OpenRaiser/Intern-Atlas huggingface dataset:
Radon Transform and Harmonical Analysis Using Lines for 3D Rotation Estimation without Correpondences from Omnidirectional Vision
Authors: Luis Falcón-Morales, Eduardo Bayro-Corrochano
Venue: ICCV
Year: 2007

Because of their large field of view, omnidirectional images can assist with localization tasks to robotic navigation algorithms. Since the images taken by omnidirectional sensors can be mapped to the sphere, the problem of attitude estimation of a 3D camera rotation can be treated as a problem of estimating rotations between spherical images. Usually, this rotation estimation problem has been solved using point correspondences or gradient information of the points of the images, with the respective computational time consuming of those point algorithms. We present an effective solution to the attitude estimation problem using line information of the images through the Radon transform, because line algorithms are less time consuming that those using points. In the formulation of the Radon transform we include a similarity function on the cross product of two images which assigns a weight to all lines pairs, and where this similarity function is integrated over all lines pairs that satisfy a constraint for lines. That is, we formulate the problem to obtain the Euler angles of the 3D camera rotation as a correlation of functions defined on the product of spheres S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> times S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> which are acted upon by elements of the direct product groups of rotations SO(3) timesSO(3). Because of the spherical treatment of the data, our approach utilizes the spherical fourier transform and spherical harmonics to produce a solution in the Fourier domain.
Efficient Computation of the Inverse Gradient on Irregular Domains
Authors: Gunnar Farnebäck, Joakim Rydell, Tino Ebbers, Mats T. Andersson, Hans Knutsson
Venue: ICCV
Year: 2007

The inverse gradient problem, finding a scalar field f with a gradient near a given vector field <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</b> on some bounded and connected domain Ω ϵ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">𝓃</sup> , can be solved by means of a Poisson equation with inhomogeneous Neumann boundary conditions. We present an elementary derivation of this partial differential equation and an efficient multigrid-based method to numerically compute the inverse gradient on non-rectangular domains. The utility of the method is demonstrated by a range of important medical applications such as phase unwrapping, pressure computation, inverse deformation fields, and fiber bundle tracking.
Learning Higher-order Transition Models in Medium-scale Camera Networks
Authors: Ryan Farrell, David S. Doermann, Larry S. Davis
Venue: ICCV
Year: 2007

We present a Bayesian framework for learning higher- order transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks.
Human Pose Estimation using Motion Exemplars
Authors: Alireza Fathi, Greg Mori
Venue: ICCV
Year: 2007

We present a motion exemplar approach for finding body configuration in monocular videos. A motion correlation technique is employed to measure the motion similarity at various space-time locations between the input video and stored video templates. These observations are used to predict the conditional state, distributions of exemplars and joint positions. Exemplar sequence selection and joint position estimation are then solved with approximate inference using Gibbs sampling and gradient ascent. The presented approach is able to find joint positions accurately for people with textured clothing. Results are presented on a dataset containing slow, fast and incline walk videos of various people from different view angles. The results demonstrate an overall improvement compared to previous methods.
Shape from Focus and Defocus: Convexity, Quasiconvexity and Defocus-Invariant Textures
Authors: Paolo Favaro
Venue: ICCV
Year: 2007

In this paper we analyze the convexity and the quasiconvexity of shape from focus/defocus and image restoration. We show that these problems are strictly quasiconvex for a family of Bregman's divergences, and in particular for least-squares. In addition to giving novel analytical insight to these problems, this study can be readily exploited to design algorithms: One can do away with global minimizers and obtain the same optimal solution by employing simple and efficient local methods. We experimentally validate this investigation by comparing two minimization algorithms: one based on a local method (gradient-flow) and another based on a global method (graph cuts). We show that both algorithms find the global optimum. Finally, we fully characterize defocus-invariant textures, a class of textures that do not allow depth recovery. We show how to decompose textures into defocus-invariant and defocus-varying components, and how this decomposition can be used to dramatically improve depth estimates.
Interacting with Projected Media on Deformable Surfaces
Authors: Siska Fitriani, Wooi-Boon Goh
Venue: ICCV
Year: 2007

This paper presents a novel human-computer interface for projector/camera-based applications that uses a deformable interaction surface. We discuss its design and implementation within the context of a radically different approach for controlling home appliances by pressing virtual buttons that are projected on soft deformable surfaces such as a sofa pillow. Effective real-time computer vision algorithms for implementing pointing and selection action detection for such an interface are discussed. Experimental results highlight the parameters and factors that have significant effect on the overall performance of such an interface.
Learning priors for calibrating families of stereo cameras
Authors: Andrew W. Fitzgibbon, Duncan P. Robertson, Antonio Criminisi, Srikumar Ramalingam, Andrew Blake 0001
Venue: ICCV
Year: 2007

Online camera recalibration is necessary for long-term deployment of computer vision systems. Existing algorithms assume that the source of recalibration information is a set of features in a general 3D scene; and that enough features are observed that the calibration problem is well-constrained. However; these assumptions are frequently invalid outside the laboratory. Real-world scenes often lack texture, contain repeated texture, or are mostly planar, making calibration difficult or impossible. In this paper we consider the calibration of families of stereo cameras, where each camera is assumed to have parameters drawn from a common but unknown prior distribution. We show how estimation of this prior using a small-number of offline-calibrated cameras (e.g. from the same production line) allows online calibration of additional cameras using a small number of point correspondences; and that using the estimated prior significantly increases the accuracy and robustness of stereo camera calibration.
Dense Multiscale Motion Extraction from Cardiac Cine MR Tagging using HARP Technology
Authors: Luc Florack, Hans C. van Assen, Avan Suinesiaputra
Venue: ICCV
Year: 2007

We propose an operational method to extract the left ventricle (LV) systole dynamics using harmonic phase (HARP) images extracted from tagged cardiac MR sequences. Established techniques to generate HARP sequences provide independent evidence for motion extraction, in the sense that the combined linear system for scalar brightness conservation, applied to the HARP images, can be uniquely solved for a dense field of motion parameters without the need for regularization. In contrast to some of the previously proposed popular methods, no segmentation or tracking of tags over time, nor interpolation of a sparse motion field explicitly coupled to the tag pattern is required, and the problem of tag fading is bypassed. An important novelty is the incorporation of automatic local scale selection so as to obtain a robust solution, which not only yields a stable, but also a smoothly varying motion field of the (healthy) LV myocardial wall. The scheme relies on an integer parameter representing order of approximation, and allows one to simultaneously obtain a dense field of differential tensors capturing the low order differential structure of the motion field, which is useful for the computation of relevant local quantities such as strain rates and material acceleration fields. The methodology is generic and straightforward to implement, and can be generalized to 3D and, in principle, to account for higher order differential structure.
Spatially Varying Classification with Localization Certainty in Level Set Segmentation
Authors: Jenny Folkesson, Carl-Fredrik Westin
Venue: ICCV
Year: 2007

We introduce a segmentation framework which extends spatially varying classification to not only incorporate anatomical localization from shape estimation, but to also encode certainty of the localization by local shape variability. The method iterates between a classification step where a statistical classifier learned from feature selection is extended with anatomical localization features, and a shape estimation step where, given the class probability maps, shape is inferred by particle filtering using a level set shape model that accounts for local degrees of anatomical variability. The spatially varying classification is embedded in a geodesic active region framework which allows for local deviations from the inferred shape using an iteratively updated classification based region term. The method is evaluated on late gadolinium enhanced cardiac MRI and is to our knowledge the first automatic segmentation method demonstrated on this type of data.
Curvature Estimation for Enhancement of Crossing Curves
Authors: Erik Franken, Remco Duits, Bart M. ter Haar Romeny
Venue: ICCV
Year: 2007

In this paper we describe a method for estimating curvature of elongated structures in images. The curvature estimation is performed on an invertible orientation score, which is a 3D entity obtained from a 2D image by convolution with a rotating kernel. By considering the group structure we can define left-invariant derivatives, which are essential to construct operations on the orientation score that amount to rotationally invariant operations on the corresponding image. The problem of estimating curvature of an oriented structure is stated as a minimization problem, which can be solved by eigenvector analysis of a matrix constructed from the non-symmetric Hessian matrix. The experiments show the method performs well for a wide range of curvatures and noise levels. The method clearly outperforms a related curvature estimation method by Van Ginkel et al. that tends to give estimates that are too small. We show how we can incorporate the curvature estimate in our method for coherence-enhancing diffusion in orientation scores. This method has superior performance in enhancing crossing contours, which is demonstrated on medical images.
Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification
Authors: Andrea Frome, Yoram Singer, Fei Sha, Jitendra Malik
Venue: ICCV
Year: 2007

We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions-a different parameterized function for every image of our training set-whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark.
Bottom-up saliency is a discriminant process
Authors: Dashan Gao 0001, Nuno Vasconcelos
Venue: ICCV
Year: 2007

A bottom-up visual saliency detector is proposed, following a decision-theoretic formulation of saliency, previously developed for top-down processing (object recognition) [5]. The saliency of a given location of the visual field is defined as the power of a Gabor-like feature set to discriminate between the visual appearance of 1) a neighborhood centered at that location (the center) and 2) a neighborhood that surrounds it (the surround). Discrimination is defined in an information-theoretic sense and the optimal saliency detector derived for a class of stimuli that complies with known statistical properties of natural images, so as to achieve a computationally efficient solution. The resulting saliency detector is shown to replicate the fundamental properties of the psychophysics of pre-attentive vision, including stimulus pop-out, inability to detect feature conjunctions, asymmetries with respect to feature presence vs. absence, and compliance with Weber's law. It is also shown that the detector produces better predictions of human eye fixations than two previously proposed bottom-up saliency detectors.
Direct Estimation of Non-Rigid Registrations with Image-Based Self-Occlusion Reasoning
Authors: Vincent Gay-Bellile, Adrien Bartoli, Patrick Sayd
Venue: ICCV
Year: 2007

The registration problem for images of a deforming surface has been well studied. External occlusions are usually well-handled. In 2D image-based registration, self- occlusions are more challenging. Consequently, the surface is usually assumed to be only slightly self-occluding. This paper is about image-based non-rigid registration with self-occlusion reasoning. A specific framework explicitly modeling self-occlusions is proposed. It is combined with an intensity-based, i.e. direct, data term for registration. Self-occlusions are detected as shrinking areas in the 2D warp. Experimental results on several challenging datasets show that our approach successfully registers images with self-occlusions while effectively detecting the occluded regions.
Improving Stereo Sub-Pixel Accuracy for Long Range Stereo
Authors: Stefan K. Gehrig, Uwe Franke
Venue: ICCV
Year: 2007

Dense stereo algorithms are able to estimate disparities at all pixels including untextured regions. Typically these disparities are evaluated at integer disparity steps. A subsequent sub-pixel interpolation often fails to propagate smoothness constraints on a sub-pixel level. The determination of sub-pixel accurate disparities is an active field of research, however, most sub-pixel estimation algorithms focus on textured image areas in order to show their precision. We propose to increase the sub-pixel accuracy in low- textured regions in three possible ways: First, we present an analysis that shows the benefit of evaluating the disparity space at fractional disparities. Second, we introduce a new disparity smoothing algorithm that preserves depth discontinuities and enforces smoothness on a sub-pixel level. Third, we present a novel stereo constraint (gravitational constraint) that assumes sorted disparity values in vertical direction and guides global algorithms to reduce false matches, especially in low-textured regions. Our goal in this work is to obtain an accurate 3D reconstruction. Large- scale 3D reconstruction will benefit heavily from these sub- pixel refinements, especially with a multi-baseline extension. Results based on semi-global matching , obtained with the above mentioned algorithmic extensions are shown for the Middlebury stereo ground truth data sets. The presented improvements, called ImproveSubPix, turn out to be one of the top-performing algorithms when evaluating the set on a sub-pixel level while being computationally efficient. Additional results are presented for urban scenes. The three improvements are independent of the underlying type of stereo algorithm and can also be applied to sparse stereo algorithms.
Robust Image Registration using Mixtures of t-distributions
Authors: Demetrios Gerogiannis, Christophoros Nikou, Aristidis Likas
Venue: ICCV
Year: 2007

We propose a pixel similarity-based algorithm enabling accurate rigid registration between single and multimodal images presenting gross dissimilarities due to noise, missing data or outlying measures. The method relies on the partitioning of a reference image by a Student's t-mixture model (SMM). This partition is then projected onto the image to be registered. The main idea is that a t-component in the reference image corresponds to a t-component in the image to be registered. If the images are correctly registered the weighted sum of distances between the corresponding components is minimized. The use of SMM components is justified by the property that they have heavier tails than standard Gaussians, thus providing robustness to outliers. Experimental results indicate that, even in the case of images presenting low SNR or important amount of dissimilarities due to temporal changes, the proposed algorithm compares favorably to the histogram-based mutual information method that is widely used in a variety of applications.
Phase Based Modelling of Dynamic Textures
Authors: Bernard Ghanem, Narendra Ahuja
Venue: ICCV
Year: 2007

This paper presents a model of spatiotemporal variations in a dynamic texture (DT) sequence. Most recent work on DT modelling represents images in a DT sequence as the responses of a linear dynamical system (LDS) to noise. Despite its merits, this model has limitations because it attempts to model temporal variations in pixel intensities which do not take advantage of global motion coherence. We propose a model that relates texture dynamics to the variation of the Fourier phase, which captures the relationships among the motions of all pixels (i.e. global motion) within the texture, as well as the appearance of the texture. Unlike LDS, our model does not require segmentation or cropping during the training stage, which allows it to handle DT sequences containing a static background. We test the performance of this model on recognition and synthesis of DT's. Experiments with a dataset that we have compiled demonstrate that our phase based model outperforms LDS.
BRDF Acquisition with Basis Illumination
Authors: Abhijeet Ghosh, Shruthi Achutha, Wolfgang Heidrich, Matthew O&apos, Toole
Venue: ICCV
Year: 2007

Realistic descriptions of surface reflectance have long been a topic of interest in both computer vision and computer graphics research. In this paper, we describe a novel and fast approach for the acquisition of bidirectional reflectance distribution functions (BRDFs). We develop a novel theory for directly measuring BRDFs in a basis representation by projecting incident light as a sequence of basis functions from a spherical zone of directions. We derive an orthonormal basis over spherical zones that is ideally suited for this task. BRDF values outside the zonal directions are extrapolated by re-projecting the zonal measurements into a spherical harmonics basis, or by fitting analytical reflection models to the data. We verify this approach with a compact optical setup that requires no moving parts and only a small number of image measurements. Using this approach, a BRDF can be measured in just a few minutes.
When is a Discrete Diffusion a Scale-Space?
Authors: Ramunas Girdziusas, Jorma Laaksonen
Venue: ICCV
Year: 2007

Necessary and sufficient conditions are discussed which state when the Euler-inspired forward diffusion in a discrete space-time is a scale-space in the sense of both the total and sign variation diminishing. We emphasize that the problem is algebraic and reduces to characterization of the elements of the generalized Laplacian so that the diffusion propagators are positive definite. As a key-product, explicit analytical expressions are found for the principal minors of the frequently-applied class of tridiagonal (Jacobi) matrices. Further generalizations are outlined by introducing novel techniques of evaluating matrix determinants.
Linear solution for the pose estimation of noncentral catadioptric systems
Authors: Nuno Gonçalves 0001, Helder Araújo
Venue: ICCV
Year: 2007

This paper presents a linear method to estimate the pose of a noncentral catadioptric system with a quadric shaped mirror in relation to a world reference frame (or local reference frame without loss of generality). The vision system is assumed to be calibrated. The method uses also as input data the structure of the scene. It is proved that any reflection point should belong to an analytical quadric that intersects the mirror quadric itself. This constraint can be written linearly in the 3D scene point coordinates (in the camera reference frame). The unknown pose screw transformation that relates camera and world reference frames can then be used in the linear model, allowing for the construction of a linear equation in the pose transformation elements. Additional constraints are used to force the estimated rotation elements to build an orthogonal matrix. Tests with simulated data and also on real images with different mirrors proved the method to be consistent and to estimate the pose accurately. However, it was also observed that the method is sensitive to noise. The results are compared with another method.