Academic literature on the topic 'Computer vision in data analytics and signal processing'
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Journal articles on the topic "Computer vision in data analytics and signal processing"
Höferlin, Benjamin, Markus Höferlin, Gunther Heidemann, and Daniel Weiskopf. "Scalable video visual analytics." Information Visualization 14, no. 1 (June 5, 2013): 10–26. http://dx.doi.org/10.1177/1473871613488571.
Full textChadebecq, François, Francisco Vasconcelos, Evangelos Mazomenos, and Danail Stoyanov. "Computer Vision in the Surgical Operating Room." Visceral Medicine 36, no. 6 (2020): 456–62. http://dx.doi.org/10.1159/000511934.
Full textLemenkova, Polina, Raphaël De Plaen, Thomas Lecocq, and Olivier Debeir. "Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium." Sensors 23, no. 1 (December 21, 2022): 56. http://dx.doi.org/10.3390/s23010056.
Full textSarada, B., M. Vinayaka Murthy, and V. Udaya Rani. "Combined secure approach based on whale optimization to improve the data classification for data analytics." Pattern Recognition Letters 152 (December 2021): 327–32. http://dx.doi.org/10.1016/j.patrec.2021.10.018.
Full textGotz, David, and Harry Stavropoulos. "DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data." IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (December 31, 2014): 1783–92. http://dx.doi.org/10.1109/tvcg.2014.2346682.
Full textXiaoru Yuan, He Xiao, Hanqi Guo, Peihong Guo, W. Kendall, Jian Huang, and Yongxian Zhang. "Scalable Multi-variate Analytics of Seismic and Satellite-based Observational Data." IEEE Transactions on Visualization and Computer Graphics 16, no. 6 (November 2010): 1413–20. http://dx.doi.org/10.1109/tvcg.2010.192.
Full textKurzhals, Kuno, and Daniel Weiskopf. "Space-Time Visual Analytics of Eye-Tracking Data for Dynamic Stimuli." IEEE Transactions on Visualization and Computer Graphics 19, no. 12 (December 2013): 2129–38. http://dx.doi.org/10.1109/tvcg.2013.194.
Full textHe, Jialuan, Zirui Xing, Tianqi Xiang, Xin Zhang, Yinghai Zhou, Chuanyu Xi, and Hai Lu. "Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring." Sensors 21, no. 17 (August 24, 2021): 5688. http://dx.doi.org/10.3390/s21175688.
Full textXu, Bao Shu, and Ze Lin Shi. "Performance Bound of Position Estimation in Image Matching." Key Engineering Materials 500 (January 2012): 766–72. http://dx.doi.org/10.4028/www.scientific.net/kem.500.766.
Full textWagner, Jorge, Wolfgang Stuerzlinger, and Luciana Nedel. "Comparing and Combining Virtual Hand and Virtual Ray Pointer Interactions for Data Manipulation in Immersive Analytics." IEEE Transactions on Visualization and Computer Graphics 27, no. 5 (May 2021): 2513–23. http://dx.doi.org/10.1109/tvcg.2021.3067759.
Full textDissertations / Theses on the topic "Computer vision in data analytics and signal processing"
Javadi, Mohammad Saleh. "Computer Vision Algorithms for Intelligent Transportation Systems Applications." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17166.
Full textNilsson, Lovisa. "Data-Driven Methods for Sonar Imaging." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176249.
Full textSimioni, Maicon Cezar. "Monitoramento da frequência cardíaca via método de magnificação de vídeo e Euleriana em tempo real." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/1373.
Full textMonitoring vital signs in patients is used to obtain relevant data for medical decisions in a fast way. However, this measurement is both inefficient and difficult, if not impossible in certain cases, such as in burnt victims, due to the impossibility of placing the electrodes directly on the skin or in infants, because of the fragility of skin. This study aims to develop of a system for continuous acquisition of photopletismografics (PPG) signals for the telemetry of heart rate in real time in a low cost platform using the OpenCV library and the method developed by MIT called the Eulerian Video Magnification, amplifying variations that are imperceptible to the naked eye. To develop the system were used the hardware platform Raspberry Pi version B with ARM11 700MHz processor and 512MB RAM. The heart rate data collected from the experi- ments were compared with data collected by a finger oximeter model More Fitness MF-425 it was chosen, by using the same working principle "PPG"to effect the measurement. After data collection was estimated the confidence interval to measure system accuracy, which corresponded to 96,5% compared to the oximeter used. It became clear that the developed system used to measure heart rate via magnification method of Eulerian live video is a low-cost technology (approximately R$ 300.00) compared to the multiparameter monitors used for monitoring critically patients, ranging in cost from R$ 8,000.00 to R$ 34,000.00. So also, it contributes to cost reduction in the treatment to the patient in need of constant monitoring, enabling with the savings generated by the acquisition and deployment of this technology makes possible greater investment in other areas of hospitals.
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Sandberg, David. "Model-Based Video Coding Using a Colour and Depth Camera." Thesis, Linköpings universitet, Datorseende, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68737.
Full textI detta examensarbete har en modellbaserad videokodningsalgoritm utvecklats som använder data från en djup- och färgkamera, exempelvis Microsoft Kinect. Det finns flera fördelar med en modellbaserad representation av en video över den mer vanligt förekommande blockbaserade varianten, vilket används av bland annat H.264. Några exempel är möjligheten att rendera videon i 3D samt från alternativa vyer, placera in objekt i videon samt möjlighet för användaren att interagera med scenen. Detta examensarbete påvisar en väldigt effektiv metod för komprimering av scengeometri. Resultaten av den presenterade algoritmen visar att möjligheten att uppnå väldigt låg bithastighet med jämförelsebara resultat med H.264-standarden.
Skepetzis, Vasilios, and Pontus Hedman. "The Effect of Beautification Filters on Image Recognition : "Are filtered social media images viable Open Source Intelligence?"." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799.
Full textBeyou, Sébastien. "Estimation de la vitesse des courants marins à partir de séquences d'images satellitaires." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00870722.
Full textMalmgren, Henrik. "Revision of an artificial neural network enabling industrial sorting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392690.
Full text(8771429), Ashley S. Dale. "3D OBJECT DETECTION USING VIRTUAL ENVIRONMENT ASSISTED DEEP NETWORK TRAINING." Thesis, 2021.
Find full textAn RGBZ synthetic dataset consisting of five object classes in a variety of virtual environments and orientations was combined with a small sample of real-world image data and used to train the Mask R-CNN (MR-CNN) architecture in a variety of configurations. When the MR-CNN architecture was initialized with MS COCO weights and the heads were trained with a mix of synthetic data and real world data, F1 scores improved in four of the five classes: The average maximum F1-score of all classes and all epochs for the networks trained with synthetic data is F1∗ = 0.91, compared to F1 = 0.89 for the networks trained exclusively with real data, and the standard deviation of the maximum mean F1-score for synthetically trained networks is σ∗ F1 = 0.015, compared to σF 1 = 0.020 for the networks trained exclusively with real data. Various backgrounds in synthetic data were shown to have negligible impact on F1 scores, opening the door to abstract backgrounds and minimizing the need for intensive synthetic data fabrication. When the MR-CNN architecture was initialized with MS COCO weights and depth data was included in the training data, the net- work was shown to rely heavily on the initial convolutional input to feed features into the network, the image depth channel was shown to influence mask generation, and the image color channels were shown to influence object classification. A set of latent variables for a subset of the synthetic datatset was generated with a Variational Autoencoder then analyzed using Principle Component Analysis and Uniform Manifold Projection and Approximation (UMAP). The UMAP analysis showed no meaningful distinction between real-world and synthetic data, and a small bias towards clustering based on image background.
Books on the topic "Computer vision in data analytics and signal processing"
1936-, Aggarwal J. K., North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Research Workshop on Multisensor Fusion for Computer Vision (1989 : Grenoble, France), eds. Multisensor fusion for computer vision. Berlin: Springer-Verlag, 1993.
Find full textKatsaggelos, Aggelos K. Signal Recovery Techniques for Image and Video Compression and Transmission. Boston, MA: Springer US, 1998.
Find full textChen, Li M. Digital Functions and Data Reconstruction: Digital-Discrete Methods. New York, NY: Springer New York, 2013.
Find full textSoille, Pierre. Mathematical Morphology and Its Applications to Image and Signal Processing: 10th International Symposium, ISMM 2011, Verbania-Intra, Italy, July 6-8, 2011. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Find full textHendriks, Cris L. Luengo. Mathematical Morphology and Its Applications to Signal and Image Processing: 11th International Symposium, ISMM 2013, Uppsala, Sweden, May 27-29, 2013. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Find full textChaudhuri, Subhasis. Hyperspectral Image Fusion. New York, NY: Springer New York, 2013.
Find full textCarlo, Arcelli, Cordella Luigi P, and Sanniti di Baja Gabriella, eds. Advances in visual form analysis: Proceedings of the Third International Workshop on Visual Form, Capri, Italy, May 28-30, 1997. Singapore: World Scientific, 1997.
Find full textPetra, Perner, Salvetti Ovidio, Siekmann Jörg H, and SpringerLink (Online service), eds. Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry: Third International Conference, MDA 2008 Leipzig, Germany, July 14, 2008 Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.
Find full textOvidio, Salvetti, and SpringerLink (Online service), eds. Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry: International Conferences MDA 2006/2007, Leipzig, Germany, July 18, 2007. Selected Papers. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2007.
Find full textSencar, Husrev T. Digital Image Forensics: There is More to a Picture than Meets the Eye. New York, NY: Springer New York, 2013.
Find full textBook chapters on the topic "Computer vision in data analytics and signal processing"
Rot, Peter, Peter Peer, and Vitomir Štruc. "Detecting Soft-Biometric Privacy Enhancement." In Handbook of Digital Face Manipulation and Detection, 391–411. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_18.
Full textRani, Jyotsna, Ram Kumar, Abahan Sarkar, and Fazal A. Talukdar. "A Study on Various Image Processing Techniques and Hardware Implementation Using Xilinx System Generator." In Computer Vision, 930–45. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch038.
Full textBaharadwaj, Nitin, Sheena Wadhwa, Pragya Goel, Isha Sethi, Chanpreet Singh Arora, Aviral Goel, Sonika Bhatnagar, and Harish Parthasarathy. "De-Noising, Clustering, Classification, and Representation of Microarray Data for Disease Diagnostics." In Research Developments in Computer Vision and Image Processing, 149–74. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4558-5.ch009.
Full textPurushothaman, Geethanjali. "Bio-Inspired Techniques in Rehabilitation Engineering for Control of Assistive Devices." In Computer Vision, 2065–82. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch090.
Full textGranero, Marco Aurélio, Marco Antônio Gutierrez, and Eduardo Tavares Costa. "Rebuilding IVUS images from raw data of the RF signal exported by IVUS equipment." In Emerging Trends in Image Processing, Computer Vision and Pattern Recognition, 87–97. Elsevier, 2015. http://dx.doi.org/10.1016/b978-0-12-802045-6.00006-5.
Full textMohanchandra, Kusuma, and Snehanshu Saha. "Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms." In Cognitive Analytics, 1577–97. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch081.
Full textHota, Rudra Narayan, Kishore Jonna, and P. Radha Krishna. "Video Stream Mining for On-Road Traffic Density Analytics." In Pattern Discovery Using Sequence Data Mining, 182–94. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-056-9.ch011.
Full textSingh, Shatakshi, Kanika Gautam, Prachi Singhal, Sunil Kumar Jangir, and Manish Kumar. "A Survey on Intelligence Tools for Data Analytics." In Advances in Data Mining and Database Management, 73–95. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3053-5.ch005.
Full textAbdul Karim, Samsul Ariffin, Nur Atiqah Binti Zulkifli, A'fza Binti Shafie, Muhammad Sarfraz, Abdul Ghaffar, and Kottakkaran Sooppy Nisar. "Medical Image Zooming by Using Rational Bicubic Ball Function." In Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies, 146–61. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-4444-0.ch008.
Full textVocaturo, Eugenio. "Image Classification Techniques." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 22–49. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch003.
Full textConference papers on the topic "Computer vision in data analytics and signal processing"
Katarya, Rahul, and Sajal Jain. "Exploration of Big Data Analytics in Healthcare Analytics." In 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP). IEEE, 2020. http://dx.doi.org/10.1109/icccsp49186.2020.9315192.
Full text"Computer Vision Techniques for Target Detection in Ground Penetrating Radar Data." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.786-034.
Full textNylanden, Teemu, Heikki Kultala, Ilkka Hautala, Jani Boutellier, Jari Hannuksela, and Olli Silven. "Programmable data parallel accelerator for mobile computer vision." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418271.
Full textBaluja, Shumeet, and Michele Covell. "Audio Fingerprinting: Combining Computer Vision & Data Stream Processing." In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366210.
Full textWu, Mark, Chen Heng, Haibin Zhu, and Haoyang Cai. "COVID-19 detection based on Computer Vision and Big Data." In 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2022. http://dx.doi.org/10.1109/icsp54964.2022.9778429.
Full textAngulo, Carlos A., Christian D. Hernandez, Gabriel Rincon, Carlos A. Boada, Javier Castillo, and Carlos A. Fajardo. "Accelerating huffman decoding of seismic data on GPUs." In 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA). IEEE, 2015. http://dx.doi.org/10.1109/stsiva.2015.7330430.
Full textChatar, Crispin, Suhas Suresha, Laetitia Shao, Soumya Gupta, and Indranil Roychoudhury. "Determining Rig State from Computer Vision Analytics." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204086-ms.
Full textContreras Contreras, Ghiordy Ferney, Byron Medina Delgado, Dinael Guevara Ibarra, Cristiano Leite de Castro, and Brayan Rene Acevedo Jaimes. "Cluster CV2: a Computer Vision Approach to Spatial Identification of Data Clusters." In 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA). IEEE, 2019. http://dx.doi.org/10.1109/stsiva.2019.8730239.
Full textSalazar-Castro, J. A., Y. C. Rosas-Narvaez, A. D. Pantoja, Juan C. Alvarado-Perez, and Diego H. Peluffo-Ordonez. "Interactive interface for efficient data visualization via a geometric approach." In 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA). IEEE, 2015. http://dx.doi.org/10.1109/stsiva.2015.7330397.
Full textCastelar, Jairo A., Carlos A. Angulo, and Carlos A. Fajardo. "Parallel decompression of seismic data on GPU using a lifting wavelet algorithm." In 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA). IEEE, 2015. http://dx.doi.org/10.1109/stsiva.2015.7330432.
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