OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic Learning and Modelling of Object Dynamics for Tracking
Authors: Terence Tay, Kah Kay Sung
Venue: ICCV
Year: 2001

The problem of tracking can be decomposed and independently addressed in two steps, namely the prediction step and the verification step. In this paper we present a new approach of addressing the prediction step that is based on modelling joint probability densities of successive states of tracked objects. This approach has the advantage that it is conceptually general such that given sufficient training data, it is capable of modelling a wide range of complex dynamics. Furthermore, we show that this conceptual prediction framework can be implemented in a tractable manner using a Gaussian mixture representation which allows predictions to be generated efficiently. We then descibe experiments that demonstrate these benefits.
Robust Principal Component Analysis for Computer Vision
Authors: Fernando De la Torre, Michael J. Black
Venue: ICCV
Year: 2001

Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance and motion. One drawback of typical PCA methods is that they are least squares estimation techniques and hence fail to account for "outliers" which are common in realistic training sets. In computer vision applications, outliers typically occur within a sample (image) due to pixels that are corrupted by noise, alignment errors, or occlusion. We review previous approaches for making PCA robust to outliers and present a new method that uses an intra-sample outlier process to account for pixel outliers. We develop the theory of Robust Principal Component Analysis (RPCA) and describe a robust M-estimation algorithm for learning linear multi-variate representations of high dimensional data such as images. Quantitative comparisons with traditional PCA and previous robust algorithms illustrate the benefits of RPCA when outliers are present. Details of the algorithm are described and a software implementation is being made publically available.
AMILab: A 2D/3D Image Processing Software
Authors: Agustín Trujillo-Pino, Karl Krissian
Venue: ICCV
Year: 2001

A freeware graphic window environment oriented to 2D/3D image processing is presented. The freeware, so-called AMILab, can be described from five different points of view: image processing environment, 3D visualization tool, image processing library, programming library, and pedagogic tool.
Image Segmentation by Data Driven Markov Chain Monte Carlo
Authors: Zhuowen Tu, Song Chun Zhu, Heung-Yeung Shum
Venue: ICCV
Year: 2001

This paper presents a computational paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian, statistical framework. The paper contributes to image segmentation in three aspects. Firstly, it designs effective and well balanced Markov Chain dynamics to explore the solution space and makes the split and merge process reversible at a middle level vision formulation. Thus it achieves globally optimal solution independent of initial segmentations. Secondly, instead of computing a single maximum a posteriori solution, it proposes a mathematical principle for computing multiple distinct solutions to incorporates intrinsic ambiguities in image segmentation. A k-adventurers algorithm is proposed for extracting distinct multiple solutions from the Markov chain sequence. Thirdly, it utilizes data-driven (bottom-up) techniques, such as clustering and edge detection, to compute importance proposal probabilities, which effectively drive the Markov chain dynamics and achieve tremendous speedup in comparison to traditional jump-diffusion method. Thus DDM-CMC paradigm provides a unifying framework where the role of existing segmentation algorithms, such as; edge detection, clustering, region growing, split-merge, SNAKEs, region competition, are revealed as either realizing Markov chain dynamics or computing importance proposal probabilities. We report some results on color and grey level image segmentation in this paper and refer to a detailed report and a web site for extensive discussion.
Flux Maximizing Geometric Flows
Authors: Alexander Vasilevskiy, Kaleem Siddiqi
Venue: ICCV
Year: 2001

Several geometric active contour models have been proposed for segmentation in computer vision and image analysis. The essential idea is to evolve a curve (in 2D) or a surface (in 3D) under constraints from image forces so that it clings to features of interest in an intensity image. Recent variations on this theme take into account properties of enclosed regions and allow for multiple curves or surfaces to be simultaneously represented. However, it is still unclear how to apply these techniques to images of narrow elongated structures, such as blood vessels, where intensity contrast may be low and reliable region statistics cannot be computed. To address this problem, we derive the gradient flows which maximize the rate of increase of flux of an appropriate vector field through a curve (in 2D) or a surface (in 3D). The key idea is to exploit the direction of the vector field along with its magnitude. The calculations lead to a simple and elegant interpretation which is essentially parameter free and has the same form in both dimensions. We illustrate its advantages with several level-set-based segmentations of 2D and 3D angiography images of blood vessels.
Stereo Matching by Compact Windows via Minimum Ratio Cycle
Authors: Olga Veksler
Venue: ICCV
Year: 2001

Window size and shape selection is a difficult problem in area based stereo. We propose an algorithm which chooses an appropriate window shape by optimizing over a large class of "compact" windows. We call them compact because their ratio of perimeter to area tends to be small. We believe that this is the first window matching algorithm which can explicitly construct non-rectangular windows. Efficient optimization over the compact window class is achieved via the minimum ratio cycle algorithm. In practice it takes time linear in the size of the largest window in our class. Still the straightforward approach to find the optimal window for each pixel-disparity pair is too slow. We develop pruning heuristics which gave practically the same results while reducing running time from minutes to seconds. Our experiments show that unlike fixed window algorithms, our method avoids blurring disparity boundaries as well as constructs large windows in low textured areas. The algorithm has few parameters which are easy to choose, and the same parameters work well for different image pairs.
Optimal Motion Estimation from Multiview Normalized Epipolar Constraint
Authors: René Vidal, Yi Ma 0001, Shawn Hsu, Shankar Sastry 0001
Venue: ICCV
Year: 2001

In this paper, we study the structure from motion problem as a constrained nonlinear least squares problem which minimizes the so called reprojection error subject to all constraints among multiple images. By converting this constrained optimization problem to an unconstrained one, we obtain a multiview version of the normalized epipolar constraint of two views. Such a multiview normalized epipolar constraint serves as a statistically optimal objective function for motion (and structure) estimation. Since such a function is defined naturally on a product of Stiefel manifolds, we show how to use geometric optimization techniques to minimize it. We present experimental results on real images to evaluate the proposed algorithm.
Robust Real-Time Face Detection
Authors: Paul A. Viola, Michael J. Jones 0001
Venue: ICCV
Year: 2001

We have constructed a frontal face detection system which achieves detection and false positive rates which are equivalent to the best published results [7, 5, 6, 4, 1]. This face detection system is most clearly distinguished from previous approaches in its ability to detect faces extremely rapidly. Operating on 384 by 288 pixel images, faces are detected at 15 frames per second on a conventional 700 MHz Intel Pentium III. In other face detection systems, auxiliary information, such as image differences in video sequences, or pixel color in color images, have been used to achieve high frame rates. Our system achieves high frame rates working only with the information present in a single grey scale image. These alternative sources of information can also be integrated with our system to achieve even higher frame rates.
Error Analysis of Pure Rotation-Based Self-Calibration
Authors: Leslie Wang, Sing Bing Kang, Heung-Yeung Shum, Guangyou Xu
Venue: ICCV
Year: 2001

Self-calibration using pure rotation is a well-known technique and has been shown to be a reliable means for recovering intrinsic camera parameters. However, in practice, it is virtually impossible to ensure that the camera motion for this type of self-calibration is a pure rotation. In this paper, we present an error analysis of recovered intrinsic camera parameters due to the presence of translation. We derived closed-form error expressions for a single pair of images with nondegenerate motion; for multiple rotations for which there are no closed-form solutions, analysis was done through repeated experiments. Among others, we show that translation-independent solutions do exist under certain practical conditions. Our analysis can be used to help choose the least error-prone approach (if multiple approaches exist) for a given set of conditions.
Shape Deformation: SVM Regression and Application to Medical Image Segmentation
Authors: Song Wang, Weiyu Zhu, Zhi-Pei Liang
Venue: ICCV
Year: 2001

This paper presents a novel landmark-based shape deformation method. This method effectively solves two problems inherent in landmark-based shape deformation: (a) identification of landmark points from a given input image, and (b) regularized deformation the shape of an an object defined in a template. The second problem is solved using a new constrained support vector machine (SVM) regression technique, in which a thin-plate kernel is utilized to provide non-rigid shape deformations. This method offers several advantages over existing landmark-based methods. First, it has a unique capability to detect and use multiple candidate landmark points in an input image to improve landmark detection. Second, it can handle the case of missing landmarks, which often arises in dealing with occluded images. We have applied the proposed method to extract the scalp contours from brain cryosection images with very encouraging results.
Control of Home Appliances Using Face and Hand Sign Recognition
Authors: Hiroki Watanabe, Hitoshi Hongo, Mamoru Yasumoto, Yoshinori Niwa, Kazuhiko Yamamoto
Venue: ICCV
Year: 2001

This article discusses face and hand sign recognition and its applications. We have already proposed a method that can recognize faces and hand signs using hierarchical discriminant analysis. Here, we implement this method on a laptop computer and develop a real-time system that can control home appliances by recognizing faces and hand signs. Our system can control various appliances in the home by remote control. Moreover, since our system can recognize the user, it can assign distinct commands to the hand signs of each user and limit the appliances and functions that users can control.
Cheirality in Epipolar Geometry
Authors: Tomás Werner, Tomás Pajdla
Venue: ICCV
Year: 2001

The image points in two images satisfy epipolar constraint. However, not all sets of points satisfying epipolar constraint correspond to any real geometry because there can exist no cameras and scene points projecting to given image points such that all image points have positive depth. Using the cheirability theory due to Hartley and previous work an oriented projective geometry, we give necessary and sufficient conditions for an image point set to correspond to any real geometry. For images from conventional cameras, this condition is simple and given in terms of epipolar lines and epipoles. Surprising, this is not sufficient for central panoramic cameras. Apart from giving the insight to epipolar geometry, among the applications are reducing the search space and ruling out impossible matches in stereo, and ruling out impossible solutions for a fundamental matrix computed from seven points.
Camera Calibration and 3D Reconstruction from Single Images Using Parallelepipeds
Authors: Marta Wilczkowiak, Edmond Boyer, Peter F. Sturm
Venue: ICCV
Year: 2001

In this paper parallelepipeds and their use in camera calibration and 3D reconstruction processes are studied. Parallelepipeds naturally characterize rigidity constraints present in a scene, such as parallelism and orthogonality. A subclass of parallelepipeds-the cuboids-has been frequently used over the past to partially calibrate cameras. However, the full potential of parallelepipeds, in camera calibration as well as in scene reconstruction, has never been clearly established. We propose a new framework for the use of parallelepipeds which is based on an extensive study of this potential. In particular, we exhibit the complete duality that exists between the intrinsic metric characteristics of a parallelepiped and the intrinsic parameters of a camera. Our framework allows to fully exploit parallelepipeds and thus overcomes several limitations of calibration approaches based on cuboids. To illustrate this framework, we present an original and very efficient interactive method for 3D reconstruction from single images. This method allows to quickly build a scene model from a single uncalibrated image.
Video Georegistration: Algorithm and Quantitative Evaluation
Authors: Richard P. Wildes, David J. Hirvonen, Steven C. Hsu, Rakesh Kumar 0001, W. Brian Lehman, Bogdan Matei, Wen-Yi Zhao
Venue: ICCV
Year: 2001

An algorithm is presented for video georegistration, with a particular concern for aerial video, i.e., video captured from an airborne platform. The algorithm's input is a video stream with telemetry (camera model specification sufficient to define an initial estimate of the view) and geodetically calibrated reference imagery (coaligned digital orthoimage and elevation map). The output is a spatial registration of the video to the reference so that it inherits the available geodetic coordinates. The video is processed in a continuous fashion to yield a corresponding stream of georegistered results. Quantitative results of evaluating the developed approach with real world aerial video also are presented. The results suggest that the developed approach may provide valuable input to the analysis and interpretation of aerial video.
On Projection Matrices and their Applications in Computer Vision
Authors: Lior Wolf, Amnon Shashua
Venue: ICCV
Year: 2001

Projection matrices from projective spaces # # to # # have long been used in multiple-view geometry to model the perspective projection created by the pin-hole camera. In this work we introduce higher-dimensional mappings # # ## # # , # ### ## ## # for the representation of various applications in which the world we view is no longer rigid. We also describe the multi-view constraints from these new projection matrices (where ###) and methods for extracting the (non-rigid) structure and motion for each application. 1
Affine 3-D Reconstruction from Two Projective Images of Independently Translating Planes
Authors: Lior Wolf, Amnon Shashua
Venue: ICCV
Year: 2001

Consider two views of a multi-body scene consisting of k planar bodies moving in pure translation one relative to the other. We show that the fundamental matrices, one per body, live in a 3-dimensional subspace, which when represented as a step-3 extensor is the common transversal on the collection of extensors defined by the homograph matrices H/sub 1/,...,H/sub k/ of the moving planes. We show that as much as five bodies are necessary for recovering the common transversal from the homograph matrices, from which we show how to recover the fundamental matrices and the affine calibration between the two cameras.
Self-Supervised Learning for Object Recognition based on Kernel Discriminant-EM Algorithm
Authors: Ying Wu 0001, Thomas S. Huang, Kentaro Toyama
Venue: ICCV
Year: 2001

It is often tedious and expensive to label large training data sets for learning-based object recognition systems. This problem could be alleviated by self-supervised learning techniques, which take a hybrid of labeled and unlabeled training data to learn classifiers. Discriminant-EM (D-EM) proposed a framework for such tasks and current D-EM algorithm employed linear discriminant analysis. However, the algorithm is limited by its dependence on linear transformations. This paper extends the linear D-EM to nonlinear kernel algorithm, Kernel D-EM, based on kernel multiple discriminant analysis (KMDA). KMDA provides better ability to simplify the probabilistic structures of data distributions in a discrimination space. We propose two novel data-sampling schemes for efficient training of kernel discriminants. Experimental results show that classifiers using KMDA learning compare with SVM performance on standard benchmark tests, and that Kernel D-EM outperforms a variety of supervised and semi-supervised learning algorithms for a hand-gesture recognition task and fingertip tracking task.
3D-Mode: A 3D Modelling and Measurement System Using a Few Photos
Authors: Gang Xu, Takahisa Nakayama, Takeshi Kajikawa, Jun-ichi Terai
Venue: ICCV
Year: 2001

We demonstrate "3D-Mode," a software system that builds 3D models of objects and scenes by taking a few (minimally 2) photographs using a digital camera at possibly largely separated positions. It has recently been commercialized by 3D Media Co., Ltd. 3D-Mode has the following steps: (1) taking photos; (2) manually obtaining and matching a few feature points on the object; (3) automatically computing 3D structure and motion; (4) automatic Delaunay triangulation and manual modification; (5) automatic acquisition of texture for each triangular patch; (6) if necessary, assign a new coordinate system and scale of space. The system does not need input of camera parameters, but requires manual input of feature points in a special manner such that matching information is input simultaneously. Once epipolar lines are available, they are used to help locate corresponding points in other images.
Stereoscopic Segmentation
Authors: Anthony J. Yezzi, Stefano Soatto
Venue: ICCV
Year: 2001

We cast the problem of multiframe stereo reconstruction of a smooth shape as the global region segmentation of a collection of images of the scene. Dually, the problem of segmenting multiple calibrated images of an object becomes that of estimating the solid shape that gives rise to such images. We assume that the radiance has smooth statistics. This assumption covers Lambertian scenes with smooth or constant albedo as well as fine homogeneous textures, which are known challenges to stereo algorithms based on local correspondence. We pose the segmentation problem within a variational framework, and use fast level set methods to approximate the optimal solution numerically. Our algorithm does nor work in the presence of strong textures, where traditional reconstruction algorithms do. It enjoys significant robustness to noise under the assumptions it is designed for.
Feature Based Object Recognition using Statistical Occlusion Models with One-to-one Correspondence
Authors: Zhengrong Ying, David A. Castañón
Venue: ICCV
Year: 2001

In this paper we present a new Bayesian framework for partially occluded object recognition with one-to-one correspondence. We introduce two different statistical models for occlusion: One model assumes that each feature in the model can be occluded independent of whether any other features are occluded, whereas the second model uses spatially correlated occlusion to represent the extent of occlusion. Using these models, the object recognition problem reduces to finding the object hypothesis with largest generalized likelihood We develop fast algorithms for finding the optimal one-to-one correspondence between scene features and object model features to compute the generalized likelihood. We evaluate our algorithms using examples extracted from synthetic aperture radar imagery, and illustrate the performance advantages of our approach over alternative algorithms proposed by others.
The KGBR Viewpoint-Lighting Ambiguity and its Resolution by Generic Constraints
Authors: Alan L. Yuille, James M. Coughlan, Scott Konishi
Venue: ICCV
Year: 2001

We describe a novel viewpoint-lighting ambiguity which we call the KGBR. This ambiguity assumes orthographic projecting or an affine camera, and uses Lambertian reflectance functions including case/attached shadows and multiple light sources. A KGBR transform alters the geometry (by a three-dimensional affine transformation) and albedo properties of objects. If two objects are related by a KGBR transform then for any viewpoint and lighting of the first object there exists a corresponding viewpoint and lighting of the second object so that the images are identical up to an affine transformation. The Generalized Bas Relief (GBR) ambiguity is obtained as a special case of the KGBR. We describe generic viewpoint and lighting assumptions and show that either, or both, resolve this ambiguity by biasing towards objects with planar geometry.
Real-Time Automated Concurrent Visual Tracking of Many Animals and Subsequent Behavioral Compilation
Authors: John S. Zelek, David J. Bullock
Venue: ICCV
Year: 2001

One of our major research focus areas is real-time visual tracking and monitoring of moving and static objects in a video sequence. In particular we are interested in (1) object localization (also referred to as the focus of attention) which involves identifying the object of interest, (2) tracking the object using a model of the object which was initiated in step 1, and (3) understanding the accumulation of movements of the object over time (i.e., behavior). The objects of interiest for the purpose of this proposal are pigs. Automatically monitoring pigs via a non-invasively placed camera in their pens is interesting because the pigs are monitored in their natural habitat. Visual tracking involves modelling the object of interest and keeping track of its position and orientation through time. Issues include tracker recovery from error and preventing the tracker from jumping to other pigs. we have been able to demonstrate tracking pigs at about 10-15 Hz, however, the tracker tends to drift off the target eventually. We have only experimented with a single pig but our initial tests indicate that we can probably track at least 10 pigs simultaneously. Some unknowns include determining how quickly the pigs move and the type of motions, including quick jerky movements. Our preliminary investigations revealed that a blob tracker is insufficient for producing accurate traces.
Cloning Your Own Face with a Desktop Camera
Authors: Zhengyou Zhang, Zicheng Liu 0001, Dennis Adler, Michael F. Cohen, Erik Hanson, Ying Shan
Venue: ICCV
Year: 2001

We have developed an easy and cost-effective system that constructs textured 3D animated face models from videos with minimal user interaction. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning the head from one side to the other. After five manual clicks on two images to tell the system where the eye corners, nose top and mouth corners are, the system automatically generates a realistic looking 3D human head model and the constructed model can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate hisher face model in a few minutes. The face model can then be imported in hisher favorite game, and the user sees themselves and their friends take part in the game they are playing. We will demonstrate the system on a laptop computer live at the conference, and participants can try it to model their own faces.