OpenRaiser/Intern-Atlas huggingface dataset:
Performance Analysis and Validation of a Paracatadioptric Omnistereo System
Authors: Xiaojin Gong, Anbumani Subramanian, Christopher L. Wyatt, Daniel J. Stilwell
Venue: ICCV
Year: 2007

In this paper we present a vector-based 3D localization formula for a paracatadioptric omnistereo system. Based on vector representation, the performance of this stereo system is analyzed numerically, including the maximum detectable range and the uncertainty of 3D localization, with respect to the flexible stereo configuration of the system, positions of scene points, as well as errors in correspondence matching and errors in stereo configuration. The results of performance analysis are used to guide the trajectory of an autonomous surface vehicle (ASV), which is equipped with a paracatadioptric omnidirectional camera, in a map building application.
Discrete camera calibration from the information distance between pixel streams
Authors: Etienne Grossmann, Francesco Orabona, José António Gaspar
Venue: ICCV
Year: 2007

We consider the problem of estimating the relative orientation of a number of individual photocells-or pixels-that hold fixed relative positions. The photocells measure the intensity of on a pencil of lines. We assume that the light-field thus sampled is changing, e.g. as the result of motion of the sensors and use the obtained measurements to estimate the orientations of the photocells. We explore an information-theoretic and geometric approach: based on real-world data, we build anon-parametric functional relation linking the information distance between the data streams of two photocells, and the angular separation between the photocells. Then, given data streams produced by arrays of pixels in similar conditions, we use the functional relation to estimate the angles between pixels. Finally, we embed the estimated angles in the unit 3D sphere to obtain the estimated layout of the array.
Ricci Flow for 3D Shape Analysis
Authors: Xianfeng Gu, Sen Wang, Junho Kim, Yun Zeng, Yang Wang 0001, Hong Qin 0001, Dimitris Samaras
Venue: ICCV
Year: 2007

Ricci flow is a powerful curvature flow method in geometric analysis. This work is the first application of surface Ricci flow in computer vision. We show that previous methods based on conformal geometries, such as harmonic maps and least-square conformal maps, which can only handle 3D shapes with simple topology are subsumed by our Ricci flow based method which can handle surfaces with arbitrary topology. Because the Ricci flow method is intrinsic and depends on the surface metric only, it is invariant to rigid motion, scaling, and isometric and conformal deformations. The solution to Ricci flow is unique and its computation is robust to noise. Our Ricci flow based method can convert all 3D problems into 2D domains and offers a general framework for 3D surface analysis. Large non-rigid deformations can be registered with feature constraints, hence we introduce a method that constrains Ricci flow computation using feature points and feature curves. Finally, we demonstrate the applicability of this intrinsic shape representation through standard shape analysis problems, such as 3D shape matching and registration.
COST: An Approach for Camera Selection and Multi-Object Inference Ordering in Dynamic Scenes
Authors: Abhinav Gupta 0001, Anurag Mittal, Larry S. Davis
Venue: ICCV
Year: 2007

Development of multiple camera based vision systems for analysis of dynamic objects such as humans is challenging due to occlusions and similarity in the appearance of a person with the background and other people- visual "confusion". Since occlusion and confusion depends on the presence of other people in the scene, it leads to a dependency structure where there are often loops in the resulting Bayesian network. While approaches such as loopy belief propagation can be used for inference, they are computationally expensive and convergence is not guaranteed in many situations. We present a unified approach, COST, that reasons about such dependencies and yields an order for the inference of each person in a group of people and a set of cameras to be used for inferences for a person. Using the probabilistic distribution of the positions and appearances of people, COST performs visibility and confusion analysis for each part of each person and computes the amount of information that can be computed with and without more accurate estimation of the positions of other people. We present an optimization problem to select set of cameras and inference dependencies for each person which attempts to minimize the computational cost under given performance constraints. Results show the efficiency of COST in improving the performance of such systems and reducing the computational resources required.
Fast Invariant Riemannian DT-MRI Regularization
Authors: Yaniv Gur, Nir A. Sochen
Venue: ICCV
Year: 2007

We present regularization by invariant denoising/smoothing of Diffusion Tensor MRI (DTI). Our solution to the problem emerges from a pure geometric point of view. The image domain and the image's values are combined together and described as a (mathematical) fiber bundle. The space of all possible DT images is the space of sections of this fiber bundle. DT image is a map that attaches a three-dimensional symmetric and positive-definite (SPD) matrix to each volume element. We treat the more general space P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> of n-dimensional SPD matrices and introduce a natural GL(n)-invariant metric via the underlying algebraic structure. A metric over sections of the fiber bundle is induced then in terms of the natural metric on P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> . This turns P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> tensors, and in general P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> tensors, into a Riemannian symmetric spaces. By means of the Beltrami framework we define a GL(n)-invariant functional over the space of sections. Then, by calculus of variations we derive the invariant equations of motion. We show that by choosing the Iwasawa coordinates the analytical calculations as well as the numerical implementation become simple. These coordinates evolve with respect to the geometry of the section via the induced metric. The numerical implementation of these flows via standard finite difference schemes is straightforward. The result is a full GL(n) invariant algorithm which is at least as fast and efficient as the Log-Euclidean method. Finally, we demonstrate this framework on real DTI data.
Structure from Statistics - Unsupervised Activity Analysis using Suffix Trees
Authors: Raffay Hamid, Siddhartha Maddi, Aaron F. Bobick, Irfan A. Essa
Venue: ICCV
Year: 2007

Models of activity structure for unconstrained environments are generally not available a priori. Recent representational approaches to this end are limited by their computational complexity, and ability to capture activity structure only up to some fixed temporal scale. In this work, we propose Suffix Trees as an activity representation to efficiently extract structure of activities by analyzing their constituent event-subsequences over multiple temporal scales. We empirically compare Suffix Trees with some of the previous approaches in terms of feature cardinality, discriminative prowess, noise sensitivity and activity-class discovery. Finally, exploiting properties of Suffix Trees, we present a novel perspective on anomalous subsequences of activities, and propose an algorithm to detect them in linear-time. We present comparative results over experimental data, collected from a kitchen environment to demonstrate the competence of our proposed framework.
Spherical-Homoscedastic Shapes
Authors: Onur C. Hamsici, Aleix M. Martínez
Venue: ICCV
Year: 2007

Shape analysis requires invariance under translation, scale and rotation. Translation and scale invariance can be realized by normalizing shape vectors with respect to their mean and norm. This maps the shape feature vectors onto the surface of a hypersphere. After normalization, the shape vectors can be made rotational invariant by modelling the resulting data using complex scalar rotation invariant distributions defined on the complex hypersphere, e.g., using the complex Bingham distribution. However, the use of these distributions is hampered by the difficulty in estimating their parameters, which is shown to be very costly or impossible in most cases. The purpose of this paper is twofold. First, we show under which conditions the classification results obtained with complex Binghams are identical to those obtained with the easy-to-estimate complex Normal distribution. Second, we derive a kernel function which (intrinsically) maps the data into a space where the above conditions are satisfied and, hence, where the normal model can be successfully used. This results in a simple, low-cost algorithm for representing and classifying shapes. We demonstrate the use of this technique in several experimental results for object and face recognition. Comparisons to other statistical shape representation/classification approaches demonstrate the superiority of the proposed algorithms in classification accuracy and computational time.
Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering
Authors: Bohyung Han, Seong-Wook Joo, Larry S. Davis
Venue: ICCV
Year: 2007

Even though sensor fusion techniques based on particle filters have been applied to object tracking, their implementations have been limited to combining measurements from multiple sensors by the simple product of individual likelihoods. Therefore, the number of observations is increased as many times as the number of sensors, and the combined observation may become unreliable through blind integration of sensor observations—especially if some sensors are too noisy and non-discriminative. We describe a methodology to model interactions between multiple sensors and to estimate the current state by using a mixture of Bayesian filters—one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, an adaptive particle arrangement system is constructed in which each particle is allocated to only one of the sensors for observation and a different number of samples is assigned to each sensor using prior distribution and partial observations. We apply this technique to visual tracking in logical and physical sensor fusion frameworks, and demonstrate its effectiveness through tracking results.
Scale-Invariant Features on the Sphere
Authors: Peter Hansen 0003, Peter Corke, Wageeh W. Boles, Kostas Daniilidis
Venue: ICCV
Year: 2007

This paper considers an application of scale-invariant feature detection using scale-space analysis suitable for use with wide field of view cameras. Rather than obtain scale- space images via convolution with the Gaussian function on the image plane, we map the image to the sphere and obtain scale-space images as the solution to the heat (diffusion) equation on the sphere which is implemented in the frequency domain using spherical harmonics. The percentage correlation of scale-invariant features that may be matched between any two wide-angle images subject to change in camera pose is then compared using each of these methods. We also present a means by which the required sampling bandwidth may be determined and propose a suitable anti-aliasing filter which may be used when this bandwidth exceeds the maximum permissible due to computational requirements. The results show improved performance using scale-space images obtained as the solution of the diffusion equation on the sphere, with additional improvements observed using the anti-aliasing filter.
A Layer-Based Restoration Framework for Variable-Aperture Photography
Authors: Samuel W. Hasinoff, Kiriakos N. Kutulakos
Venue: ICCV
Year: 2007

We present variable-aperture photography, a new method for analyzing sets of images captured with different aperture settings, with all other camera parameters fixed. We show that by casting the problem in an image restoration framework, we can simultaneously account for defocus, high dynamic range exposure (HDR), and noise, all of which are confounded according to aperture. Our formulation is based on a layered decomposition of the scene that models occlusion effects in detail. Recovering such a scene representation allows us to adjust the camera parameters in post-capture, to achieve changes in focus setting or depth-of-field—with all results available in HDR. Our method is designed to work with very few input images: we demonstrate results from real sequences obtained using the three-image "aperture bracketing" mode found on consumer digital SLR cameras.
On the Extraction of Curve Skeletons using Gradient Vector Flow
Authors: M. Sabry Hassouna, Aly A. Farag
Venue: ICCV
Year: 2007

In this paper, we propose a new variational framework for computing continuous curve skeletons from discrete objects that are suitable for structural shape representation. We have derived a new energy function, which is proportional to some medialness function, such that the minimum cost path between any two medial voxels in the shape is a curve skeleton. We have employed two different medialness functions; the Euclidean distance field and a variant of the magnitude of the gradient vector flow (GVF), resulting in two different energy functions. The first energy controls the identification of the shape topological nodes from which curve skeletons start, while the second one controls the extraction of curve skeletons. The accuracy and robustness of the proposed framework are validated both quantitatively and qualitatively against competing techniques as well as several 3D shapes of different complexity.
pLSA for Sparse Arrays With Tsallis Pseudo-Additive Divergence: Noise Robustness and Algorithm
Authors: Tamir Hazan, Roee Hardoon, Amnon Shashua
Venue: ICCV
Year: 2007

We introduce the Tsallis divergence error measure in the context of pLSA matrix and tensor decompositions showing much improved performance in the presence of noise. The focus of our approach is on one hand to provide an optimization framework which extends (in the sense of a one parameter family) the Maximum Likelihood framework and on the other hand is theoretically guaranteed to provide robustness under clutter, noise and outliers in the measurement matrix under certain conditions. Specifically, the conditions under which our approach excels is when the measurement array (co-occurrences) is sparse — which happens in the application domain of "bag of visual words".
Depth Map Regeneration via Improved Graph Cuts Using a Novel Omnidirectional Stereo Sensor
Authors: Lei He, Chuanjiang Luo, Feng Zhu 0014, Yingming Hao, Jinjun Ou, Jing Zhou
Venue: ICCV
Year: 2007

An integrated framework mainly focusing on stereo matching has been presented in this paper to obtain dense depth maps for a mobile robot that is equipped with a novel omnidirectional stereo vision sensor that is designed to obtain height information. The vision sensor is composed of a common perspective camera and two hyperbolic mirrors, which are separately fixed inside a glass cylinder. As the separation between the two mirrors provides much enlarged baseline, the precision of the system has improved correspondingly. Nevertheless, the large disparity space and image particularities that are different from general stereo vision system result in poor performance using common methods. To satisfy the reliability requirement by mobile robot navigation, we use improved graph cuts method, in which more appropriate three-variable smootheness model is proposed for general priors corresponding to more reasonable piecewise smoothness assumption since the well-known swap move algorithm can be applied to a wider class of functions. We also show the necessary modification to handle panoramic images, including deformed matching template, adaptable template scale. Experiment shows that this proposed vision system is feasible as a practical stereo sensor for accurate depth map generation.
A Statistical Approach to Determine Symmetrical Solutions for the Registration of 3D Knee Implant Models to Sagittal Fluoroscopy Images
Authors: Jeroen Hermans, Johan Bellemans, Dirk Vandermeulen, Paul Suetens
Venue: ICCV
Year: 2007

During the registration of 3D CAD models of metallic knee implant components to single-plane sagittal fluoroscopy images, the 3D pose of each implant component is estimated by maximizing the similarity between its 2D image appearance and the observed fluoroscopy image. Because knee implant components are highly symmetrical with respect to the sagittal plane, two significantly different model poses result in 2D image projections very similar to an observed sagittal fluoroscopy image. Traditional 2D/3D registration algorithms tend to converge to one of these symmetrical poses discarding the other one. This paper presents a method which simultaneously estimates both symmetrical solutions. In order not to limit the proposed method to 3D models with an exact plane of symmetry, a completely data-driven symmetry constraint is used which is imposed to the estimated pose parameters. The algorithm is embedded in a statistical framework which is optimized using a deterministic annealing expectation-maximization approach. The validity of the method is demonstrated by registration of the tibial knee implant component to real and simulated fluoroscopy images.
Registration of anatomical images using geodesic paths of diffeomorphisms parameterized with stationary vector fields
Authors: Monica Hernandez, Matías N. Bossa, Salvador Olmos
Venue: ICCV
Year: 2007

Computational Anatomy aims for the study of the statistical variability in anatomical structures. Variability is encoded by the transformations existing among populations of anatomical images. These transformations are usually computed from diffeomorphic registration based on the large deformation paradigm. In this framework diffeomorphisms are usually computed as end points of paths on the Riemannian manifold of diffeomorphisms parameterized by non-stationary vector fields. Recently, an alternative parameterization based on stationary vector fields has been developed. In this article we propose to use this stationary parameterization for diffeomorphic registration. We formulate the variational problem related to this registration scenario and derive the associated Euler-Lagrange equations. We evaluate the performance of the non-stationary vs the stationary parameterizations in real and synthetic 3D-MRI datasets. Compared to the non-stationary parameterization, our proposal provides similar accuracy in terms of image matching and deformation smoothness while drastically reducing memory and time requirements.
N3M: Natural 3D Markers for Real-Time Object Detection and Pose Estimation
Authors: Stefan Hinterstoisser, Selim Benhimane, Nassir Navab
Venue: ICCV
Year: 2007

In this paper, a new approach for object detection and pose estimation is introduced. The contribution consists in the conception of entities permitting stable detection and reliable pose estimation of a given object. Thanks to a well- defined off-line learning phase, we design local and minimal subsets of feature points that have, at the same time, distinctive photometric and geometric properties. We call these entities Natural 3D Markers (N3Ms). Constraints on the selection and the distribution of the subsets coupled with a multi-level validation approach result in a detection at high frame rates and allow us to determine the precise pose of the object. The method is robust against noise, partial occlusions, background clutter and illumination changes. The experiments show its superiority to existing standard methods. The validation was carried out using simulated ground truth data. Excellent results on real data demonstrated the usefulness of this approach for many computer vision applications.
Bilinear Models for Spatio-Temporal Point Distribution Analysis: Application to Extrapolation of Whole Heart Cardiac Dynamics
Authors: Corné Hoogendoorn, Federico Sukno, Sebastián Ordas, Alejandro F. Frangi
Venue: ICCV
Year: 2007

In this work we introduce the usage of bilinear models as a means of factorising the shape variation induced by subject variability and the contraction of the human heart. We show that it is feasible to reconstruct the shape of the heart at a certain point in the cardiac cycle if we are given a small number of shapes representing the same heart at different points in the same cycle, using the bilinear model. Depending on pathology and the ratios between healthy and pathological hearts in the training set, RMS reconstruction errors measured between 1.39 and 16.58 millimetres, with a median of 6.79 and 90th percentile of 9.95 millimetres.
Discriminant Embedding for Local Image Descriptors
Authors: Gang Hua 0001, Matthew A. Brown, Simon A. J. Winder
Venue: ICCV
Year: 2007

Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and visual recognition. However, such descriptors are generally parameterised in very high dimensional spaces e.g. 128 dimensions in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. Furthermore, these descriptors have traditionally been carefully hand crafted by manually tuning many parameters. In this paper, we tackle both of these problems by formulating descriptor design as a non- parametric dimensionality reduction problem. In contrast to previous approaches that use only the global statistics of the inputs, we adopt a discriminative approach. Starting from a large training set of labelled match/non-match pairs, we pursue lower dimensional embeddings that are optimised for their discriminative power. Extensive comparative experiments demonstrate that we can exceed the performance of the current state of the art techniques such as SIFT with far fewer dimensions, and with virtually no parameters to be tuned by hand.
Incremental Learning of Boosted Face Detector
Authors: Chang Huang, Haizhou Ai, Takayoshi Yamashita, Shihong Lao, Masato Kawade
Venue: ICCV
Year: 2007

In recent years, boosting has been successfully applied to many practical problems in pattern recognition and computer vision fields such as object detection and tracking. As boosting is an offline training process with beforehand collected data, once learned, it cannot make use of any newly arriving ones. However, an offline boosted detector is to be exploited online and inevitably there must be some special cases that are not covered by those beforehand collected training data. As a result, the inadaptable detector often performs badly in diverse and changeful environments which are ordinary for many real-life applications. To alleviate this problem, this paper proposes an incremental learning algorithm to effectively adjust a boosted strong classifier with domain-partitioning weak hypotheses to online samples, which adopts a novel approach to efficient estimation of training losses received from offline samples. By this means, the offline learned general-purpose detectors can be adapted to special online situations at a low extra cost, and still retains good generalization ability for common environments. The experiments show convincing results of our incremental learning approach on challenging face detection problems with partial occlusions and extreme illuminations.