OpenRaiser/Intern-Atlas huggingface dataset:
Robust multilinear principal component analysis
Authors: Kohei Inoue, Kenji Hara, Kiichi Urahama
Venue: ICCV
Year: 2009

We propose two methods for robustifying multilinear principal component analysis (MPCA) which is an extension of the conventional PCA for reducing the dimensions of vectors to higher-order tensors. For two kinds of outliers, i.e., sample outliers and intra-sample outliers, we derive iterative algorithms on the basis of the Lagrange multipliers. We also demonstrate that the proposed methods outperform the original MPCA when datasets contain such outliers experimentally.
Robust motion estimation using trajectory spectrum learning: Application to aortic and mitral valve modeling from 4D TEE
Authors: Razvan Ioan Ionasec, Yang Wang 0001, Bogdan Georgescu, Ingmar Voigt, Nassir Navab, Dorin Comaniciu
Venue: ICCV
Year: 2009

In this paper we propose a robust and efficient approach to localizing and estimating the motion of non-rigid and articulated objects using marginal trajectory spectrum learning. Detecting the motion directly in the Euclidean space is often found difficult to guarantee a smooth and accurate result and might be affected by drifting. These issues, however, can be addressed effectively by formulating the motion estimation problem as spectrum detection in the trajectory space. The full trajectory space can be decomposed into orthogonal subspaces defined by generic bases, such as the Discrete Fourier Transform (DFT). The obtained representation is shown to be compact, facilitating efficient learning and optimization in its marginal spaces. In the training stage, local features are extended in the temporal domain to integrate the time coherence constraint and selected via boosting to form strong classifiers. An incremental optimization is performed in sparse marginal spaces learned from the training data. To maximize efficiency and robustness we constrain the search based on clusters of hypotheses defined in each subspace. Experiments demonstrate the performance of the proposed method on articulated motion estimation of aortic and mitral valves from ultrasound data. Our method is evaluated on 65 4D TEE sequences (1516 volumes) with the accuracy in the range of the inter-user variability of expert users. It provides in less than 60 seconds with an precision of 1.36 ± 0.32mm a personalized 4D model of aortic and mitral valves crucial for the clinical workflow.
Human pose estimation using consistent max-covering
Authors: Hao Jiang
Venue: ICCV
Year: 2009

We propose a novel consistent max-covering scheme for human pose estimation. Consistent max-covering formulates pose estimation as the covering of body part polygons on an object silhouette so that the body part tiles maximally cover the foreground, match local image features, and satisfy body linkage plan and color constraints. It uses high order constraints to anchor multiple body parts simultaneously; the hyper-edges in the part relation graph are essential for detecting complex poses. Because of using multiple clues in pose estimation, this method is resistant to cluttered foregrounds. We propose an efficient linear relaxation method to solve the consistent max-covering problem. Experiments on a variety of images and videos show that the proposed method is more robust than locally constrained methods for human pose estimation.
Domain adaptive semantic diffusion for large scale context-based video annotation
Authors: Yu-Gang Jiang 0001, Jun Wang 0006, Shih-Fu Chang, Chong-Wah Ngo
Venue: ICCV
Year: 2009

Learning to cope with domain change has been known as a challenging problem in many real-world applications. This paper proposes a novel and efficient approach, named domain adaptive semantic diffusion (DASD), to exploit semantic context while considering the domain-shift-of-context for large scale video concept annotation. Starting with a large set of concept detectors, the proposed DASD refines the initial annotation results using graph diffusion technique, which preserves the consistency and smoothness of the annotation over a semantic graph. Different from the existing graph learning methods which capture relations among data samples, the semantic graph treats concepts as nodes and the concept affinities as the weights of edges. Particularly, the DASD approach is capable of simultaneously improving the annotation results and adapting the concept affinities to new test data. The adaptation provides a means to handle domain change between training and test data, which occurs very often in video annotation task. We conduct extensive experiments to improve annotation results of 374 concepts over 340 hours of videos from TRECVID 2005-2007 data sets. Results show consistent and significant performance gain over various baselines. In addition, the proposed approach is very efficient, completing DASD over 374 concepts within just 2 milliseconds for each video shot on a regular PC.
Piecewise-consistent color mappings of images acquired under various conditions
Authors: Sefy Kagarlitsky, Yael Moses, Yacov Hel-Or
Venue: ICCV
Year: 2009

Many applications in computer vision require comparisons between two images of the same scene. Comparison applications usually assume that corresponding regions in the two images have similar colors. However, this assumption is not always true. One way to deal with this problem is to apply a color mapping to one of the images. In this paper we address the challenge of computing color mappings between pairs of images acquired under different acquisition conditions, and possibly by different cameras. For images taken from different viewpoints, our proposed method overcomes the lack of pixel correspondence. For images taken under different illumination, we show that no single color mapping exists, and we address and solve a new problem of computing a minimal set of piecewise color mappings. When both viewpoint and illumination vary, our method can only handle planar regions of the scene. In this case, the scene planar regions are simultaneously co-segmented in the two images, and piecewise color mappings for these regions are calculated. We demonstrate applications of the proposed method for each of these cases.
An information theoretic approach for tracker performance evaluation
Authors: Edward K. Kao, Matthew P. Daggett, Michael B. Hurley
Venue: ICCV
Year: 2009

Automated tracking of vehicles and people is essential for the effective utilization of imagery in wide area surveillance applications. In order to determine the best tracking algorithm and parameters for a given application, a comprehensive evaluation procedure is required. However, despite half a century of research in multi-target tracking, there is no consensus on how to score the overall performance of these trackers. Existing evaluation approaches assess tracker performance through measures of correspondence between ground truth tracks and system tracks using metrics such as track detection rate, track completeness, track fragmentation rate, and track ID change rate. However, each of these only provides a partial measure of performance and no good method exists to combine them into a holistic metric. Towards this end, this paper presents a pair of information theoretic metrics with similar behavior to the Receiver Operating Characteristic (ROC) curves of signal detection theory. Overall performance is evaluated with the percentage of truth information that a tracker captured and the total amount of false information that it reported. Information content is quantified through conditional entropy and mutual information computations using numerical estimates of the probability of association between the truth and the system tracks. This paper demonstrates how these information quality metrics provide a comprehensive evaluation of overall tracker performance and how they can be used to perform tracker comparisons and parameter tuning on wide-area surveillance imagery and other applications. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Which faces to tag: Adding prior constraints into active learning
Authors: Ashish Kapoor, Gang Hua 0001, Amir Akbarzadeh, Simon Baker
Venue: ICCV
Year: 2009

We introduce an algorithm that guides the user to tag faces in the best possible order during a face recognition assisted tagging scenario. In particular, we extend the active learning paradigm to take advantage of constraints known a priori. For example, in the context of personal photo collections, if two faces come from the same source photograph, we know that they must be of different people. Similarly, in the context of video, we know that the faces from a single track must be of the same person. Given a set of unlabeled images and constraints, we use a probabilistic discriminative model that models the posterior distributions by propagating label information using a message passing scheme. The uncertainty estimate provided by the model naturally allows for active learning paradigms where the user is consulted after each iteration to tag additional faces. Our experiments show that performing active learning while incorporating a priori constraints provides a significant boost in many real-world face recognition tasks.
Incremental Multiple Kernel Learning for object recognition
Authors: Aniruddha Kembhavi, Behjat Siddiquie, Roland Miezianko, Scott McCloskey, Larry S. Davis
Venue: ICCV
Year: 2009

A good training dataset, representative of the test images expected in a given application, is critical for ensuring good performance of a visual categorization system. Obtaining task specific datasets of visual categories is, however, far more tedious than obtaining a generic dataset of the same classes. We propose an Incremental Multiple Kernel Learning (IMKL) approach to object recognition that initializes on a generic training database and then tunes itself to the classification task at hand. Our system simultaneously updates the training dataset as well as the weights used to combine multiple information sources. We demonstrate our system on a vehicle classification problem in a video stream overlooking a traffic intersection. Our system updates itself with images of vehicles in poses more commonly observed in the scene, as well as with image patches of the background, leading to an increase in performance. A considerable change in the kernel combination weights is observed as the system gathers scene specific training data over time. The system is also seen to adapt itself to the illumination change in the scene as day transitions to night.
Body-relative navigation guidance using uncalibrated cameras
Authors: Olivier Koch, Seth J. Teller
Venue: ICCV
Year: 2009

We present a vision-based method that assists human navigation within unfamiliar environments. Our main contribution is a novel algorithm that learns the correlation between user egomotion and feature matches on a wearable set of uncalibrated cameras. The primary advantage of this method is that it provides robust guidance cues in the user's body frame, and is tolerant to small changes in the camera configuration. We couple this method with a topological mapping algorithm that provides global localization within the traversed environment. We validate our approach with ground-truth experiments and demonstrate the method on several real-world datasets spanning two kilometers of indoor and outdoor walking excursions.
Graph cuts using a Riemannian metric induced by tensor voting
Authors: Hyung Il Koo, Nam Ik Cho
Venue: ICCV
Year: 2009

In this paper, we present a new algorithm that combines the advantages of tensor voting into graph cuts. Tensor voting has been a popular tool for a number of early vision problems since it can use principles of perceptual grouping, which are not well considered in graph cuts. We attempt to encode the power of tensor voting into an energy minimization framework. For this, we assume that the tensor map obtained by tensor voting induces a Riemannian metric in image domain, and the metric is constructed according to the conventional ways of tensor interpretation. Finally, by embedding the induced Riemannian metric into the graph via edge weights, the graph cuts algorithm can have priors considering principles of perceptual grouping. The proposed method can be used in the labeling of occluded regions, object segmentation using only edge information, and boundary regularization.
Shadow cameras: Reciprocal views from illumination masks
Authors: Sanjeev J. Koppal, Srinivasa G. Narasimhan
Venue: ICCV
Year: 2009

Scene appearance from the point of view of a light source is called a reciprocal or dual view. Since there exists a large diversity in illumination, these virtual views may be non-perspective and multi-viewpoint in nature. In this paper, we demonstrate the use of occluding masks to recover these dual views, which we term shadow cameras. We first show how to render a single reciprocal scene view by swapping the camera and light source positions. We extend this technique for multiple views by building a virtual shadow camera array with static masks and a moving source. We also capture non-perspective views such as orthographic, cross-slit and a pushbroom variant, while introducing novel applications such as converting between camera projections and removing refractive and catadioptric distortions. Finally, since a shadow camera is artificial, we can manipulate any of its intrinsic parameters, such as camera skew, to create perspective distortions.
Riemannian Bayesian estimation of diffusion tensor images
Authors: Kai Krajsek, Marion I. Menzel, Hanno Scharr
Venue: ICCV
Year: 2009

Diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive imaging technique allowing to estimate the molecular self-diffusion tensors of water within surrounding tissue. Due to the low signal-to-noise ratio of magnetic resonance images, reconstructed tensor images usually require some sort of regularization in a post-processing step. Previous approaches are either suboptimal with respect to the reconstructing or regularization step. This paper presents a Bayesian approach for simultaneous reconstructing and regularization of DT-MR images that allows to resolve the disadvantages of previous approaches. To this end, estimation theoretical concepts are generalized to tensor valued images that are considered as Riemannian manifolds. Doing so allows us to derive a maximum a posterior estimator of the tensor image that considers both the statistical characteristics of the Rician noise occurring in MR images as well as the nonlinear structure of tensor valued images. Experiments on synthetic data as well as real DT-MRI data validate the advantage of considering both statistical as well as geometrical characteristics of DT-MRI.
Kernelized locality-sensitive hashing for scalable image search
Authors: Brian Kulis, Kristen Grauman
Venue: ICCV
Year: 2009

Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sub-linear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several large-scale datasets, and show that it enables accurate and fast performance for example-based object classification, feature matching, and content-based retrieval.
Estimating natural illumination from a single outdoor image
Authors: Jean-François Lalonde, Alexei A. Efros, Srinivasa G. Narasimhan
Venue: ICCV
Year: 2009

Given a single outdoor image, we present a method for estimating the likely illumination conditions of the scene. In particular, we compute the probability distribution over the sun position and visibility. The method relies on a combination of weak cues that can be extracted from different portions of the image: the sky, the vertical surfaces, and the ground. While no single cue can reliably estimate illumination by itself, each one can reinforce the others to yield a more robust estimate. This is combined with a data-driven prior computed over a dataset of 6 million Internet photos. We present quantitative results on a webcam dataset with annotated sun positions, as well as qualitative results on consumer-grade photographs downloaded from Internet. Based on the estimated illumination, we show how to realistically insert synthetic 3-D objects into the scene.
A probabilistic framework for partial intrinsic symmetries in geometric data
Authors: Ruxandra Lasowski, Art Tevs, Hans-Peter Seidel, Michael Wand 0001
Venue: ICCV
Year: 2009

In this paper, we present a novel algorithm for partial intrinsic symmetry detection in 3D geometry. Unlike previous work, our algorithm is based on a conceptually simple and straightforward probabilistic formulation of partial shape matching: based on a Markov random field model, we obtain a probability distribution over all possible intrinsic matches of a shape to itself, which reveals the symmetry structure of the object. Rather than examining this exponentially sized distribution directly, which is infeasible, we approximate marginals of this distribution using sum-product loopy belief propagation and show how the symmetry information can subsequently be extracted from this condensed representation. Using a parallel implementation on graphics hardware, we are able to extract symmetries of deformable shapes in general poses efficiently. We apply our algorithm on several standard 3D models, demonstrating that a concise probabilistic model yields a practical and general symmetry detection algorithm.
Spectral clustering of linear subspaces for motion segmentation
Authors: Fabien Lauer, Christoph Schnörr
Venue: ICCV
Year: 2009

This paper studies automatic segmentation of multiple motions from tracked feature points through spectral embedding and clustering of linear subspaces. We show that the dimension of the ambient space is crucial for separability, and that low dimensions chosen in prior work are not optimal. We suggest lower and upper bounds together with a data-driven procedure for choosing the optimal ambient dimension. Application of our approach to the Hopkins155 video benchmark database uniformly outperforms a range of state-of-the-art methods both in terms of segmentation accuracy and computational speed.
FLoSS: Facility location for subspace segmentation
Authors: Nevena Lazic, Inmar E. Givoni, Brendan J. Frey, Parham Aarabi
Venue: ICCV
Year: 2009

Subspace segmentation is the task of segmenting data lying on multiple linear subspaces. Its applications in computer vision include motion segmentation in video, structure-from-motion, and image clustering. In this work, we describe a novel approach for subspace segmentation that uses probabilistic inference via a message-passing algorithm. We cast the subspace segmentation problem as that of choosing the best subset of linear subspaces from a set of candidate subspaces constructed from the data. Under this formulation, subspace segmentation corresponds to facility location, a well studied operational research problem. Approximate solutions to this NP-hard optimization problem can be found by performing maximum-a-posteriori (MAP) inference in a probabilistic graphical model. We describe the graphical model and a message-passing inference algorithm. We demonstrate the performance of Facility Location for Subspace Segmentation, or FLoSS, on synthetic data as well as on 3D multi-body video motion segmentation from point correspondences.
Robust matching of building facades under large viewpoint changes
Authors: Jimmy Addison Lee, Kin Choong Yow 0001, Alex Y. S. Chia
Venue: ICCV
Year: 2009

This paper presents a novel approach to finding point correspondences between images of building facades with wide viewpoint variations, and at the same time returning a large list of true matches between the images. Such images comprise repetitive and symmetric patterns, which render popular algorithms e.g., SIFT to be ineffective. Feature descriptors such as SIFT that are based on region patches are also unstable under large viewing angle variations. In this paper, we integrate both the appearance and geometric properties of an image to find unique matches. First we extract hypotheses of building facades based on a robust line fitting algorithm. Each hypothesis is defined by a planar convex quadrilateral in the image, which we call a “q-region”, and the four corners of each q-region provide the inputs from which a projective transformation model is derived. Next, a set of interest points are extracted from the images and are used to evaluate the correctness of the transformation model. The transformation model with the largest set of matched interest points is selected as the correct model, and this model also returns the best pair of corresponding q-regions and the most number of point correspondences in the two images. Extensive experimental results demonstrate the robustness of our approach in which we achieve a tenfold increase in true matches when compared to state of the art techniques such as SIFT and MSER.
Feature-centric Efficient Subwindow Search
Authors: Alain D. Lehmann, Bastian Leibe, Luc Van Gool
Venue: ICCV
Year: 2009

Many object detection systems rely on linear classifiers embedded in a sliding-window scheme. Such exhaustive search involves massive computation. Efficient Subwindow Search (ESS) avoids this by means of branch and bound. However, ESS makes an unfavourable memory tradeoff. Memory usage scales with both image size and overall object model size. This risks becoming prohibitive in a multiclass system. In this paper, we make the connection between sliding-window and Hough-based object detection explicit. Then, we show that the feature-centric view of the latter also nicely fits with the branch and bound paradigm, while it avoids the ESS memory tradeoff. Moreover, on-line integral image calculations are not needed. Both theoretical and quantitative comparisons with the ESS bound are provided, showing that none of this comes at the expense of performance.
A new multiview spacetime-consistent depth recovery framework for free viewpoint video rendering
Authors: Cheng Lei, Xida Chen, Yee-Hong Yang
Venue: ICCV
Year: 2009

In this paper, we present a new approach for recovering spacetime-consistent depth maps from multiple video sequences captured by stationary, synchronized and calibrated cameras for depth based free viewpoint video rendering. Our two-pass approach is generalized from the recently proposed region-tree based binocular stereo matching method. In each pass, to enforce temporal consistency between successive depth maps, the traditional region-tree is extended into a temporal one by including connections to “temporal neighbor regions” in previous video frames, which are identified using estimated optical flow information. For enforcing spatial consistency, multi-view geometric constraints are used to identify inconsistencies between depth maps among different views which are captured in an inconsistency map for each view. Iterative optimizations are performed to progressively correct inconsistencies through inconsistency maps based depth hypotheses pruning and visibility reasoning. Furthermore, the background depth and color information is generated from the results of the first pass and is used in the second pass to enforce sequence-wise temporal consistency and to aid in identifying and correcting spatial inconsistencies. The extensive experimental evaluations have shown that our proposed approach is very effective in producing spatially and temporally consistent depth maps.