CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995498
Kevin Schelten, S. Roth
{"title":"Connecting non-quadratic variational models and MRFs","authors":"Kevin Schelten, S. Roth","doi":"10.1109/CVPR.2011.5995498","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995498","url":null,"abstract":"Spatially-discrete Markov random fields (MRFs) and spatially-continuous variational approaches are ubiquitous in low-level vision, including image restoration, segmentation, optical flow, and stereo. Even though both families of approaches are fairly similar on an intuitive level, they are frequently seen as being technically rather distinct since they operate on different domains. In this paper we explore their connections and develop a direct, rigorous link with a particular emphasis on first-order regularizers. By representing spatially-continuous functions as linear combinations of finite elements with local support and performing explicit integration of the variational objective, we derive MRF potentials that make the resulting MRF energy equivalent to the variational energy functional. In contrast to previous attempts, we provide an explicit connection for modern non-quadratic regularizers and also integrate the data term. The established connection opens certain classes of MRFs to spatially-continuous interpretations and variational formulations to a broad range of probabilistic learning and inference algorithms.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125381830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995745
R. Hartley, Khurrum Aftab, J. Trumpf
{"title":"L1 rotation averaging using the Weiszfeld algorithm","authors":"R. Hartley, Khurrum Aftab, J. Trumpf","doi":"10.1109/CVPR.2011.5995745","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995745","url":null,"abstract":"We consider the problem of rotation averaging under the L1 norm. This problem is related to the classic Fermat-Weber problem for finding the geometric median of a set of points in IRn. We apply the classical Weiszfeld algorithm to this problem, adapting it iteratively in tangent spaces of SO(3) to obtain a provably convergent algorithm for finding the L1 mean. This results in an extremely simple and rapid averaging algorithm, without the need for line search. The choice of L1 mean (also called geometric median) is motivated by its greater robustness compared with rotation averaging under the L2 norm (the usual averaging process). We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global L1 optimum) and multiple rotation averaging (for which no such proof exists). The algorithm is demonstrated to give markedly improved results, compared with L2 averaging. We achieve a median rotation error of 0.82 degrees on the 595 images of the Notre Dame image set.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126608037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995715
Shayok Chakraborty, V. Balasubramanian, S. Panchanathan
{"title":"Dynamic batch mode active learning","authors":"Shayok Chakraborty, V. Balasubramanian, S. Panchanathan","doi":"10.1109/CVPR.2011.5995715","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995715","url":null,"abstract":"Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts towards a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning. However, existing work in this field has primarily been heuristic and static. In this work, we propose a novel optimization-based framework for dynamic batch mode active learning, where the batch size as well as the selection criteria are combined in a single formulation. The solution procedure has the same computational complexity as existing state-of-the-art static batch mode active learning techniques. Our results on four challenging biometric datasets portray the efficacy of the proposed framework and also certify the potential of this approach in being used for real world biometric recognition applications.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126645016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995726
B. Flach, D. Schlesinger
{"title":"Modelling composite shapes by Gibbs random fields","authors":"B. Flach, D. Schlesinger","doi":"10.1109/CVPR.2011.5995726","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995726","url":null,"abstract":"We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express spatial relations between shape parts and simple shapes simultaneously. This allows to model and recognise complex shapes as spatial compositions of simpler parts.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115560919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995333
Jason Chang, John W. Fisher III
{"title":"Efficient MCMC sampling with implicit shape representations","authors":"Jason Chang, John W. Fisher III","doi":"10.1109/CVPR.2011.5995333","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995333","url":null,"abstract":"We present a method for sampling from the posterior distribution of implicitly defined segmentations conditioned on the observed image. Segmentation is often formulated as an energy minimization or statistical inference problem in which either the optimal or most probable configuration is the goal. Exponentiating the negative energy functional provides a Bayesian interpretation in which the solutions are equivalent. Sampling methods enable evaluation of distribution properties that characterize the solution space via the computation of marginal event probabilities. We develop a Metropolis-Hastings sampling algorithm over level-sets which improves upon previous methods by allowing for topological changes while simultaneously decreasing computational times by orders of magnitude. An M-ary extension to the method is provided.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116048804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995727
Lionel Baboud, Martin Čadík, E. Eisemann, H. Seidel
{"title":"Automatic photo-to-terrain alignment for the annotation of mountain pictures","authors":"Lionel Baboud, Martin Čadík, E. Eisemann, H. Seidel","doi":"10.1109/CVPR.2011.5995727","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995727","url":null,"abstract":"We present a system for the annotation and augmentation of mountain photographs. The key issue resides in the registration of a given photograph with a 3D geo-referenced terrain model. Typical outdoor images contain little structural information, particularly mountain scenes whose aspect changes drastically across seasons and varying weather conditions. Existing approaches usually fail on such difficult scenarios. To avoid the burden of manual registration, we propose a novel automatic technique. Given only a viewpoint and FOV estimates, the technique is able to automatically derive the pose of the camera relative to the geometric terrain model. We make use of silhouette edges, which are among most reliable features that can be detected in the targeted situations. Using an edge detection algorithm, our technique then searches for the best match with silhouette edges rendered using the synthetic model. We develop a robust matching metric allowing us to cope with the inevitable noise affecting detected edges (e.g. due to clouds, snow, rocks, forests, or any phenomenon not encoded in the digital model). Once registered against the model, photographs can easily be augmented with annotations (e.g. topographic data, peak names, paths), which would otherwise imply a tedious fusion process. We further illustrate various other applications, such as 3D model-assisted image enhancement, or, inversely, texturing of digital models.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116423038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995737
Luca Del Pero, Jinyan Guan, Ernesto Brau, J. Schlecht, Kobus Barnard
{"title":"Sampling bedrooms","authors":"Luca Del Pero, Jinyan Guan, Ernesto Brau, J. Schlecht, Kobus Barnard","doi":"10.1109/CVPR.2011.5995737","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995737","url":null,"abstract":"We propose a top down approach for understanding indoor scenes such as bedrooms and living rooms. These environments typically have the Manhattan world property that many surfaces are parallel to three principle ones. Further, the 3D geometry of the room and objects within it can largely be approximated by non overlapping simple structures such as single blocks (e.g. the room boundary), thin blocks (e.g. picture frames), and objects that are well modeled by single blocks (e.g. simple beds). We separately model the 3D geometry, the imaging process (camera parameters), and edge likelihood, to provide a generative statistical model for image data. We fit this model using data driven MCMC sampling. We combine reversible jump Metropolis Hastings samples for discrete changes in the model such as the number of blocks, and stochastic dynamics to estimate continuous parameter values in a particular parameter space that includes block positions, block sizes, and camera parameters. We tested our approach on two datasets using room box pixel orientation. Despite using only bounding box geometry and, in particular, not training on appearance, our method achieves results approaching those of others. We also introduce a new evaluation method for this domain based on ground truth camera parameters, which we found to be more sensitive to the task of understanding scene geometry.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114159974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995387
Jungmin Lee, Minsu Cho, Kyoung Mu Lee
{"title":"Hyper-graph matching via reweighted random walks","authors":"Jungmin Lee, Minsu Cho, Kyoung Mu Lee","doi":"10.1109/CVPR.2011.5995387","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995387","url":null,"abstract":"Establishing correspondences between two feature sets is a fundamental issue in computer vision, pattern recognition, and machine learning. This problem can be well formulated as graph matching in which nodes represent feature points while edges describe pairwise relations between feature points. Recently, several researches have tried to embed higher-order relations of feature points by hyper-graph matching formulations. In this paper, we generalize the previous hyper-graph matching formulations to cover relations of features in arbitrary orders, and propose a novel state-of-the-art algorithm by reinterpreting the random walk concept on the hyper-graph in a probabilistic manner. Adopting personalized jumps with a reweighting scheme, the algorithm effectively reflects the one-to-one matching constraints during the random walk process. Comparative experiments on synthetic data and real images show that the proposed method clearly outperforms existing algorithms especially in the presence of noise and outliers.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128973899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995665
P. G. Lee, Ying Wu
{"title":"Nonlocal matting","authors":"P. G. Lee, Ying Wu","doi":"10.1109/CVPR.2011.5995665","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995665","url":null,"abstract":"This work attempts to considerably reduce the amount of user effort in the natural image matting problem. The key observation is that the nonlocal principle, introduced to denoise images, can be successfully applied to the alpha matte to obtain sparsity in matte representation, and therefore dramatically reduce the number of pixels a user needs to manually label. We show how to avoid making the user provide redundant and unnecessary input, develop a method for clustering the image pixels for the user to label, and a method to perform high-quality matte extraction. We show that this algorithm is therefore faster, easier, and higher quality than state of the art methods.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129032104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995666
Guangliang Chen, M. Maggioni
{"title":"Multiscale geometric and spectral analysis of plane arrangements","authors":"Guangliang Chen, M. Maggioni","doi":"10.1109/CVPR.2011.5995666","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995666","url":null,"abstract":"Modeling data by multiple low-dimensional planes is an important problem in many applications such as computer vision and pattern recognition. In the most general setting where only coordinates of the data are given, the problem asks to determine the optimal model parameters (i.e., number of planes and their dimensions), estimate the model planes, and cluster the data accordingly. Though many algorithms have been proposed, most of them need to assume prior knowledge of the model parameters and thus address only the last two components of the problem. In this paper we propose an efficient algorithm based on multiscale SVD analysis and spectral methods to tackle the problem in full generality. We also demonstrate its state-of-the-art performance on both synthetic and real data.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129882488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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