OpenRaiser/Intern-Atlas huggingface dataset:
Probability Models for High Dynamic Range Imaging
Authors: Chris Pal, Richard Szeliski, Matthew Uyttendaele, Nebojsa Jojic
Venue: CVPR
Year: 2004

Methods for expanding the dynamic range of digital photographs by combining images taken at different exposures have recently received a lot of attention. Current techniques assume that the photometric transfer function of a given camera is the same (modulo an overall exposure change) for all the input images. Unfortunately, this is rarely the case with today's camera, which may perform complex nonlinear color and intensity transforms on each picture. In this paper, we show how the use of probability models for the imaging system and weak prior models for the response functions enable us to estimate a different function for each image using only pixel intensity values. Our approach also allows us to characterize the uncertainty inherent in each pixel measurement. We can therefore produce statistically optimal estimates for the hidden variables in our model representing scene irradiance. We present results using this method to statistically characterize camera imaging functions and construct high-quality high dynamic range (HDR) images using only image pixel information.
A Fast Multigrid Implicit Algorithm for the Evolution of Geodesic Active Contours
Authors: George Papandreou, Petros Maragos
Venue: CVPR
Year: 2004

Active contour models are among the most popular PDE-based tools in computer vision. In this paper we present a new algorithm for the fast evolution of geodesic active contours and compare it with other established numerical schemes. The new algorithm employs a full time-implicit and unconditionally stable numerical scheme and applies multigrid methods for the efficient solution of the occurring sparse linear system. When we utilize very big time-steps for the numerical evolution of the front, the proposed scheme has increased accuracy and better rotational invariance properties compared with the alternative AOS scheme. This allows for the rapid evolution and convergence of the contour to its final configuration after only very few iterations. Standard pyramidal and/or narrowband techniques can be easily integrated into our algorithm and further accelerate the curve evolution. Experimental results in object boundary detection demonstrate the power of the method.
View Independent Human Body Pose Estimation from a Single Perspective Image
Authors: Vasu Parameswaran, Rama Chellappa
Venue: CVPR
Year: 2004

Recovering the 3D coordinates of various joints of the human body from an image is a critical first step for several model-based human tracking and optical motion capture systems. Unlike previous approaches that have used a restrictive camera model or assumed a calibrated camera, our work deals with the general case of a perspective uncalibrated camera and is thus well suited for archived video. The input to the system is an image of the human body and correspondences of several body landmarks, while the output is the set of 3D coordinates of the landmarks in a body-centric coordinate system. Using ideas from 3D model based invariants, we set up a polynomial system of equations in the unknown head pitch, yaw and roll angles. If we are able to make the often-valid assumption that the torso twist is small, there are finite numbers of solutions to the head-orientation that can be computed readily. Once the head orientation is computed, the epipolar geometry of the camera is recovered, leading to solutions to the 3D joint positions. Results are presented on synthetic and real images.
Local Smoothing for Manifold Learning
Authors: Jin Hyeong Park, Zhenyue Zhang, Hongyuan Zha, Rangachar Kasturi
Venue: CVPR
Year: 2004

We propose methods for outlier handling and noise reduction using weighted local linear smoothing for a set of noisy points sampled from a nonlinear manifold. Weighted PCA is used as a building block for our methods and we suggest an iterative weight selection scheme for robust local linear fitting together with an outlier detection method based on minimal spanning trees to further improve robustness. We also develop an efficient and effective bias-reduction method to deal with the "trim the peak and fill the valley" phenomenon in local linear smoothing. Synthetic examples along with several image data sets are presented to show that manifold learning methods combined with weighted local linear smoothing give more accurate results.
Similarity Measure and Learning with Gray Level Aura Matrices (GLAM) for Texture Image Retrieval
Authors: Xuejie Qin, Yee-Hong Yang
Venue: CVPR
Year: 2004

We present a new similarity measure for texture images based on the gray level aura matrices (GLAM), originally proposed by Elfadel and Picard for modeling textures. With the new similarity measure, a support vector machine (SVM) is used to learn pattern similarities for texture image retrieval. In our approach, a texture image is first segmented into clusters of gray level sets. Defined based on the aura measures, a normalized aura matrix is calculated between the gray level sets of the image. The similarity between two texture images computed by the distance of their corresponding normalized aura matrices is defined as the aura matrix distance. The smaller the distance, the more similar are the two textures. To enable the learning of similarity for texture image retrieval, an existing SVM method is adapted to our application, but with a different similarity measure function, different texture feature vectors, and a different similarity ranking scheme for the final retrieved images based on the GLAM. We compare our approach experimentally with existing approaches by performing texture image retrieval from the Brodatz database and the Vistex database. The experimental results show that the proposed approach has performance significantly better than existing approaches with an average successful retrieval rate of 99% - 100% vs 89% - 92% using other approaches.
Simultaneous Calibration and Tracking with a Network of Non-Overlapping Sensors
Authors: Ali Rahimi, Brian Dunagan, Trevor Darrell
Venue: CVPR
Year: 2004

We describe a method for simultaneously recovering the trajectory of a target and the external calibration parameters of non-overlapping cameras in a multi-camera system. Each camera is assumed to measure the location of a moving target within its field of view with respect to the camera's ground-plane coordinate system. Calibrating the network of cameras requires aligning each camera's ground-plane coordinate system with a global ground-plane coordinate system. Prior knowledge about the target's dynamics can compensate for the lack of overlap between the camera fields of view. The target is allowed to move freely with varying speed and direction. We demonstrate the idea with a network of indoor wireless cameras.
Calibrating an Air-Ground Control System from Motion Correspondences
Authors: Rahul Rao, Camillo J. Taylor, Vijay Kumar 0001
Venue: CVPR
Year: 2004

In this paper we consider the problem of controlling the motion of a vehicle moving on a ground plane based on aerial imagery. In the course of this work we propose a novel analysis of the relationship between the velocity of the vehicle on the ground plane and the velocity of its projection in the image. We show that this relationship provides information about a subset of the parameters of the homography relating the ground plane to the aerial image plane and describe how we can recover this relationship from available measurements.
Multi-Classifier Framework for Atlas-Based Image Segmentation
Authors: Torsten Rohlfing, Calvin R. Maurer Jr.
Venue: CVPR
Year: 2004

We develop and evaluate in this paper a multi-classifier framework for atlas-based segmentation, a popular segmentation method in biomedical image analysis. An atlas is a spatial map of classes (e.g., anatomical structures), which is usually derived from a reference individual by manual segmentation. An atlas-based classification is generated by registering an image to an atlas, that is, by computing a semantically correct coordinate mapping between the two. In the present paper the registration algorithm is an intensity-based non-rigid method that computes a free-form deformation (FFD) defined on a uniform grid of control points. The transformation is regularized by a weighted smoothness constraint term. Different atlases, as well as different parameterizations of the registration algorithm, lead to different and somewhat independent atlas-based classifiers. The outputs of these classifiers can be combined in order to improve overall classification accuracy. In an evaluation study, biomedical images from seven subjects are segmented (1) using three individual atlases; (2) using one atlas and three different resolutions of the FFD control point grid, (3) using one atlas and three different regularization constraint weights. In each case, the three individual segmentations are combined by Sum Rule fusion. For each individual and for each combined segmentation, its recognition rate (relative number of correctly labeled image voxels) is computed against a manual gold-standard segmentation. In all cases, classifier combination consistently improved classification accuracy. The biggest improvement was achieved using multiple atlases, a smaller gain resulted from multiple regularization constraint weights, and a marginal gain resulted from multiple control point spacings. We conclude that multi-classifier methods have a natural application to atlas-based segmentation and the potential to increase classification accuracy in real-world segmentation problems.
Hyperspectral Texture Classification Using Generalized Markov Fields
Authors: Subhadip Sarkar, Glenn Healey
Venue: CVPR
Year: 2004

We present a generalized random field model in a random environment to classify hyperspectral textures. The model generalizes traditional random field models by allowing the spatial interaction parameters of the field to be random variables. Principal component analysis is used to reduce the dimensionality of the data set to a small number of spectral bands that capture almost all of the energy in the original hyperspectral textures. Using the model we obtain a compact feature vector that efficiently computes within and between band information. Using a set of hyperspectral samples, we evaluate the performance of this model for classifying textures and compare the results with other approaches.
Clear Underwater Vision
Authors: Yoav Y. Schechner, Nir Karpel
Venue: CVPR
Year: 2004

Underwater imaging is important for scientific research and technology, as well as for popular activities. We present a computer vision approach which easily removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. We show that the main degradation effects can be associated with partial polarization of light. We therefore present an algorithm which inverts the image formation process, to recover a good visibility image of the object. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by product, a distance map of the scene is derived as well. We successfully used our approach when experimenting in the sea using a system we built. We obtained great improvement of scene contrast and color correction, and nearly doubled the underwater visibility range.
Uncontrolled Modulation Imaging
Authors: Yoav Y. Schechner, Shree K. Nayar
Venue: CVPR
Year: 2004

To obtain high dynamic range or hyperspectral images, multiple frames of the same field of view are acquired while the imaging settings are modulated; images are taken at different exposures or through different wavelength bands. A major problem associated with such modulations has been the need for perfect synchronization between image acquisition and modulation control. In the past, this problem has been addressed by using sophisticated servo-control mechanisms. In this work, we show that the process of modulation imaging can be made much simpler by using vision algorithms to automatically relate each acquired frame to its corresponding modulation level. This correspondence is determined solely from the acquired image sequence and does not require measurement or control of the modulation. The image acquisition and the modulation work continuously, in parallel, and independently. We refer to this approach as computational synchronization. It makes the imaging process simple and easy to implement. We have developed a prototype modulation imaging system that uses computational synchronization and used it to acquire high dynamic range and multispectral images.
A Non-Parametric Approach for Independent Component Analysis Using Kernel Density Estimation
Authors: Kuntal Sengupta, Prabir Burman, Rajeev Sharma
Venue: CVPR
Year: 2004

Learning using independent component analysis (ICA) has found a wide range of applications in the area of computer vision and pattern analysis, ranging from face recognition to speech separation. This paper presents a non-parametric approach to the ICA problem that is robust towards outlier effects. The algorithm, for the first time in the field of ICA, adopts an intuitive and direct approach, focusing on the very definition of independence itself; i.e. the joint probability density function (pdf) of independent sources is factorial over the marginal distributions. In the proposed algorithm, kernel density estimation is employed to approximate the underlying distributions. There are two major advantages of our algorithm. First, existing algorithms focus on learning the independent components by attempting to fulfill necessary conditions (but not sufficient) for independence. For example, the Jade algorithm attempts to approximate independence by minimizing higher order statistics, which are not robust to outliers. Comparatively, our technique is inherently robust towards outlier effects. Second, since the learning employs kernel density estimation, it is naturally free from the assumptions of source distributions (unlike the Infomax algorithm). Experimental results show that the algorithm is able to perform separation of sources in the presence of outliers, whereas existing algorithms like Jade and Infomax break down under such conditions. The results have also shown that the proposed non-parametric approach is generally source distribution independent. In addition, it is able to separate non-Gaussian zero-kurtotic signals unlike the traditional ICA algorithms like Jade and Infomax.
Linear Model Hashing and Batch RANSAC for Rapid and Accurate Object Recognition
Authors: Ying Shan, Bogdan Matei, Harpreet S. Sawhney, Rakesh Kumar 0001, Daniel F. Huber, Martial Hebert
Venue: CVPR
Year: 2004

This paper proposes a joint feature-based model indexing and geometric constraint based alignment pipeline for efficient and accurate recognition of 3D objects from a large model database. Traditional approaches either first prune the model database using indexing without geometric alignment or directly perform recognition based alignment. The indexing based pruning methods without geometric constraints can miss the correct models under imperfections such as noise, clutter and obscurations. Alignment based verification methods have to linearly verify each model in the database and hence do not scale up. The proposed techniques use spin images as semi-local shape descriptors and locality-sensitive hashing (LSH) to index into a joint spin image database for all the models. The indexed models represented in the pruned set are further pruned using progressively complex geometric constraints. A simple geometric configuration of multiple spin images, for instance a doublet, is first used to check for geometric consistency. Subsequently, full Euclidean geometric constraints are applied using RANSAC-based techniques on the pruned spin images and the models to verify specific object identity. As a result, the combined indexing and geometric alignment based pipeline is able to focus on matching the most promising models, and generate far less pose hypotheses while maintaining the same level of performance as the sequential alignment based recognition. Furthermore, compared to geometric indexing techniques like geometric hashing, the construction time and storage complexity for the proposed technique remains linear in the number of features rather than higher order polynomial. Experiments on a 56 3D model database show promising results.
2D-Shape Analysis Using Conformal Mapping
Authors: Eitan Sharon, David Mumford
Venue: CVPR
Year: 2004

The study of 2D shapes and their similarities is a central problem in the field of vision. It arises in particular from the task of classifying and recognizing objects from their observed silhouette. Defining natural distances between 2D shapes creates a metric space of shapes, whose mathematical structure is inherently relevant to the classification task. One intriguing metric space comes from using conformal mappings of 2D shapes into each other, via the theory of Teichmuller spaces. In this space, every simple closed curve in the plane (a "shape") is represented by a "fingerprint", which is a diffeomorphism of the unit circle to itself (a differentiable and invertible, periodic function). More precisely, every shape defines to a unique equivalence class of such diffeomorphisms up to right multiplication by a Mobius map. The fingerprint does not change if the shape is varied by translations and scaling and any such equivalence class comes from some shape. This coset space, equipped with the infinitesimal Weil-Petersson (WP) Riemannian norm is a metric space. In this space, it appears very likely to be true that the shortest path between each two shapes is unique, and is given by a geodesic connecting them. Their distance from each other is given by integrating the WP-norm along that geodesic. In this paper we concentrate on solving the "welding" problem of "sewing" together conformally the interior and exterior of the unit circle, glued on the unit circle by a given diffeomorphism, to obtain the unique 2D shape associated with this diffeomorphism. These allow us to go back and forth between 2D shapes and their representing diffeomorphisms in this "space of shapes".
Propagation Networks for Recognition of Partially Ordered Sequential Action
Authors: Yifan Shi, Yan Huang 0017, David Minnen, Aaron F. Bobick, Irfan A. Essa
Venue: CVPR
Year: 2004

We present propagation networks (P-nets), a novel approach for representing and recognizing sequential activities that include parallel streams of action. We represent each activity using partially ordered intervals. Each interval is restricted by both temporal and logical constraints, including information about its duration and its temporal relationship with other intervals. P-nets associate one node with each temporal interval. Each node is triggered according to a probability density function that depends on the state of its parent nodes. Each node also has an associated observation function that characterizes supporting perceptual evidence. To facilitate real-time analysis, we introduce a particle filter framework to explore the conditional state space. We modify the original condensation algorithm to more efficiently sample a discrete state space (D-condensation). Experiments in the domain of blood glucose monitor calibration demonstrate both the representational power of P-nets and the effectiveness of the D-condensation algorithm.
Invariant Operators, Small Samples, and the Bias-Variance Dilemma
Authors: Xiaojin Shi, Roberto Manduchi
Venue: CVPR
Year: 2004

Invariant features or operators are often used to shield the recognition process from the effect of "nuisance" parameters, such as rotations, foreshortening, or illumination changes. From an information-theoretic point of view, imposing invariance results in reduced (rather than improved) system performance. In fact, in the case of small training samples, the situation is reversed, and invariant operators may reduce the misclassification rate. We propose an analysis of this interesting behavior based on the bias-variance dilemma, and present experimental results confirming our theoretical expectations. In addition, we introduce the concept of "randomized invariants" for training, which can be used to mitigate the effect of small sample size.
Tracking Loose-Limbed People
Authors: Leonid Sigal, Sidharth Bhatia, Stefan Roth 0001, Michael J. Black, Michael Isard
Venue: CVPR
Year: 2004

We pose the problem of 3D human tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loosely-connected limbs. Conditional probabilities relating the 3D pose of connected limbs are learned from motion-captured training data. Similarly, we learn probabilistic models for the temporal evolution of each limb (forward and backward in time). Human pose and motion estimation is then solved with non-parametric belief propagation using a variation of particle filtering that can be applied over a general loopy graph. The loose-limbed model and decentralized graph structure facilitate the use of low-level visual cues. We adopt simple limb and head detectors to provide "bottom-up" information that is incorporated into the inference process at every time-step; these detectors permit automatic initialization and aid recovery from transient tracking failures. We illustrate the method by automatically tracking a walking person in video imagery using four calibrated cameras. Our experimental apparatus includes a marker-based motion capture system aligned with the coordinate frame of the calibrated cameras with which we quantitatively evaluate the accuracy of our 3D person tracker.
Camera Network Calibration from Dynamic Silhouettes
Authors: Sudipta N. Sinha, Marc Pollefeys, Leonard McMillan
Venue: CVPR
Year: 2004

In this paper we present an automatic method for calibrating a network of cameras from only silhouettes. This is particularly useful for shape-from-silhouette or visual-hull systems, as no additional data is needed for calibration. The key novel contribution of this work is an algorithm to robustly compute the epipolar geometry from dynamic silhouettes. We use the fundamental matrices computed by this method to determine the projective reconstruction of the complete camera configuration. This is refined into a metric reconstruction using self-calibration. We validate our approach by calibrating a four camera visual-hull system from archive data where the dynamic object is a moving person. Once the calibration parameters have been computed, we use a visual-hull algorithm to reconstruct the dynamic object from its silhouettes.
Variational Mixture Smoothing for Non-Linear Dynamical Systems
Authors: Cristian Sminchisescu, Allan D. Jepson
Venue: CVPR
Year: 2004

We present an algorithm for computing joint state, smoothed, density estimates for non-linear dynamical systems in a Bayesian setting. Many visual tracking problems can be formulated as probabilistic inference over time series, but we are not aware of mixture smoothers that would apply to weakly identifiable models, where multimodality is persistent rather than transient (e.g. monocular 3D human tracking). Such processes, in principle, exclude iterated Kalman smoothers, whereas flexible MCMC methods or sample based particle smoothers encounter computational difficulties: accurately locating an exponential number of probable joint state modes representing high-dimensional trajectories, rapidly mixing between those or resampling probable configurations missed during filtering. In this paper we present an alternative, layered, mixture density smoothing algorithm that exploits the accuracy of efficient optimization within a Bayesian approximation framework. The distribution is progressively refined by combining polynomial time search over the embedded network of temporal observation likelihood peaks, MAP continuous trajectory estimates, and Bayesian variational adjustment of the resulting joint mixture approximation. Our results demonstrate the effectiveness of the method on the problem of inferring multiple plausible 3D human motion trajectories from monocular video.