OpenRaiser/Intern-Atlas huggingface dataset:
Biologically MotivatedPrecise and Simple Calibration and Reconstruction Using a Stereo Light Microscope
Authors: Lars Eckert, Rolf-Rainer Grigat
Venue: ICCV
Year: 2001

Stereoscopic calibration and reconstruction is applied to the specialized optics of a binocular monobjective stereo light microscope. Such a microscope exhibits a special kind of image distortion. Despite the difficulty of modelling the microscope, a simple calibration method as well as a fast and simple, yet precise, reconstruction algorithm is developed. Their fundamental scheme is based upon biological binocular vision. The reconstruction uses polynomial approximations up to a degree of 2 and thus has a very low computational complexity. The polynomial coefficients are identified during calibration and their number is minimal by construction. No lens data is required. Both the calibration and reconstruction algorithm are robust against a rigid motion of the microscope. Their power is proven with real data using an off-the-shelf PC.
Probabilistic Framework for Segmenting People Under Occlusion
Authors: Ahmed M. Elgammal, Larry S. Davis
Venue: ICCV
Year: 2001

In this paper we address the problem of segmenting foreground regions corresponding to a group of people given models of their appearance that were initialized before occlusion. We present a general framework that uses maximum likelihood estimation to estimate the best arrangement for people in terms of 2D translation that yields a segmentation for the foreground region. Given the segmentation result we conduct occlusion reasoning to recover relative depth information and we show how to utilize this depth information in the same segmentation framework. We also present a more practical solution for the segmentation problem that is online to avoid searching an exponential space of hypothesis. The person model is based on segmenting the body into regions in order to spatially localize the color-features corresponding to the way people are dressed. Modeling these regions involves modeling their appearance (color distributions) as well us their spatial distribution with respect to the body. We use a non-parametric approach bused on kernel density estimation to represent the color distribution of each region and therefore we do not restrict the clothing to be of uniform color instead it can be any mixture of colors and/or patterns. We also present a method to automatically initialize these models and learn them before the occlusion.
Blind Removal of Image Non-Linearities
Authors: Hany Farid, Alin C. Popescu
Venue: ICCV
Year: 2001

This paper presents a technique for blindly removing image non-linearities in the absence of any calibration information or explicit knowledge of the imaging device. The basic approach exploits the fact that a non-linearity introduces specific higher-order correlations in the frequency domain (beyond second-order). These correlations can be detected using tools from polyspectral analysis. The non-linearities can then be estimated and removed by simply minimizing these correlations.
Real-time Virtual Object Insertion
Authors: Paolo Favaro, Hailin Jin, Stefano Soatto
Venue: ICCV
Year: 2001

We present a system to insert virtual objects into real image sequences in real time. The system consists of offthe- shelf hardware (a camera connected to a Pentium PC) and software to (a) automatically select and track region features despite changes in illumination, (b) estimate threedimensional position and orientation of surface patches relative to an inertial reference frame despite individual pointfeatures appearing and disappearing, (c) insert a texturemapped virtual object into the scene so as to make it appear to be part of the scene and moving with it. This is all done in real time. The multi-thread C++ code, which is readily interfaced with a frame grabber as well as Matlab for development, will be made available to the public at the demonstration.
Shape from Texture and Integrability
Authors: David A. Forsyth
Venue: ICCV
Year: 2001

We describe a shape from texture method that constructs a maximum a posteriori estimate of surface coefficients using both the deformation of individual texture elements-as in local methods-and the overall distribution of elements-as in global methods. The method described applies to a much larger family of textures than any previous method, local or global. We demonstrate an analogy with shape from shading, and use this to produce a numerical method. Examples of reconstructions for synthetic images of surfaces are provided, and compared with ground truth. The method is defined for orthographic views, but can be generalised to perspective views simply.
Visual Learning by Integrating Descriptive and Generative Methods
Authors: Cheng-en Guo, Song Chun Zhu, Ying Nian Wu
Venue: ICCV
Year: 2001

This paper presents a mathematical framework for visual learning that integrates two popular statistical learning paradigms in the literature: (I). Descriptive learning, such as Markov random fields and minimax entropy learning, and (II). Generative learning, such as PCA, ICA, TCA, image coding and HMM. We apply this integrated learning framework to texton modeling, and we assume that an observed texture image is generated by multiple layers of hidden stochastic "texton processes" with each texton being a window function, like a mini-template or a wavelet, under affine transformations. The spatial arrangements of the textons are characterized by minimax entropy models. The texton processes generate images by occlusion or linear addition. Thus given a raw input image, the learning framework achieves four goals: (i). Computing the appearance of the textons. (ii) Inferring the hidden stochastic texton processes. (iii). Learning Gibbs models for each texton process and (iv). Verifying the learnt textons and Gibbs models through random sampling and texture synthesis. The integrated framework subsumes the minimax entropy learning paradigm and creates a richer class of probability models for visual patterns, which are suited for middle level vision representations. Furthermore we show that the integration of description and generative methods yields a natural and general framework of visual learning. We demonstrate the proposed framework and algorithms on many real images.
A Background Model Initialization Algorithm for Video Surveillance
Authors: Daniel Gutchess, Miroslav Trajkovic, Eric Cohen-Solal, Damian M. Lyons, Anil K. Jain 0001
Venue: ICCV
Year: 2001

Many motion detection and tracking algorithms rely on the process of background subtraction, a technique which detects changes from a model of the background scene. We present a new algorithm for the purpose of background model initialization. The algorithm takes as input a video sequence in which moving objects are present, and outputs a statistical background model describing the static parts of the scene. Multiple hypotheses of the background value at each pixel are generated by locating periods of stable intensity in the sequence. The likelihood of each hypothesis is then evaluated using optical flow information from the neighborhood around the pixel, and the most likely hypothesis is chosen to represent the background. Our results are compared with those of several standard background modeling techniques using surveillance video of humans in indoor environments.
Noise in Bilinear Problems
Authors: John A. Haddon, David A. Forsyth
Venue: ICCV
Year: 2001

Despite the wide application of bilinear problems to problems both in computer vision and in other fields, their behaviour under the effects of noise is still poorly understood. In this paper, we show analytically that marginal distributions on the solution components of a bilinear problem can be bimodal, even with Gaussian measurement error. We demonstrate and compare three different methods of estimating the covariance of a solution. We show that the Hessian at the mode substantially underestimates covariance. Many problems in computer vision can be posed as bilinear problems: i.e. one must find a solution to a set of equations of the form.
Multiple Motion Scene Reconstruction from Uncalibrated Views
Authors: Mei Han, Takeo Kanade
Venue: ICCV
Year: 2001

We describe a reconstruction method of multiple motion scenes, which are the scenes containing multiple moving objects, from uncalibrated views. Assuming that the objects are moving with constant velocities, the method recovers the scene structure, the trajectories of the moving objects, the camera motion and the camera intrinsic parameters (except skews) simultaneously. The number of the moving objects is automatically detected without prior motion segmentation. The method is based on a unified geometrical representation of the static scene and the moving objects. It first performs a projective reconstruction using a bilinear factorization algorithm, and then converts the projective solution to a Euclidean one by enforcing metric constraints. Experimental results on synthetic and real images are presented.
Face Recognition with Support Vector Machines: Global versus Component-based Approach
Authors: Bernd Heisele, Purdy Ho, Tomaso A. Poggio
Venue: ICCV
Year: 2001

We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40/spl deg/ in depth. The component system clearly outperformed both global systems on all tests.
Harmonics Extraction Based on Higher Order Statistics Spectrum Decomposition for A Unified Texture Model
Authors: Yong Huang, Kap Luk Chan, ZhongYang Huang
Venue: ICCV
Year: 2001

By considering a texture being composed of two orthogonal components in a unified texture model, the deterministic component and the indeterministic component, a method of harmonics extraction from a Higher Order Statistics (HOS) based spectral decomposition is developed. The method estimates the power spectrum based on the diagonal slice of the fourth-order cumulants From this spectrum, the harmonic frequencies can be easily extracted even for noisy images. The simulation and experimental results indicate that this method is effective for texture decomposition and performs better than the traditional lower order statistics based decomposition methods.
Geometrical Fundamentals of Polycentric Panoramas
Authors: Fay Huang, Shou-Kang Wei, Reinhard Klette
Venue: ICCV
Year: 2001

This paper proposes polycentric panoramas as a general model of panoramic images. The model formalizes essential characteristics of panoramic geometry. It is able to describe a wide range of panoramic images, including those potentially of future interest, or previously introduced such as single-center, multi-perspective, or concentric panoramas. This paper presents geometrical fundamentals towards stereo applications based on sets of polycentric panoramas. We discuss the image acquisition model, epipolar geometry and a 3D reconstruction approach for this general model of polycentric panoramas. Our theorems on epipolar curve and 3D reconstruction hold for any pair of polycentric panoramas. Corollaries demonstrate that the proposed mathematical model clarifies the understanding and characterization of more specific models. Epipolar curves of special cases are illustrated on panoramic images acquired by a high resolution line-camera.
Separation of Multiple Objects in Motion Images by Clustering
Authors: Kohei Inoue, Kiichi Urahama
Venue: ICCV
Year: 2001

A method is presented for estimating the number of rigid objects moving independently and separating the feature points tracked on a motion image into individual objects by clustering. In the method, feature points are firstly mapped into a low dimensional space suitable for grouping them into each object. In this low dimensional space, clusters are extracted sequentially by a graph spectral method. The number of clusters i.e. objects can be estimated on the basis of the variation in the cohesiveness of extracted clusters. We show by numerical experiments that the present method is robust to moderate measurement noises and distortion by perspective projection.
Human Tracking with Mixtures of Trees
Authors: Sergey Ioffe, David A. Forsyth
Venue: ICCV
Year: 2001

Tree-structured probabilistic models admit simple, fast inference. However they are not well suited to phenonena such as occlusion, where multiple components of an object may disappear simultaneously. We address this problem with mixtures of trees, and demonstrate an efficient and compact representation of this mixture, which admits simple learning and inference algorithms. We use this method to build an automated tracker for Muybridge sequences of a variety of human activities. Tracking is difficult, because the temporal dependencies rule out simple inference methods. We show how to use our model for efficient inference, using a method that employs alternate spatial and temporal inference. The result is a cracker that (a) uses a very loose motion model, and so can track many different activities at a variable frame rate and (b) is entirely, automatic.
Region Extraction from Multiple Images
Authors: Hiroshi Ishikawa 0002, Ian Jermyn
Venue: ICCV
Year: 2001

We present a method for region identification in multiple images. A set of regions in different images and the correspondences on their boundaries can be thought of as a boundary in the multi-dimensional space formed by the product of the individual image domains. We minimize an energy functional on the space of such boundaries, thereby identifying simultaneously both the optimal regions in each image and the optimal correspondences on their boundaries. We use a ratio form for the energy functional, thus enabling the global minimization of the energy functional using a polynomial time graph algorithm, among other desirable properties. We choose a simple form for this energy that favours boundaries that lie on high intensity gradients in each image, while encouraging correspondences between boundaries in different images that match intensity values. The latter tendency is weighted by a novel heuristic energy that encourages the boundaries to lie on disparity or optical flow discontinuities, although no dense optical flow or disparity map is computed.
A Framework for Segmentation of Talk and Game Shows
Authors: Omar Javed, Zeeshan Rasheed, Mubarak Shah
Venue: ICCV
Year: 2001

In this paper, we present a method to remove commercials from talk and game show videos and to segment these videos into host and guest shots. In our approach, we mainly rely on information contained in shot transitions, rather than analyzing the scene content of individual frames. We utilize the inherent differences in scene structure of commercials and talk shows to differentiate between them. Similarly, we make use of the well-defined structure of talk shows, which can be exploited to classify shots as host or guest shots. The entire show is first segmented into camera shots based on color histogram. Then, we construct a data-structure (shot connectivity graph) which links similar shots over time. Analysis of the shot connectivity graph helps us to automatically separate commercials from program segments. This is done by first detecting stories, and then assigning a weight to each story based on its likelihood of being a commercial. Further analysis on stories is done to distinguish shots of the hosts from shots of the guests. We have tested our approach on several full-length shows (including commercials) and have achieved video segmentation with high accuracy. The whole scheme is fast and works even on low quality video (160/spl times/120 pixel images at 5 Hz).
Segmentation of the Left Ventricle in Cardiac MR Images
Authors: Marie-Pierre Jolly, Nicolae Duta, Gareth Funka-Lea
Venue: ICCV
Year: 2001

This paper describes a segmentation technique to automatically extract the myocardium in 4D cardiac MR images for quantitative cardiac analysis and the diagnosis of patients. Three different modules are presented. The automatic localization algorithm is able to approximately locate the left ventricle in an image using a maximum discrimination technique. Then, the local deformation algorithm can deform active contours so that they align to the edges in the image to produce the desired outlining of the myocardium. Finally, the global localization algorithm is able to propagate segmented contours from one image in the data set to all the others. We have experimented with the proposed method on a large number of patients and present some examples to show the strengths and pitfalls of our algorithm.
A Simple and Efficient Template Matching Algorithm
Authors: Frédéric Jurie, Michel Dhome
Venue: ICCV
Year: 2001

We propose a general framework for object tracking in video images. It consists of low-order parametric models for the image motion of a target region. These models are used to predict the movement and to track the target. The difference of intensity between the pixels belonging to the current region and the pixels of the selected target (learnt during an off-line stage) allows a straightforward prediction of the region position in the current image. The proposed algorithm allows to track in real time (less than 10 ms) any planar textured target under homographic motions. This algorithm is very simple (a few lines of code) and very efficient (less than 10 ms on a 150 MHz hardware).
Ambiguous Configurations for the 1D Structure and Motion Problem
Authors: Fredrik Kahl, Kalle Åström
Venue: ICCV
Year: 2001

In this paper we investigate, determine and classify the critical configurations for solving structure and motion problems for 1D retina vision. We give a complete categorization of all ambiguous configurations for a 1D perspective camera irrespective of the number of points and views. Both calibrated and uncalibrated cameras are considered. Several examples and illustrations are provided to explain the results and to provide geometrical insight.
Multiple View Geometry of Non-planar Algebraic Curves
Authors: Jeremy Yermiyahou Kaminski, Michael Fryers, Amnon Shashua, Mina Teicher
Venue: ICCV
Year: 2001

We introduce a number of new results in the context of multi-view geometry from general algebraic curves. We start with the derivation of the extended Kruppa's equations which are responsible for describing the epipolar constraint of two projections of a general (non-planar) algebraic curve. As part of the derivation of those constraints we address the issue of dimension analysis and as a result establish the minimal number of algebraic curves required for a solution of the epipolar geometry as a function of their degree and genus. We then establish new results on the reconstruction of general algebraic curves from multiple views. We address three different representations of curves: (i) the regular point representation for which we show that the reconstruction from two views of a curve of degree d admits two solutions, one of degree d and the other of degree d(d-1), (ii) the dual space representation (tangents) for which we derive a lower bound for the number of views necessary for reconstruction as a function of the curve degree and genus, and (iii) a new representation (to computer vision) based on the set of lines meeting the curve which does not require any curve fitting in image space, for which we also derive lower bounds for the number of views necessary for reconstruction as a function of the curve degree alone.
Do We Really Have to Consider Covariance Matrices for Image Features?
Authors: Yasushi Kanazawa, Ken-ichi Kanatani
Venue: ICCV
Year: 2001

Many studies have been made in the past for optimization using covariance matrices of feature points. We first describe how to compute the covariance matrix of a feature point from the gray levels by integrating existing methods. Then, we experimentally examine if thus computed covariance matrices really reflect the accuracy of the feature points. To test this, we do subpixel template matching and compute the homography and the fundamental matrix. Our conclusion is rather surprising, pointing out important elements often overlooked.
A Linear Dual-Space Approach to 3D Surface Reconstruction from Occluding Contours using Algebraic Surfaces
Authors: Kongbin Kang, Jean-Philippe Tarel, Richard Fishman, David B. Cooper
Venue: ICCV
Year: 2001

We present a linear approach to the 3D reconstruction problem from occluding contours using algebraic surfaces. The problem of noise and missing data in the occluding contours extracted from the images leads us to this approach. Our approach is based first on the intensive use of the duality property between 3D points and tangent planes, and second on the algebraic representation of 3D surfaces by implicit polynomials of degree 2 and higher.
Plane-based Projective Reconstruction
Authors: Robert Kaucic, Richard I. Hartley, Nicolas Y. Dano
Venue: ICCV
Year: 2001

A linear method for computing a projective reconstruction from a large number of images is presented and then evaluated. The method uses planar homographies between views to linearize the resecting of the cameras. Constraints based on the fundamental matrix, trifocus tensor or quadrifocal tensor are used to derive relationship between the position vectors of all the cameras at once. The resulting set of equations are solved using a SVD. The algorithm is computationally efficient as it is linear in the number of matched points used. A key feature of the algorithm is that all of the images are processed simultaneously, as in the Sturm-Triggs factorization method, but it differs in not requiring that all points be visible in all views. An additional advantage is that it works with any mixture of line and point correspondence through the constraints these impose on the multilinear tensors. Experiments on both synthetic and real data confirm the method's utility.
Automated 3D PDM Construction Using Deformable Models
Authors: Michael Kaus, Vladimir Pekar, Cristian Lorenz, Roel Truyen, Steven Lobregt, Jens A. Richolt, Jürgen Weese
Venue: ICCV
Year: 2001

In recent years several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a-priori knowledge. Examples include principal component analysis (PCA) of manually or semi-automatically placed corresponding landmarks on the learning shapes (point distribution models, PDM), which is time consuming and subjective. However automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of 3D PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using CT data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.