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Relevant bibliographies by topics / Computer vision; robust model fitting; preference

Academic literature on the topic 'Computer vision; robust model fitting; preference'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Computer vision; robust model fitting; preference"

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Zhao, Xi, Yun Zhang, Shoulie Xie, Qianqing Qin, Shiqian Wu, and Bin Luo. "Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting." Sensors 20, no. 11 (May 27, 2020): 3037. http://dx.doi.org/10.3390/s20113037.

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Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting.

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Wang, Hanzi, and David Suter. "Using symmetry in robust model fitting." Pattern Recognition Letters 24, no. 16 (December 2003): 2953–66. http://dx.doi.org/10.1016/s0167-8655(03)00156-9.

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Wang, Yiru, Yinlong Liu, Xuechen Li, Chen Wang, Manning Wang, and Zhijian Song. "GORFLM: Globally Optimal Robust Fitting for Linear Model." Signal Processing: Image Communication 84 (May 2020): 115834. http://dx.doi.org/10.1016/j.image.2020.115834.

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Pham, Trung T., Tat-Jun Chin, Jin Yu, and David Suter. "The Random Cluster Model for Robust Geometric Fitting." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 8 (August 2014): 1658–71. http://dx.doi.org/10.1109/tpami.2013.2296310.

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Zhang, Zongliang, Jonathan Li, Yulan Guo, Xin Li, Yangbin Lin, Guobao Xiao, and Cheng Wang. "Robust procedural model fitting with a new geometric similarity estimator." Pattern Recognition 85 (January 2019): 120–31. http://dx.doi.org/10.1016/j.patcog.2018.07.027.

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Wimmer, M., F. Stulp, S. Pietzsch, and B. Radig. "Learning Local Objective Functions for Robust Face Model Fitting." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 8 (August 2008): 1357–70. http://dx.doi.org/10.1109/tpami.2007.70793.

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WANG, HAI-JUN, and MING LIU. "ACTIVE CONTOURS DRIVEN BY LOCAL GAUSSIAN DISTRIBUTION FITTING ENERGY BASED ON LOCAL ENTROPY." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 06 (September 2013): 1355008. http://dx.doi.org/10.1142/s0218001413550082.

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This paper presents a scheme of improvement on the local Gaussian distribution fitting energy (LGDF) model in terms of robustness to initialization and noise. The LGDF energy is redefined as a weighted energy integral. The weights are defined based on local entropy deriving from a gray level distribution of local image, which enables the proposed model to be robust to the initialization. Experimental results prove that the proposed model is more robustness to noise than the original LGDF model, local binary fitting (LBF) model and local image fitting (LIF) model.

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Wong, Hoi Sim, Tat-Jun Chin, Jin Yu, and David Suter. "A simultaneous sample-and-filter strategy for robust multi-structure model fitting." Computer Vision and Image Understanding 117, no. 12 (December 2013): 1755–69. http://dx.doi.org/10.1016/j.cviu.2013.08.007.

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Wang, Hanzi, and David Suter. "MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation." International Journal of Computer Vision 59, no. 2 (September 2004): 139–66. http://dx.doi.org/10.1023/b:visi.0000022287.61260.b0.

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Verma, Rachna, and Arvind Kumar Verma. "A Clustering and Outlier Detection Scheme for Robust Parametric Model Estimation for Plane Fitting." Applied Mechanics and Materials 789-790 (September 2015): 770–75. http://dx.doi.org/10.4028/www.scientific.net/amm.789-790.770.

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Extraction of geometric information and reconstruction of a parametric model from the data points captured by various sensors or generated by various image preprocessing algorithms is a vital research issue for many computer vision and robotics applications. The aim is to reconstruct 3D objects, consisting of planar patches, in a scene from its point cloud captured by a sensor set. A reconstructed scene has many applications such as stereo vision, robot navigation, medical imaging, etc. Unfortunately, the captured point cloud often gets corrupted due to sensor errors/malfunctioning and preprocessing algorithms. The corrupted data pose difficulty in accurate estimation of underlying geometric model parameters. In this paper, a new algorithm has been proposed to efficiently and accurately estimate the model parameters in heavily corrupted data points. The method is based on forming clusters of estimated planes with reference to a fixed plane. Clustering is accomplished on the basis of angles and distances of estimated planes from the reference plane. The proposed method is implemented over a wide range of data points. It is a robust technique and observed to outperform the widely used RANSAC algorithm in terms of accuracy and computational efficiency.

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Dissertations / Theses on the topic "Computer vision; robust model fitting; preference"

1

Wang, Hanzi. "Robust statistics for computer vision : model fitting, image segmentation and visual motion analysis." Monash University, Dept. of Electrical and Computer Systems Engineering, 2004. http://arrow.monash.edu.au/hdl/1959.1/5345.

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Wong, Hoi Sim. "A preference analysis approach to robust geometric model fitting in computer vision." Thesis, 2013. http://hdl.handle.net/2440/82075.

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Robust model fitting is a crucial task in numerous computer vision applications, where the information of interest is often expressed as a mathematical model. The goal of model fitting is to estimate the model parameters that “best” explain the observed data. However, robust model fitting is a challenging problem in computer vision, since vision data are (1) unavoidably contaminated by outliers due to imperfections in data acquisition and preprocessing, and (2) often contain multiple instances (or structures) of a model. Many robust fitting methods involve generating hypotheses using random sampling, and then (1) score the hypotheses using a robust criterion or (2) use a mode seeking or clustering method to elicit the potential structures in the data. Obtaining a good set of hypotheses is crucial for success, however this is often timeconsuming, especially for heavily contaminated data. In addition, many irrelevant hypotheses are unavoidably generated during sampling process. This frequently becomes an obstacle for accurate estimation, and has been ignored in previous methods. In particular, mode seeking-based fitting methods are very sensitive to the proportion of good/bad hypotheses. This thesis proposes several sampling methods for rapid and effective generation of good hypotheses, and hypothesis filtering methods to remove bad hypotheses for accurate estimation. The techniques developed here can be easily integrated into existing fitting methods to significantly improve fitting accuracy. We also propose a hierarchical fitting method, which recognizes that details in real-life data are organized hierarchically (i.e., large structures cascading down to finer structures). This can avoid excessive parameter tuning to obtain a particular fitting result, whereas existing fitting methods often fit data with a single number of structures and permit only one interpretation of the data. The algorithms in this thesis are motivated by preference (or ranking) analysis, which has been widely used in areas such as information retrieval, artificial intelligence and marketing. Preference analysis provides a sophisticated non-parametric approach to analyzing the data and hypotheses in model fitting problems. The algorithms developed here are shown to be more reliable than previous methods, and to perform well in various vision tasks.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2013

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Book chapters on the topic "Computer vision; robust model fitting; preference"

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Tiwari, Lokender, Saket Anand, and Sushil Mittal. "Robust Multi-Model Fitting Using Density and Preference Analysis." In Computer Vision – ACCV 2016, 308–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54190-7_19.

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Chai, Dengfeng, and Qunsheng Peng. "Image Feature Detection as Robust Model Fitting." In Computer Vision – ACCV 2006, 673–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612704_67.

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Sariyanidi, Evangelos, Casey J. Zampella, Robert T. Schultz, and Birkan Tunc. "Inequality-Constrained and Robust 3D Face Model Fitting." In Computer Vision – ECCV 2020, 433–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58545-7_25.

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Ngo, Thanh Trung, Hajime Nagahara, Ryusuke Sagawa, Yasuhiro Mukaigawa, Masahiko Yachida, and Yasushi Yagi. "Adaptive-Scale Robust Estimator Using Distribution Model Fitting." In Computer Vision – ACCV 2009, 287–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12297-2_28.

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Cootes, Tim F., Mircea C. Ionita, Claudia Lindner, and Patrick Sauer. "Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting." In Computer Vision – ECCV 2012, 278–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33786-4_21.

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Conference papers on the topic "Computer vision; robust model fitting; preference"

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Magri, Luca, and Andrea Fusiello. "Robust Multiple Model Fitting with Preference Analysis and Low-rank Approximation." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.20.

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Wang, Hanzi, Guobao Xiao, Yan Yan, and David Suter. "Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.332.

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Tepper, Mariano, and Guillermo Sapiro. "Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.77.

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Trung Thanh Pham, Tat-Jun Chin, Jin Yu, and D. Suter. "The Random Cluster Model for robust geometric fitting." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247740.

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Romdhani and Vetter. "Efficient, robust and accurate fitting of a 3D morphable model." In ICCV 2003: 9th International Conference on Computer Vision. IEEE, 2003. http://dx.doi.org/10.1109/iccv.2003.1238314.

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"ACTIVE APPEARANCE MODEL FITTING UNDER OCCLUSION USING FAST-ROBUST PCA." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001768701290136.

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Yu, Jin, Tat-Jun Chin, and David Suter. "A global optimization approach to robust multi-model fitting." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995608.

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Kluger, Florian, Eric Brachmann, Hanno Ackermann, Carsten Rother, Michael Ying Yang, and Bodo Rosenhahn. "CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00469.

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Wimmer, Matthias, Freek Stulp, and Bernd Radig. "Enabling Users to Guide the Design of Robust Model Fitting Algorithms." In 2007 IEEE 11th International Conference on Computer Vision. IEEE, 2007. http://dx.doi.org/10.1109/iccv.2007.4409121.

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Zheng, Yinqiang, Shigeki Sugimoto, and Masatoshi Okutomi. "Deterministically maximizing feasible subsystem for robust model fitting with unit norm constraint." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995640.

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