OpenRaiser/Intern-Atlas huggingface dataset:
Conditional State Space Models for Discriminative Motion Estimation
Authors: Minyoung Kim 0001, Vladimir Pavlovic 0001
Venue: ICCV
Year: 2007

We consider the problem of predicting a sequence of real-valued multivariate states from a given measurement sequence. Its typical application in computer vision is the task of motion estimation. State Space Models are widely used generative probabilistic models for the problem. Instead of jointly modeling states and measurements, we propose a novel discriminative undirected graphical model which conditions the states on the measurements while exploiting the sequential structure of the problem. The major benefits of this approach are: (1) It focuses on the ultimate prediction task while avoiding probably unnecessary effort in modeling the measurement density, (2) It relaxes generative models' assumption that the measurements are independent given the states, and (3) The proposed inference algorithm takes linear time in the measurement dimension as opposed to the cubic time for Kalman filtering, which allows us to incorporate large numbers of measurement features. We show that the parameter learning can be cast as an instance of convex optimization. We also provide efficient convex optimization methods based on theorems from linear algebra. The performance of the proposed model is evaluated on both synthetic data and the human body pose estimation from silhouette videos.
A 3D Teacher for Car Detection in Aerial Images
Authors: Stefan Kluckner, Georg Pacher, Helmut Grabner, Horst Bischof, Joachim Bauer
Venue: ICCV
Year: 2007

This paper demonstrates how to reduce the hand labeling effort considerably by 3D information in an object detection task. In particular, we demonstrate how an efficient car detector for aerial images with minimal hand labeling effort can be build. We use an on-line boosting algorithm to incrementally improve the detection results. Initially, we train the classifier with a single positive (car) example, randomly drawn from a fixed number of given samples. When applying this detector to an image we obtain many false positive detections. We use information from a stereo matcher to detect some of these false positives (e.g. detected cars on a facade) and feed back this information to the classifier as negative updates. This improves the detector considerably, thus reducing the number of false positives. We show that we obtain similar results to hand labeling by iteratively applying this strategy. The performance of our algorithm is demonstrated on digital aerial images of urban environments.
Applications of parametric maxflow in computer vision
Authors: Vladimir Kolmogorov, Yuri Boykov, Carsten Rother
Venue: ICCV
Year: 2007

The maximum flow algorithm for minimizing energy functions of binary variables has become a standard tool in computer vision. In many cases, unary costs of the energy depend linearly on parameter λ. In this paper we study vision applications for which it is important to solve the maxflow problem for different λ's. An example is a weighting between data and regularization terms in image segmentation or stereo: it is desirable to vary it both during training (to learn λ from ground truth data) and testing (to select best λ using high-knowledge constraints, e.g. user input). We review algorithmic aspects of this parametric maximum flow problem previously unknown in vision, such as the ability to compute all breakpoints of λ and corresponding optimal configurations infinite time. These results allow, in particular, to minimize the ratio of some geometric functional, such as flux of a vector field over length (or area). Previously, such functional were tackled with shortest path techniques applicable only in 2D. We give theoretical improvements for "PDE cuts" [5]. We present experimental results for image segmentation, 3D reconstruction, and the cosegmentation problem.
Classification of Weakly-Labeled Data with Partial Equivalence Relations
Authors: Sanjiv Kumar, Henry A. Rowley
Venue: ICCV
Year: 2007

In many vision problems, instead of having fully labeled training data it is easier to obtain the input in small groups, where the data in each group is constrained to be from the same class but the actual class label is not known. Such constraints give rise to partial equivalence relations. The absence of class labels prevents the use of standard discriminative methods in this scenario. On the other hand, the state-of-the-art techniques that use partial equivalence relations, e.g., relevant component analysis, learn projections that are optimal for data representation, but not discrimination. We show that this leads to poor performance in several real-world applications, especially those with high-dimensional data. In this paper, we present a novel discriminative technique for the classification of weakly-labeled data which exploits the null-space of data scatter matrices to achieve good classification accuracy. We demonstrate the superior performance of both linear and nonlinear versions of our approach on face recognition, clustering, and image retrieval tasks. Results are reported on standard datasets as well as real-world images and videos from the Web.
Support Kernel Machines for Object Recognition
Authors: Ankita Kumar, Cristian Sminchisescu
Venue: ICCV
Year: 2007

Kernel classifiers based on Support Vector Machines (SVM) have recently achieved state-of-the art results on several popular datasets like Caltech or Pascal. This was possible by combining the advantages of SVM - convexity and the availability of efficient optimizers, with 'hyperkernels' - linear combinations of kernels computed at multiple levels of image encoding. The use of hyperkernels faces the challenge of choosing the kernel weights, the use of possibly irrelevant, poorly performing kernels, and an increased number of parameters that can lead to overfitting. In this paper we advocate the transition from SVMs to Support Kernel Machines (SKM) - models that estimate both the parameters of a sparse linear combination of kernels, and the parameters of a discriminative classifier. We exploit recent kernel learning techniques, not previously used in computer vision, that show how learning SKMs can be formulated as a convex optimization problem, which can be solved efficiently using Sequential Minimal Optimization. We study kernel learning for several multi-level image encodings for supervised object recognition and report competitive results on several datasets, including INRIA pedestrian, Caltech 101 and the newly created Caltech 256.
An Invariant Large Margin Nearest Neighbour Classifier
Authors: M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
Venue: ICCV
Year: 2007

The k-nearest neighbour (kNN) rule is a simple and effective method for multi-way classification that is much used in Computer Vision. However, its performance depends heavily on the distance metric being employed. The recently proposed large margin nearest neighbour (LMNN) classifier [21] learns a distance metric for kNN classification and thereby improves its accuracy. Learning involves optimizing a convex problem using semidefinite programming (SDP). We extend the LMNN framework to incorporate knowledge about invariance of the data. The main contributions of our work are three fold: (i) Invariances to multivariate polynomial transformations are incorporated without explicitly adding more training data during learning - these can approximate common transformations such as rotations and affinities; (ii) the incorporation of different regularizes on the parameters being learnt; and (Hi) for all these variations, we show that the distance metric can still be obtained by solving a convex SDP problem. We call the resulting formulation invariant LMNN (lLMNN) classifier. We test our approach to learn a metric for matching (i) feature vectors from the standard Iris dataset; and (ii) faces obtained from TV video (an episode of 'Buffy the Vampire Slayer'). We compare our method with the state of the art classifiers and demonstrate improvements.
Flexible Mirror Imaging
Authors: Sujit Kuthirummal, Shree K. Nayar
Venue: ICCV
Year: 2007

The field of view of a traditional camera has a fixed shape. This severely restricts how scene elements can be composed into an image. We present a novel imaging system that uses a flexible mirror in conjunction with a camera to overcome this limitation. By deforming the mirror, our system can produce fields of view with a wide range of shapes and sizes. A captured image is typically a multi-perspective view of the scene with spatially varying resolution. As a result, scene objects appear distorted. To minimize these distortions, we have developed an efficient algorithm that maps a captured image to one with almost uniform resolution. To determine this mapping we need to know the shape of the mirror. For this, we have developed a simple calibration method that automatically estimates the mirror shape from its boundary, which is visible in the captured image. We present a number of examples that demonstrate that a flexible field of view imaging system can be used to compose scenes in ways that have not been possible before. This flexibility can be exploited in applications such as video surveillance and monitoring.
Modeling the Marginal Distributions of Complex Wavelet Coefficient Magnitudes for the Classification of Zoom-Endoscopy Images
Authors: Roland Kwitt, Andreas Uhl
Venue: ICCV
Year: 2007

In this paper, we propose a set of new image features for the classification of zoom-endoscopy images. The feature extraction step is based on fitting a two-parameter Weibull distribution to the wavelet coefficient magnitudes of sub-bands obtained from a complex wavelet transform variant. We show, that the shape and scale parameter possess more discriminative power than the classic mean and standard deviation based features for complex subband coefficient magnitudes. Furthermore, we discuss why the commonly used Rayleigh distribution model is suboptimal in our case.
Diffusion Tensor Estimation by Maximizing Rician Likelihood
Authors: Bennett A. Landman, Pierre-Louis Bazin, Jerry L. Prince
Venue: ICCV
Year: 2007

Diffusion tensor imaging (DTI) is widely used to characterize white matter in health and disease. Previous approaches to the estimation of diffusion tensors have either been statistically suboptimal or have used Gaussian approximations of the underlying noise structure, which is Rician in reality. This can cause quantities derived from these tensors - e.g., fractional anisotropy and apparent diffusion coefficient - to diverge from their true values, potentially leading to artifactual changes that confound clinically significant ones. This paper presents a novel maximum likelihood approach to tensor estimation, denoted Diffusion Tensor Estimation by Maximizing Rician Likelihood (DTEMRL). In contrast to previous approaches, DTEMRL considers the joint distribution of all observed data in the context of an augmented tensor model to account for variable levels of Rician noise. To improve numeric stability and prevent non-physical solutions, DTEMRL incorporates a robust characterization of positive definite tensors and a new estimator of underlying noise variance. In simulated and clinical data, mean squared error metrics show consistent and significant improvements from low clinical SNR to high SNR. DTEMRL may be readily supplemented with spatial regularization or a priori tensor distributions for Bayesian tensor estimation.
Motion Analysis of Endovascular Stent-Grafts by MDL Based Registration
Authors: Georg Langs, Nikos Paragios, Rene Donner, Pascal Desgranges, Alain Rahmouni, Hicham Kobeiter
Venue: ICCV
Year: 2007

The endovascular repair of a traumatic rupture of the thoracic aorta - that would otherwise lead to the death of the patient - is performed by delivering a stent-graft into the vessel at the rupture location. The age range of the affected patients is large and the stent-graft will stay in the body for the remaining life. The technique is relatively new, and no experience with regard to long-term effects, and durability exists. To predict long-term complications, such as ruptures or destructive interactions with surrounding tissue during the life of the patient, it is important to understand the - rather intense and constant - movement of the stent- graft during the cardiac cycle. A computed tomography with heart gating (gated CT) acquires sequences that show the region of the stent-graft at different time points. We analyze the motion of stent-grafts with a model based approach. Stent-grafts are represented as sparse sets of axis points extracted from the gated CT, and motion patterns are captured by a minimum description length based group-wise registration of the stent-graft at different time points. No parameterization or a priori definition of the topology is necessary, and highly variable elasticity properties in the data volume can by accounted for by the sparse statistical model, that captures correlations and motion components of the stent-graft. We report results for deformation models and registration accuracy for 5 patients.
Temporally Consistent Reconstruction from Multiple Video Streams Using Enhanced Belief Propagation
Authors: E. Scott Larsen, Philippos Mordohai, Marc Pollefeys, Henry Fuchs
Venue: ICCV
Year: 2007

We present an approach for 3D reconstruction from multiple video streams taken by static, synchronized and calibrated cameras that is capable of enforcing temporal consistency on the reconstruction of successive frames. Our goal is to improve the quality of the reconstruction by finding corresponding pixels in subsequent frames of the same camera using optical flow, and also to at least maintain the quality of the single time-frame reconstruction when these correspondences are wrong or cannot be found. This allows us to process scenes with fast motion, occlusions and self- occlusions where optical flow fails for large numbers of pixels. To this end, we modify the belief propagation algorithm to operate on a 3D graph that includes both spatial and temporal neighbors and to be able to discard messages from outlying neighbors. We also propose methods for introducing a bias and for suppressing noise typically observed in uniform regions. The bias encapsulates information about the background and aids in achieving a temporally consistent reconstruction and in the mitigation of occlusion related errors. We present results on publicly available real video sequences. We also present quantitative comparisons with results obtained by other researchers.
Visual Tracking by Affine Kernel Fitting Using Color and Object Boundary
Authors: Ido Leichter, Michael Lindenbaum, Ehud Rivlin
Venue: ICCV
Year: 2007

Kernel-based trackers aggregate image features within the support of a kernel (a mask) regardless of their spatial structure. These trackers spatially fit the kernel (usually in location and in scale) such that a function of the aggregate is optimized. We propose a kernel-based visual tracker that exploits the constancy of color and the presence of color edges along the target boundary. The tracker estimates the best affinity of a spatially aligned pair of kernels, one of which is color-related and the other of which is object boundary-related. In a sense, this work extends previous kernel-based trackers by incorporating the object boundary cue into the tracking process and by allowing the kernels to be affinely transformed instead of only translated and isotropically scaled. These two extensions make for more precise target localization. Moreover, a more accurately localized target facilitates safer updating of its reference color model, further enhancing the tracker's robustness. The improved tracking is demonstrated for several challenging image sequences.
LogCut - Efficient Graph Cut Optimization for Markov Random Fields
Authors: Victor S. Lempitsky, Carsten Rother, Andrew Blake 0001
Venue: ICCV
Year: 2007

Markov Random Fields (MRFs) are ubiquitous in low- level computer vision. In this paper, we propose a new approach to the optimization of multi-labeled MRFs. Similarly to a-expansion it is based on iterative application of binary graph cut. However, the number of binary graph cuts required to compute a labelling grows only logarithmically with the size of label space, instead of linearly. We demonstrate that for applications such as optical flow, image restoration, and high resolution stereo, this gives an order of magnitude speed-up, for comparable energies. Iterations are performed by "fusion" of solutions, done with QPBO which is related to graph cut but can deal with non-submodularity. At convergence, the method achieves optima on a par with the best competitors, and sometimes even exceeds them.
3D object recognition from range images using pyramid matching
Authors: Xinju Li, Igor Guskov
Venue: ICCV
Year: 2007

Recognition of 3D objects from different viewpoints is a difficult problem. In this paper, we propose a new method to recognize 3D range images by matching local surface descriptors. The input 3D surfaces are first converted into a set of local shape descriptors computed on surface patches defined by detected salient features. We compute the similarities between input 3D images by matching their descriptors with a pyramid kernel function. The similarity matrix of the images is used to train for classification using SVM, and new images can be recognized by comparing with the training set. The approach is evaluated on both synthetic and real 3D data with complex shapes.
Robust Visual Tracking Based on Incremental Tensor Subspace Learning
Authors: Xi Li 0001, Weiming Hu 0004, Zhongfei Zhang, Xiaoqin Zhang 0002, Guan Luo
Venue: ICCV
Year: 2007

Most existing subspace analysis-based tracking algorithms utilize a flattened vector to represent a target, resulting in a high dimensional data learning problem. Recently, subspace analysis is incorporated into the multilinear framework which offline constructs a representation of image ensembles using high-order tensors. This reduces spatio-temporal redundancies substantially, whereas the computational and memory cost is high. In this paper, we present an effective online tensor subspace learning algorithm which models the appearance changes of a target by incrementally learning a low-order tensor eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking then is led by the state inference within the framework in which a particle filter is used for propagating sample distributions over the time. A novel likelihood function, based on the tensor reconstruction error norm, is developed to measure the similarity between the test image and the learned tensor subspace model during the tracking. Theoretic analysis and experimental evaluations against a state-of-the-art method demonstrate the promise and effectiveness of this algorithm.
Exploiting Occluding Contours for Real-Time 3D Tracking: A Unified Approach
Authors: Gang Li, Yanghai Tsin, Yakup Genc
Venue: ICCV
Year: 2007

Model-based 3D object tracking is fast and robust using 3D edges. However, traditional edge-based approaches have difficulty handling occluding contours of curved surfaces, since they are not static model edges but change with the viewpoint. In this paper we propose a unified approach to edge-based tracking where 3D edges including occluding contours are utilized. This is achieved through an analysis of local surface differential geometry, which provides the foundation for incorporating occluding contours of curved surfaces into edge-based tracking. This approach uses a simple parametrization of both types of model edges within the same framework. The proposed method has been tested within the context of an existing edge-based tracking system. The system can track both types of model edges in a very fast and robust manner. Experimental results on both synthetic and real scenes are provided, which confirm that occluding contours improve real-time 3D tracking performance.
Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series
Authors: Rui Li 0053, Tai-Peng Tian, Stan Sclaroff
Venue: ICCV
Year: 2007

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.
Estimation of the Epipole using Optical Flow at Antipodal Points
Authors: John Lim, Nick Barnes
Venue: ICCV
Year: 2007

This paper develops an algorithm for estimating the epipole or direction of translation of a moving monocular observer. To this end, we use constraints arising from two points that are antipodal on the image sphere. The antipodal point condition is necessary for decoupling rotation from translation. One such pair of points constrains the epipole to lie on a plane, and using two pairs of points, we have two such planes. The intersection of these two planes gives an estimate of the epipole. This means we require image motion measurements at two pairs of antipodal points to obtain an estimate. Repeating this will yield a set of possible solutions and a variety of methods could be applied to obtain a robust and refined estimate from this set. One robust and simple method is chosen for illustrative purposes and results on real images are shown. With real sequences, results of below 2deg error in the estimate of the epipole can be obtained. Since antipodal points on an image sphere are required, this algorithm must use some kind of omnidirectional or large field-of-view (FOV) sensor.
An Interactive Approach to Pose-Assisted and Appearance-based Segmentation of Humans
Authors: Zhe Lin 0001, Larry S. Davis, David S. Doermann, Daniel DeMenthon
Venue: ICCV
Year: 2007

An interactive human segmentation approach is described. Given regions of interest provided by users, the approach iteratively estimates segmentation via a generalized EM algorithm. Specifically, it encodes both spatial and color information in a nonparametric kernel density estimator, and incorporates local MRF constraints and global pose inferences to propagate beliefs over image space iteratively to determine a coherent segmentation. This ensures the segmented humans resemble the shapes of human poses. Additionally, a layered occlusion model and a probabilistic occlusion reasoning method are proposed to handle segmentation of multiple humans in occlusion. The approach is tested on a wide variety of images containing single or multiple occluded humans, and the segmentation performance is evaluated quantitatively.