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Mixture Trees for Modeling and Fast Conditional Sampling with Applications in Vision and Graphics

F. Dellaert, V. Kwatra, Sang Min Oh
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)  
We show that the mixture tree models data efficiently at multiple resolutions, and present fast conditional sampling as one of many possible applications.  ...  In particular, the development of this datastructure was spurred by a multi-target tracking application, where memory-based motion modeling calls for fast conditional sampling from large empirical densities  ...  Acknowledgments This work was funded under NSF Award IIS-0219850, "ITR: Observing, Tracking and Modeling Social Multi-agent Systems".  ... 
doi:10.1109/cvpr.2005.224 dblp:conf/cvpr/DellaertKO05 fatcat:635wcgpdqrbxfkn7kv5tfhiwyu

Nonparametric belief propagation

Erik B. Sudderth, Alexander T. Ihler, Michael Isard, William T. Freeman, Alan S. Willsky
2010 Communications of the ACM  
Freeman, and A. Willsky, and "PAMPAS: Real-Valued Graphical Models for Computer Vision," by M. Isard.  ...  In this work we describe an extension of BP to continuous variable models, generalizing particle filtering, and Gaussian mixture filtering techniques for time series to more complex models.  ...  For the graphical models of interest here, however, exact sampling from p(x | y) is intractable.  ... 
doi:10.1145/1831407.1831431 fatcat:lbsxkfdvwbgttc4qaybyxuywlm

Titelei/Inhaltsverzeichnis [chapter]

Oliver Müller
2018 Graphical Model MAP Inference with Continuous Label Space in Computer Vision  
Joint tracking and segmentation is formulated as a high-order probabilistic graphical model over continuous state variables.  ...  It is shown that slice sampling leads to fast convergence and does not rely on hyper-parameter tuning as opposed to competing approaches based on Metropolis-Hastings or heuristic samplers.  ...  Michael Ying Yang for his support and for sharing his deep knowledge on probabilistic graphical models with me.  ... 
doi:10.51202/9783186860101-i fatcat:ed4olqtfw5bxfcbopsnlmtmmke

One-Shot Inference in Markov Random Fields

Hao Xiong, Yuanzhen Guo, Yibo Yang, Nicholas Ruozzi
2019 Conference on Uncertainty in Artificial Intelligence  
We prove that our approach overcomes weaknesses of existing ones and demonstrate its efficacy on both synthetic models and real-world applications.  ...  In this work, we propose a novel variational inference strategy that is efficient for repeated inference tasks, flexible enough to handle both continuous and discrete random variables, and scalable enough  ...  This limits the applicability of these methods to specific subsets of continuous graphical models.  ... 
dblp:conf/uai/XiongGYR19 fatcat:3i6htvzx7rhfnkevc3exzilkwq

Julia Language in Machine Learning: Algorithms, Applications, and Open Issues [article]

Kaifeng Gao, Jingzhi Tu, Zenan Huo, Gang Mei, Francesco Piccialli, Salvatore Cuomo
2020 arXiv   pre-print
The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity.  ...  Then, it investigates applications of the machine learning algorithms implemented with the Julia language.  ...  Model creation uses 70% of the sample data, and the remaining 30% of the samples are used for model validation.  ... 
arXiv:2003.10146v1 fatcat:f2ocidpu4rchnokkc46qzrjgyu

The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models

Varun Jampani, Sebastian Nowozin, Matthew Loper, Peter V. Gehler
2015 Computer Vision and Image Understanding  
We implement this idea in a principled way with an "informed sampler" and in careful experiments demonstrate it on challenging generative models which contain renderer programs as their components.  ...  Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs.  ...  For this experiment we also experimented with another conditional density estimation approach using a forest of random regression trees.  ... 
doi:10.1016/j.cviu.2015.03.002 fatcat:fzr7ufszarhdtk26ffaqwgfrbe

Learning to Estimate Scenes from Images

William T. Freeman, Egon C. Pasztor
1998 Neural Information Processing Systems  
From synthetic data , we model the relationship between image and scene patches, and between a scene patch and neighboring scene patches.  ...  We demonstrate the technique for motion analysis and estimating high resolution images from low-resolution ones.  ...  Viola, and Y. \Veiss for helpful discussions.  ... 
dblp:conf/nips/FreemanP98 fatcat:smywdkwmubgvtdwjucuceyzboy

Julia language in machine learning: Algorithms, applications, and open issues

Kaifeng Gao, Gang Mei, Francesco Piccialli, Salvatore Cuomo, Jingzhi Tu, Zenan Huo
2020 Computer Science Review  
The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity.  ...  Then, it investigates applications of the machine learning algorithms implemented with the Julia language.  ...  Project for Science and Technology (2020AA002).  ... 
doi:10.1016/j.cosrev.2020.100254 fatcat:gdt66djfvjfqpjou3lvemxsxfy

Diagnostics of Surface Errors by Embedded Vision System and its Classification by Machine Learning Algorithms

Kamil Židek, Alexander Hošovský, Ján Dubják
2015 Key Engineering Materials  
for error classification in machine vision systems.  ...  The Article deals with usability and advantages of embedded vision systems for surface error detection and usability of advanced algorithms, technics and methods from machine learning and artificial intelligence  ...  Acknowledgement The research was supported by the Project VEGA 1/0911/14 Implementation of wireless technologies into the design of new products and services to protect human health.  ... 
doi:10.4028/ fatcat:5ftrgx7j6barphdlx5vy3r3kpm

Picture: A probabilistic programming language for scene perception

Tejas D Kulkarni, Pushmeet Kohli, Joshua B Tenenbaum, Vikash Mansinghka
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision.  ...  Here we present Picture, a probabilistic programming language for scene understanding that allows researchers to express complex generative vision models, while automatically solving them using fast general-purpose  ...  Acknowledgements We thank Thomas Vetter for giving us access to the Basel face model. T. Kulkarni was graciously supported by the Leventhal Fellowship.  ... 
doi:10.1109/cvpr.2015.7299068 dblp:conf/cvpr/KulkarniKTM15 fatcat:kwyidcyug5esxiu5jykt6knim4

Mixtures of dynamic textures

A.B. Chan, N. Vasconcelos
2005 Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1  
Finally, we demonstrate the applicability of the proposed model to problems that have traditionally been challenging for computer vision.  ...  We derive the EM algorithm for learning a mixture of dynamic textures, and relate the learning algorithm and the dynamic texture mixture model to previous works.  ...  The dynamic texture is completely specified with the parameters Θ = {A, Q, C, R, µ, S}, and is represented as a graphical model in Figure 1a .  ... 
doi:10.1109/iccv.2005.151 dblp:conf/iccv/ChanV05 fatcat:wzvqfnogr5bnnbqtakmgmdzex4

Discrete Markov image modeling and inference on the quadtree

J.-M. Laferte, P. Perez, F. Heitz
2000 IEEE Transactions on Image Processing  
Noncasual Markov (or energy-based) models are widely used in early vision applications for the representation of images in high-dimensional inverse problems.  ...  The practical relevance of the different models and inference algorithms is investigated in the context of image classification problem, on both synthetic and natural images.  ...  Fabre, Irisa/Inria-Rennes, for stimulating discussions.  ... 
doi:10.1109/83.826777 pmid:18255411 fatcat:mqgp4sbcazdsndy3d2zudxqi5a

Interface '99

Arnold Goodman
2000 SIGKDD Explorations  
In addition, it is the newest link in a bridge between the Interface and KDD begun by References 2-4 and the sessions on KDD at Interface '98 and Interface '99.  ...  This personal overview of Interface '99 is intended to communicate its meaning and relevance to SIGKDD, as well as provide valuable information on trends within the Interface for data miners seeking to  ...  "Simplifying Mixture Models with Applications" by William Szewczyk at NSA with David W.  ... 
doi:10.1145/846183.846207 fatcat:ncoticeezndhpknzyx3huk66hu

Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model

Xiang Yu, Junzhou Huang, Shaoting Zhang, Wang Yan, Dimitris N. Metaxas
2013 2013 IEEE International Conference on Computer Vision  
For deformation, the first step uses mean-shift local search with constrained local model to rapidly approach the global optimum.  ...  We present a two-stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations.  ...  It is fast and effective for most near-frontal faces, but lacks flexibility dealing with large pose variations. Sivic et al. [11] used mixture of tree structure to estimate landmarks. Uricar et al.  ... 
doi:10.1109/iccv.2013.244 dblp:conf/iccv/YuHZYM13 fatcat:vb76nthjyvhlpa4luvaksu32lu

Deformable Bayesian networks for data clustering and fusion

Kittipat Kampa, Jose C. Principe, J. Tory Cobb, Anand Rangarajan, Russell S. Harmon, John H. Holloway, Jr., J. Thomas Broach
2011 Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI  
(MAP) solution with the uncertainty of the estimate in the form of a probability distribution which is desired for a variety of applications.  ...  In this work, we propose DEformable BAyesian Networks (DEBAN), a probabilistic graphical model framework where model selection and statistical inference can be viewed as two key ingredients in the same  ...  In order to exploit a graphical model to an application, there are 2 main issues to consider: 1) the graphical model structure learning problem and 2) the inference problem.  ... 
doi:10.1117/12.883096 fatcat:u4agr42sdjcplas27whjz5vi2i
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