| 1. |
- Felsberg, Michael, 1974-, et al.
(författare)
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Online Learning of Correspondences between Images
- 2013
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE Computer Society. - 0162-8828 .- 1939-3539. ; 35:1, s. 118-129
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Tidskriftsartikel (refereegranskat)abstract
- We propose a novel method for iterative learning of point correspondences between image sequences. Points moving on surfaces in 3D space are projected into two images. Given a point in either view, the considered problem is to determine the corresponding location in the other view. The geometry and distortions of the projections are unknown as is the shape of the surface. Given several pairs of point-sets but no access to the 3D scene, correspondence mappings can be found by excessive global optimization or by the fundamental matrix if a perspective projective model is assumed. However, an iterative solution on sequences of point-set pairs with general imaging geometry is preferable. We derive such a method that optimizes the mapping based on Neyman's chi-square divergence between the densities representing the uncertainties of the estimated and the actual locations. The densities are represented as channel vectors computed with a basis function approach. The mapping between these vectors is updated with each new pair of images such that fast convergence and high accuracy are achieved. The resulting algorithm runs in real-time and is superior to state-of-the-art methods in terms of convergence and accuracy in a number of experiments.
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| 2. |
- Ionescu, Catalin, et al.
(författare)
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Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments.
- 2014
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539. ; 36:7, s. 1325-1339
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Tidskriftsartikel (refereegranskat)abstract
- We introduce a new dataset, Human3.6M, of 3.6 Million 3D Human poses, acquired by recording the performance of 11 subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models. Besides increasing the size the current state of the art datasets by several orders of magnitude, we aim to complement such datasets with a diverse set of poses encountered in typical human activities (taking photos, posing, greeting, eating, etc.), with synchronized image, motion capture and depth data, and with accurate 3D body scans of all subjects involved. We also provide mixed reality videos where 3D human models are animated using motion capture data and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide large scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. The dataset and code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, are available online at http://vision.imar.ro/human3.6m.
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| 3. |
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| 4. |
- Leordeanu, Marius, et al.
(författare)
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Generalized Boundaries from Multiple Image Interpretations
- 2014
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539. ; 36:7, s. 1312-1324
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Tidskriftsartikel (refereegranskat)abstract
- Boundary detection is a fundamental computer vision problem that is essential for a variety of tasks, such as contour and region segmentation, symmetry detection and object recognition and categorization. We propose a generalized formulation for boundary detection, with closed-form solution, applicable to the localization of different types of boundaries, such as object edges in natural images and occlusion boundaries from video. Our generalized boundary detection method (Gb) simultaneously combines low-level and mid-level image representations in a single eigenvalue problem and solves for the optimal continuous boundary orientation and strength. The closed-form solution to boundary detection enables our algorithm to achieve state-of-the-art results at a significantly lower computational cost than current methods. We also propose two complementary novel components that can seamlessly be combined with Gb: first, we introduce a soft-segmentation procedure that provides region input layers to our boundary detection algorithm for a significant improvement in accuracy, at negligible computational cost; second, we present an efficient method for contour grouping and reasoning, which when applied as a final post-processing stage, further increases the boundary detection performance.
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| 5. |
- Ma, Zhanyu, et al.
(författare)
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Bayesian Estimation of Beta Mixture Models with Variational Inference
- 2011
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 0162-8828 .- 1939-3539. ; 33:11, s. 2160-2173
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Tidskriftsartikel (refereegranskat)abstract
- Bayesian estimation of the parameters in beta mixture models (BMM) is analytically intractable. The numerical solutionsto simulate the posterior distribution are available, but incur high computational cost. In this paper, we introduce an approximation tothe prior/posterior distribution of the parameters in the beta distribution and propose an analytically tractable (closed-form) Bayesianapproach to the parameter estimation. The approach is based on the variational inference (VI) framework. Following the principles ofthe VI framework and utilizing the relative convexity bound, the extended factorized approximation method is applied to approximate thedistribution of the parameters in BMM. In a fully Bayesian model where all the parameters of the BMM are considered as variables andassigned proper distributions, our approach can asymptotically find the optimal estimate of the parameters posterior distribution. Also,the model complexity can be determined based on the data. The closed-form solution is proposed so that no iterative numericalcalculation is required. Meanwhile, our approach avoids the drawback of overfitting in the conventional expectation maximizationalgorithm. The good performance of this approach is verified by experiments with both synthetic and real data.
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| 6. |
- Moreno, Rodrigo, et al.
(författare)
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On improving the efficiency of tensor voting
- 2011
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - Washington, DC, USA : IEEE Computer Society. - 0162-8828 .- 1939-3539. ; 33:11, s. 2215-2228
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: The stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used inapplications where efficiency is an issue since they have a complexity of order O(1). Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.
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| 7. |
- Ortega-Garcia, Javier, et al.
(författare)
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The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
- 2010
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - Piscataway, N.J. : IEEE Press. - 0162-8828 .- 1939-3539. ; 32:6, s. 1097-1111
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Tidskriftsartikel (refereegranskat)abstract
- A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008. © 2010 IEEE.
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| 8. |
- Pena, Jose M, 1974-, et al.
(författare)
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On the Complexity of Discrete Feature Selection for Optimal Classification
- 2010
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE Institute of Electrical and Electronics. - 0162-8828 .- 1939-3539. ; 32:8, s. 1517-U1522
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Tidskriftsartikel (refereegranskat)abstract
- Consider a classification problem involving only discrete features that are represented as random variables with some prescribed discrete sample space. In this paper, we study the complexity of two feature selection problems. The first problem consists in finding a feature subset of a given size k that has minimal Bayes risk. We show that for any increasing ordering of the Bayes risks of the feature subsets (consistent with an obvious monotonicity constraint), there exists a probability distribution that exhibits that ordering. This implies that solving the first problem requires an exhaustive search over the feature subsets of size k. The second problem consists of finding the minimal feature subset that has minimal Bayes risk. In the light of the complexity of the first problem, one may think that solving the second problem requires an exhaustive search over all of the feature subsets. We show that, under mild assumptions, this is not true. We also study the practical implications of our solutions to the second problem.
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| 9. |
- Taghia, Jalil, et al.
(författare)
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Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference
- 2014
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 0162-8828 .- 1939-3539. ; 36:9, s. 1701-1715
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the Bayesian estimation of the von-Mises Fisher (vMF) mixture model with variational inference (VI). The learning task in VI consists of optimization of the variational posterior distribution. However, the exact solution by VI does not lead to an analytically tractable solution due to the evaluation of intractable moments involving functional forms of the Bessel function in their arguments. To derive a closed-form solution, we further lower bound the evidence lower bound where the bound is tight at one point in the parameter distribution. While having the value of the bound guaranteed to increase during maximization, we derive an analytically tractable approximation to the posterior distribution which has the same functional form as the assigned prior distribution. The proposed algorithm requires no iterative numerical calculation in the re-estimation procedure, and it can potentially determine the model complexity and avoid the over-fitting problem associated with conventional approaches based on the expectation maximization. Moreover, we derive an analytically tractable approximation to the predictive density of the Bayesian mixture model of vMF distributions. The performance of the proposed approach is verified by experiments with both synthetic and real data.
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| 10. |
- Wittek, Peter, et al.
(författare)
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Compactly Supported Basis Functions as Support Vector Kernels for Classification
- 2011
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE computer. - 0162-8828 .- 1939-3539. ; 33:10, s. 2039-2050
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Tidskriftsartikel (refereegranskat)abstract
- Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L(2) space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.
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