OpenRaiser/Intern-Atlas huggingface dataset:
A Real Time System for Robust 3D Voxel Reconstruction of Human Motions
Authors: German K. M. Cheung, Takeo Kanade, Jean-Yves Bouguet, Mark Holler
Venue: CVPR
Year: 2000

We present a multi-PC/camera system that can perform 3D reconstruction and ellipsoid fitting of moving humans in real time. The system consists of five cameras. Each camera is connected to a PC which locally extracts the silhouettes of the moving person in the image captured by the camera. The five silhouette images are then sent, via local network, to a host computer to perform 3D voxel-based reconstruction by an algorithm called SPOT. Ellipsoids are then used to fit the reconstructed data. By using a simple and user-friendly interface, the user can display and observe, in real time and from any view-point, the 3D models of the moving human body. With a rate of higher than 15 frames per second, the system is able to capture non-intrusively, a sequence of human motions.
Sensor Networked Mobile Robotics
Authors: Thomas Christensen, Mads Noergaard, Claus B. Madsen, Adam W. Hoover
Venue: CVPR
Year: 2000

The dominant architecture for mobile robot perception uses sensors on-board the robot, providing a first-person perspective on the environment. We demonstrate a novel mobile robot architecture that uses an environment-based sensor network, which provides third-person perception. The idea is that a mobile robot working in the area tunes in to broadcasts from the video camera network (or in this case from an environment-based computer processing the video frames) to receive sensor data. This distributed sensing configuration offers several advantages over on-board sensing.
A New Algorithm for Non-Rigid Point Matching
Authors: Haili Chui, Anand Rangarajan 0001
Venue: CVPR
Year: 2000

We present a new robust point matching algorithm (RPM) that can jointly estimate the correspondence and non-rigid transformations between two point-sets that may be of different sizes. The algorithm utilizes the soft assign for the correspondence and the thin-plate spline for the non-rigid mapping. Embedded within a deterministic annealing framework, the algorithm can automatically reject a fraction of the points as outliers. Experiments on both 2D synthetic point-sets with varying degrees of deformation, noise and outliers, and on real 3D sulcal point-sets (extracted from brain MRI) demonstrate the robustness of the algorithm.
Real-Time Tracking of Non-Rigid Objects Using Mean Shift
Authors: Dorin Comaniciu, Visvanathan Ramesh, Peter Meer
Venue: CVPR
Year: 2000

A new method for real time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and finds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) and the target candidates is expressed by a metric derived from the Bhattacharyya coefficient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution. The capability of the tracker to handle in real time partial occlusions, significant clutter, and target scale variations, is demonstrated for several image sequences.
A New Regularized Approach for Contour Morphing
Authors: Ge Cong, Bahram Parvin
Venue: CVPR
Year: 2000

In this paper, we propose a new approach for interpolating curves (contour morphing) in time, which is a process of gradually changing a source curve (known) through intermediate curves (unknown) into a target curve (known). The novelty of our approach is in the deployment of a new regularization term and the corresponding Euler equation. Our method is applicable to implicit curve representation and it establishes a relationship between curve interpolation and a two dimensional function. This is achieved by minimizing the supremum of the gradient, which leads to the infinite Laplacian equation (ILE). ILE is optimal in the sense that interpolated curves are equally distributed along their normal direction. We point out that the existing distance field manipulation (DFM) methods are only an approximation to the proposed optimal solution and that the relationship between ILE and DFM is not local as it has been asserted before. The proposed interpolation can also be used to construct multiscale curve representation.
Articulated-Pose Estimation Using Brightness and Depth-Constancy Constraints
Authors: Michele Covell, Ali Rahimi, Michael Harville, Trevor Darrell
Venue: CVPR
Year: 2000

This paper explores several approaches for articulated-pose estimation, assuming that video-rate depth information is available, from either stereo cameras or other sensors. We use these depth measurements in the traditional linear brightness constraint equation, as well as in a depth constraint equation. To capture the joint constraints, we combine the brightness and depth constraints with twist mathematics. We address several important issues in the formation of the constraint equations, including updating the body rotation matrix without using a first-order matrix approximation and removing the coupling between the rotation and translation updates. The resulting constraint equations are linear on a modified parameter set. After solving these linear constraints, there is a single closed-form non-linear transformation to return the updates to the original pose parameters. We show results for tracking body pose in oblique views of synthetic walking sequences and in moving-camera views of synthetic jumping-jack sequences. We also show results for tracking body pose in side views of a real walking sequence.
Robust Periodic Motion and Motion Symmetry Detection
Authors: Ross Cutler, Larry S. Davis
Venue: CVPR
Year: 2000

We describe a robust technique for detecting nonstationary periodic motion from a moving and static camera. We also describe a robust technique for discriminating motion symmetries (periodic motion classification), which we apply to classifying running humans (bipeds) and canines (quadrupeds). The system has been implemented to run in real-time (30 Hz) on standard PC workstations.
Structure from Motion without Correspondence
Authors: Frank Dellaert, Steven M. Seitz, Charles E. Thorpe, Sebastian Thrun
Venue: CVPR
Year: 2000

A method is presented to recover 3D scene structure and camera motion from multiple images without the need for correspondence information. The problem is framed as finding the maximum likelihood structure and motion given only the 2D measurements, integrating over all possible assignments of 3D features to 2D measurements. This goal is achieved by means of an algorithm which iteratively refines a probability distribution over the set of all correspondence assignments. At each iteration a new structure from motion problem is solved, using as input a set of 'virtual measurements' derived from this probability distribution. The distribution needed can be efficiently obtained by Markov Chain Monte Carlo sampling. The approach is cast within the framework of Expectation-Maximization, which guarantees convergence to a local maximizer of the likelihood. The algorithm works well in practice, as will be demonstrated using results on several real image sequences.
Adaptive Metric nearest Neighbor Classification
Authors: Carlotta Domeniconi, Dimitrios Gunopulos, Jing Peng
Venue: CVPR
Year: 2000

Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a locally adaptive nearest neighbor classification method to try to minimize bias. We use a Chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are highly adaptive to query locations. Neighborhoods are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities tend to be smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using a variety of simulated and real world data.
Detecting Dynamic Behavior in Compressed Fingerprint Videos: Distortion
Authors: Chitra Dorai, Nalini K. Ratha, Ruud M. Bolle
Venue: CVPR
Year: 2000

Distortions in fingerprint images arising from the elasticity of finger skin and the pressure and movement of fingers during image capture lead to great difficulties in establishing a match between multiple images acquired from a single finger. In a single fingerprint image depicting a finger at some given instant of time, it is difficult to get any distortion information. Further, static two-dimensional or three-dimensional (electronic) copies of fingerprints can be fabricated and used to spoof remote biometric security systems since the input required by the systems is not a function of time. This paper addresses these issues, by proposing the novel use of fingerprint video sequences to investigate and exploit dynamic behaviors manifested by fingers over time during image acquisition. In particular, we present a novel approach to detect and estimate distortion occurring in fingerprint video streams. Our approach directly works on MPEG-{1, 2} encoded fingerprint video bitstreams to estimate interfield flow without decompression, and uses flow characteristics to investigate temporal behaviour of the fingerprints. The joint temporal and motion analysis leads to a novel technique to detect and characterize distortion reliably. The proposed method has been tested on the NIST 24 database and the results are very promising.
A Combined Feature-Texture Similarity Measure for Face Alignment under
Authors: Lixin Fan, Kah Kay Sung
Venue: CVPR
Year: 2000

We formulate face alignment as a model-based parameter estimation problem in this paper. First, we work within a framework that combines two separate subspace models to represent frontal face patterns and pose change independently. The combined unified nonlinear model represents varying pose faces with a complex manifold. Then, we use a feature based similarity measure (FBSM) to evaluate image differences in terms of pose, and match unknown pose faces with the model image using a combined feature-feature similarity measure (FTSM). Noticeable properties of the combined FTSM include (1) its sensitivity to spatial differences between feature points in two images, which is crucial to aligning two initially faraway poses; (2) easy determination of hill-climb directions in parameter space, without computing gradients of error functions. Experimental results demonstrate that, in the absence of significant clutter, a face alignment algorithm using the combined FTSM, can reliably align varying pose faces under different lighting conditions, even when initial poses are far off.
Tomographic Reconstruction Using Curve Evolution
Authors: Haihua Feng, David A. Castañón, William Clement Karl
Venue: CVPR
Year: 2000

In this paper, we develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques. We use a low order parametric model to describe the shape and texture of the object support as well as the background. This model uses a set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects. The problem of determining the unknown contour and texture coefficients of the object and background medium is then formulated as a non-linear estimation problem. By designing a new, "tomographic flow", the resulting problem is recast into a curve evolution problem and an efficient algorithm based on level set techniques is developed. The performance of the curve evolution method is demonstrated using examples with noisy Radon transformed data and noisy ground penetrating radar data. The reconstruction results and computational cost are compared with those of conventional regularization methods. The results indicate that our curve evolution methods achieve improved shape reconstruction with reduced computation requirements.
Towards Reliable Fusion of Segmented Surface Descriptions
Authors: Daniel Fischer 0002, Peter Kohlhepp
Venue: CVPR
Year: 2000

This paper presents a novel solution for integrating range views into 3D geometry models. Unlike previous approaches, sparse surface and relation attributes are fused directly, without relying on the original range data or a dense mesh of data points. Corresponding surface patches in overlapping views are found and registered automatically. The main focus is on the integration of registered views. Two algorithms for merging partial boundary polygons, one following the 'explained' and the other following the 'enlarging' contour, are presented and methods for updating surface areas, quadric approximations and other attributes are provided. The technique is relevant for 3D map making in autonomous vehicles: the current surface map is used for symbolic action planning while being extended by incoming sensor data. Preliminary experimental results indicate that the prototype system copes well with large measurement and segmentation errors.
Inconsistencies in Edge Detector Evaluation
Authors: Lance A. Forbes, Bruce A. Draper
Venue: CVPR
Year: 2000

In recent years, increasing effort has gone into evaluating computer vision algorithms in general, and edge detection algorithms in particular. Most of the evaluation techniques use only a few test images, leaving open the question of how broadly their results can be interpreted. Our research tests the consistency of the receiver operating characteristic (ROC) curve, and demonstrates why consistent edge detector evaluation is difficult to achieve. We show how easily the framework can be manipulated to rank any of three modern edge detectors in any order by making minor changes to the test imagery. We also note that at least some of the inconsistency is the result of the erratic nature of the algorithms themselves, suggesting that it is still possible to create better edge detectors.
Provably Fast Algorithms for Contour Tracking
Authors: Daniel Freedman, Michael S. Brandstein
Venue: CVPR
Year: 2000

A new tracker is presented. Two sets are identified: one which contains all possible curves as found in the image, and a second which contains all curves which characterize the object of interest. The former is constructed out of edge-points in the image, while the latter is learned prior to running. The tracked curve is taken to be the element of the first set which is nearest the second set. The formalism for the learned set of curves allows for mathematically well understood groups of transformations (e.g. affine, projective) to be treated on the same footing as less well understood deformations, which may be learned from training curves. An algorithm is proposed to solve the tracking problem, and its properties are theoretically demonstrated: it solves the global optimization problem, and does so with certain complexity bounds. Experimental results applying the proposed algorithm to the tracking of a moving finger are presented, and compared with the results of a condensation approach.
Detection of Obstacles in the Flight Path of an Aircraft
Authors: Tarak Gandhi, Mau-Tsuen Yang, Rangachar Kasturi, Octavia I. Camps, Lee D. Coraor, Jeffrey McCandless
Venue: CVPR
Year: 2000

The National Aeronautics and Space Administration (NASA), along with members of the aircraft industry, recently developed technologies for a new supersonic aircraft. One of the technological areas considered for this aircraft is the use of video cameras and image processing equipment to aid the pilot in detecting other aircraft in the sky. The detection techniques should provide high detection probability for obstacles that can vary from sub-pixel to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. This paper describes approaches to detect airborne obstacles on collision course and crossing trajectories in video images captured from an airborne aircraft. In both cases the approaches consist of an image processing stage to identify possible obstacles followed by a tracking stage to distinguish between true obstacles and image clutter, based on their behavior. The crossing target detection algorithm was also implemented on a pipelined architecture from DataCube and runs in real time. Both algorithms have been successfully tested on flight tests conducted by NASA.
Error Analysis of Background Adaption
Authors: Xiang Gao, Terrance E. Boult, Frans Coetzee, Visvanathan Ramesh
Venue: CVPR
Year: 2000

Background modeling is a common component in video surveillance systems and is used to quickly identify regions of interest. To increase the robustness of background subtraction techniques, researchers have developed techniques to update the background model and also developed probabilistic/statistical approaches for thresholding the difference. This paper presents an error analysis of this type of background modeling and pixel labeling, providing both theoretical analysis and experimental validation. Evaluation is centered around the tradeoff of probability of false alarm and probability of miss detection, and this paper shows how to efficiently compute these probabilities front simpler values that are more easily measured. It includes an analysis for both static and dynamic background modeling. The paper also examines the assumptions of Gaussian and mixture of Gaussian models for a pixel.
Perceptual Grouping and Segmentation by Stochastic Clustering
Authors: Yoram Gdalyahu, Noam Shental, Daphna Weinshall
Venue: CVPR
Year: 2000

We use cluster analysis as a unifying principle for problems from low, middle and high level vision. The clustering problem is viewed as graph partitioning, where nodes represent data elements and the weights of the edges represent pairwise similarities. Our algorithm generates samples of cuts in this graph, by using David Karger's contraction algorithm, and computes an "average" cut which provides the basis for our solution to the clustering problem. The stochastic nature of our method makes it robust against noise, including accidental edges and small spurious clusters. The complexity of our algorithm is very low: O(N log/sup 2/ N)for N objects and a fired accuracy level. Without additional computational cost, our algorithm provides a hierarchy of nested partitions. We demonstrate the superiority of our method for image segmentation on a few real color images. Our second application includes the concatenation of edges in a cluttered scene (perceptual grouping), where we show that the same clustering algorithm achieves as good a grouping, if not better as more specialized methods.
Image Retrieval and Segmentation based on Color Invariants
Authors: Jan-Mark Geusebroek, Dennis C. Koelma, Arnold W. M. Smeulders, Theo Gevers
Venue: CVPR
Year: 2000

We will demonstrate our CVPR2000 paper "Measurement of Color Invariants" for the cases of image retrieval based on query by example and for color image segmentation. Both are of importance in content based access of image and video data. We demonstrate the usefulness of the proposed color invariants in image retrieval by example systems. We show that an image retrieval query should include the type of invariance expected in the result. We demonstrate such queries by using the "ImageSurf" retrieval system. Segmentation of images based on the proposed color invariants is demonstrated by the "PicToVision" system. The system provides image processing functionality through the world wide web, and is publicly accessible at www.science. uva.nl/research/isis.pictovision.html.
Measurement of Color Invariants
Authors: Jan-Mark Geusebroek, Arnold W. M. Smeulders, Rein van den Boomgaard
Venue: CVPR
Year: 2000

This paper presents the measurement of object reflectance from color images. We exploit the Gaussian scale-space paradigm to define framework for the robust measurement of object reflectance from color images. Illumination and geometrical invariant properties are derived from a physical reflectance model based on the Kubelka-Munk theory. Imaging conditions are assumed to be white illumination and matte, dull object or general object, respectively. Invariance is denoted by +, whereas sensitivity to the imaging condition is indicated by -. Invariance, discriminative power and localization accuracy of the color invariants is extensively investigated, showing the invariants to be successful in discounting shadow, illumination intensity, highlights, and noise. Experiments show the different invariants to be highly discriminative while maintaining invariance properties. The presented framework for color measurement is well-founded in physics as well as measurement science. The framework is thoroughly evaluated experimentally. Hence is considered more adequate than existing methods for the measurement of invariant color features.
A Formal Classification of 3D Medial Axis Points and Their Local Geometry
Authors: Peter J. Giblin, Benjamin B. Kimia
Venue: CVPR
Year: 2000

This paper proposes a novel hypergraph skeletal representation for 3D shape based on a formal derivation of the generic structure of its medial axis. By classifying each skeletal point by its order of contact, we shout that generically the medial axis consists of five types of points which are then organized into sheets, curves, and points: (i) sheets (manifolds with boundary) which are the locus of bitangent spheres with regular tangency/sup 1/ A/sub 1//sup 2/. Two types of curves (ii) the intersection curve of three sheets and the locus of centers of tritangent spheres, A/sub 1//sup 3/, and (iii) the boundary of sheets which are the locus of centers of spheres whose radius equals the larger principle curvature, i.e., higher order contact A/sub 3/ points; and two types of points (iv) centers of quad-tangent spheres, A/sub 1//sup 4/, and, (v) centers of spheres with one regular tangency and one higher order tangency, A/sub 1/A/sub 3/ The geometry of the 3D medial axis thus consists of sheets (A/sub 1//sup 2/) bounded by one type of curve (A/sub 3/) on their free end, which corresponds to ridges on the surface, and attached to two other sheets at another type of curves (A/sub 1//sup 3/), which support a generalized cylinder description. The A/sub 3/ curves can only end in A/sub 1/A/sub 3/ points where they must meet an A/sub 1//sup 3/ curve. The A/sub 1//sup 3/ curves can either meet one A/sub 3/ curve or meet three other A/sub 1//sup 3/ curve at an A/sub 1//sup 4/ point. This formal result leads to a compact representation for 3D shape, referred to as the medial axis hypergraph representation consisting of nodes (A/sub 1//sup 4/ and A/sub 1/A/sub 3/ points), links between pairs of nodes (A/sub 1//sup 3/ and A/sub 3/ curves) and hyperlinks between groups of links (A/sub 1//sup 2/ sheets). The description of the local geometry at nodes by itself is sufficient to capture qualitative aspects of shapes, in analogy to 2D. We derive a pointwise reconstruction formula to reconstruct a surface from this medial axis hypergraph. Thus, the hypergraph completely characterizes 3D shape and lays the theoretical foundation for its use in recognition, morphing, design and manipulation of shapes.