OpenRaiser/Intern-Atlas huggingface dataset:
Flying a Toy Plane
Authors: Donald Tanguay
Venue: CVPR
Year: 2000

The availability of powerful computing and inexpensive cameras is engendering a new era of real-time consumer-grade computer vision. We have devised a vision system for cheap, unencumbered user input with six degrees of freedom and have applied it to virtual flight on a typical consumer platform. Sitting before a monitor-mounted camera, a user can intuitively pilot a flight simulator by relative position (X, Y and Z) and orientation (roll, pitch, and yaw) of a specially-marked toy plane held in the hand. This paper describes the design, acquisition, tracking, and pose estimation of our object model. We have achieved a robust scheme capable of operating in a demanding environment with unpredictable lights, backgrounds, and camera properties. The success of our approach relies heavily on tracking. We allow multiple hypotheses and benefit from the intensity invariance and fast, semiglobal trackability of the cliff feature, which we introduce as a useful feature abstraction. While this work demonstrates an integrated approach for solving a specific problem, the ideas can be applied elsewhere.
Dynamic Layer Representation with Applications to Tracking
Authors: Hai Tao, Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: CVPR
Year: 2000

A dynamic layer representation is proposed for tracking moving objects. Previous work on layered representations has largely concentrated on two-/multi-frame batch formulations, and tracking research has not addressed the issue of joint estimation of object motion ownership and appearance. The paper extends the estimation of layers in a dynamic scene to incremental estimation formulation and demonstrates how this naturally solves the tracking problem. The three components of the dynamic layer representation, namely, layer motion, ownership, and appearance, are estimated simultaneously over time in a MAP framework. In order to enforce a global shape constraint and to maintain the layer segmentation over time, a parametric segmentation prior is proposed. The generalized EM algorithm is employed to compute the optimal solution. We show the results on real-time tracking of multiple moving or static objects in a cluttered scene imaged from a moving aerial video camera. The moving objects may do complex motions, and have complex interactions such as passing. By using both the appearance and the segmentation information, many difficult tracking tasks are reliably handled.
Handwritten Digit Recognition with a Novel Vision Model that Extracts Linearly Separable Features
Authors: Loo-Nin Teow, Kia-Fock Loe
Venue: CVPR
Year: 2000

We use well-established results in biological vision to construct a novel vision model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear classifier on these features, our model is relatively simple yet outperforms other models on the same data set.
Fusion of Color, Shading and Boundary Information for Factory Pipe Segmentation
Authors: Bertrand Thirion, Benedicte Bascle, Visvanathan Ramesh, Nassir Navab
Venue: CVPR
Year: 2000

Image segmentation has traditionally been thought of us a low/mid-level vision process incorporating no high level constraints. However, in complex and uncontrolled environments, such bottom-up strategies have drawbacks that lead to large misclassification rates. Remedies to this situation include taking into account (1) contextual and application constraints, (2) user input and feedback to incrementally improve the performance of the system. We attempt to incorporate these in the context of pipeline segmentation in industrial images. This problem is of practical importance for the 3D reconstruction of factory environments. However it poses several fundamental challenges mainly due to shading. Highlights and textural variations, etc. Our system performs pipe segmentation by fusing methods from physics-based vision, edge and texture analysis, probabilistic learning and the use of the graph-cut formalism.
Recognizing Upper Face Action Units for Facial Expression Analysis
Authors: Ying-li Tian, Takeo Kanade, Jeffrey F. Cohn
Venue: CVPR
Year: 2000

We develop an automatic system to analyze subtle changes in upper face expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal image sequence. Our system recognizes fine-grained changes in facial expression based on Facial Action Coding System (FACS) action units (AUs). Multi-state facial component models are proposed for tracting and modeling different facial features, including eyes, brews, cheeks, and furrows. Then we convert the results of tracking to detailed parametric descriptions of the facial features. These feature parameters are fed to a neural network which recognizes 7 upper face action units. A recognition rate of 95% is obtained for the test data that include both single action units and AU combinations.
Tracking of Elongated Structures Using Statistical Snakes
Authors: Ricardo Toledo, Xavier Orriols, Xavier Binefa, Petia Radeva, Jordi Vitrià, Juan José Villanueva
Venue: CVPR
Year: 2000

In this paper we introduce a statistic snake that learns and tracks image features by means of statistic learning techniques. Using probabilistic principal component analysis a feature description is obtained from a training set of object profiles. In our approach a sound statistical model is introduced to define a likelihood estimate of the grey-level local image profiles together with their local orientation. This likelihood estimate allows to define a probabilistic potential field of the snake where the elastic curve deforms to maximise the overall probability of detecting learned image features. To improve the convergence of snake deformation, we enhance the likelihood map by a physics-based model simulating a dipole-dipole interaction. A new extended local coherent interaction is introduced defined in terms of extended structure tensor of the image to give priority to parallel coherence vectors.
A Curve Evolution Approach to Smoothing and Segmentation Using the Mumford-Shah Functional
Authors: Andy Tsai, Anthony J. Yezzi, Alan S. Willsky
Venue: CVPR
Year: 2000

In this work, we approach the classic Mumford-Shah problem from a curve evolution perspective. In particular we let a given family of curves define the boundaries between regions in an image within which the data are modeled by piecewise smooth functions plus noise as in the standard Mumford-Shah functional. The gradient descent equation of this functional is then used to evolve the curve. Each gradient descent step involves solving a corresponding optimal estimation problem which connects the Mumford-Shah functional and our curve evolution implementation with the theory of boundary-value stochastic processes. The resulting active contour model, therefore, inherits the attractive ability of the Mumford-Shah technique to generate, in a coupled Mumford-Shah a smooth reconstruction of the image and a segmentation as well. We demonstrate applications of our method to problems in which data quality is spatially varying and to problems in which sets of pixel measurements are missing. Finally, we demonstrate a hierarchical implementation of our model which leads to a fast and efficient algorithm capable of dealing with important image features such as triple points.
A Probabilistic Architecture for Content-Based Image Retrieval
Authors: Nuno Vasconcelos, Andrew Lippman
Venue: CVPR
Year: 2000

The design of an effective architecture for content-based retrieval from visual libraries requires careful consideration of the interplay between feature selection, feature representation, and similarity metric. We present a solution where all the modules strive to optimize the same performance criteria: the probability of retrieval error. This solution consists of a Bayesian retrieval criteria (shown to generalize the most prevalent similarity metrics in current use) and an embedded mixture representation over a multiresolution feature space (shown to provide a good trade-off between retrieval accuracy, invariance, perceptual relevance of similarity, and complexity). The new representation extends standard models (histogram and Gaussian) by providing simultaneous support for high-dimensional features and multi-modal densities and performs well on color texture, and generic image databases.
Shape and Motion Carving in 6D
Authors: Sundar Vedula, Simon Baker, Steven M. Seitz, Takeo Kanade
Venue: CVPR
Year: 2000

The motion of a non-rigid scene over time imposes more constraints on its structure than those derived from images at a single time instant alone. An algorithm is presented for simultaneously recovering dense scene shape and scene flow (i.e. the instantaneous 3D motion at every point in the scene). The algorithm operates by carving away hexels, or points in the 6D space of all possible shapes and flows that are inconsistent with the images captures at either time instant, or across time. The recovered shape is demonstrated to be more accurate than that recovered using images at a single time instant. Applications of the combined scene shape and flow include motion capture for animation, retiming of videos, and non-rigid motion analysis.
Histogram-Based Object Recognition Using Shape-from-Shading
Authors: Philip L. Worthington, Edwin R. Hancock
Venue: CVPR
Year: 2000

This paper presents an experimental study of the problem of object recognition using surface attributes delivered by shape-from-shading. We focus our attention on histogram-based representations. Several surface attributes are considered. These include slant and tilt angles, mean and Gaussian curvature, and, shape-index and curvedness. Our study reveals that recognition rates of 98% are achievable under controlled lighting conditions. When there is significant illumination variation then recognition rates of 80% are achievable.
Color Tracking by Transductive Learning
Authors: Ying Wu 0001, Thomas S. Huang
Venue: CVPR
Year: 2000

One of the difficulties of color tracking is that color changes in different lighting conditions, and static color models would be inadequate to capture the nonstationary color distribution over time. Although some work has been done on adaptive color models, this problem still needs further investigation. Different from many other approaches, we formulate the nonstationary color tracking problem as a transductive learning problem, in which the generalization of a trained color classifier is only defined on the pixels in a specific image, rather than the whole color space. This formulation offers a way to design and transduce color classifiers through non-stationary color distribution. Instead of assuming a color transition model. We assume that some unlabeled pixels in a new image frame can be "confidently" labeled by a "weak classifier" according to a preset confidence level. The proposed Discriminant-EM (D-EM) algorithm offers an effective way to transduce color classifiers as well as automatically select a good color space. Experiments show that D-EM successfully handles some problems in color tracking. As a component our natural gesture interface, this algorithm gives tight bounding boxes of the hand or face regions in video sequences.
Discriminant-EM Algorithm with Application to Image Retrieval
Authors: Ying Wu 0001, Qi Tian 0001, Thomas S. Huang
Venue: CVPR
Year: 2000

In many vision applications, the practice of supervised learning faces several difficulties, one of which is that insufficient labeled training data result in poor generalization. In image retrieval, we have very few labeled images from query and relevance feedback so that it is hard to automatically weight image features and select similarity metrics for image classification. This paper investigates the possibility of including an unlabeled data set to make up the insufficiency of labeled data. Different from most current research in image retrieval, the proposed approach tries to cast image retrieval as a transductive learning problem, in which the generalization of an image classifier is only defined on a set of images such as the given image database. Formulating this transductive problem in a probabilistic framework the proposed algorithm, Discriminant EM (D-EM) not only estimates the parameters of a generative model but also finds a linear transformation to relax the assumption of probabilistic structure of data distributions as well as select good features automatically. Our experiments show that D-EM has a satisfactory performance in image retrieval applications. D-EM algorithm has the potential to many other applications.
A Linear Algorithm for Camera Self-Calibration, Motion and Structure Recovery for Multi-Planar Scenes from Two Perspective Images
Authors: Gang Xu, Jun-ichi Terai, Heung-Yeung Shum
Venue: CVPR
Year: 2000

In this paper we show that given two homography matrices for two planes in space, there is a linear algorithm for the rotation and translation between the two cameras, the focal lengths of the two cameras and the plane equations in the space. Using the estimates as an initial guess, we can further optimize the solution by minimizing the difference between observations and reprojections. Experimental results are shown. We also provide a discussion about the relationship between this approach and the Kruppa equation.
Geodesic Distance Evolution of Surfaces: A New Method for Matching Surfaces
Authors: Hussein M. Yahia, Etienne G. Huot, Isabelle Herlin, Isaac Cohen
Venue: CVPR
Year: 2000

The general problem of surface matching is considered in this study. The process described in this work hinges on a geodesic distance equation for a family of surfaces embedded in the graph of a cost function. The cost function represents the geometrical matching criterion between the two 3D surfaces. This graph is a hypersurface in 4-dimensional space, and the theory presented herein is a generalization of the geodesic curve evolution method introduced by R. Kimmel et al. (1995). It also generalizes the 2D matching process developed in Cohen and Herlin (1998). An Eulerian level-set formulation of the geodesic surface evolution is also used, leading to a numerical scheme for solving partial differential equations originating from hyperbolic conservation laws, which has proven to be very robust and stable. The method is applied on examples showing both small and large deformations, and arbitrary topological changes.
A Geometric Approach to Train Support Vector Machines
Authors: Ming-Hsuan Yang 0001, Narendra Ahuja
Venue: CVPR
Year: 2000

Support Vector Machines (SVMs) have shown great potential in numerous visual learning and pattern recognition problems. The optimal decision surface of a SVM is constructed from its support vectors which are conventionally determined by solving a quadratic programming (QP) problem. However, solving a large optimization problem is challenging since it is computationally intensive and the memory requirement grows with square of the training vectors. In this paper, we propose a geometric method to extract a small superset of support vectors, which we call guard vectors, to construct the optimal decision surface. Specifically, the guard vectors are found by solving a set of linear programming problems. Experimental results on synthetic and real data sets show that the proposed method is more efficient than conventional methods using QPs and requires much less memory.
Feature Based Visualization of Geophysical Data
Authors: Qing Yang 0002, Bahram Parvin
Venue: CVPR
Year: 2000

Our goal is to develop a feature based framework for data mining and forecasting from geophysical data fields. These data may be generated from either numerical simulation models or space based platforms. This paper focuses on pertinent features from sea surface temperature (SST) fields that are observed with the AVHRR satellite. Our contribution consist of three components: (1) A method for tracking feature velocities from from fluid motion with incompressibility constraint, (2) a method for localizing singular events such as vortices and saddle points from underlying feature velocities, and (3) application of our protocol to 12 years of high resolution real data to reveal novel seasonal and inter-annual trends based on computed events.
Two-Stage Robust Optical Flow Estimation
Authors: Ming Ye, Robert M. Haralick
Venue: CVPR
Year: 2000

We formulate optical flow estimation as a two-stage regression problem. Based on characteristics of these two regression models and conclusions on modern regression methods, we choose a least trimmed squares followed by a weighted least squares estimator to solve the optical flow constraint (OFC); and at places where this one-stage robust method fails due to poor derivative quality, we use a least trimmed squares estimator to make the facet model fitting robust. This two-stage robust scheme produces significantly higher accuracy than non-robust algorithms and those only using robust methods at the OFC stage. On the synthetic data, the one-stage robust method has an average error of 7.7% against 24% of Black's and 19% of the pure LS method; and the two-stage robust method further reduces the error by half near motion boundaries. Advantages are also demonstrated on real data.
Detecting and Tracking Human Face and Eye Using Space-Varying Sensor and an Active Vision Head
Authors: Mohammed Yeasin, Yasuo Kuniyoshi
Venue: CVPR
Year: 2000

We have developed a system for detecting and tracking human face and eye in an unstructured environment. We adopt a biologically plausible retinally connected neural network architecture and integrate it with an active vision system. While the active vision system tracks the object moving in real time, the neural network detects the face and eye location from the video stream at a slower rate. The paper provides a systematic way of creating and selecting examples for training the network by exploring the link between theory and practice. Experimental results on a real sequence of images from a space-varying sensor depicts the performance of the system.
Physical Panoramic Pyramid and Noise Sensitivity in Pyramids
Authors: Weihong Yin, Terrance E. Boult
Venue: CVPR
Year: 2000

Multi-resolution techniques have been used in a wide range of vision applications. Unfortunately, the costly operation of building a proper pyramid strongly reduces its value as a tool for reducing computational cost. A new approach, physical panoramic pyramid, is introduced in this paper. Physical panoramic pyramid measures multiple resolutions simultaneously resulting in multi-resolution panoramic images. No computation is needed to construct these image pyramids. We also analyze general noise sensitivity in image pyramids, including the interaction of the loss of resolution, random background noise and aliasing noise. The paper also discusses the issue of indexing between the neighboring layer the viewpoint variation and the applications of the physical panoramic pyramid.
Order Parameters for Minimax Entropy Distributions: When Does High Level Knowledge Help?
Authors: Alan L. Yuille, James M. Coughlan, Song Chun Zhu, Ying Nian Wu
Venue: CVPR
Year: 2000

Many problems in vision can be formulated as Bayesian inference. It is important to determine the accuracy of these inferences and how they depend on the problem domain. In recent work, Coughlan and Yuille showed that, for a restricted class of problems, the performance of Bayesian inference could be summarized by an order parameter K which depends on the probability distributions which characterize the problem domain. In this paper we generalize the theory of order parameters so that it applies to domains for which the probability models can be obtained by Minimax Entropy learning theory. By analyzing order parameters it is possible to determine whether a target can be detected using a general purpose "generic" model or whether a more specific "high-level" model is needed. At critical values of the order parameters the problem becomes unsolvable without the addition of extra prior knowledge.
Integrated 3D Scene Flow and Structure Recovery from Multiview Image Sequences
Authors: Ye Zhang, Chandra Kambhamettu
Venue: CVPR
Year: 2000

Scene flow is the 3D motion field of points in the world. Given N (N>1) image sequences gathered with a N-eye stereo camera or N calibrated cameras, we present a novel system which integrates 3D scene flow and structure recovery in order to complement each other's performance. We do not assume rigidity of the scene motion, thus allowing for non-rigid motion in the scene. In our work, images are segmented into small regions. We assume that each small region is undergoing similar motion, represented by a 3D affine model. Nonlinear motion model fitting based on both optical flow constraints and stereo constraints is then carried over each image region in order to simultaneously estimate 3D motion correspondences and structure. To ensure the robustness, several regularization constraints are also introduced. A recursive algorithm is designed to incorporate the local and regularization constraints. Experimental results on both synthetic and real data demonstrate the effectiveness of our integrated 3D motion and structure analysis scheme.
Illuminant Direction Determination for Multiple Light Sources
Authors: Yufei Zhang 0012, Yee-Hong Yang
Venue: CVPR
Year: 2000

In this paper, a novel scheme of extracting multiple illuminant directions from an image of a Lambertian sphere of known size is proposed. The illuminant direction detection process is based on the concept of critical points introduced in the paper. We show that the illuminant directions have a close relationship to those critical points and that, by identifying those critical points as many as possible, illuminate direction may be recovered if certain conditions are satisfied. Our preliminary, experimental results show that illumination information can be obtained accurately.
Illumination-Insensitive Face Recognition Using Symmetric Shape-from-Shading
Authors: Wen-Yi Zhao, Rama Chellappa
Venue: CVPR
Year: 2000

Sensitivity to variations in illumination is a fundamental and challenging problem in face recognition. In this paper, we describe a new method based on symmetric shape-from-shading (SSFS) to develop a face recognition system that is robust to changes in illumination. The basic idea of this approach is to use the SSFS algorithm as a tool to obtain a prototype image which is illumination-normalized. It has been shown that the SSFS algorithm has a unique point-wise solution. But it is still difficult to recover accurate shape information given a single real face image with complex shape and varying albedo. In stead, we utilize the fact that all faces share a similar shape making the direct computation of the prototype image from a given face image feasible. Finally, to demonstrate the efficacy of our method, we have applied it to several publicly available face databases.