OpenRaiser/Intern-Atlas huggingface dataset:
A Boundary Extraction Method Based on Dual-T-Snakes and Dynamic Programming
Authors: Gilson A. Giraldi, Edilberto Strauss, Antonio A. F. Oliveira
Venue: CVPR
Year: 2000

The original proposal of active contour models, also called snakes, for image segmentation, suffers from a strong sensitivity to its initial position and can not deal with topological changes. The sensitivity to initialization can be addressed by dynamic programming (DP) techniques which have the advantage of guaranteeing the global minimum and of being more stable numerically than the variational approaches. Their disadvantages are the storage requirements and computational complexity. In this paper we address these limitations of DP by reducing the region of interest (search space) through the use of the Dual-T-Snake approach. The solution of this method consists of two curves enclosing each object boundary which allows the definition of a more efficient search space for a DP technique. The resulting method (Dual-T-Snake plus DP) inherits the capability of changing the topology and avoiding local minima from the Dual-T-Snake and the global optimal properties of the dynamic programming. It can be also extended for 3D.
Fixed Topology Skeletons
Authors: Polina Golland, W. Eric L. Grimson
Venue: CVPR
Year: 2000

In this paper, we present a novel approach to robust skeleton extraction. We use undirected graphs to model connectivity of the skeleton points. The graph topology remains unchanged throughout the skeleton computation, which greatly reduces sensitivity of the skeleton to noise in the shape outline. Furthermore, this representation naturally defines an ordering of the points along the skeleton. The process of skeleton extraction can be formulated as energy minimization in this framework. We provide an iterative, snake-like algorithm for the skeleton estimation using distance transform. Fixed topology skeletons are useful if the global shape of the object is known ahead of time, such as for people silhouettes, hand outlines, medical structures, images of letters and digits. Small changes in the object outline should be either ignored, or detected and analyzed, but they do not change the general structure of the underlying skeleton. Example applications include tracking, object recognition and shape analysis.
Stereo Based Gesture Recognition Invariant to 3D Pose and Lighting
Authors: Radek Grzeszczuk, Gary R. Bradski, Michael H. Chu, Jean-Yves Bouguet
Venue: CVPR
Year: 2000

This paper describes a stereo-based approach for gesture recognition that works well under extreme lighting conditions and tolerates a large range of hand poses. The approach proposes a hybrid gesture representation that models the user's arm as a 3D line and uses images to represent the hand gestures. The algorithm finds the arm orientation and the hand location from the disparity data and uses this information to initialize a color based segmentation algorithm that cleanly separates the hand from the background. Finally, our approach uses the arm orientation to compute a frontal view of the hand through perspective unwarping producing easily recognizable hand gesture templates. The classification algorithm uses statistical moments of the binarized gesture templates to find the match. We achieve 96% recognition rates under varying lighting and 3D poses for a set of 6 gestures.
Multi-View 3D Analysis with Applications for Augmented Reality and Enhanced Video Visualization
Authors: Yanlin Guo, Steven C. Hsu, Supun Samarasekera, Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: CVPR
Year: 2000

The article presents methods for 3D scene geometry recovery/refinement and pose estimation from motion imagery in two representative scenarios. First, we present a method for pose estimation and scene geometry recovery from extended sequences without prior knowledge of the scheme. Second, we discuss how to recover camera poses when a rough scene model is provided. We show how to extend and refine the scene model using the recovered poses. Finally, we present applications of the above techniques for 3D imagery manipulation such as enhanced visualization for video and 3D insertion of synthetic objects in the imagery.
Histogram Preserving Image Transformations
Authors: Efstathios Hadjidemetriou, Michael D. Grossberg, Shree K. Nayar
Venue: CVPR
Year: 2000

Histograms are used to analyze and classify images. They have been found experimentally to have low sensitivity to certain types of image morphisms, for example, viewpoint changes and object deformations. However the precise effect of these image morphisms on the histogram has not been studied. In this work we derive the complete class of local transformations that preserve the histogram or simply scale its magnitude. To achieve this the transformations are represented as solutions to families of vector fields acting on the image. It is then shown that weak perspective projection and paraperspective projection belong to this class and simply scale the histogram. The results on weak perspective projection, together with the effect of illumination, are used to compute the histogram of the projection of 3D polyhedral objects. We verify the analytical results with several examples. Moreover we present and test a system that recognizes and approximates the poses of 3D polyhedral objects independent of viewpoint.
Reconstruction of a Scene with Multiple Linearly Moving Objects
Authors: Mei Han, Takeo Kanade
Venue: CVPR
Year: 2000

We describe an algorithm for reconstructing a scene containing multiple moving objects. Given a monocular image sequence, we recover the scene structure, the trajectories of the moving objects and the camera motion simultaneously. The number of the moving objects is automatically detected without prior motion segmentation. Assuming that the objects are moving linearly with constant speeds, we propose a unified geometrical representation of the static scene and the moving objects. This representation enables the embedding of the motion constraints into the scene structure, which leads to a factorization-based algorithm. Experimental results on synthetic and real images are presented.
Detecting and Tracking Eyes by Using Their Physiological Properties, Dynamics, and Appearance
Authors: Antonio Haro, Myron Flickner, Irfan A. Essa
Venue: CVPR
Year: 2000

Reliable detection and tracking of eyes is an important requirement for attentive user interfaces. In this paper, we present a methodology for detecting eyes robustly in indoor environments in real-time. We exploit the physiological properties and appearance of eyes as well as head/eye motion dynamics. Infrared lighting is used to capture the physiological properties of eyes, Kalman trackers are used to model eye/head dynamics, and a probabilistic based appearance model is used to represent eye appearance. By combining three separate modalities, with specific enhancements within each modality, our approach allows eyes to be treated as robust features that can be used for other higher-level processing.
Reconstruction from Six-Point Sequences
Authors: Richard I. Hartley, Nicolas Y. Dano
Venue: CVPR
Year: 2000

An algorithm is given for computing projective structure from a set of six points seen in a sequence of many images. The method is based on the notion of duality between cameras and points first pointed out by Carlsson and Weinshall. The current implementation avoids the weakness inherent in previous implementations of this method in which numerical accuracy is compromised by the distortion of image point error distributions under projective transformation. It is shown in this paper that one may compute the dual fundamental matrix by minimizing a cost function giving a first-order approximation to geometric distance error in the original untransformed image measurements. This is done by a modification of a standard near-optimal method for computing the fundamental matrix. Subsequently, the error measurements are adjusted optimally to conform with exact imaging geometry by application of the triangulation method of Hartley-Sturm.
Stereo without Depth Search and Metric Calibration
Authors: Hiroshi Hattori, Atsuto Maki
Venue: CVPR
Year: 2000

We propose a new stereo method for 2D navigation in a dynamic environment such as roads without depth search and metric camera calibration. Conventionally, there is an effective stereo method based on the constraint that an observer moves on the ground plane, namely the GP constraint. Although the GP constraint is often violated in an outdoor environment due to the movement of the observer, i.e., camera vibrations and inclination, it can be updated using some features on the ground plane only if the stereo cameras are weakly calibrated. However, the conventional stereo method has several drawbacks. First, it is rather difficult to solve for the general epipolar geometry summarized in the fundamental matrix. Second, although the updated GP constraint often becomes imperfect, it lacks an effective contrivance for the noise reduction. Third, there is no measure to assess the danger of detected obstacles. To solve these problems, we develop a domain-specific stereo method which utilizes various attributes of roads. We introduce the "pseudo-projective camera model" that provides a good approximation to the projective camera in road scenes and accordingly define the linear epipolar geometry for a pair of pseudo-projective cameras. Furthermore, we show that extraction of two parallel lines lying on the road is effective to update the GP constraint, overcome the noise issue and even to estimate the degree of danger. Through experiments we demonstrate that our method is efficient and applicable to a variety of outdoor scenes.
Catadioptric Sensors that Approximate Wide-Angle Perspective Projections
Authors: R. Andrew Hicks, Ruzena Bajcsy
Venue: CVPR
Year: 2000

We present two families of reflective surfaces that are capable of providing a wide field of view, and yet still approximate a perspective projection to a high degree. These surfaces are derived by considering a plane perpendicular to the axis of a surface of revolution and finding the equations governing the distortion of the image of the plane in this surface. We then view this relation as a differential equation and prescribe the distortion term to be linear. By choosing appropriate initial conditions for the differential equation and solving it numerically, we derive the surface shape and obtain a precise estimate as to what degree the resulting sensor can approximate a perspective projection. Thus these surfaces act as computational sensors, allowing for a wide-angle perspective view of a scene without processing the image in software. The applications of such a sensor should be numerous, including surveillance, robotics and tradition photography.
Representation and Recognition of Complex Human Motion
Authors: Jesse Hoey, James J. Little
Venue: CVPR
Year: 2000

The quest for a vision system capable of representing and recognizing arbitrary motions benefits from a low dimensional, non-specific representation of flow fields, to be used in high level classification tasks. We present Zernike polynomials as an ideal candidate for such a representation. The basis of Zernike polynomials is complete and orthogonal and can be used for describing many types of motion at many scales. Starting from image sequences, locally smooth image velocities are derived using a robust estimation procedure, from which are computed compact representations of the flow using the Zernike basis. Continuous density hidden Markov models are trained using the temporal sequences of vectors thus obtained, and are used for subsequent classification. We present results of our method applied to image sequences of facial expressions both with and without significant rigid head motion and to sequences of lip motion from a known database. We demonstrate that the Zernike representation yields results competitive with those obtained using principal components, while not committing to specific types of motion. It is therefore ideal as a fundamental building block for a vision system capable of classifying arbitrary motion types.
Learning Patterns from Images by Combining Soft Decisions and Hard Decisions
Authors: Pengyu Hong, Thomas S. Huang, Roy Wang
Venue: CVPR
Year: 2000

We present a novel approach for learning patterns (sub-images) shared by multiple images without prior knowledge about the number and the positions of the patterns in the images. The patterns may undergo kinds of rigid and non-rigid transformations. To reduce the searching space, the images are pre-segmented and represented by attribute relation graphs (ARGs). The problem is then formulated as learning the isomorphic subgraph, called pattern ARG (PARG) from multiple sample ARGs (SARG) with regard to the attribute similarity and the relation similarity. An inexact graph-matching algorithm is proposed to establish the correspondence between each SARG and the PARG. Inexact graph matching and model editing based on Bayes' decision rule are incorporated into Generalized Expectation and Maximization (GEM) algorithm. The modified GEM algorithm combines soft decisions and hard decisions together to learn both the appearance and the structure of the PARG. In the experiments, the learned PARG successfully captures the appearance and spatial information of the concept shared by the images.
Representation and Optimal Recognition of Human Activities
Authors: Somboon Hongeng, François Brémond, Ramakant Nevatia
Venue: CVPR
Year: 2000

Towards the goal of realizing a generic automatic human activity recognition system, a new formalism is proposed. Activities are described by a chained hierarchical representation using three type of entities: image features, mobile object properties and scenarios. Taking image features of tracked moving regions from an image sequence as input, mobile object properties are first computed by specific methods while noise is suppressed by statistical methods. Scenarios are recognized from mobile object properties based on Bayesian analysis. Several scenarios are recognized by an algorithm using a probabilistic finite-state automaton (a variant of structured HMM). A demonstration of the optimality of this recognition method is discussed. Finally, the validity and the effectiveness of our approach is demonstrated on both real-world and perturbed data.
Pose Estimation, Model Refinement, and Enhanced Visualization Using Video
Authors: Stephen C. Hsu, Supun Samarasekera, Rakesh Kumar 0001, Harpreet S. Sawhney
Venue: CVPR
Year: 2000

In this paper we present methods for exploitation and enhanced visualization of video given a prior coarse untextured polyhedral model of a scene. Since it is necessary to estimate the 3D poses of the moving camera, we develop an algorithm where tracked features are used to predict the pose between frames and the predicted poses are refined by a coarse to fine process of aligning projected 3D model line segments to oriented image gradient energy pyramids. The estimated poses can be used to update the model with information derived from video, and to re-project and visualize the video from different points of view with a larger scene context. Via image registration, we update the placement of objects in the model and the 3D shape of new or erroneously modeled objects, then map video texture to the model. Experimental results are presented for long aerial and ground level videos of a large-scale urban scene.
Multisensor Integration for Building Modeling
Authors: Andres Huertas, Zu Whan Kim, Ramakant Nevatia
Venue: CVPR
Year: 2000

Machine perception can benefit from the use of features extracted from data provided by a variety of sensor modalities. Recent advances in sensor design makes it possible to incorporate multiple sensors into vision systems for increased capability. Two important issues must be considered for the integration task. The sensors must be spatially coregistered and the phenomenologies must be compatible. In this paper we address these issues as they apply to the problem of automatic modeling of building structures from aerial views. We present a methodology to incorporate cues extracted from IFSAR (Interferometric Synthetic Aperture Radar) to significantly improve the performance and the quality of the results of an existing system that relies on electro-optical panchromatic images, while reducing processing time. Quantitative evaluations are given.
A Robust and Efficient Motion Segmentation Based on Orthogonal Projection Matrix of Shape Space
Authors: Naoyuki Ichimura
Venue: CVPR
Year: 2000

A novel algorithm for motion segmentation is proposed. The algorithm uses the fact that shape of an object with homogeneous motion is represented as 4 dimensional linear space. Thus motion segmentation is done as the decomposition of shape space of multiple objects into a set of 4 dimensional subspace. The decomposition is realized using the discriminant analysis of orthogonal projection matrix of shape space. Since only discriminant analysis of ID data is needed, this analysis is quite simple. The algorithm based on the analysis is robust for data with noise and outliers, because the analysis can extract useful information for motion segmentation while rejecting useless one. The implementation results show that the proposed method is robust and efficient enough to do online task for real scenes.
Real-Time 3-D Motion and Structure of Point-Features: A Front-End for Vision-Based Control and Interaction
Authors: Hailin Jin, Paolo Favaro, Stefano Soatto
Venue: CVPR
Year: 2000

We present a system that consists of one camera connected to a personal computer that can (a) select and track a number of high-contrast point features on a sequence of images, (b) estimate their three-dimensional motion and position relative to an inertial reference frame, assuming rigidity, (c) handle occlusions that cause point-features to disappear as well as new features to appear. The system can also (d) perform partial self-calibration and (e) check for consistency of the rigidity assumption, although these features are not implemented in the current release. All of this is done automatically and in real-time (30 Hz) for 40-50 point features using commercial off-the-shelf hardware. The system is based on an algorithm presented by Chiuso et al. (2000), the properties of which have been analyzed by Chiuso and Soatto (2000). In particular, the algorithm is provably observable, provably minimal and provably stable- under suitable conditions. The core of the system, consisting of C++ code ready to interface with a frame grabber as well as Matlab code for development, is available at http://ee.wustl.edu/-soatto/research.html. We demonstrate the system by showing its use as (1) an ego-motion estimator, (2) an object tracker, and (3) an interactive input device, all without any modification of the system settings.
Stereoscopic Shading: Integrating Mult1Frame Shape Cues in a Variational Framework
Authors: Hailin Jin, Stefano Soatto, Anthony J. Yezzi
Venue: CVPR
Year: 2000

We address the problem of integrating multi-frame stereo and shading cues within the framework of optimization in the infinite-dimensional space of piecewise smooth surfaces. Cue integration then reduces to the determination of regions where prior assumptions on the reflectance of the surfaces can be enforced. By combining cues, our formulation allows defining a well-posed problem even when reconstruction from stereo or shading in isolation would be ill-posed. For a simplified model we prove the necessary conditions for optimality, and propose an iterative optimization algorithm, which we implement using ultra-narrowband level set methods.
Surface Landmark Selection and Matching in Natural Terrain
Authors: Andrew E. Johnson 0002
Venue: CVPR
Year: 2000

In this paper we present an algorithm for robust absolute position estimation in natural terrain based on landmarks extracted from dense 3-D surfaces. Our landmarks are constructed by concatenating pose dependent oriented surface points with pose invariant surface signatures into a single feature vector; this definition of landmarks allows a priori pose information to be used to constrain the search for landmark matches. The first step in our algorithm is to extract landmarks from stable and salient surface patches. These landmarks are then stored in a closest point search structure with which landmarks are matched efficiently using available pose constraints and invariant values. Finally, an iterative pose estimation algorithm, based on least median squares, is wrapped around landmark matching to eliminate outliers and estimate absolute position. To validate our algorithm, we show hundreds of absolute position estimation results from three different natural scenes. These results show that our algorithm can incorporate constraints on position and attitude for efficient landmark matching and match small and dense scene surface patches to large and coarse model surfaces.
Transformed Hidden Markov Models: Estimating Mixture Models of Images and Inferring Spatial Transformations in Video Sequence
Authors: Nebojsa Jojic, Nemanja Petrovic, Thomas S. Huang, Brendan J. Frey
Venue: CVPR
Year: 2000

In this paper we describe a novel generative model for video analysis called the transformed hidden Markov model (THMM). The video sequence is modeled as a set of frames generated by transforming a small number of class images that summarize the sequence. For each frame, the transformation and the class are discrete latent variables that depend on the previous class and transformation in the sequence. The set of possible transformations is defined in advance, and it can include a variety of transformation such as translation, rotation and shearing. In each stage of such a Markov model, a new frame is generated from a transformed Gaussian distribution based on the class/transformation combination generated by the Markov chain. This model can be viewed as an extension of a transformed mixture of Gaussians through time. We use this model to cluster unlabeled video segments and form a video summary in an unsupervised fashion. We also use the trained models to perform tracking, image stabilization and filtering. We demonstrate that the THMM is capable of combining long term dependencies in video sequences (repeating similar frames in remote parts of the sequence) with short term dependencies (such as short term image frame similarities and motion patterns) to better summarize and process a video sequence even in the presence of high levels of white or structured noise (such as foreground occlusion).
Local Appearance for Robust Object Recognition
Authors: Deeptiman Jugessur, Gregory Dudek
Venue: CVPR
Year: 2000

We present an approach to appearance-based object recognition using single camera images. Our approach is based on using an attention mechanism to obtain visual features that are generic, robust and informative. The features themselves are recognized using principal components an the frequency domain. In this paper we show how the visual characteristics of only a small number of such features can be used for appearance-based object recognition that is not confounded by planar rotations or background clutter.
Are Multifractal Multipermuted Multinomial Measures Good Enough for Unsupervised Image Segmentation?
Authors: Lui Kam, Jacques Blanc-Talon
Venue: CVPR
Year: 2000

By extending multinomial measures, a new class of self-similar multifractal measures is developed for texture representation. Two multifractal features have been shown to be suitable for texture discrimination and classification. Their use within a supervised segmentation framework provides us with satisfactory results. In this paper we complete the survey on these features by showing their rotation invariant property and their scaling behaviour. Both properties are particularly important for analyzing aerial images because the geographical elements can appear in different orientations and scales. Then, an automatic clustering algorithm based on a watershed technique is used for the segmentation of real world images. The experimental results are encouraging.