Literatura académica sobre el tema "REAL IMAGE PREDICTION"
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Artículos de revistas sobre el tema "REAL IMAGE PREDICTION"
Takezawa, Takuma y Yukihiko Yamashita. "Wavelet Based Image Coding via Image Component Prediction Using Neural Networks". International Journal of Machine Learning and Computing 11, n.º 2 (marzo de 2021): 137–42. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1026.
Texto completoHong, Yan, Li Niu y Jianfu Zhang. "Shadow Generation for Composite Image in Real-World Scenes". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 1 (28 de junio de 2022): 914–22. http://dx.doi.org/10.1609/aaai.v36i1.19974.
Texto completoSather, A. P., S. D. M. Jones y D. R. C. Bailey. "Real-time ultrasound image analysis for the estimation of carcass yield and pork quality". Canadian Journal of Animal Science 76, n.º 1 (1 de marzo de 1996): 55–62. http://dx.doi.org/10.4141/cjas96-008.
Texto completoTham, Hwee Sheng, Razaidi Hussin y Rizalafande Che Ismail. "A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect". IOP Conference Series: Earth and Environmental Science 1064, n.º 1 (1 de julio de 2022): 012048. http://dx.doi.org/10.1088/1755-1315/1064/1/012048.
Texto completoPintelas, Emmanuel, Meletis Liaskos, Ioannis E. Livieris, Sotiris Kotsiantis y Panagiotis Pintelas. "Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction". Journal of Imaging 6, n.º 6 (28 de mayo de 2020): 37. http://dx.doi.org/10.3390/jimaging6060037.
Texto completoSnider, Eric J., Sofia I. Hernandez-Torres y Ryan Hennessey. "Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting". Diagnostics 13, n.º 3 (23 de enero de 2023): 417. http://dx.doi.org/10.3390/diagnostics13030417.
Texto completoFroning, Dieter, Eugen Hoppe y Ralf Peters. "The Applicability of Machine Learning Methods to the Characterization of Fibrous Gas Diffusion Layers". Applied Sciences 13, n.º 12 (9 de junio de 2023): 6981. http://dx.doi.org/10.3390/app13126981.
Texto completoMoskolaï, Waytehad Rose, Wahabou Abdou, Albert Dipanda y Kolyang. "Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review". Remote Sensing 13, n.º 23 (27 de noviembre de 2021): 4822. http://dx.doi.org/10.3390/rs13234822.
Texto completoRajesh, E., Shajahan Basheer, Rajesh Kumar Dhanaraj, Soni Yadav, Seifedine Kadry, Muhammad Attique Khan, Ye Jin Kim y Jae-Hyuk Cha. "Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner". Diagnostics 13, n.º 1 (28 de diciembre de 2022): 95. http://dx.doi.org/10.3390/diagnostics13010095.
Texto completoBhimte, Sumit, Hrishikesh hasabnis, Rohit Shirsath, Saurabh Sonar y Mahendra Salunke. "Severity Prediction System for Real Time Pothole Detection". Journal of University of Shanghai for Science and Technology 23, n.º 07 (29 de julio de 2021): 1328–34. http://dx.doi.org/10.51201/jusst/21/07356.
Texto completoTesis sobre el tema "REAL IMAGE PREDICTION"
Raykhel, Ilya. "Real-time automatic price prediction for eBay online trading /". Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2697.pdf.
Texto completoYin, Ling. "Automatic Stereoscopic 3D Chroma-Key Matting Using Perceptual Analysis and Prediction". Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31851.
Texto completoKaneva, Biliana K. "Large databases of real and synthetic images for feature evaluation and prediction". Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/71478.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (p. 157-167).
Image features are widely used in computer vision applications from stereo matching to panorama stitching to object and scene recognition. They exploit image regularities to capture structure in images both locally, using a patch around an interest point, and globally, over the entire image. Image features need to be distinctive and robust toward variations in scene content, camera viewpoint and illumination conditions. Common tasks are matching local features across images and finding semantically meaningful matches amongst a large set of images. If there is enough structure or regularity in the images, we should be able not only to find good matches but also to predict parts of the objects or the scene that were not directly captured by the camera. One of the difficulties in evaluating the performance of image features in both the prediction and matching tasks is the availability of ground truth data. In this dissertation, we take two different approaches. First, we propose using a photorealistic virtual world for evaluating local feature descriptors and leaning new feature detectors. Acquiring ground truth data and, in particular pixel to pixel correspondences between images, in complex 3D scenes under different viewpoint and illumination conditions in a controlled way is nearly impossible in a real world setting. Instead, we use a high-resolution 3D model of a city to gain complete and repeatable control of the environment. We calibrate our virtual world evaluations by comparing against feature rankings made from photographic data of the same subject matter (the Statue of Liberty). We then use our virtual world to study the effects on descriptor performance of controlled changes in viewpoint and illumination. We further employ machine learning techniques to train a model that would recognize visually rich interest points and optimize the performance of a given descriptor. In the latter part of the thesis, we take advantage of the large amounts of image data available on the Internet to explore the regularities in outdoor scenes and, more specifically, the matching and prediction tasks in street level images. Generally, people are very adept at predicting what they might encounter as they navigate through the world. They use all of their prior experience to make such predictions even when placed in unfamiliar environment. We propose a system that can predict what lies just beyond the boundaries of the image using a large photo collection of images of the same class, but not from the same location in the real world. We evaluate the performance of the system using different global or quantized densely extracted local features. We demonstrate how to build seamless transitions between the query and prediction images, thus creating a photorealistic virtual space from real world images.
by Biliana K. Kaneva.
Ph.D.
Vestin, Albin y Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms". Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
Texto completoKHICHI, MANISH. "DEEPFAKE OR REAL IMAGE PREDICTION USING MESONET". Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18952.
Texto completoLee, Hsua-Yun y 李炫運. "Real-Time Multi-Object Tracking Algorithm Using Improved Object-Image-Completeness and Prediction-Search". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/8n9yym.
Texto completo國立中興大學
電機工程學系所
101
Except for general camera, currently most of robust techniques for multi-object tracking are to use other assistant sensors such as IR, stereo image sensor or multiple cameras for acquiring other information (e.g. depth field data) to segment objects from background. There are also many researches in single image sequence, however, most of them can''t accurately segment objects from background in complex background, moreover, can''t track the overlapped objects. Furthermore, they usually use the complex algorithm. This thesis presents a method which avoids the common practice of using a complex algorithm for multi-object tracking based on single image sequence to achieve low cost and real-time. We propose a “Prediction-Search” to lower the computation for real-time demand. Furthermore, we also propose an improved “Object-Image-Completeness” to improve the broken image issue for target objects. In addition to “Prediction-Search”, we add the distance and color comparison algorithms for tracking assistant to make the tracking robust. The “Prediction-Search” has a very low computation to achieve tracking task so that the tracking speed will be very fast. In general, most of tracks will be completed by “Prediction-Search”, and the minority need distance comparison, and a few of the minority need color comparison, so the tracking speed will be very fast. Regarding the real tracking speed, our system can keep 30 frames/sec tracking speed based on 30 frames/sec input image sequence for real time demand in our test platform. In tracking performance, even if the objects are overlapping each other, the proposed algorithm still can track each object. We have implemented the tracking task for 18 objects, 3 objects overlap, different figure objects, different size objects, irregular path objects, walking peoples and running peoples.
Harding, G. y M. Bloj. "Real and predicted influence of image manipulations on eye movements during scene recognition". 2010. http://hdl.handle.net/10454/6004.
Texto completoLibros sobre el tema "REAL IMAGE PREDICTION"
Carr, Michael William. International Marine's weather predicting simplified: How to read weather charts and satellite images. Camden, Me: International Marine, 1999.
Buscar texto completoCapítulos de libros sobre el tema "REAL IMAGE PREDICTION"
Robinson, Robert, Ozan Oktay, Wenjia Bai, Vanya V. Valindria, Mihir M. Sanghvi, Nay Aung, José M. Paiva et al. "Real-Time Prediction of Segmentation Quality". En Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 578–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00937-3_66.
Texto completoBertini, M., A. Del Bimbo y W. Nunziati. "Soccer Videos Highlight Prediction and Annotation in Real Time". En Image Analysis and Processing – ICIAP 2005, 637–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_78.
Texto completoJoldes, Grand Roman, Adam Wittek, Mathieu Couton, Simon K. Warfield y Karol Miller. "Real-Time Prediction of Brain Shift Using Nonlinear Finite Element Algorithms". En Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, 300–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04271-3_37.
Texto completoZuo, Fei y Peter H. N. de With. "Real-Time Facial Feature Extraction by Cascaded Parameter Prediction and Image Optimization". En Lecture Notes in Computer Science, 651–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_79.
Texto completoZhong, Lijun, Qifeng Yu, Jiexin Zhou, Xiaohu Zhang y Yani Lu. "Real-Time Interpretation Method for Shooting-Range Image Based on Position Prediction". En Lecture Notes in Computer Science, 68–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34120-6_6.
Texto completoDing, Yukun, Dewen Zeng, Mingqi Li, Hongwen Fei, Haiyun Yuan, Meiping Huang, Jian Zhuang y Yiyu Shi. "Towards Efficient Human-Machine Collaboration: Real-Time Correction Effort Prediction for Ultrasound Data Acquisition". En Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 461–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87193-2_44.
Texto completoMuthukumar, Pratyush, Emmanuel Cocom, Jeanne Holm, Dawn Comer, Anthony Lyons, Irene Burga, Christa Hasenkopf y Mohammad Pourhomayoun. "Real-Time Spatiotemporal Air Pollution Prediction with Deep Convolutional LSTM Through Satellite Image Analysis". En Advances in Data Science and Information Engineering, 315–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71704-9_20.
Texto completoSingh, Chandan, Wooseok Ha y Bin Yu. "Interpreting and Improving Deep-Learning Models with Reality Checks". En xxAI - Beyond Explainable AI, 229–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_12.
Texto completoKlingner, Marvin y Tim Fingscheidt. "Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised Task". En Deep Neural Networks and Data for Automated Driving, 149–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_5.
Texto completoSchwan, Constanze y Wolfram Schenck. "Design of Interpretable Machine Learning Tasks for the Application to Industrial Order Picking". En Technologien für die intelligente Automation, 291–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-662-64283-2_21.
Texto completoActas de conferencias sobre el tema "REAL IMAGE PREDICTION"
Venkataswamy, Prashanth, M. Omair Ahmad y M. N. S. Swamy. "Real-time Image Aesthetic Score Prediction for Portable Devices". En 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2020. http://dx.doi.org/10.1109/mwscas48704.2020.9184491.
Texto completoSarvan, N. "Analysis Prediction Template Toolkit (APTT) for real-time image processing". En 7th International Conference on Image Processing and its Applications. IEE, 1999. http://dx.doi.org/10.1049/cp:19990293.
Texto completoHussain, Akhtar, Nitin Afzulpur, Muhammad Waseem Ashraf, Shahzadi Tayyaba y Abdul Rehman Abbasi. "Detecting unintended gesture in real-time video for mental state prediction". En 2011 International Conference on Graphic and Image Processing. SPIE, 2011. http://dx.doi.org/10.1117/12.913576.
Texto completoChen, Xiaokang, Yajie Xing y Gang Zeng. "Real-Time Semantic Scene Completion Via Feature Aggregation And Conditioned Prediction". En 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191318.
Texto completo"Online and Real-time Network for Video Pedestrian Intent Prediction". En 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2022. http://dx.doi.org/10.1109/dicta56598.2022.10034602.
Texto completoLv, Mingming, Yuanlong Hou, Rongzhong Liu y Runmin Hou. "Fast template matching based on grey prediction for real-time object tracking". En Eighth International Conference on Graphic and Image Processing, editado por Yulin Wang, Tuan D. Pham, Vit Vozenilek, David Zhang y Yi Xie. SPIE, 2017. http://dx.doi.org/10.1117/12.2266225.
Texto completoHandrich, Sebastian, Laslo Dinges, Frerk Saxen, Ayoub Al-Hamadi y Sven Wachmuth. "Simultaneous Prediction of Valence / Arousal and Emotion Categories in Real-time". En 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2019. http://dx.doi.org/10.1109/icsipa45851.2019.8977743.
Texto completoZhaoxia, Xu, Xing Renpeng, Lin Yong y Shan Tiecheng. "Real-time prediction of urban traffic flow based on data mining". En 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2021. http://dx.doi.org/10.1109/ipec51340.2021.9421212.
Texto completoRahman, A. K. M. Mahbubur, Md Iftekhar Tanveer, Asm Iftekhar Anam y Mohammed Yeasin. "IMAPS: A smart phone based real-time framework for prediction of affect in natural dyadic conversation". En 2012 Visual Communications and Image Processing (VCIP). IEEE, 2012. http://dx.doi.org/10.1109/vcip.2012.6410828.
Texto completoTong, Wei, Yubing Gao, Edmond Q. Wu y Li-Min Zhu. "Self-Supervised Depth Estimation Based on the Consistency of Synthetic-real Image Prediction". En 2023 International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE, 2023. http://dx.doi.org/10.1109/icarm58088.2023.10218857.
Texto completoInformes sobre el tema "REAL IMAGE PREDICTION"
Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor y Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, enero de 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
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