhanced. Secondly, we feed back the enhanced visible images given by the ON-antagonism system to the active cells in the center-surrounding antagonism receptive field. The fused [Formula: see text]VIS[Formula: see text]IR signal are obtained by feeding back the OFF-enhanced infrared image to the corresponding surrounding-depressing neurons. Then we feed back the enhanced visible signal from OFF-antagonism system to the depressing cells in the center-surrounding antagonism receptive field. The ON-enhanced infrared image is taken as the input signal of the corresponding active cells in the neurons, then the cell response of infrared-enhance-visible is produced in the process, it is denoted as [Formula: see text]IR[Formula: see text]VIS. The three kinds of signal are considered as R, G and B components in the output composite image. Finally, some experiments are performed in order to evaluate the performance of the proposed method. The information entropy, average gradient and objective image fusion measure are used to assess the performance of the proposed method objectively. Some traditional digital signal processing-based fusion methods are also evaluated for comparison in the experiments. In this paper, the Quantitative assessment indices show that the proposed fusion model is superior to the classical Waxman’s model, and some of its performance is better than the other image fusion methods.
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Qi, Liangtao, Lei Qiu, and Xiao Zhou. "Fault diagnosis method of mechanical power system based on image processing technology." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142091409. http://dx.doi.org/10.1177/1729881420914093.
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Fault detection and diagnosis become one of today’s hot spots, which describes that image information is an important form of fault information, it can quickly through the image processing technique, and can accurately extract the characteristic signal. This article selects the color of the particle image, the integrated use of digital image processing, pattern recognition theory, the characteristic parameters of tribology knowledge, as well as the extraction, optimization, and digital; verifies the feasibility of iron spectrum of abrasive fault recognition, and provides a new efficient ferrographic wear particle image recognition method. Firstly, the grindstone image of the original color diesel engine was preprocessed, and the grindstone image of the ferrograph was identified by directly selecting grindstone from the preprocessed ferrograph image and selecting the target grindstone. According to the two types of abrasive particles, the characteristic parameters were first classified, and then the values of the characteristic parameters were obtained through the training and learning of the sample abrasive particles. In view of the large number of characteristic parameters of ferro-spectrum abrasive particles, this article determined the characteristic parameters suitable for the identification of abrasive particles in this article through the feature optimization and proved the correctness of the identification of characteristic parameters of abrasive particles through the test.
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Liang, En-Hui, and Edward K. Wong. "Hierarchical algorithms for morphological image processing." Pattern Recognition 26, no. 4 (April 1993): 511–29. http://dx.doi.org/10.1016/0031-3203(93)90107-8.
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Qian, Kai, and Prabir Bhattacharya. "Binary image processing by polynomial approach." Pattern Recognition Letters 11, no. 6 (June 1990): 395–403. http://dx.doi.org/10.1016/0167-8655(90)90110-n.
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Tharmakulasingam, Mukunthan, Nouman S. Chaudhry, Manoharanehru Branavan, Wamadeva Balachandran, Aurore C. Poirier, Mohammed A. Rohaim, Muhammad Munir, Roberto M. La Ragione, and Anil Fernando. "An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2." Electronics 10, no. 17 (August 26, 2021): 2065. http://dx.doi.org/10.3390/electronics10172065.
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An artificial intelligence-assisted low-cost portable device for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is presented here. This standalone temperature-controlled device houses tubes designed for conducting reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays. Moreover, the device utilises tubes illuminated by LEDs, an in-built camera, and a small onboard computer with automated image acquisition and processing algorithms. This intelligent device significantly reduces the normal assay run time and removes the subjectivity associated with operator interpretation of colourimetric RT-LAMP results. To further improve this device’s usability, a mobile app has been integrated into the system to control the LAMP assay environment and to visually display the assay results by connecting the device to a smartphone via Bluetooth. This study was undertaken using ~5000 images produced from the ~200 LAMP amplification assays using the prototype device. Synthetic RNA and a small panel of positive and negative SARS-CoV-2 patient samples were assayed for this study. State-of-the-art image processing and artificial intelligence algorithms were applied to these images to analyse them and to select the most efficient algorithm. The template matching algorithm for image extraction and MobileNet CNN architecture for classification results provided 98.0% accuracy with an average run time of 20 min to confirm the endpoint result. Two working points were chosen based on the best compromise between sensitivity and specificity. The high sensitivity point has a sensitivity value of 99.12% and specificity value of 70.8%, while at the high specificity point, the sensitivity is 96.05% and specificity 93.59%. Furthermore, this device provides an efficient and cost-effective platform for non-health professionals to detect not only SARS-CoV-2 but also other pathogens in resource-limited laboratories, factories, airports, schools, universities, and homes.
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Zheng, Zhiyan, Ruixuan He, Cuijun Lin, and Chunyu Huang. "Multimodal Magnetic Resonance Imaging to Diagnose Knee Osteoarthritis under Artificial Intelligence." Computational Intelligence and Neuroscience 2022 (June 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/6488889.
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This work aimed to investigate the application value of the multimodal magnetic resonance imaging (MRI) algorithm based on the low-rank decomposition denoising (LRDD) in the diagnosis of knee osteoarthritis (KOA), so as to offer a better examination method in the clinic. Seventy-eight patients with KOA were selected as the research objects, and they all underwent T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fat suppression T2WI (SE-T2WI), and fat saturation T2WI (FS-T2WI). All obtained images were processed by using the I-LRDD algorithm. According to the degree of articular cartilage lesions under arthroscopy, the patients were divided into a group I, a group II, a group III, and a group IV. The sensitivity, specificity, accuracy, and consistency of KOA diagnosis of T1WI, T2WI, SE-T2WI, and FS-T2WI were analyzed by referring to the results of arthroscopy. The results showed that the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the I-LRDD algorithm used in this work were higher than those of image block priori denoising (IBPD) and LRDD, and the time consumption was lower than that of IBDP and LRDD ( p < 0.05). The sensitivity, specificity, accuracy, and consistency (Kappa value) of multimodal MRI in the diagnosis of KOA were 88.61%, 85.3%, 87.37%, and 0.73%, respectively, which were higher than those of T1WI, T2WI, SE-T2WI, and FS-T2WI. The sensitivity, specificity, accuracy, and consistency of multimodal MRI in diagnosing lesions in group IV were 95%, 96.10%, 95.88%, and 0.70%, respectively, which were much higher than those in groups I, II, and III ( p < 0.05). In conclusion, the LRDD algorithm shows a good image processing efficacy, and the multimodal MRI showed a good diagnosis effect on KOA, which was worthy of promotion clinically.
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Xu, Hui-hong, and Dong-yuan Ge. "A novel image edge smoothing method based on convolutional neural network." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092167. http://dx.doi.org/10.1177/1729881420921676.
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In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network’s understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, main part of the image or to suppress image noise and high-frequency interference components, which could make the image’s brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. At present, there are still problems such as easy blurring of the edges of the image, poor overall smoothing effect, obvious step effect, and lack of robustness to noise on image smoothing. Based on the convolutional neural network, this article proposes a method for edge detection and deep learning for image smoothing. The results show that the research method proposed in this article solves the problem of edge detection and information capture better, significantly improves the edge effect, and protects the effectiveness of edge information. At the same time, it reduces the signal-to-noise ratio of the smoothed image and greatly improves the effect of image smoothing.
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DOUGHERTY, EDWARD R., and CHARLES R. GIARDINA. "MORPHOLOGY ON UMBRA MATRICES." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 02 (June 1988): 367–85. http://dx.doi.org/10.1142/s0218001488000224.
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The umbra transform serves as a connection between gray-scale morphology and the classical two-valued morphology of G. Matheron and H. Hadwiger. From a general set-theoretic perspective, the umbra transform of an image (or signal) results in an infinite set, even in the discrete case. By employing bound matrix image representation it is possible to represent the umbra by a finite data structure, the result being an approach that is both intuitive and computational. Moreover, the method is essentially dimensionally independent and thus applies to both morphological image and signal processing.
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Jiang, Fangyun, Xiaoping Fu, Kai Kuang, and Dan Fan. "Artificial Intelligence Algorithm-Based Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis by CT Image." Computational and Mathematical Methods in Medicine 2022 (April 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/3871994.
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The aim of this study was to investigate the effect of low-dose CT enterography (CTE) based on modified guided image filtering (GIF) algorithm in the differential diagnosis of ulcerative colitis (UC) and Crohn’s disease (CD). Methods. One hundred and twenty patients with suspected diagnosis of IBD were studied. They were randomly divided into control group (routine CT examination) and observation group (low-dose CTE examination based on improved GIF algorithm), with 60 cases in each group. Comprehensive diagnosis was used as the standard to assess the diagnostic effect. Results. (1) The peak signal-to-noise ratio (PSNR) (26.02 dB) and structural similarity (SSIM) (0.8921) of the algorithm were higher than those of GIF (17.22 dB/0.8491), weighted guided image filtering (WGIF) (23.78 dB/0.8489), and gradient domain guided image filtering (GGIF) (23.77 dB/0.7567) ( P < 0.05 ); (2) the diagnostic sensitivity (91.49%), specificity (92.31%), accuracy (91.67%), positive predictive value (97.73%), and negative predictive value (75%) of the observation group were higher than those of the control group ( P < 0.05 ); the sensitivity and specificity of CTE in the diagnosis of UD and CD were 96.77% and 81.25% and 98.33% and 93.33%, respectively ( P < 0.05 ); there were significant differences in symmetrical intestinal wall thickening and smooth serosal surface between UD and CD ( P < 0.05 ). Conclusion. (1) The improved GIF algorithm has a more effective application value in the denoising processing of low-dose CT images and can better improve the image quality; (2) the accuracy of CTE in the diagnosis of IBD is high, and CTE is of great value in the differential diagnosis of UD and CD.
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Tang, Jian, Wenxiu Yu, Guoxin Zhao, Xiangdong Jiao, and Xuepeng Ding. "Application of Bispectrum Dimensionality Reduction Method in Ultrasonic Echo Signal Processing." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 6 (November 20, 2022): 1053–60. http://dx.doi.org/10.20965/jaciii.2022.p1053.
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Processing ultrasonic echo signals to obtain high-precision residual thickness information of the pipeline wall is the key to nondestructive testing of corrosion of a long-distance pipeline. The traditional power spectrum estimation method assumes that an analyzed echo signal is Gaussian, and the useful information is insufficiently extracted, which leads to errors in the processing results. In this paper, to solve this problem, the bispectrum, which requires the least amount of computation in higher-order spectral estimation, is proposed to process an echo signal with a non-minimum phase and non-Gaussian characteristics. The bispectrum is projected onto a one-dimensional frequency space using the dimensionality reduction method, and one-dimensional diagonal slices of the bispectrum are extracted to analyze the characteristics of the echo signal, which significantly improves the intuitiveness of data processing. The experimental results show that the bispectrum dimensionality reduction method has high accuracy in processing ultrasonic echo signals, and the relative error of the residua wall thickness is below 2%. A C-scan image displaying the shape, size, depth, and other characteristics of pipeline corrosion obtained by the proposed method is much better than that using the traditional power spectrum estimation method. Therefore, the proposed method is suitable for nondestructive testing of corrosion of long-distance pipelines.
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Li, Shutao, James T. Kwok, and Yaonan Wang. "Multifocus image fusion using artificial neural networks." Pattern Recognition Letters 23, no. 8 (June 2002): 985–97. http://dx.doi.org/10.1016/s0167-8655(02)00029-6.
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Latinović, Nikola, Ilija Popadić, Branko Tomić, Aleksandar Simić, Petar Milanović, Srećko Nijemčević, Miroslav Perić, and Mladen Veinović. "Signal Processing Platform for Long-Range Multi-Spectral Electro-Optical Systems." Sensors 22, no. 3 (February 8, 2022): 1294. http://dx.doi.org/10.3390/s22031294.
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In this paper, we present a hardware and software platform for signal processing (SPP) in long-range, multi-spectral, electro-optical systems (MSEOS). Such systems integrate various cameras such as lowlight color, medium or long-wave-infrared thermal and short-wave-infrared cameras together with other sensors such as laser range finders, radars, GPS receivers, etc. on rotational pan-tilt positioner platforms. An SPP is designed with the main goal to control all components of an MSEOS and execute complex signal processing algorithms such as video stabilization, artificial intelligence-based target detection, target tracking, video enhancement, target illumination, multi-sensory image fusion, etc. Such algorithms might be very computationally demanding, so an SPP enables them to run by splitting processing tasks between a field-programmable gate array (FPGA) unit, a multicore microprocessor (MCuP) and a graphic processing unit (GPU). Additionally, multiple SPPs can be linked together via an internal Gbps Ethernet-based network to balance the processing load. A detailed description of the SPP system and experimental results of workloads for typical algorithms on demonstrational MSEOS are given. Finally, we give remarks regarding upgrading SPPs as novel FPGAs, MCuPs and GPUs become available.
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Várkonyi-Kóczy, Annamária R. "Special Issue on Selected Papers WISP'99." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 1 (January 20, 2001): 1. http://dx.doi.org/10.20965/jaciii.2001.p0001.
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Today's complex industrial and engineering systems - especially with the appearance of large-scale embedded and/or real-time systems - confront researchers and engineers with completely new challenges. Measurement and signal processing systems are involved in almost all kinds of activities in that field where control problems, system identification problems, industrial technologies, etc., are to be solved, i.e., when signals, parameters, or attributes must be measured, monitored, approximated, or determined somehow. In a large number of cases, traditional information processing tools and equipment fail to handle these problems. Not only is the handling of previously unseen spatial and temporal complexity questionable but such problems have also to be addressed such as the interaction and communication of subsystems based on entirely different modeling and information expression methods, the handling of abrupt changes within the environment and/or the processing system, the possible temporal shortage of computational power and/or loss of some data due to the former. Signal processing should even in these cases provide outputs of acceptable quality to continue the operation of the complete system, producing data for qualitative evaluations and supporting decisions. It means the introduction of new ideas for specifying, designing, implementing, and operating sophisticated signal processing systems. Intelligent - artificial intelligence, soft computing, anytime, etc. - methods are serious candidates for handling many theoretical and practical problems, providing a better description, and, in many cases, are the best if not the only alternatives for emphasizing significant aspects of system behavior. These techniques, however, are relatively new methods and up until now, not widely used in the field of signal processing because some of the critical questions related to design and verification are not answered properly and because uncertainty is maintained quite differently than in classical metrology. After the initiation of the 1999 IEEE International Workshop on Intelligent Signal Processing, WISP'99, which was the first event to start linking scientific communities working in the fields of intelligent systems and signal processing and hoping that it will attract more and more scientists and engineers in these hot topics, this special issue continues this pioneering work by offering a selection of nine papers fitting into the profile of the journal from the numerous high quality ones presented at WISP'99. These excellent papers deal with different aspects of advanced computational intelligence in signal processing, including the application of neural networks, fuzzy techniques, genetic and anytime algorithms in modeling, signal processing, noise cancellation, identification, and pattern recognition, multisensorial information fusion and intelligent classification in image processing, exact and nonexact complexity reduction, and nonclassical and mixed data and uncertainty representation and handling. As an editor of this special issue, I would like to express my thanks to all of the contributors and my belief in that the excellent research results it contains provide the basis for further strengthening and spreading of advanced computational intelligence in signal processing opening wide possibilities for new theoretical and practical achievements.