OpenRaiser/Intern-Atlas huggingface dataset:
What Do Planar Shadows Tell About Scene Geometry?
Authors: Jean-Yves Bouguet, Markus Weber, Pietro Perona
Venue: CVPR
Year: 1999

A method for reconstructing 3D scene geometry from a set of projected shadows is presented. It is composed of two stages. First, the scene geometry is retrieved up to three scalar unknowns using only the information contained in the observed shadow edges on the image plane. Then, the three remaining unknowns are computed making use of the known depths at three points. This technique improves upon previous results in that it does not require the presence of a reference plane in the background. A mathematical analysis is presented using dual-space geometry, a formalism that provides adequate tools to carry out all the derivations in a compact and intuitive manner. A linear algorithm based on singular value decomposition (SVD) is presented leading to a closed form solution for reconstruction.
Edge Detector Evaluation Using Empirical ROC Curves
Authors: Kevin W. Bowyer, Christine Kranenburg, Sean Dougherty
Venue: CVPR
Year: 1999

A method is demonstrated to evaluate edge detector performance using receiver operating characteristic curves. It involves matching edges to manually specified ground truth to count true positive and false positive detections. Edge detector parameter settings are trained and tested on different images, and aggregate test ROC curves presented for two sets of 10 images. The performance of eight different edge detectors is compared. The Canny and Heitger detectors provide the best performance.
A New Bayesian Framework for Object Recognition
Authors: Yuri Boykov, Daniel P. Huttenlocher
Venue: CVPR
Year: 1999

We introduce an approach to feature-based object recognition, using maximum a posteriori (MAP) estimation under a Markov random field (MRF) model. This approach provides an efficienct solution for a wide class of priors that explicitly model dependencies between individual features of an object. These priors capture phenomena such as the fact that unmatched features due to partial occlusion are generally spatially correlated rather than independent. The main focus of this paper is a special case of the framework that yields a particularly efficient approximation method. We call this special case spatially coherent matching (SCM), as it reflects the spatial correlation among neighboring features of an object. The SCM method operates directly on the image feature map, rather than relying on the graph-based methods used in the general framework. We present some Monte Carlo experiments showing that SCM yields substantial improvements over Hausdorff matching for cluttered scenes and partially occluded objects.
Calibration of Image Sequences for Model Visualization
Authors: Adrian Broadhurst, Roberto Cipolla
Venue: CVPR
Year: 1999

The object of this paper is to find a quick and accurate method for computing the projection matrices of an image sequence, so that the error is distributed evenly along the sequence. It assumes that a set of correspondences between points in the images is known, and that these points represent rigid points in the world. This paper extends the algebraic minimisation approach developed by Hartley so that it can be used for long image sequences. This is achieved by initially computing a trifocal tensor using the three most extreme views. The intermediate views are then computed linearly using the trifocal tensor. An iterative algorithm as presented which perturbs the twelve entries of one camera matrix so that the algebraic error along the whole sequence is minimised.
Shape from Video
Authors: Tomás Brodský, Cornelia Fermüller, Yiannis Aloimonos
Venue: CVPR
Year: 1999

This paper presents a novel technique for recovering the shape of a static scene from a video sequence due to a rigidly moving camera. The solution procedure consists of two stages. In the first stage, the rigid motion of the camera at each instant in time is recovered. This provides the transformation between successive viewing positions. The solution is achieved through new constraints which relate 3D motion and shape directly to the image derivatives. These constraints allow to combine the processes of 3D motion estimation and segmentation by exploiting the geometry and statistics inherent in the data. In the second stage the scene surfaces are reconstructed through an optimization procedure which utilizes data from all the frames of the video sequence. A number of experimental results demonstrate the potential of the approach.
Eigenshapes for 3D Object Recognition in Range Data
Authors: Richard J. Campbell, Patrick J. Flynn
Venue: CVPR
Year: 1999

Much of the recent research in object recognition has adopted an appearance-based scheme, wherein objects to be recognized are represented as a collection of prototypes in a multidimensional space spanned by a number of characteristic vectors (eigen-images) obtained from training views. In this paper, we extend the appearance-based recognition scheme to handle range (shape) data. The result of training is a set of 'eigensurfaces' that capture the gross shape of the objects. These techniques are used to form a system that recognizes objects under an arbitrary rotational pose transformation. The system has been tested on a 20 object database including free-form objects and a 54 object database of manufactured parts. Experiments with the system point out advantages and also highlight challenges that must be studied in future research.
Toward Recovering Shape and Motion of 3D Curves from Multi-View Image Sequences
Authors: Rodrigo L. Carceroni, Kiriakos N. Kutulakos
Venue: CVPR
Year: 1999

We introduce a framework for recovering the 3D shape and motion of unknown, arbitrarily-moving curves from two or more image sequences acquired simultaneously from distinct points in space. We use this framework to (1) identify ambiguities in the multi-view recovery of (rigid or nonrigid) 3D motion for arbitrary curves, and (2) identify a novel spatio-temporal constraint that couples the problems of 3D shape and 3D motion recovery in the multi-view case. We show that this constraint leads to a simple hypothesize-and-test algorithm for estimating 3D curve shape and motion simultaneously. Experiments performed with synthetic data suggest that, in addition to recovering 3D curve motion, our approach yields shape estimates of higher accuracy than those obtained when stereo analysis alone is applied to a multi-view sequence.
Fast, Reliable Head Tracking under Varying Illumination
Authors: Marco La Cascia, Stan Sclaroff
Venue: CVPR
Year: 1999

An improved technique for 3D head tracking under varying illumination conditions is proposed. The head is modeled as a texture mapped cylinder. Tracking is formulated as an image registration problem in the cylinder's texture map image. To solve the registration problem in the presence of lighting variation and head motion, the residual error of registration is modeled as a linear combination of texture warping templates and orthogonal illumination templates. Fast and stable on-line tracking is then achieved via regularized weighted least squares minimization of the registration error. The regularization term tends to limit potential ambiguities that arise in the warping and illumination templates. Tracking does not require a precise initial of the model; the system is initialized automatically using a simple 2D face detector. The only assumption is that the target is facing the camera in the first frame of the sequence. Experiments in tracking are reported.
Evaluation of Texture Segmentation Algorithms
Authors: Kyong I. Chang, Kevin W. Bowyer, Munish Sivagurunath
Venue: CVPR
Year: 1999

This paper presents a method of evaluating unsupervised texture segmentation algorithms. The control scheme of texture segmentation has been conceptualized as two modular processes: (1) feature computation and (2) segmentation of homogeneous regions based on the feature values. Three feature extraction methods are considered: gray level co-occurrence matrix, Laws' texture energy and Gabor multi-channel filtering. Three segmentation algorithms are considered: fuzzy c-means clustering, square-error clustering and split-and-merge. A set of 35 real scene images with manually-specified ground truth was compiled. Performance is measured against ground truth on real images using region-based and pixel-based performance metrics.
A Volumetric Stereo Matching Method: Application to Image-Based Modeling
Authors: Qian Chen 0022, Gérard G. Medioni
Venue: CVPR
Year: 1999

We formulate stereo matching as an extremal surface extraction problem. This is made possible by embedding the disparity surface inside a volume where the surface is composed of voxels with locally maximal similarity values. This formulation naturally implements the coherence principle, and allows us to incorporate most known global constraints. Time efficiency is achieved by executing the algorithm in a coarse-to-fine fashion, and only populating the full volume at the coarsest level. To make the system more practical, we present a rectification algorithm based on the fundamental matrix, avoiding full camera calibration. We present results on standard stereo pairs, and on our own data set. The results are qualitatively evaluated in terms of both the generated disparity maps and the 3-D models.
Efficient Iterative Solution to M-View Projective Reconstruction Problem
Authors: Qian Chen 0022, Gérard G. Medioni
Venue: CVPR
Year: 1999

We propose an efficient solution to the general M-view projective reconstruction problem, using matrix factorization and iterative least squares. The method can accept input with missing data, meaning that not all points are necessarily visible in all views. It runs much faster than the often-used non-linear minimization method, while preserving the accuracy of the latter. The key idea is to convert the minimization problem into a series of weighted least squares sub-problems with drastically reduced matrix sizes. Additionally, we show that good initial values can always be obtained. Experimental results on both synthetic and real data are presented. Potential applications are also demonstrated.
An Integral Formulation for Differential Photometric Stereo
Authors: James J. Clark, Holly Pekau
Venue: CVPR
Year: 1999

In this paper we present an integral formulation of the active differential photometric stereo algorithm proposed by Clark (1992) and by Iwahori et al. [1992, 1994). The algorithm presented in this paper does not require measurement of derivatives of image quantities, but requires instead the computation of integrals of image quantities. Thus the algorithm is more robust to sensor noise and light source position errors than the Clark-Iwahori algorithm. We show that the algorithm presented in the paper can be efficiently implemented in practice with a planar distributed light source, and present experimental results demonstrating the efficacy of the algorithm.
Detecting and Tracking Moving Objects for Video Surveillance
Authors: Isaac Cohen, Gérard G. Medioni
Venue: CVPR
Year: 1999

We address the problem of detection and tracking of moving objects in a video stream obtained from a moving airborne platform. The proposed method relies on a graph representation of moving objects which allows to derive and maintain a dynamic template of each moving object by enforcing their temporal coherence. This inferred template along with the graph representation used in our approach allows us to characterize objects trajectories as an optimal path in a graph. The proposed tracker allows to deal with partial occlusions, stop and go motion in very challenging situations. We demonstrate results on a number of different real sequences. We then define an evaluation methodology to quantify our results and show how tracking overcome detection errors.
Calibration of an Outdoor Active Camera System
Authors: Robert T. Collins, Yanghai Tsin
Venue: CVPR
Year: 1999

A parametric camera model and calibration procedures are developed for an outdoor active camera system with pan, tilt and zoom control. Unlike traditional methods, active camera motion plays a key role in the calibration process, and no special laboratory setups are required. Intrinsic parameters are estimated automatically by fitting parametric models to the optic flow induced by rotating and zooming. No knowledge of 3D scene structure is needed. Extrinsic parameters are calculated by actively rotating the camera to sight a sparse set of surveyed landmarks over a virtual hemispherical field of view, yielding a well-conditioned pose estimation problem.
A Probabilistic Framework for Embedded Face and Facial Expression Recognition
Authors: Antonio Colmenarez, Brendan J. Frey, Thomas S. Huang
Venue: CVPR
Year: 1999

We present a Bayesian recognition framework in which a model of the whole face is enhanced by models of facial feature position and appearances. Face recognition and facial expression recognition are carried out using maximum likelihood decisions. The algorithm finds the model and facial expression that maximizes the likelihood of a test image. In this framework, facial appearance matching is improved by facial expression matching. Also, changes in facial features due to expressions are used together with facial deformation. Patterns to jointly perform expression recognition. In our current implementation, the face is divided into 9 facial features grouped in 4 regions which are detected and tracked automatically in video segments. The feature images are modeled using Gaussian distributions on a principal component sub-space. The training procedure is supervised; we use video segments of people in which the facial expressions have been segmented and labeled by hand. We report results on face and facial expression recognition using a video database of 18 people and 6 expressions.
Model Based Segmentation of Nuclei
Authors: Ge Cong, Bahram Parvin
Venue: CVPR
Year: 1999

A new approach for segmentation of nuclei observed with an epi-fluorescence microscope is presented. The technique is model based and uses local feature activities such as step-edge segments, roof-edge segments, and concave corners to construct a set of initial hypotheses. These local feature activities are extracted using either local or global operators to form a possible set of hypotheses. Each hypothesis is expressed as a hyperquadric for better stability, compactness, and error handling. The search space is expressed as an assignment matrix with an appropriate cost function to ensure local adjacency, and global consistency. Each possible configuration of a set of nuclei defines a path, and the path with the least error corresponds to best representation. This result is then presented to an operator who verifies and eliminates a small number of errors.
Extracting Textured Vertical Facades From Controlled Close-Range Imagery
Authors: Satyan R. Coorg, Seth J. Teller
Venue: CVPR
Year: 1999

We are developing a system to extract geodetic, textured CAD models from thousands of initially uncontrolled, close-range ground and aerial images of urban scenes. Here we describe one component of the system, which operates after the imagery has been controlled or geo-referenced. This fully automatic component detects significant vertical facades in the scene, then extrudes them to meet an inferred, triangulated terrain and procedurally generated roof polygons. The algorithm then estimates for each surface a computer graphics texture, or diffuse reflectance map, from the many available observations of that surface. We present the results of the algorithm on a complex dataset: nearly 4,000 high-resolution digital images of a small (200 meter square) office park, acquired from close range under highly varying lighting conditions, amidst significant occlusion due both to multiple inter-occluding structures, and dense foliage. While the results are of less fidelity than that would be achievable by an interactive system, our algorithm is the first to be demonstrated on such a large, real-world dataset.
Simultaneous Extraction of Functional Face Subspaces
Authors: Nicholas Costen, Timothy F. Cootes, Gareth J. Edwards, Christopher J. Taylor 0001
Venue: CVPR
Year: 1999

Facial variation divides into a number of functional subspaces. An improved method of measuring these was designed within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity, expression) were obtained by Principal Components Analysis on appropriate groups of faces. An iterative algorithm was applied to image codings to maximise the probability of coding across these non-orthogonal subspaces before obtaining the projection on each sub-space and recalculating the spaces. This procedure enhances identity recognition, reduces overall sub-space variance and produces Principal Components with greater span and less contamination.
Dynamic Occluding Contours: A New External-Energy Term for Snakes
Authors: Michele Covell, Trevor Darrell
Venue: CVPR
Year: 1999

Dynamic contours, or snakes, provide an effective method for tracking complex moving objects for segmentation and recognition tasks, but have difficulty tracking occluding boundaries on cluttered backgrounds. To compensate for this shortcoming, dynamic contours often rely on detailed object-shape or motion models to distinguish between the boundary of the tracked object and other boundaries in the background. In this paper we present a complementary approach to detailed object models: We improve the discriminative power of the local image measurements that drive the tracking process. We describe a new, robust external-energy term for dynamic contours that can track occluding boundaries without detailed object models. We show how our image model improves tracking in cluttered scenes, and describe how a fine-grained image-segmentation mask is created directly from the local image measurements used for tracking.
Real-Time Periodic Motion Detection, Analysis, and Applications
Authors: Ross Cutler, Larry S. Davis
Venue: CVPR
Year: 1999

We describe a new technique to detect and analyze periodic motion as seen from both a static and moving camera. By tracking objects of interest, we compute an object's self-similarity as it evolves in time. For periodic motion, the self-similarity measure is also periodic, and we apply time-frequency analysis to detect and characterize the periodic motion. A real-time system has been implemented to track and classify objects using periodicity. Examples of object classification, person counting, and non-stationary periodicity are provided.
Combining Information using Hard Constraints
Authors: Douglas DeCarlo, Dimitris N. Metaxas
Venue: CVPR
Year: 1999

In this paper we show how the use of hard constraints in solving estimation problems, by allowing multiple sources of information to be taken into account during optimization, increases robustness and improves efficiency over alternative methods such as the statistical combination of separate optimization results. Our argument is based on an empirical evaluation of the technique which uses a model-based optical flow constraint in a deformable model framework for tracking a face. The flow constraint makes the model-to-edge alignment optimization problem easier by projecting away the portion of the search space that optical flow makes unlikely, while a Kalman filter is used to reconcile hard constraints with the uncertainty in the optical flow data. Using these hard constraints, the system converges more quickly at each iteration and avoids local minima in solutions that cause other methods to lose track. We conjecture that this use of constraints will be effective in any integration application where there are disparities in the difficulty of computational problems associated with the we of different information sources.
Using the Condensation Algorithm for Robust, Vision-based Mobile Robot Localization
Authors: Frank Dellaert, Wolfram Burgard, Dieter Fox, Sebastian Thrun
Venue: CVPR
Year: 1999

To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm, a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be rued in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.
Color Image Segmentation
Authors: Yining Deng, B. S. Manjunath, Hyundoo Shin
Venue: CVPR
Year: 1999

In this work, a new approach to fully automatic color image segmentation, called JSEG, is presented. First, colors in the image are quantized to several representing classes that can be used to differentiate regions in the image. Then, image pixel colors are replaced by their corresponding color class labels, thus forming a class-map of the image. A criterion for "good" segmentation using this class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image", in which high and low values correspond to possible region boundaries and region centers, respectively. A region growing method is then used to segment the image based on the multi-scale J-images. Experiments show that JSEG provides good segmentation results on a variety of images.
Stereo Correspondence from Motion Correspondence
Authors: Fadi Dornaika, Ronald Chung
Venue: CVPR
Year: 1999

This paper introduces a new framework for stereo correspondence recovery using one motion of a stereo rig. Both the stereo correspondence and the motion of the stereo rig are unknown. By combining the stereo geometry and the motion correspondence we are able to infer the stereo correspondence from motion correspondence without having to systematically use the intensity-based stereo matching algorithms. The stereo correspondence recovery consists of two consecutive steps: the first step uses metric data associated with the stereo rig while the second step uses feature correspondences only. Experiments involving real stereo pairs indicate the feasibility and robustness of the approach.
Visual Tracking and Control using Lie Algebras
Authors: Tom Drummond, Roberto Cipolla
Venue: CVPR
Year: 1999

A novel approach to visual servoing is presented, which takes advantage of the structure of the Lie algebra of affine transformations. The aim of this project is to use feedback from a visual sensor to guide a robot arm to a target position. The sensor is placed in the end effector of the robot, the 'camera-in-hand' approach, and thus provides direct feedback of the robot motion relative to the target scene via observed transformations of the scene. These scene transformations are obtained by measuring the affine deformations of a target planar contour, captured by use of an active contour, or snake. Deformations of the snake are constrained using the Lie groups of affine and projective transformations. Properties of the Lie algebra of affine transformations are exploited to integrate observed deformations to the target contour which can be compensated with appropriate robot motion using a non-linear control structure. These techniques have been implemented using a video camera to control a 5 DoF robot arm. Experiments with this implementation are presented, together with a discussion of the results.