OpenRaiser/Intern-Atlas huggingface dataset:
Multiview 3D warps
Authors: Alessio Del Bue, Adrien Bartoli
Venue: ICCV
Year: 2011

Image registration and 3D reconstruction are fundamental computer vision and medical imaging problems. They are particularly challenging when the input data are images of a deforming body obtained by a single moving camera. We propose a new modelling framework, the multiview 3D warps. Existing models are twofold: they estimate inter-image warps which are often inconsistent between the different images and do not model the underlying 3D structure, or reconstruct just a sparse set of points. In contrast, our multiview 3D warps combine the advantages of both; they have an explicit 3D component and a set of 3D deformations combined with projection to 2D. They thus capture the dense deforming body's time-varying shape and camera pose. The advantages over the classical solutions are numerous: thanks to our feature-based estimation method for the multiview 3D warps, one can not only augment the original images but also retarget or clone the observed body's 3D deformations by changing the pose. Experimental results on simulated and real data are reported, confirming the advantages of our framework over existing methods.
Incremental on-line semi-supervised learning for segmenting the left ventricle of the heart from ultrasound data
Authors: Gustavo Carneiro 0001, Jacinto C. Nascimento
Venue: ICCV
Year: 2011

Recently, there has been an increasing interest in the investigation of statistical pattern recognition models for the fully automatic segmentation of the left ventricle (LV) of the heart from ultrasound data. The main vulnerability of these models resides in the need of large manually annotated training sets for the parameter estimation procedure. The issue is that these training sets need to be annotated by clinicians, which makes this training set acquisition process quite expensive. Therefore, reducing the dependence on large training sets is important for a more extensive exploration of statistical models in the LV segmentation problem. In this paper, we present a novel incremental on-line semi-supervised learning model that reduces the need of large training sets for estimating the parameters of statistical models. Compared to other semi-supervised techniques, our method yields an on-line incremental re-training and segmentation instead of the off-line incremental re-training and segmentation more commonly found in the literature. Another innovation of our approach is that we use a statistical model based on deep learning architectures, which are easily adapted to this on-line incremental learning framework. We show that our fully automatic LV segmentation method achieves state-of-the-art accuracy with training sets containing less than twenty annotated images.
A nonparametric Riemannian framework on tensor field with application to foreground segmentation
Authors: Rui Caseiro, João F. Henriques, Pedro Martins 0004, Jorge P. Batista
Venue: ICCV
Year: 2011

Background modelling on tensor field has recently been proposed for foreground detection tasks. Taking into account the Riemannian structure of the tensor manifold, recent research has focused on developing parametric methods on the tensor domain e.g. gaussians mixtures (GMM) [7]. However, in some scenarios, simple parametric models do not accurately explain the physical processes. Kernel density estimators (KDE) have been successful to model, on Euclidean sample spaces the nonparametric nature of complex, time varying, and non-static backgrounds [8]. Founded on the mathematically rigorous KDE paradigm on general Riemannian manifolds [15], we define a KDE specifically to operate on the tensor manifold. We present a mathematically-sound framework for nonparametric modeling on tensor field to foreground segmentation. We endow the tensor manifold with two well-founded Riemannian metrics, i.e. Affine-Invariant and Log-Euclidean. Theoretical aspects are defined and the metrics are compared experimentally. Theoretic analysis and experimental results demonstrate the promise/effectiveness of the framework.
Stereo time-of-flight
Authors: Victor Castañeda, Diana Mateus, Nassir Navab
Venue: ICCV
Year: 2011

This paper describes a novel method to acquire depth images using a pair of ToF (Time of Flight) cameras. As opposed to approaches that filter, calibrate or do 3D reconstructions posterior to the image acquisition, we propose to combine the measurements of the two cameras at the acquisition level. To do so, we define a three-stages procedure, during which we actively modify the infrared lighting of the scene: first, the two cameras emit an infrared signal one after the other (stages 1 and 2), and then, simultaneously (stage 3). Assuming the scene is static during the three stages, we gather the depth measurements obtained with both cameras and define a cost function to optimize the two depth images. A quantitative evaluation of the performance of the proposed method for different objects and stereo configurations is provided based on a simulation of the ToF cameras. Results on real images are also presented. Both in simulation and real images the stereo-ToF acquisition produces more accurate depth measurements.
An adaptive coupled-layer visual model for robust visual tracking
Authors: Luka Cehovin, Matej Kristan, Ales Leonardis
Venue: ICCV
Year: 2011

This paper addresses the problem of tracking objects which undergo rapid and significant appearance changes. We propose a novel coupled-layer visual model that combines the target's global and local appearance. The local layer in this model is a set of local patches that geometrically constrain the changes in the target's appearance. This layer probabilistically adapts to the target's geometric deformation, while its structure is updated by removing and adding the local patches. The addition of the patches is constrained by the global layer that probabilistically models target's global visual properties such as color, shape and apparent local motion. The global visual properties are updated during tracking using the stable patches from the local layer. By this coupled constraint paradigm between the adaptation of the global and the local layer, we achieve a more robust tracking through significant appearance changes. Indeed, the experimental results on challenging sequences confirm that our tracker outperforms the related state-of-the-art trackers by having smaller failure rate as well as better accuracy.
BiCoS: A Bi-level co-segmentation method for image classification
Authors: Yuning Chai, Victor S. Lempitsky, Andrew Zisserman
Venue: ICCV
Year: 2011

The objective of this paper is the unsupervised segmentation of image training sets into foreground and background in order to improve image classification performance. To this end we introduce a new scalable, alternation-based algorithm for co-segmentation, BiCoS, which is simpler than many of its predecessors, and yet has superior performance on standard benchmark image datasets.
A selective spatio-temporal interest point detector for human action recognition in complex scenes
Authors: Bhaskar Chakraborty, Michael B. Holte, Thomas B. Moeslund, Jordi Gonzàlez 0001, F. Xavier Roca
Venue: ICCV
Year: 2011

Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance.
What an image reveals about material reflectance
Authors: Manmohan Krishna Chandraker, Ravi Ramamoorthi
Venue: ICCV
Year: 2011

We derive precise conditions under which material reflectance properties may be estimated from a single image of a homogeneous curved surface (canonically a sphere), lit by a directional source. Based on the observation that light is reflected along certain (a priori unknown) preferred directions such as the half-angle, we propose a semiparametric BRDF abstraction that lies between purely parametric and purely data-driven models. Formulating BRDF estimation as a particular type of semiparametric regression, both the preferred directions and the form of BRDF variation along them can be estimated from data. Our approach has significant theoretical, algorithmic and empirical benefits, lends insights into material behavior and enables novel applications. While it is well-known that fitting multi-lobe BRDFs may be ill-posed under certain conditions, prior to this work, precise results for the well-posedness of BRDF estimation had remained elusive. Since our BRDF representation is derived from physical intuition, but relies on data, we avoid pitfalls of both parametric (low generalizability) and non-parametric regression (low interpretability, curse of dimensionality). Finally, we discuss several applications such as single-image relighting, light source estimation and physically meaningful BRDF editing.
Multi-view 3D reconstruction for scenes under the refractive plane with known vertical direction
Authors: Yao-Jen Chang, Tsuhan Chen
Venue: ICCV
Year: 2011

Images taken from scenes under water suffer distortion due to refraction. While refraction causes magnification with mild distortion on the observed images, severe distortions in geometry reconstruction would be resulted if the refractive distortion is not properly handled. Different from the radial distortion model, the refractive distortion depends on the scene depth seen from each light ray as well as the camera pose relative to the refractive surface. Therefore, it's crucial to obtain a good estimate of scene depth, camera pose and optical center to alleviate the impact of refractive distortion. In this work, we formulate the forward and back projections of light rays involving a refractive plane for the perspective camera model by explicitly modeling refractive distortion as a function of depth. Furthermore, for cameras with an inertial measurement unit (IMU), we show that a linear solution to the relative pose and a closed-form solution to the absolute pose can be derived with known camera vertical directions. We incorporate our formulations with the general structure from motion framework followed by the patch-based multiview stereo algorithm to obtain a 3D reconstruction of the scene. We show through experiments that the explicit modeling of depth-dependent refractive distortion physically leads to more accurate scene reconstructions.
Panoramic stereo video textures
Authors: Vincent Chapdelaine-Couture, Michael S. Langer, Sébastien Roy 0001
Venue: ICCV
Year: 2011

A panoramic stereo (or omnistereo) pair of images provides depth information from stereo up to 360 degrees around a central observer. Because omnistereo lenses or mirrors do not yet exist, synthesizing omnistereo images requires multiple stereo camera positions and baseline orientations. Recent omnistereo methods stitch together many small field of view images called slits which are captured by one or two cameras following a circular motion. However, these methods produce omnistereo images for static scenes only. The situation is much more challenging for dynamic scenes since stitching needs to occur over both space and time and should synchronize the motion between left and right views as much as possible. This paper presents the first ever method for synthesizing panoramic stereo video textures. The method uses full frames rather than slits and uses blending across seams rather than smoothing or matching based on graph cuts. The method produces loopable panoramic stereo videos that can be displayed up to 360 degrees around a viewer.
Unstructured light scanning to overcome interreflections
Authors: Vincent Chapdelaine-Couture, Nicolas Martin, Sébastien Roy 0001
Venue: ICCV
Year: 2011

Reconstruction from structured light can be greatly affected by interreflections between surfaces in the scene. This paper introduces band-pass white noise patterns designed specifically to reduce interreflections, and still be robust to standard challenges in scanning systems such as scene depth discontinuities, defocus and low camera-projector pixel ratio. While this approach uses unstructured light patterns that increase the number of required projected images, it is up to our knowledge the first method that is able to recover scene disparities in the presence of both scene discontinuities and interreflections. Furthermore, the method does not require calibration (geometric nor photometric) or post-processing such as dynamic programming or phase unwrapping. We show results for a challenging scene and compare them to correspondences obtained with the well-known Gray code and Phase-shift methods.
Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation
Authors: Yutian Chen 0001, Andrew Gelfand, Charless C. Fowlkes, Max Welling
Venue: ICCV
Year: 2011

We present a new method to combine possibly inconsistent locally (piecewise) trained conditional models p(y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sub> |x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sub> ) into pseudo-samples from a global model. Our method does not require training of a CRF, but instead generates samples by iterating forward a weakly chaotic dynamical system. The new method is illustrated on image segmentation tasks where classifiers based on local appearance cues are combined with pairwise boundary cues.
Silhouette-based object phenotype recognition using 3D shape priors
Authors: Yu Chen 0009, Tae-Kyun Kim 0001, Roberto Cipolla
Venue: ICCV
Year: 2011

This paper tackles the novel challenging problem of 3D object phenotype recognition from a single 2D silhouette. To bridge the large pose (articulation or deformation) and camera viewpoint changes between the gallery images and query image, we propose a novel probabilistic inference algorithm based on 3D shape priors. Our approach combines both generative and discriminative learning. We use latent probabilistic generative models to capture 3D shape and pose variations from a set of 3D mesh models. Based on these 3D shape priors, we generate a large number of projections for different phenotype classes, poses, and camera viewpoints, and implement Random Forests to efficiently solve the shape and pose inference problems. By model selection in terms of the silhouette coherency between the query and the projections of 3D shapes synthesized using the galleries, we achieve the phenotype recognition result as well as a fast approximate 3D reconstruction of the query. To verify the efficacy of the proposed approach, we present new datasets which contain over 500 images of various human and shark phenotypes and motions. The experimental results clearly show the benefits of using the 3D priors in the proposed method over previous 2D-based methods.
Illumination demultiplexing from a single image
Authors: Christine Chen, Daniel A. Vaquero, Matthew Turk 0001
Venue: ICCV
Year: 2011

A class of techniques in computer vision and graphics is based on capturing multiple images of a scene under different illumination conditions. These techniques explore variations in illumination from image to image to extract interesting information about the scene. However, their applicability to dynamic environments is limited due to the need for robust motion compensation algorithms. To overcome this issue, we propose a method to separate multiple illuminants from a single image. Given an image of a scene simultaneously illuminated by multiple light sources, our method generates individual images as if they had been illuminated by each of the light sources separately. To facilitate the illumination separation process, we encode each light source with a distinct sinusoidal pattern, strategically selected given the relative position of each light with respect to the camera, such that the observed sinusoids become independent of the scene geometry. The individual illuminants are then demultiplexed by analyzing local frequencies. We show applications of our approach in image-based relighting, photometric stereo, and multiflash imaging.
Multi-label visual classification with label exclusive context
Authors: Xiangyu Chen, Xiao-Tong Yuan, Qiang Chen 0007, Shuicheng Yan, Tat-Seng Chua
Venue: ICCV
Year: 2011

We introduce in this paper a novel approach to multi-label image classification which incorporates a new type of context - label exclusive context - with linear representation and classification. Given a set of exclusive label groups that describe the negative relationship among class labels, our method, namely LELR for Label Exclusive Linear Representation, enforces repulsive assignment of the labels from each group to a query image. The problem can be formulated as an exclusive Lasso (eLasso) model with group overlaps and affine transformation. Since existing eLasso solvers are not directly applicable to solving such an variant of eLasso in our setting, we propose a Nesterov's smoothing approximation algorithm for efficient optimization. Extensive comparing experiments on the challenging real-world visual classification benchmarks demonstrate the effectiveness of incorporating label exclusive context into visual classification.
Multi-task low-rank affinity pursuit for image segmentation
Authors: Bin Cheng 0001, Guangcan Liu, Jingdong Wang 0001, ZhongYang Huang, Shuicheng Yan
Venue: ICCV
Year: 2011

This paper investigates how to boost region-based image segmentation by pursuing a new solution to fuse multiple types of image features. A collaborative image segmentation framework, called multi-task low-rank affinity pursuit, is presented for such a purpose. Given an image described with multiple types of features, we aim at inferring a unified affinity matrix that implicitly encodes the segmentation of the image. This is achieved by seeking the sparsity-consistent low-rank affinities from the joint decompositions of multiple feature matrices into pairs of sparse and low-rank matrices, the latter of which is expressed as the production of the image feature matrix and its corresponding image affinity matrix. The inference process is formulated as a constrained nuclear norm and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2;1</sub> -norm minimization problem, which is convex and can be solved efficiently with the Augmented Lagrange Multiplier method. Compared to previous methods, which are usually based on a single type of features, the proposed method seamlessly integrates multiple types of features to jointly produce the affinity matrix within a single inference step, and produces more accurate and reliable segmentation results. Experiments on the MSRC dataset and Berkeley segmentation dataset well validate the superiority of using multiple features over single feature and also the superiority of our method over conventional methods for feature fusion. Moreover, our method is shown to be very competitive while comparing to other state-of-the-art methods.
Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence
Authors: Anoop Cherian, Suvrit Sra, Arindam Banerjee 0001, Nikolaos Papanikolopoulos
Venue: ICCV
Year: 2011

Covariance matrices provide compact, informative feature descriptors for use in several computer vision applications, such as people-appearance tracking, diffusion-tensor imaging, activity recognition, among others. A key task in many of these applications is to compare different covariance matrices using a (dis)similarity function. A natural choice here is the Riemannian metric corresponding to the manifold inhabited by covariance matrices. But computations involving this metric are expensive, especially for large matrices and even more so, in gradient-based algorithms. To alleviate these difficulties, we advocate a novel dissimilarity measure for covariance matrices: the Jensen-Bregman LogDet Divergence. This divergence enjoys several useful theoretical properties, but its greatest benefits are: (i) lower computational costs (compared to standard approaches); and (ii) amenability for use in nearest-neighbor retrieval. We show numerous experiments to substantiate these claims.
Learning component-level sparse representation using histogram information for image classification
Authors: Chen-Kuo Chiang, Chih-Hsueh Duan, Shang-Hong Lai, Shih-Fu Chang
Venue: ICCV
Year: 2011

A novel component-level dictionary learning framework which exploits image group characteristics within sparse coding is introduced in this work. Unlike previous methods, which select the dictionaries that best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and component level importance within one unified framework to give a discriminative representation for image groups. The importance measures how well each feature component represents the image group property with the dictionary by using histogram information. Then, dictionaries are updated iteratively to reduce the influence of unimportant components, thus refining the sparse representation for each image group. In the end, by keeping the top K important components, a compact representation is derived for the sparse coding dictionary. Experimental results on several public datasets are shown to demonstrate the superior performance of the proposed algorithm compared to the-state-of-the-art methods.
A dimensionality result for multiple homography matrices
Authors: Wojciech Chojnacki, Anton van den Hengel
Venue: ICCV
Year: 2011

It is shown that the set of all I-element collections of interdependent homography matrices describing homographies induced by I planes in the 3D scene between two views has dimension 4I + 7. This improves on an earlier result which gave an upper bound for the dimension in question, and solves a long-standing open problem. The significance of the present result lies in that it is critical to the identification of the full set of constraints to which collections of interdependent homography matrices are subject, which in turn is critical to the design of constrained optimisation techniques for estimating such collections from image data.