OpenRaiser/Intern-Atlas huggingface dataset:
Iterative Projective Reconstruction from Multiple Views
Authors: Shyjan Mahamud, Martial Hebert
Venue: CVPR
Year: 2000

We propose an iterative method for the recovery of the projective structure and motion from multiple images. It has been recently noted that by scaling the measurement matrix by the true projective depths, recovery of the structure and motion is possible by factorization. The reliable determination of the projective depths is crucial to the success of this approach. The previous approach recovers these projective depths using pairwise constraints among images. We first discuss a few important drawbacks with this approach. We then propose an iterative method where we simultaneously recover both the projective depths as well as the structure and motion that avoids some of these drawbacks by utilizing all of the available data uniformly. The new approach makes use of a subspace constraint on the projections of a 3D point onto an arbitrary number of images. The projective depths are readily determined by solving a generalized eigenvalue problem derived from the subspace constraint. We also formulate a dual subspace constraint on all the points in a given image, which can be used for verifying the projective geometry of a scene or object that was modeled. We prove the monotonic convergence of the iterative scheme to a local maximum. We show the robustness of the approach on both synthetic and real data despite large perspective distortions and varying initializations.
Mixture Models and the Segmentation of Multimodal Textures
Authors: Roberto Manduchi
Venue: CVPR
Year: 2000

A problem with using mixture-of-Gaussian models for unsupervised texture segmentation is that a "multimodal" texture (such as can often be encountered in natural images) cannot be well represented by a single Gaussian cluster. We propose a divide-and-conquer method that groups together Gaussian clusters (estimated via Expectation Maximization) into homogeneous texture classes. This method allows to successfully segment even rather complex textures, as demonstrated by experimental tests on natural images.
A General Method for Errors-in-Variables Problems in Computer Vision
Authors: Bogdan Matei, Peter Meer
Venue: CVPR
Year: 2000

The Errors-in-Variables (EIV) model from statistics is often employed in computer vision though only rarely under this name. In an EIV model all the measurements are corrupted by noise while the a priori information is captured with a nonlinear constraint among the true (unknown) values of these measurements. To estimate the model parameters and the uncorrupted data, the constraint can be linearized, i.e., embedded in a higher dimensional space. We show that linearization introduces data-dependent (heteroscedastic) noise and propose an iterative procedure, the heteroscedastic EIV (HEIV) estimator to obtain consistent estimates in the most general, multivariate case. Analytical expressions for the covariances of the parameter estimates and corrected data points, a generic method for the enforcement of ancillary constraints arising from the underlying geometry are also given. The HEIV estimator minimizes the first order approximation of the geometric distances between the measurements and the true data points, and thus can be a substitute for the widely used Levenberg-Marquardt based direct solution of the original nonlinear problem. The HEIV estimator has however the advantage of a weaker dependence on the initial solution and a faster convergence. In comparison to Kanatani's renormalization paradigm (an earlier solution of the same problem) the HEIV estimator has more solid theoretical foundations which translate into better numerical behavior We show that the HEIV estimator can provide an accurate solution to most 3D vision estimation tasks, and illustrate its performance through two case studies: calibration and the estimation of the fundamental matrix.
Dynamic Memory: Architecture for Real Time Integration of Visual Perception, Camera Action, and Network Communication
Authors: Takashi Matsuyama, Shinsaku Hiura, Toshikazu Wada, Kazuyuki Murase, A. Yoshioka
Venue: CVPR
Year: 2000

In a Cooperative Distributed Vision system a group of communicating Active Vision Agents (AVA, in short, i.e. real time image processor with an active video camera and high speed network interface) cooperate to fulfil a meaningful task such as moving object tracking and dynamic scene visualization. A key issue to design and implement an AVA rests in the dynamic integration of Visual Perception, Camera Action, and Network Communication. This paper proposes a novel dynamic system architecture named Dynamic Memory Architecture, where perception, action, and communication modules share what we call the Dynamic Memory. It maintains not only temporal histories of state variables such as pan-tilt angles of the camera and the target object location but also their predicted values in the future. Perception, action, and communication modules are implemented as parallel processes which dynamically read from and write into the memory according to their own individual dynamics. The dynamic memory supports such asynchronous dynamic interactions (i.e. data exchanges between the modules) without wasting time for synchronization. This no-wait asynchronous module interaction capability greatly facilitates the implementation of real time reactive systems such as moving object tracking. Moreover, the dynamic memory supports the virtual synchronization between multiple AVAs, which facilitates the cooperative object tracking by communicating AVAs. A prototype system for real time moving object tracking demonstrated the effectiveness of the proposed idea.
A Batch/Recursive Algorithm for 3D Scene Reconstruction
Authors: Philip F. McLauchlan
Venue: CVPR
Year: 2000

We present a new formulation of sequential least-squares applied to scene and motion reconstruction from image features. We argue that recursive techniques will become more important both for real-time control applications and also interactive vision applications. The aim is to approximate as well as possible the result of the batch bundle adjustment method. In previously published work we described an algorithm which works well if the same features are visible throughout the sequence. Here we show how to deal with new features in a way that avoids deterioration of the results. The main theoretical advance here is showing how to adjust the system information matrix when scene/camera parameters are removed from the reconstruction. We show how this procedure affects the sparseness of the information matrix, and thus how to achieve an efficient recursive solution to the reconstruction problem.
Recovering Projection Geometry: How a Cheap Camera Can Outperform an Expensive Stereo System
Authors: Matthias Mitschke, Nassir Navab
Venue: CVPR
Year: 2000

Recovering the projection geometry of an X-ray system or an augmented reality video see-through Head Mounted Display (HMD) are mathematically quite similar. Recent work in both medical imaging and augmented reality use external optical sensors in order to recover the motion of the imaging system. In this paper, we take the example of the recovery of an X-ray projection geometry. We show that the mathematical problem, which needs to be solved, is equivalent to the hand-eye calibration well studied in both computer vision and robotics community. We present a comparative study for the recovery of the motion and therefore projection, geometry using five different hand-eye calibration methods proposed in the literature. We compare the motion estimation results using expensive external stereo-based tracking systems with one obtained by using an integrated optical camera. The paper concludes by shouting that ever, if the motion estimation is more accurate when using an external sensor, the projection geometry is better estimated by the integrated optical camera. These results are of crucial importance to both medical imaging and augmented reality communities.
Scene Modeling for Wide Area Surveillance and Image Synthesis
Authors: Anurag Mittal, Daniel P. Huttenlocher
Venue: CVPR
Year: 2000

We present a method for modeling a scene that is observed by a moving camera, where only a portion of the scene is visible at any time. This method uses mixture models to represent pixels in a panoramic view, and to construct a "background image" that contains only static (non-moving) parts of the scene. The method can be used to reliably detect moving objects in a video sequence, detect patterns of activity over a wide field of view, and remove moving objects from a video or panoramic mosaic. The method also yields improved results in detecting moving objects and in constructing mosaics in the presence of moving objects, when compared with techniques that are not based on scene modeling. We present examples illustrating the results.
Solving the Rotation-Estimation Problem by Using the Perspective Three-Point Algorithm
Authors: Toshio Moriya, Haruo Takeda
Venue: CVPR
Year: 2000

We present a closed-form solution for the rotation-estimation problem, by introducing the same algorithm used for solving the perspective three-point problem. The rotation-estimation problem is defined as estimating the relative rotation of two camera images when the translation is known. The perspective three-point problem, in which the pose of a single camera is estimated from the location of three points, is a classical problem in the field of computer vision, and a closed-form solution for it has already been established. The two problems are not obviously similar, but we show by making some parameter exchanges that they are essentially equivalent. Actual applications using the rotation-estimation problem are also explained.
Intelligent Selection Tools
Authors: Eric N. Mortensen, L. Jack Reese, William A. Barrett
Venue: CVPR
Year: 2000

Intelligent Scissors and Intelligent Paint are complementary interactive image segmentation tools that allow a user to quickly and accurately select objects of interest. This demonstration provides a means for participants to experience the dynamic nature of these tools.
Improved Motion Stereo Matching Based on a Modified Dynamic Programming
Authors: Mikhail G. Mozerov, Vitaly Kober, Tae-Sun Choi
Venue: CVPR
Year: 2000

A new method for computing precise depth map estimates of 3D shape of a moving object is proposed. 3D shape recovery in motion stereo is formulated as a matching optimization problem of multiple stereo images. The proposed method is a heuristic modification of dynamic programming applied to two-dimensional optimization problem. 3D shape recovery using real motion stereo images demonstrates a good performance of the algorithm in terms of reconstruction accuracy.
Face Recognition by Distribution Specific Feature Extraction
Authors: Kenji Nagao
Venue: CVPR
Year: 2000

A new pattern recognition approach to face recognition is presented that can deal with drastic differences in the appearance of a face. Given a pair of sample sets of facial images with potential correspondences, each being drawn from a distinctive distribution, the algorithm reliability finds correspondences over those different distributions. Unlike the traditional approaches that model the face images as having a consistent distribution and so use the same feature extraction function for both of the image sets, the new method applies to each sample a function specific to the distribution from which it is drawn. This function is derived by maximizing a newly defined class-separability criterion over the different distributions. Results of face recognition are presented on images including drivers' license pictures. Drastic improvements are shown over algorithms based on the traditional Fisher's discriminant analysis.
Visual Servoing for Automatic and Uncalibrated Needle Placement for Percutaneous Procedures
Authors: Nassir Navab, Benedicte Bascle, Michael H. Loser, Bernhard Geiger, Russell H. Taylor
Venue: CVPR
Year: 2000

This paper presents a new approach to image-based guidance of a needle or surgical tool during percutaneous procedures. The method is based on visual servoing. It requires no prior calibration or registration. The technique provides highly precise 3D-alignment of the tool with respect to an anatomic target. By taking advantage of projective geometry and projective invariants, this can be achieved in a fixed number (12) of iterations. In addition the approach estimates the required insertion depth. Experiments include automatic 3D alignment and insertion of a needle held by a medical robot into a pig kidney under X-ray fluoroscopy.
Lines in One Orthographic and Two Perspective Views
Authors: Nassir Navab, Yakup Genc, Mirko Appel
Venue: CVPR
Year: 2000

In this paper we introduce a linear algorithm to recover the Euclidean motion between one orthographic and two perspective cameras from straight line correspondences filling the gap in the analysis of motion estimation from line correspondences for various projection models. The general relationship between lines in three views is described by the trifocal tensor. Euclidean structure from motion for three perspective views is a special case in which the relationship is defined by a collection of three matrices. We describe the case of two calibrated perspective views and an orthographic view. Similar to the other cases, our linear algorithm requires 13 or more line correspondences to recover 27 coefficients of the trifocal tensor.
360 x 360 Mosaics
Authors: Shree K. Nayar, Amruta Karmarkar
Venue: CVPR
Year: 2000

Current mosaicing methods use narrow field of view cameras to acquire image data. This poses problems when computing a complete spherical mosaic. First, a large number of images are needed to capture a sphere. Second, errors in mosaicing make it difficult to complete the spherical mosaic without seams. Third, with a hand-held camera it is hard for the user to ensure complete coverage of the sphere. This paper presents two approaches to spherical mosaicing. The first is to rotate a 360 degree camera about a single axis to capture a sequence of 360 degree strips. The unknown rotations between the strips are estimated and the strips are blended together to obtain a spherical mosaic. The second approach seeks to significantly enhance the resolution of the computed mosaic by capturing 360 degree slices rather than strips. A variety of slice cameras are proposed that map a thin 360 degree sheet of rays onto a large image area. This results in the capture of high resolution slices despite the use of a low resolution video camera. A slice camera is rotated using a motorized turntable to obtain regular as well as stereoscopic spherical mosaics.
Multifeature Object Tracking Using a Model-Free Approach
Authors: Hieu Tat Nguyen, Marcel Worring
Venue: CVPR
Year: 2000

In this paper we introduce a new model-free method for object tracking. The tracking is posed as a segmentation problem which we solve using the watershed algorithm. A framework is defined to compute the required topographic surface from distances to the predicted contour intensity, edges and motion edges. This multifeature tracking approach yields accurate results in the presence of object corners, image clutter and camera motion. Results on real sequences confirm the stability and robustness of the method. Objects are tracked over long sequences and in the presence of fast object motion.
Multi-Modality Model-Based Registration in the Cardiac Domain
Authors: Thomas O&apos, Donnell, Shmuel Aharon, Alok Gupta, Gareth Funka-Lea, Sandra Simon Halliburton, Richard D. White
Venue: CVPR
Year: 2000

Recent advances in CT technology have made possible the acquisition of high resolution images of the heart in which important anatomical features such as the coronary vessels are visible. The state of the art in MR Imaging techniques, on the other hand, typically provides lower resolution but can facilitate the differentiation of viable (healthy) cardiac tissue from infarcted (injured) tissue. The registration of CT and MR images of the same heart permit the association of anatomy with function. As an example, given an infarcted region, the coronary vessels responsible for irrigating that region may be isolated and targeted for treatment (e.g., coronary bypass). We demonstrate a software package, PROTEUS, which is capable of registering CT and MR images. Since the manifestation of cardiac tissue in the two modalities differs so greatly we rely on features to perform the matching. Specifically, we employ a model-based approach that relies on segmented contours delineating the inner and outer borders of the left ventricle (LV). Given these contours, our technique is completely automatic.
An On-Line Handwriting Recognition System Using Fisher Segmental Matching and Hypotheses Propagation Network
Authors: Jong Oh, Davi Geiger
Venue: CVPR
Year: 2000

We propose an on-line handwriting recognition approach that integrates local bottom-up constructs with a global top-down measure into a modular recognition engine. The bottom-up process uses local point features for hypothesizing character segmentations and the top-down part performs shape matching for evaluating the segmentations. The shape comparison, called Fisher segmental matching, is based on Fisher's linear discriminant analysis. Along with an efficient ligature modeling, the segmentations and their matching scores are integrated into a recognition engine termed Hypotheses Propagation Network, which runs a variant of the topological sort algorithm of graph search. The result is a system that is more shape-oriented less dependent on local and temporal features, modular in construction and has a rich range of opportunities for further extensions. Our system currently performs at 95% of recognition rate on cursive scripts with a 460 word dictionary.
Structure from Motion Using Points, Lines, and Intensities
Authors: John Oliensis, Michael Werman
Venue: CVPR
Year: 2000

We present a fast factorization algorithm for estimating structure and motion simultaneously from points, lines, and/or directly from the image intensities under full perspective. It generalizes the Oliensis method for points to include lines and intensities as well.
Robust Stereo Ego-motion for Long Distance Navigation
Authors: Clark F. Olson, Larry H. Matthies, Marcel Schoppers, Mark W. Maimone
Venue: CVPR
Year: 2000

Several methods for computing observer motion from monocular and stereo image sequences have been proposed. However, accurate positioning over long distances requires a higher level of robustness than previously achieved. This paper describes several mechanisms for improving robustness in the context of a maximum-likelihood stereo ego-motion method. We demonstrate that even a robust system will accumulate super-linear error in the distance traveled due to increasing orientation errors. However, when an absolute orientation sensor is incorporated, the growth is reduced to linear in the distance traveled, grows much more slowly in practice. Our experiments, including a trial with 210 stereo pairs, indicate that these techniques can achieve errors below 1% of the distance traveled. This method has been implemented to run on-board a prototype Mars rover.
Active Character Recognition Using 'A*-Like' Algorit
Authors: Jaehwa Park, Venu Govindaraju
Venue: CVPR
Year: 2000

This paper describes an Active Character Recognition methodology, henceforth referred to as ACR. We present in this paper a method that uses an active heuristic function similar to the one used by A* search algorithm that adaptively determines the length of the feature vector as well as the features themselves used to classify an input pattern. ACR adapts to factors such as the quality of the input pattern, its intrinsic similarities and differences from patterns of other classes it is being compared against and the processing time available. Furthermore, the finer resolution is accorded to only certain "zones" of the input pattern which are deemed important given the classes that are being discriminated. Experimental results support the methodology presented. Recognition rate of ACR is about 96% on the NIST data sets and the speed is better than traditional classification methods.
Impact of Dynamic Model Learning on Classification of Human Motion
Authors: Vladimir Pavlovic 0001, James M. Rehg
Venue: CVPR
Year: 2000

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and analysis of figure motion has employed either generic or highly specific hand-tailored dynamic models superficially coupled with hidden Markov models (HMMs) of motion regimes. Recently, an alternative class of learned dynamic models known as switching linear dynamic systems (SLDSs) has been cast in the framework of dynamic Bayesian networks (DBNs) and applied to analysis and tracking of the human figure. In this paper we further study the impact of learned SLDS models on analysis and tracking of human motion and contrast them to the more common HMM models. We develop a novel approximate structured variational inference algorithm for SLDS, a globally convergent DBN inference scheme, and compare it with standard SLDS inference techniques. Experimental results on learning and analysis of figure dynamics from video data indicate the significant potential of the SLDS approach.
Multimodal Speaker Detection Using Error Feedback Dynamic Bayesian Networks
Authors: Vladimir Pavlovic 0001, James M. Rehg, Ashutosh Garg, Thomas S. Huang
Venue: CVPR
Year: 2000

Design and development of novel human-computer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous multi-sensory data such as video and sound. Temporal fusion of multiple sensors has been efficiently formulated using dynamic Bayesian networks (DBNs) which allows the power of statistical inference and learning to be combined with contextual knowledge of the problem. Unfortunately simple learning methods can cause such appealing models to fail when the data exhibits complex behavior. We formulate a learning framework for DBNs based on error-feedback and statistical boosting theory. We apply this framework to the problem of audio/visual speaker detection in an interactive kiosk environment using "off-the-shelf" visual and audio sensors (face, skin, texture, mouth motion, and silence detectors). Detection results obtained in this setup demonstrate superiority of our learning framework over that of the classical ML learning in DBNs.
Cameras for Stereo Panoramic Imaging
Authors: Shmuel Peleg, Yael Pritch, Moshe Ben-Ezra
Venue: CVPR
Year: 2000

A panorama for visual stereo consists of a pair of panoramic images, where one panorama is for the left eye, and another panorama is for the right eye. A panoramic stereo pair provides a stereo sensation lip to a full 360 degrees. A stereo panorama cannot be photographed by two omnidirectional cameras from two viewpoints. It is normally constructed by mosaicing together images from a rotating stereo pair, or from a single moving camera. Capturing stereo panoramic images by a rotating camera makes it impossible to capture dynamic scenes at video rates, and limits stereo panoramic imaging to stationary scenes. This paper presents two possibilities for capturing stereo panoramic images using optics, without any moving parts. A special mirror is introduced such that viewing the scene through this mirror creates the same rays as those used with the rotating cameras. Such a mirror enables the capture of stereo panoramic movies with a regular video camera. A lens for stereo panorama is also introduced. The designs of the mirror and of the lens are based on curves whose caustic is a circle.