Добірка наукової літератури з теми "Discriminative Pose Robust Descriptors"
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Статті в журналах з теми "Discriminative Pose Robust Descriptors"
Kniaz, V. V., V. V. Fedorenko, and N. A. Fomin. "DEEP LEARNING FOR LOWTEXTURED IMAGE MATCHING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 513–18. http://dx.doi.org/10.5194/isprs-archives-xlii-2-513-2018.
Повний текст джерелаRabba, Salah, Matthew Kyan, Lei Gao, Azhar Quddus, Ali Shahidi Zandi, and Ling Guan. "Discriminative Robust Head-Pose and Gaze Estimation Using Kernel-DMCCA Features Fusion." International Journal of Semantic Computing 14, no. 01 (March 2020): 107–35. http://dx.doi.org/10.1142/s1793351x20500014.
Повний текст джерелаKawulok, Michal, Jakub Nalepa, Jolanta Kawulok, and Bogdan Smolka. "Dynamics of facial actions for assessing smile genuineness." PLOS ONE 16, no. 1 (January 5, 2021): e0244647. http://dx.doi.org/10.1371/journal.pone.0244647.
Повний текст джерелаSanyal, Soubhik, Sivaram Prasad Mudunuri, and Soma Biswas. "Discriminative pose-free descriptors for face and object matching." Pattern Recognition 67 (July 2017): 353–65. http://dx.doi.org/10.1016/j.patcog.2017.02.016.
Повний текст джерелаSingh, Geetika, and Indu Chhabra. "Discriminative Moment Feature Descriptors for Face Recognition." International Journal of Computer Vision and Image Processing 5, no. 2 (July 2015): 81–97. http://dx.doi.org/10.4018/ijcvip.2015070105.
Повний текст джерелаHajraoui, Abdellatif, and Mohamed Sabri. "Generic and Robust Method for Head Pose Estimation." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (November 1, 2016): 439. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp439-446.
Повний текст джерелаLin, Guojun, Meng Yang, Linlin Shen, Mingzhong Yang, and Mei Xie. "Robust and discriminative dictionary learning for face recognition." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (March 2018): 1840004. http://dx.doi.org/10.1142/s0219691318400040.
Повний текст джерелаSingh, Geetika, and Indu Chhabra. "Integrating Global Zernike and Local Discriminative HOG Features for Face Recognition." International Journal of Image and Graphics 16, no. 04 (October 2016): 1650021. http://dx.doi.org/10.1142/s0219467816500212.
Повний текст джерелаSCHWARTZ, WILLIAM ROBSON, and HELIO PEDRINI. "IMPROVED FRACTAL IMAGE COMPRESSION BASED ON ROBUST FEATURE DESCRIPTORS." International Journal of Image and Graphics 11, no. 04 (October 2011): 571–87. http://dx.doi.org/10.1142/s0219467811004251.
Повний текст джерелаChen, Si, Dong Yan, and Yan Yan. "Directional Correlation Filter Bank for Robust Head Pose Estimation and Face Recognition." Mathematical Problems in Engineering 2018 (October 21, 2018): 1–10. http://dx.doi.org/10.1155/2018/1923063.
Повний текст джерелаДисертації з теми "Discriminative Pose Robust Descriptors"
Ferraz, Colomina Luis. "Viewpoint invariant features and robust monocular Camera pose estimation." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/368568.
Повний текст джерелаCamera pose with respect to a real world scene determines the perspective projection of the scene on the image plane. The analysis of the deformations between pairs of images due to perspective and camera pose have led many Computer Vision researchers to deal with problems such as, the ability to detect and match the same local features in different images or recovering for each image its original camera pose. The difference between both problems lie in the locality of the image information, while for local features we look for local invariance, for camera pose we look for more global information sources, like sets of local features. Local feature detection is a cornerstone of a wide range of Computer Vision applications since it allows to match and localize specific image regions. In the first part of this work local invariance of features is tackled proposing algorithms to improve the robustness to image perturbations, perspective changes and discriminative power from two points of view: (i) accurate detection of non-redundant corner and blob image structures based on their movement along different scales, and (ii) learning robust descriptors. Concretely, we propose three scale invariant detectors, detecting one of them corners and blobs simultaneously with a negligible computational overhead. We also propose one blob affine invariant detector. In terms of descriptors, we propose to learn them using Convolutional Neural Networks and large datasets of annotated image regions under different image conditions. Despite being a topic researched for decades camera pose estimation is still an open challenge. The goal of the Perspective-n-Point (PnP) problem is to estimate the location and orientation of a calibrated camera from n known 3D-to-2D point correspondences between a previously known 3D model of a real scene and 2D features obtained from a single image. In the second part of this thesis camera pose estimation is addressed with novel PnP approaches, which reduces drastically the computational cost allowing real-time applications independently of the number of correspondences. In addition, we provide an integrated outlier rejection mechanism with a negligible computational overhead and a novel method to increase the accuracy by modelling the reprojection error of each correspondence. Finally in the case of complex and huge scenarios, with maybe hundreds of thousands of features, is difficult and computationally expensive to be able to find correct 3D-to-2D correspondences. In this case, a robust and accurate top-down approach for camera pose estimation is proposed. Our approach takes advantage of high-level classifiers, which estimates a rough camera pose, in order to constrain the 3D-to-2D correspondences to be used by our accurate and robust to outliers PnP method.
Sanyal, Soubhik. "Discriminative Descriptors for Unconstrained Face and Object Recognition." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4177.
Повний текст джерелаЧастини книг з теми "Discriminative Pose Robust Descriptors"
Chuang, Meng-Che, Jenq-Neng Hwang, and Kresimir Williams. "Automatic Fish Segmentation and Recognition for Trawl-Based Cameras." In Computer Vision, 847–74. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch034.
Повний текст джерелаТези доповідей конференцій з теми "Discriminative Pose Robust Descriptors"
Sanyal, Soubhik, Devraj Mandal, and Soma Biswas. "Aligned discriminative pose robust descriptors for face and object recognition." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296395.
Повний текст джерелаSanyal, Soubhik, Sivaram Prasad Mudunuri, and Soma Biswas. "Discriminative Pose-Free Descriptors for Face and Object Matching." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.437.
Повний текст джерелаSeo, Jeong-Jik, Hyung-Il Kim, and Yong Man Ro. "Pose-Robust and Discriminative Feature Representation by Multi-task Deep Learning for Multi-view Face Recognition." In 2015 IEEE International Symposium on Multimedia (ISM). IEEE, 2015. http://dx.doi.org/10.1109/ism.2015.93.
Повний текст джерелаNayak, Anshul, Azim Eskandarian, Prasenjit Ghorai, and Zachary Doerzaph. "A Comparative Study on Feature Descriptors for Relative Pose Estimation in Connected Vehicles." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70693.
Повний текст джерелаZhang, Yichen, Jiehong Lin, Ke Chen, Zelin Xu, Yaowei Wang, and Kui Jia. "Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/193.
Повний текст джерелаSarkhel, Ritesh, and Arnab Nandi. "Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/466.
Повний текст джерелаBhabhrawala, Talib, and Venkat Krovi. "Shape Recovery From Medical Image Data Using Extended Superquadrics." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84738.
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