OpenRaiser/Intern-Atlas huggingface dataset:
Human Tracking in Multiple Cameras
Authors: Sohaib Khan, Omar Javed, Zeeshan Rasheed, Mubarak Shah
Venue: ICCV
Year: 2001

Multiple cameras are needed to cover large environments for monitoring activity. To track people successfully in multiple perspective imagery, one needs to establish correspondence between objects captured in multiple cameras. We present a system for tracking people in multiple uncalibrated cameras. The system is able to discover spatial relationships between the camera fields of view and use this information to correspond between different perspective views of the same person. We employ the novel approach of finding the limits of field of view (FOV) of a camera as visible in the other cameras. Using this information, when a person is seen in one camera, we are able to predict all the other cameras in which this person will be visible. Moreover, we apply the FOV constraint to disambiguate between possible candidates of correspondence. We present results on sequences of up to three cameras with multiple people. The proposed approach is very fast compared to camera calibration based approaches.
Improving AR using Shadows Arising from Natural Illumination Distribution in Video Sequences
Authors: Taeone Kim, Yongduek Seo, Ki-Sang Hong
Venue: ICCV
Year: 2001

In this paper, we propose a method for generating realistic shadows of virtual objects inserted into a real video sequence. Our aim is to improve and extend the work of Sato, Sato, and Ikeuchi, (1999), which is based on a static camera, to the case of a video sequence. This extension consists of several procedures: calibration of a moving video camera and a graphic camera, removing false shadows occurring due to a shortcoming of the static camera approach for the estimation of an illumination distribution, and so on. The calibration of the moving camera is solved by camera self-calibration and, with it, we designed a flexible graphic world coordinate system embedding technique called "match move". We also show that the shortcoming of the previous static camera approach is overcome by using information from video sequence. Finally we present the experimental results of a real video sequence.
Hierarchical Pre-Segmentation without Prior Knowledge
Authors: Arjan Kuijper, Luc Florack
Venue: ICCV
Year: 2001

A new method to pre-segment images by means of a hierarchical description is proposed. This description is obtained from an investigation of the deep structure of a scale space image-the input image and the Gaussian filtered ones simultaneously. We concentrate on scale space critical points-points with vanishing gradient with respect to both spatial and scale direction. We show that these points are always saddle points. They turn out to be extremely useful, since the iso-intensity manifolds through these points provide a scale space hierarchy tree and induce a segmentation without a priori knowledge. Moreover, together with the so-called catastrophe points, these scale space saddles form the critical points of the parameterised critical curves-the curves along which the spatial saddle points move in scale space. Experimental results with respect to the hierarchy and segmentation are given, based on an artificial image and a simulated MRI.
Dimensional Analysis of Image Motion
Authors: Michael S. Langer, Richard Mann
Venue: ICCV
Year: 2001

Studies of image motion typically address motion categories on a case-by-case basis. Examples include a moving point, a moving contour, or a 2D optical flow field. The typical assumption made in these studies is that there is a unique velocity at each moving point in the image. In this paper we relax this assumption. We introduce a broader set of motion categories in which the set of motions at a moving point can be 0-D, 1-D, or 2-D. We consider one new motion category in detail, which we call optical snow. This motion category occurs, for example, when an observer translates relative to a massively cluttered scene. Examples include the motion seen by an observer moving through bushes, or falling snow seen by a stationary observer. Optical snow is characterized by a 1-D set of velocities at each moving point and as such, it cannot be analyzed using a classical computational method such as optical flow. We introduce a technique for analyzing optical snow which is based on a bow tie signature of the motion in the frequency domain. We demonstrate the effectiveness of the technique using both synthetic and real image sequences.
Incorporating Differential Constraints in a 3D Reconstruction Process. Application to Stereo
Authors: Richard Lengagne, Pascal Fua
Venue: ICCV
Year: 2001

We propose to incorporate a priori geometric constraints in a 3-D stereo reconstruction scheme to cope with the many cases where image information alone is not sufficient to accurately recover 3-D shape. Our approach is based on the iterative deformation of a 3-D surface mesh to minimize an objective function. We show that combining anisotropic meshing with a nonquadratic approach to regularization enables us to obtain satisfactory reconstruction results using triangulations with few vertices. Structural or numerical constraints can then be added locally to the reconstruction process through a constrained optimization scheme. They improve the reconstruction results and enforce their consistency with a priori knowledge about object shape. The strong description and modeling properties of differential features make them useful tools that can be efficiently used as constraints for 3-D reconstruction.
Model-Based Initialisation of Vehicle Tracking: Dependency on Illumination
Authors: Holger Leuck, Hans-Hellmut Nagel
Venue: ICCV
Year: 2001

Although a model-based vehicle tracking approach offers the promise to be more reliable than a purely data-driven one, based on the additional knowledge brought to bear during the tracking phase, a suitable initialisation of the tracking phase still presents considerable problems. Part of these difficulties are related to the appropriate choice of assumptions concerning the prevailing illumination of the recorded scene. We present an approach to automatically detect elongated shadow-casting structures in the scene and exploit these structures to automatically distinguish between directed and diffuse illumination. Extended experiments with real-world traffic scenes illustrate the principal practicality of this approach, but simultaneously reveal unexpected difficulties.
The Earth Mover's Distance is the Mallows Distance: Some Insights from Statistics
Authors: Elizaveta Levina, Peter J. Bickel
Venue: ICCV
Year: 2001

The Earth Mover's distanc1e was first introduced as a purely empirical ways to measure texture and color similarities. We show that it has a rigorous probabilistic interpretation and is conceptually equivalent to the Mallows distance on probability distributions. The two distances are exactly the same when applied to probability distributions, but behave differently when applied to unnormalized distributions with different masses, called signatures. We discuss the advantages and disadvantages of both distances, and statistical issues involved in computing them from data. We also report some texture classification results for the Mallows distance applied to texture features and compare several ways of estimating feature distributions. In addition, we list some known probabilistic properties of this distance.
Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation
Authors: Stan Z. Li, QingDong Fu, Lie Gu, Bernhard Schölkopf, Yimin Cheng, HongJiang Zhang
Venue: ICCV
Year: 2001

Face images are subject to changes in view and illumination. Such changes cause data distribution to be highly nonlinear and complex in the image space. It is desirable to learn a nonlinear mapping from the image space to a low dimensional space such that the distribution becomes simpler tighter and therefore more predictable for better modeling effaces. In this paper we present a kernel machine based approach for learning such nonlinear mappings. The aim is to provide an effective view-based representation for multi-view face detection and pose estimation. Assuming that the view is partitioned into a number of distinct ranges, one nonlinear view-subspace is learned for each (range of) view from a set of example face images of that view (range), by using kernel principal component analysis (KPCA). Projections of the data onto the view-subspaces are then computed as view-based nonlinear features. Multi-view face detection and pose estimation are performed by classifying a face into one of the facial views or into the nonface class, by using a multi-class kernel support vector classifier (KSVC). Experimental results show that fusion of evidences from multi-views can produce better results than using the result from a single view; and that our approach yields high detection and low false alarm rates in face detection and good accuracy in pose estimation, in comparison with the linear counterpart composed of linear principal component analysis (PCA) feature extraction and Fisher linear discriminant based classification (FLDC).
Modelling Faces Dynamically across Views and Over Time
Authors: Yongmin Li 0001, Shaogang Gong, Heather M. Liddell
Venue: ICCV
Year: 2001

A comprehensive novel multi-view dynamic face model is presented in this paper to address two challenging problems in face recognition and facial analysis: modelling faces with large pose variation and modelling faces dynamically in video sequences. The model consists of a sparse 3D shape model learnt from 2D images, a shape-and-pose-free texture model, and an affine geometrical model. Model fitting is performed by optimising (1) a global fitting criterion on the overall face appearance while it changes across views and over time, (2) a local fitting criterion on a set of landmarks, and (3) a temporal fitting criterion between successive frames in a video sequence. By temporally estimating the model parameters over a sequence input, the identity and geometrical information of a face is extracted separately. The former is crucial to face recognition and facial analysis. The latter is used to aid tracking and aligning faces. We demonstrate the results of successfully applying this model on faces with large variation of pose and expression over time.
View-Based Clustering of Object Appearances Based on Independent Subspace Analysis
Authors: Stan Z. Li, XiaoGuang Lv, HongJiang Zhang
Venue: ICCV
Year: 2001

In 3D object detection and recognition, an object of interest is subject to changes in view as well as in illumination and shape. For image classification purpose, it is desirable to derive a representation in which intrinsic characteristics of the object are captured in a low dimensional space while effects due to artifacts are reduced. In this paper, we propose a method for view-based unsupervised learning of object appearances. First, view-subspaces are learned from a view-unlabeled data set of multi-view appearances, using independent subspace analysis (ISA). A learned view-subspace provides a representation of appearances at that view, regardless of illumination effect. A measure, called view-subspace activity, is calculated thereby to provide a metric for view-based classification. View-based clustering is then performed by using maximum view-subspace activity (MVSA) criterion. This work is to the best of our knowledge the first devoted research on view-based clustering of images.
Efficient Dense Depth Estimation from Dense Multiperspective Panoramas
Authors: Yin Li 0003, Chi-Keung Tang, Heung-Yeung Shum
Venue: ICCV
Year: 2001

In this paper we study how to compute a dense depth map with panoramic field of view (e.g., 360 degrees) from multi-perspective panoramas. A dense sequence of multiperspective panoramas is used for better accuracy and reduced ambiguity by taking advantage of significant data redundancy. To speed up the reconstruction, we derive an approximate epipolar plane image that is associated with the planar sweeping camera setup, and use one-dimensional window for efficient matching. To address the aperture problem introduced by one-dimensional window matching, we keep a set of possible depth candidates from matching scores. These candidates are then passed to a novel two-pass tensor voting scheme to select the optimal depth. By propagating the continuity and uniqueness constraints non-iteratively in the voting process, our method produces high-quality reconstruction results even when significant occlusion is present. Experiments on challenging synthetic and real scenes demonstrate the effectiveness and efficacy of our method.
Learning Low Dimensional Invariant Signature of 3-D Object under Varying View and Illumination from 2-D Appearances
Authors: Stan Z. Li, Jie Yan, Xinwen Hou, Zeyu Li, HongJiang Zhang
Venue: ICCV
Year: 2001

In this paper, we propose an invariant signature representation for appearances of 3-D object under varying view and illumination, and a method for learning the signature from multi-view appearance examples. The signature, a nonlinear feature, provides a good basis for 3-D object detection and pose estimation due to its following properties. (I) Its location in the signature feature space is a simple function of the view and is insensitive or invariant to illumination. (2) It changes continuously as the view changes, so that the object appearances at all possible views should constitute a known simple curve segment (manifold) in the feature space. (3) The coordinates of rite object appearances in the feature space are correlated in a known way according to a predefined function of the view. The first two properties provide a basis for object detection and the third for view (pose) estimation. To compute the signature representation from input, we present a nonlinear regression method for learning a nonlinear mapping from the input (e.g. image) space to the feature space. The ideas of the signature representation and the learning method are illustrated with experimental results for the object of human face. It is shown that the face object can be effectively, modeled compactly in a 10-D nonlinear feature space. The 10-D signature presents excellent insensitivity to changes in illumination for any view. The correlation of the signature coordinates is well determined by the predefined parametric function. Applications of the proposed method in face detection and pose estimation are demonstrated.
Uncalibrated Motion Capture Exploiting Articulated Structure Constraints
Authors: David Liebowitz, Stefan Carlsson
Venue: ICCV
Year: 2001

We present an algorithm for 3D reconstruction of dynamic articulated structures, such as humans, from uncalibrated multiple views. The reconstruction exploits constraints associated with a dynamic articulated structure, specifically the conservation over time of length between rotational joints. These constraints admit metric reconstruction from at least two different images in each of two uncalibrated parallel projection cameras. The algorithm is based on a stratified approach, starting with affine reconstruction from factorization, followed by rectification to metric structure using the articulated structure constraints. The exploitation of these specific constraints allows reconstruction and self-calibration with fewer feature paints and views compared to standard self-calibration. The method is extended to pairs of cameras that are zooming, Where calibration of the cameras allows compensation for the changing scale factor in a scaled orthographic camera. Results are presented in the form of stick figures and animated 3D reconstructions using pairs of sequences from broadcast television. The technique shows promise as a means of creating 3D animations of dynamic activities such as sports events.
True Single View Point Cone Mirror Omni-Directional Catadioptric System
Authors: Shih-Schön Lin, Ruzena Bajcsy
Venue: ICCV
Year: 2001

Pinhole camera model is a simplified subset of geometric optics. In special cases like the image formation of the cone (a degenerate conic section) mirror in an omnidirectional view catadioptric system, there are more complex optical phenomena involved that the simple pinhole model can not explain. We show that using the full geometric optics model a true single viewpoint cone mirror omni-directional system can be built. We show how such a system is built first, and then show in detail how each optical phenomenon works together to make the system true single viewpoint. The new system requires only simple off-the-shelf components and still outperforms other single viewpoint omni-systems for many applications.
Region Segmentation via Deformable Model-Guided Split and Merge
Authors: Lifeng Liu, Stan Sclaroff
Venue: ICCV
Year: 2001

An improved method for deformable shape-based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. Perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group's bounding contour. A globally consistent interpretation is determined in part by the minimum description length principle. Experiments show that model-guided split and merge yields a significant improvement in segmention over a method that uses merging alone.
A Gabor Feature Classifier for Face Recognition
Authors: Chengjun Liu, Harry Wechsler
Venue: ICCV
Year: 2001

This paper describes a novel Gabor feature classifier (GFC) method for face recognition. The GFC method employs an enhanced Fisher discrimination model on an augmented Gabor feature vector, which is derived from the Gabor wavelet transformation of face images. The Gabor wavelets, whose kernels are similar to the 2D receptive field profiles of the mammalian cortical simple cells, exhibit desirable characteristics of spatial locality and orientation selectivity. As a result, the Gabor transformed face images produce salient local and discriminating features that are suitable for face recognition. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images, which involve different illumination and varied facial expressions of 200 subjects. The effectiveness of the novel GFC method is shown in terms of both absolute performance indices and comparative performance against some popular face recognition schemes such as the eigenfaces method and some other Gabor wavelet based classification methods. In particular, the novel GFC method achieves 100% recognition accuracy using only 62 features.
Learning Inhomogeneous Gibbs Model of Faces by Minimax Entropy
Authors: Ce Liu 0001, Song Chun Zhu, Heung-Yeung Shum
Venue: ICCV
Year: 2001

In this paper we propose a novel inhomogeneous Gibbs model by the minimax entropy principle, and apply it to face modeling. The maximum entropy principle generalizes the statistical properties of the observed samples and results in the Gibbs distribution, while the minimum entropy principle makes the learnt distribution close to the observed one. To capture the fine details of a face, an inhomogeneous Gibbs model is derived to learn the local statistics of facial feature paints. To alleviate the high dimensionality problem of face models, we propose to learn the distribution in a subspace reduced by principal component analysis or PCA. We demonstrate that our model effectively captures important and subtle non-Gaussian face patterns and efficiently generates good face models.
New Perspectives on Geometric Reflection Theory From Rough Surfaces
Authors: Andrew J. Lundberg, Lawrence B. Wolff, Diego A. Socolinsky
Venue: ICCV
Year: 2001

Exact wave theories of specular reflectance from rough surfaces are computationally intractable thus motivating the practical need for geometric reflectance models which treat only the geometric ray nature of light reflection. The cornerstone of geometric reflectance modeling from rough surfaces in computer vision and computer graphics over the past two decades has been the Torrance-Sparrow model. This model has worked well as an intuitive description of rough surfaces as a collection of planar Fresnel reflectors called microfacets together with the concept of geometric attenuation for light which is obscured during reflection under an assumed rough surface geometry. Experimental data and analysis show that the current conceptualization of how specularly reflected light rays geometrically interact with rough surfaces needs to be seriously revised. The Torrance-Sparrow model while in qualitative agreement with specular reflection from rough surfaces is seen to be quantitatively inaccurate. Furthermore there are conceptual inconsistencies upon which derivation of this reflectance model is based. We show how significant quantitative improvement can be achieved for a geometric reflectance model by making some fundamental revisions to notions of microfacet probability distributions and geometric attenuation. Work is currently undergoing, to relate physical surface reconstructions from Atomic Force Microscope data to reflectance data from these same surfaces.
Beyond Lambert: Reconstructing Surfaces with Arbitrary BRDFs
Authors: Sebastian Magda, David J. Kriegman, Todd E. Zickler, Peter N. Belhumeur
Venue: ICCV
Year: 2001

We address an open and hitherto neglected problem in computer vision, how to reconstruct the geometry of objects with arbitrary and possibly anisotropic bidirectional reflectance distribution functions (BRDFs). Present reconstruction techniques, whether stereo vision, structure from motion, laser range finding, etc. make explicit or implicit assumptions about the BRDF. Here, we introduce two methods that were developed by re-examining the underlying image formation process; the methods make no assumptions about the object's shape, the presence or absence of shadowing, or the nature of the BRDF which may vary over the surface. The first method takes advantage of Helmholtz reciprocity, while the second method exploits the fact that the radiance along a ray of light is constant. In particular, the first method uses stereo pairs of images in which point light sources are co-located at the centers of projection of the stereo cameras. The second method is based on double covering a scene's incident light field; the depths of surface points are estimated using a large collection of images in which the viewpoint remains fixed and a point light source illuminates the object. Results from our implementations lend empirical support to both techniques.
Visual Servoing Invariant to Changes in Camera Intrinsic Parameters
Authors: Ezio Malis
Venue: ICCV
Year: 2001

This paper presents a new visual servoing scheme which is invariant to changes in camera-intrinsic parameters. Current visual servoing techniques are based on the learning of a reference image with the same camera used during the servoing. With the new method, it is possible to position a camera (with eventually varying intrinsic parameters), with respect to a nonplanar object, given a "reference image" taken with a completely different camera. The necessary and sufficient conditions for the local asymptotic stability show that the control law is robust in the presence of large calibration errors. Local stability implies that the system can accurately track a path in the invariant space. The path can be chosen such that the camera follows a straight line in the Cartesian space. Simple sufficient conditions are given in order to keep the tracking error bounded. This promising approach has been successfully tested with an eye-in-hand robotic system.