OpenRaiser/Intern-Atlas huggingface dataset:
Illumination Subspace for Multibody Motion Segmentation
Authors: Atsuto Maki, Hiroshi Hattori
Venue: CVPR
Year: 2001

This paper presents a new method for automatically separating the motion of multiple independently moving objects in a sequence of images based on the notion of illumination subspace. We show that intensities of observed trajectories of image features on a single body lie on a linearly independent frame space with three, or fewer, dimensions. We then argue that it is possible in theory to determine the grouping of the feature points by way of separating the illumination subspaces. As a clue for practical separation, we also introduce the surface interaction matrix which is valid for Lambertian reflectance surface. Recently, several authors have presented different algorithms on this task of grouping by factorization-based procedures using the coordinates of image features. While the challenges in their approaches are to realize the robust performance in the presence of noise, we propose to incorporate the above photometric analysis available at given feature points in the conventional schemes of motion segmentation, and show that the performance is indeed stabilized through experiments on real and synthetic image sequences.
Stereo Head Calibration from a Planar Object
Authors: Henrik Malm, Anders Heyden
Venue: CVPR
Year: 2001

A technique for stereo camera calibration from a known planar calibration object is proposed. Both the intrinsic parameters and the relative orientation are calculated. The proposed algorithm uses the two homogeneous linear constraints on the image of the absolute conic arising from the plane homographies for each camera and position. In addition to these, linear constraints on the relation between the images of the absolute conic for the two cameras in the stereo pair are calculated. The derivation of these constraints is made possible by considering the rigidity of the stereo pair. Using all these constraints simultaneously in a linear system gives a stereo calibration technique that is less sensitive to noise, compared to just using the single camera constraints. As a part of the algorithm, a simple way to calculate the infinite homography from two stereo views of a plane is presented.The performance of the algorithm is shown in experiments on both simulated and real data.
Quantigraphic Imaging: Estimating the camera response and exposures from differently exposed images
Authors: Steve Mann 0001, Richard Mann
Venue: CVPR
Year: 2001

Multiple differently exposed pictures of the same subject matter arise naturally whenever a video camera having automatic exposure captures multiple frames of video with the same subject matter appearing in regions of overlap between at least some of the successive video frames. Almost all cameras have some kind of automatic exposure feature. Generally automatic exposure is center weighted, so that when a light object falls in the center of the frame the exposure is automatically decreased, whereas the exposure is automatically increased when the camera swings around to point at a darker object. In this paper, it is assumed that the spatial (e.g. projective) coordinate transformation between successive frames of the sequence is known (or equivalently that it is the identity), and the contribution of the paper is an efficient way to estimate the tonal relationship between successive frames of the sequence. In particular methods are proposed to simultaneously estimate the unknown camera response function, as well as the set of unknown relative exposure changes among images, up to a single unknown scalar constant. The method comprises a succession of guesses each of which is a refinement of the previous. The first guess is often sufficient, so that no initial solution needs to be provided by the user. Each subsequent guess is a least squares solution so that no computationally expensive optimization is required. Since the method makes use of all the data, it is extremely immune to noise. The method is tested against state-of-the art laboratory measurement instruments to confirm the accuracy of the results.
Metric Self Calibration from Screw-Transform Manifolds
Authors: Russell A. Manning, Charles R. Dyer
Venue: CVPR
Year: 2001

This paper introduces a method for metric self-calibration that is based on a novel decomposition of the fundamental matrix between two views taken by a camera with fixed internal parameters. The method blends important advantages of the Kruppa constraints and the modulus constraint: it works directly from fundamental matrices and uses a reduced-parameter representation for stability. General properties of the new decomposition are also developed, including an intuitive interpretation of the three free parameters of internal calibration. The approach is demonstrated on both real and synthetic data.
Efficient Evaluation of Classification and Recognition Systems
Authors: Ross J. Micheals, Terrance E. Boult
Venue: CVPR
Year: 2001

In this paper, a new framework for evaluating a variety of computer vision systems and components is introduced. This framework is particularly well suited for domains such as classification or recognition systems, where blind application of the i.i.d. assumption would reduce an evaluation's accuracy, such as with classification or recognition systems. With few exceptions, most previous work on vision system evaluation does not include confidence intervals, since they are difficult to calculate, and are often coupled with strict requirements. We show how a set of previously overlooked replicate statistics tools can be used to obtain tighter confidence intervals of evaluation estimates while simultaneously reducing the amount of data and computation required to reach such sound evaluatory conclusions. In the included application of the new methodology, the well-known FERET face recognition system evaluation is extended to incorporate standard errors and confidence intervals.
Dynamic bayesian framework for extracting temporal structure in video
Authors: Ankush Mittal, Loong Fah Cheong, Leung Tung Sing
Venue: CVPR
Year: 2001

In this paper, we develop the concept of descriptors based on perceptual-level motion features such as time-to-collision, shot transition and temporal motion and it is shown that by including them the representational level of the video classes is significantly enhanced, e.g. violence could be detected. The temporal context cues, which had been largely neglected by present content-based retrieval (CBR) systems, are integrated into the framework. A dynamic Bayesian framework for the CBR systems which can learn the temporal structure through the fusion of all the features is designed The experimental results for more than 4 hours of videos are presented for a number of key applications like sequence identifier, highlight extraction for sports, and detecting climax or violence.
Shape contexts enable efficient retrieval of similar shapes
Authors: Greg Mori, Serge J. Belongie, Jitendra Malik
Venue: CVPR
Year: 2001

In this paper we demonstrate that a recently introduced shape descriptor, the "shape context", can be used to quickly prune a search for similar shapes. Our representation for a shape is a discrete set of n points sampled from its internal and external contours. For each of these points, the shape context is a histogram of the relative positions of the n - 1 remaining points. We present two methods for rapid shape retrieval: one that does comparisons based on a small number of shape contexts and another that uses vector quantization in the space of shape contexts. We verify the discriminative power of these methods with tests on the Columbia (COIL-100) 3D object database and the Snodgrass and Vanderwart line drawings. The shape context-based methods are shown to quickly produce an accurate shortlist of candidates suitable for a more exact matching engine in spite of pose variation and occlusion.
Gauge Fixing for Accurate 3D Estimation
Authors: Daniel D. Morris, Ken-ichi Kanatani, Takeo Kanade
Venue: CVPR
Year: 2001

Computer vision techniques can estimate 3D shape from images, but usually only up to a scale factor. The scale factor must be obtained by a physical measurement of the scene or the camera motion. Using gauge theory, we show that how this scale factor is determined can significantly affect the accuracy of the estimated shape. And yet these considerations have been ignored in previous works where 3D shape accuracy is optimized. We investigate how scale fixing influences the accuracy of 3D reconstruction and determine what measurement should be made to maximize the shape accuracy.
Image Magnification Using Level-Set Reconstruction
Authors: Bryan S. Morse, Duane Schwartzwald
Venue: CVPR
Year: 2001

Image magnification is a common problem in imaging applications, requiring interpolation to "read between the pixels". Although many magnification/interpolation algorithms have been proposed in the literature, all methods must suffer to some degree the effects of imperfect reconstruction: false high-frequency content introduced by the underlying original sampling. Most often, these effects manifest themselves as jagged contours in the image. The paper presents a method for constrained smoothing of such artifacts that attempts to produce smooth reconstructions of the image's level curves while still maintaining image fidelity. This is similar to other iterative reconstruction algorithms and to Bayesian restoration techniques, but instead of assuming a smoothness prior for the underlying intensity function it assumes smoothness of the level curves. Results show that this technique can produce images whose error properties are equivalent to the initial approximation (interpolation) used, while their contour smoothness is both visually and quantitatively improved.
A Confidence Measure for Boundary Detection and Object Selection
Authors: Eric N. Mortensen, William A. Barrett
Venue: CVPR
Year: 2001

We introduce a confidence measure that estimates the assurance that a graph arc (or edge) corresponds to an object boundary in an image. A weighted, planar graph is imposed onto the watershed lines of a gradient magnitude image and the confidence measure is a function of the cost of fixed-length paths emanating from and extending to each end of a graph arc. The confidence measure is applied to automate the detection of object boundaries and thereby reduces (often greatly) the time and effort required for object boundary definition within a user guided image segmentation environment.
Automatic Estimation of the Projected Light Source Direction
Authors: Peter Nillius, Jan-Olof Eklundh
Venue: CVPR
Year: 2001

We present a fully automatic algorithm for estimating the projected light source direction from a single image. The requirement is that there exists a segment of an occluding contour of an object with locally Lambertian surface reflectance in the image. The algorithm consists of three stages. First a heuristic algorithm picks out potential occluding contours using color and edge information. Secondly, for each contour the light source direction is estimated using a shading model. In the final stage the results from the estimations are fused together in a Bayesian network to arrive at the most likely light source direction. The probabilistic model takes into account that the contours from the first stage might not be occluding contours. Using the same framework the contours are also classified as occluding or not. Experiments test the second stage, estimating the light source direction from an occluding contour, as well as the full algorithm.
Analysis and Synthesis of Human Faces with Pose Variations by a Parametric Piecewise Linear Subspace Method
Authors: Kazunori Okada, Christoph von der Malsburg
Venue: CVPR
Year: 2001

A framework for learning an accurate and general parametric facial model from 2D images is proposed and its application for analyzing and synthesizing facial images with pose variation is demonstrated. Our parametric piecewise linear subspace method covers a wide range of pose variation in a continuous manner through a weighted linear combination of local linear models distributed in a pose parameter space. The linear design helps to avoid typical nonlinear pitfalls such as overfitting and time-consuming learning. Experimental results show sub-degree and sub-pixel accuracy within /spl plusmn/55 degree full 3D rotation and good generalization capability over unknown head poses when learned and tested for specific persons.
On Photometric Aspects of Catadioptric Cameras
Authors: Takayuki Okatani, Koichiro Deguchi
Venue: CVPR
Year: 2001

Several imaging systems called catadioptric cameras have been developed that use a combination of lenses and mirrors to obtain wide fields of view. Geometric aspects have been well studied. This paper focuses on photometric aspects of catadioptric cameras. We discuss how geometric distortion of images of catadioptric cameras resulting from curvature of the mirror affects image irradiance. We show that image irradiance is determined only by the nature of lenses comprising the cameras and is independent of mirror shape. This is because an increase in the solid angle of space per single image pixel is canceled out by a decrease in the apparent solid angle of the lens aperture viewed from a scene point via the mirror. Thus, image irradiance of catadioptric cameras is not affected by geometric distortion due to mirror curvature. This is in contrast to cameras with ordinary lenses alone, especially fisheye lenses, in which image irradiance is actually affected by image distortion. Theoretical results as well as results of numerical simulations are presented.
A Simple Stereo Algorithm to Recover Precise Object Boundaries and Smooth Surfaces
Authors: Masatoshi Okutomi, Yasuhiro Katayama, Setsuko Oka
Venue: CVPR
Year: 2001

In area-based stereo matching, there is a problem called "boundary overreach", i.e. the recovered object boundary turns out to be wrongly located away from the real one. This is especially harmful to segmenting objects using depth information. A few approaches have been proposed to solve this problem. However, these techniques tend to degrade on smooth surfaces. That is, there seems to be a trade-off problem between recovering precise object edges and obtaining smooth surfaces. In this paper, we propose a new simple method to solve this problem. Using multiple stereo pairs and multiple windowing, our method detects the region where the boundary overreach is likely to occur (let us call it "BO region") and adopts appropriate methods for the BO and non-BO regions. Although the proposed method is quite simple, the experimental results have shown that it is very effective at recovering both sharp object edges at their correct locations and smooth object surfaces.
Production of Video Images by Computer Controlled Cameras and Its Application to TV Conference System
Authors: Masaki Onishi, Takehiko Kagebayashi, Kunio Fukunaga
Venue: CVPR
Year: 2001

TV conference systems have been widely used recently. A participant of each site proceeds with a TV conference using video image on a screen and voice of a partner site. In this case, a fixed video camera shoots a scene of a site in general. The video image taken by a fixed camera, however, is lacking in changes. Also the fixed camera does not take shots efficiency that the participants of a partner site pay attention. As one of the candidates to avoid these defects, there is a method that the computer-controlled cameras shoots the scene. In this paper we propose an algorithm of shooting the best shot by computer-controlled cameras. The shooting algorithm is mainly decided by estimating an area of the image with high degree of attention, which is given by not only visual information but also auditory information. By using an experimental system, we confirm the effectiveness of our approach by examining a questionnaire from the participants of TV conferences.
Anti-Sequences: Event Detection by Frame Stacking
Authors: Margarita Osadchy, Daniel Keren, Yaniv Gal
Venue: CVPR
Year: 2001

This paper presents a natural extension of the newly introduced "anti-face" method to event detection, both in the image and in the feature domains. In the case of the image domain (video sequences) we create spatio temporal templates by stacking the video frames, and the detection is performed on these templates. In order to recognize the motion of features in a video sequence, the spatial locations of the features are modulated in time, thus creating a one-dimensional vector which re resents the event The following applications of anti-sequences are presented 1) Detection of an object under 3D rotations in a video sequence simulated from the COIL database, 2) Visual speech recognition of spoken words, and 3) Recognition of symbols sketched with a laser pointer. The resulting detection algorithm is very fast, and is robust enough to work on small images. Also, it is capable of discriminating the desired event-template from arbitrary events, and not only events in a "negative training set".
Robust Crease Detection and Curvature Estimation of Piecewise Smooth Surfaces from Triangle Mesh Approximations Using Normal Voting
Authors: David L. Page, Andreas F. Koschan, Yiyong Sun, Joon Ki Paik, Mongi A. Abidi
Venue: CVPR
Year: 2001

In this paper, we describe a robust method for the estimation of curvature on a triangle mesh, where this mesh is a discrete approximation of a piecewise smooth surface. The proposed method avoids the computationally expensive process of surface fitting and instead employs normal voting to achieve robust results. This method detects crease discontinuities on the surface to improve estimates near those creases. Using a voting scheme, the algorithm estimates both principal curvatures and principal directions for smooth patches. The entire process requires one user parameter-the voting neighborhood size, which is a function of sampling density, feature size, and measurement noise. We present results for both synthetic and real data and compare these results to an existing algorithm developed by Taubin (1995).
On the Individuality of Fingerprints
Authors: Sharath Pankanti, Salil Prabhakar, Anil K. Jain 0001
Venue: CVPR
Year: 2001

Fingerprint identification is based on two basic premises: (i) persistence: the basic characteristics of fingerprints do not change with time; and (ii) individuality: the fingerprint is unique to an individual. The validity of the first premise has been established. While the second premise is generally accepted to be true, the underlying scientific basis of fingerprint individuality has not been formally tested. We address the problem of fingerprint individuality by quantifying the amount of information available in minutiae points to establish a correspondence between two fingerprint images. We derive an expression which estimates the probability of falsely associating minutiae-based representations from two arbitrary fingerprints. For example, the probability that a fingerprint with 36 minutiae points will share 12 minutiae points with another arbitrarily chosen fingerprint with 36 minutiae points is 6.10/spl times/10/sup -8/. These probability estimates are compared with typical fingerprint matcher accuracy results. Our results show that: (i) fingerprint matching is not infallible and leads to some false associations, (ii) the performance of automatic fingerprint matcher does not even come close to the theoretical performance, and (iii) due to the limited information content of the minutiae-based representation, automatic system designers should explore the use of non-minutiae-based information present in the fingerprints.
LDA/SVM Driven Nearest Neighbor Classification
Authors: Jing Peng, Douglas R. Heisterkamp, H. K. Dai 0001
Venue: CVPR
Year: 2001

Nearest neighbor classification relies on the assumption that class conditional probabilities are locally constant. This assumption becomes false in high dimensions with finite samples due to the curse of dimensionality. The nearest neighbor rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias. We use local support vector machine teaming to estimate an effective metric for producing neighborhoods that are elongated along less discriminant feature dimensions and constricted along most discriminant ones. As a result, the class conditional probabilities can be expected to be approximately constant in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other competing techniques using a number of data sets.
Rethinking Classical Internal Forces for Active Contour Models
Authors: Douglas P. Perrin, Christopher E. Smith
Venue: CVPR
Year: 2001

The classical active contour model has two basic internal forces: tension and curvature. These forces are included to provide cohe sion, equal control point spacing, and locally smooth shape. These classical internal forces have undesirable attributes that are in conflict with these original desired characteristics. Tension evenly spaces the control points, but also causes the models to collapse in weak image gradients. Curvature produces locally smooth curvature, but it does so by forcing the model toward a straight line. The paper returns to the original active contour model motivations to reformulate these internal forces. The desired properties are achieved without the introduction of unwanted model behavior A new spacing force and a new constant change in curvature force are introduced and their performance characteristics are discussed. The paper includes experimental results that demonstrate the efficacy and performance of the proposed reformulations.
Analysis and Detection of Shadows in Video Streams: A Comparative Evaluation
Authors: Andrea Prati 0001, Rita Cucchiara, Ivana Mikic, Mohan M. Trivedi
Venue: CVPR
Year: 2001

Robustness to changes in illumination conditions as well as viewing perspectives is an important requirement for many computer vision applications. One of the key factors in enhancing the robustness of dynamic scene analysis is that of accurate and reliable means for shadow detection. Shadow detection is critical for correct object detection in image sequences. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, the full range of problems underlying the shadow detection is identified and discussed. We classify the proposed solutions to this problem using a taxonomy of four main classes, deterministic model and non-model based, and statistical parametric and nonparametric. Novel quantitative (detection and discrimination accuracy) and qualitative metrics (scene and object independence, flexibility to shadow situations and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences.
A Relationship between Spline-Based Deformable Models and Weighted Graphs in Non-Rigid Matching
Authors: Anand Rangarajan 0001, Haili Chui, Eric Mjolsness
Venue: CVPR
Year: 2001

Deformable models are central to non-rigid motion analysis, shape matching and non-rigid medical image registration. Spline-based deformations are a very popular class of parameterizations of deformable models and have been heavily used in multiple domains. In a somewhat separate sub-field, weighted graphs are a frequently used object parameterization. Graph matching using weighted graph object parameterizations finds application in a spectrum ranging from rigid pose estimation to deformable object recognition. Here, we demonstrate a hitherto unsuspected relationship between spline-based deformable models and weighted graphs. It turns out that spline parameterizations in the kernel representation can be used to construct equivalent weighted graphs. With this connection established, we envision a cross-fertilization between these two seemingly disparate sub-fields of computer vision.