OpenRaiser/Intern-Atlas huggingface dataset:
Affine Calibration from Moving Objects
Authors: Russell A. Manning, Charles R. Dyer
Venue: ICCV
Year: 2001

This paper introduces a novel linear algorithm for determining the affine calibration between two camera views of a dynamic scene. The affine calibration is computed directly from the fundamental matrices associated with various moving objects in the scene, as well as from the fundamental matrix for the static background if the cameras are at different locations. A minimum of two fundamental matrices are required, but any number of additional fundamental matrices can be incorporated into the linear system to improve the stability of the computation. The technique is demonstrated on both real and synthetic data.
A Versatile Method for Trifocal Tensor Estimation
Authors: Bogdan Matei, Bogdan Georgescu, Peter Meer
Venue: ICCV
Year: 2001

Reliable estimation of the trifocal tensor is crucial for 3D reconstruction from uncalibrated cameras. The estimation process is based on minimizing the geometric distances between the measurements and the corrected data points, the underlying nonlinear optimization problem being most often solved with the Levenberg-Marquardt (LM) algorithm. We employ for this task the heteroscedastic errors-in-variables (HEIV) estimator and take into account both the singularity of the multivariate tensor constraint and the bifurcation which can appear for noisy data. In comparison to the Gold Standard method, the new approach is significantly faster while having the same performance, and it is less sensitive to initialization when the data is close to degenerate. Analytical expressions for the covariances of the parameter and corrected image point estimates are available for the HEIV estimator and thus the confidence regions of the corrected measurements can be delineated in the images.
FaceTracker: A Human Face Tracking and Facial Organ Localizing System
Authors: Jun Miao, Hongming Zhang 0011, Wen Gao 0001, Gang Deng, Hong Liu 0007, Xilin Chen 0001
Venue: ICCV
Year: 2001

This paper presents a tracking system based on the technique of gravity-center template to track faces and localize facial organs such as pupil, eyes, nose and mouth.
Estimation and Interpretation of Discontinuities in Optical Flow Fields
Authors: Markus Middendorf, Hans-Hellmut Nagel
Venue: ICCV
Year: 2001

A systematic categorization of an adaptively determined local estimate for an OF-vector allows to detect non-local 'walls of discontinuities' which establish an essentially closed boundary around images of objects whose motion differs from that of foreground and background. The estimation process has been refined to the point where occasionally observable-initially counter-intuitive-discontinuity blobs inside a region corresponding to the image of a moving object found an acceptable explanation. Segmentation results obtained on this basis for different real-world image sequences will be used to illustrate the approach.
Color Eigenflows: Statistical Modeling of Joint Color Changes
Authors: Erik G. Miller, Kinh Tieu
Venue: ICCV
Year: 2001

We develop a linear model of commonly observed joint color changes in images due to variation in lighting and certain non-geometric camera parameters. This is done by observing how all of the colors are mapped between two images of the same scene under various "real-world" lighting changes. We represent each instance of such a joint color mapping as a 3-D vector field in RGB color space. We show that the variance in these maps is well represented by a low-dimensional linear subspace of these vector fields. We dub the principal components of this space the color eigenflows. When applied to a new image, the maps define an image subspace (different for each new image) of plausible variations of the image as seen under a wide variety of naturally observed lighting conditions. We examine the ability of the eigenflows and a base image to reconstruct a second image taken under different lighting conditions, showing our technique to be superior to other methods. Setting a threshold on this reconstruction error gives a simple system for scene recognition.
Photometric Image-Based Rendering for Image Generation in Arbitrary Illumination
Authors: Yasuhiro Mukaigawa, Hajime Miyaki, Sadahiko Mihashi, Takeshi Shakunaga
Venue: ICCV
Year: 2001

A Photometric Image-Based Rendering (PIER) concept is proposed that facilitates the generation of an image with an arbitrary illumination. Based on this concept, we aim to generate both diffuse and specular reflections. It is not necessary to explicitly recover 3D shape and reflection properties of the scene. In order to control appearance changes caused by modifications in the lighting conditions, we utilize a set of real images taken, in a variety of lighting conditions. Since the diffuse and specular reflection components have different characteristics, we separate these components and apply different methods to each. A photometric linearization is introduced to control diffuse reflections as well as for separating the other components. This also facilitates the treatment of attached shadows as a part of the diffuse reflection. A morphing technique is utilized to generate specular reflections. This is an effective technique for dealing with glossy objects, even when the light shape is clearly observed in the specular reflection. Experimental results show that realistic images can be successfully generated using this technique.
Performance Evaluation of Stereo for Tele-presence
Authors: Jane Mulligan, Volkan Isler, Konstantinos Daniilidis
Venue: ICCV
Year: 2001

In an immersive tele-presence environment a 3D remote real scene is projected from the viewpoint of the local user. This 3D world is acquired through stereo reconstruction at the remote site. In this paper we start a performance analysis of stereo algorithms with respect to the task of immersive visualization. As opposed to usual monocular image based rendering, we are also interested in the depth error in novel views because our rendering is stereoscopic. We describe an evaluation test-bed which provides a world-wide first available set of registered dense "ground-truth" laser data and image data from multiple views. We establish metrics for novel depth views that reflect discrepancies both in the image and in 3D-space. It is well known that stereo performance is affected by both erroneous matching as well as incorrect depth triangulation. We experimentally study the effects of occlusion and low texture on the distributions of the error metrics. Then, we algebraically predict the behavior of depth and novel projection error as a function of the camera set-up and the error in the disparity. These are first steps towards building a laboratory for psychophysical judgement of depth estimates which is the ultimate performance test of tele-presence stereo.
Towards Real-Time Multi-Modality 3-D Medical Image Registration
Authors: Thomas Netsch, Peter Rösch, Arianne van Muiswinkel, Jürgen Weese
Venue: ICCV
Year: 2001

Intensity value-based registration is a widely used technique for the spatial alignment of medical images. Generally, the registration transformation is determined by iteratively optimizing a similarity measure calculated from the grey values of both images. However, such algorithms may have high computational costs, especially in the case of multi-modality registration, which makes their integration into systems difficult. At present, registration based on mutual information (MI) still requires computation times of the order of several minutes. In this contribution we focus on a new similarity measure based on local correlation (LC) which is well-suited for numerical optimization. We show that LC can be formulated as a least-squares criterion which allows the use of dedicated methods. Thus, it is possible to register MR neuro perfusion time-series (128/sup 2//spl times/30 voxel, 40 images) on a moderate workstation in real-time: the registration of an image takes about 500 ms and is therefore several times faster than image acquisition time. For the registration of CT-MR images (512/sup 2//spl times/87 CT 256/sup 2//spl times/128 MR) a multiresolution framework is used. On top of the decomposition, which requires 47 s of computation time, the optimization with an algorithm based on Ml previously described in the literature takes 97 s. In contrast, the proposed approach only takes 13 s, corresponding to a speedup about a factor of 7. Furthermore, we demonstrate that the superior computational performance of LC is not gained at the expense of accuracy. In particular experiments with dual contrast MR images providing ground truth for the registration show a comparable sub-voxel accuracy of LC and MI similarity.
Occlusion Robust Adaptive Template Tracking
Authors: Hieu Tat Nguyen, Marcel Worring, Rein van den Boomgaard
Venue: ICCV
Year: 2001

We propose a new method for tracking rigid objects in image sequences using template matching. A Kalman filter is used to make the template adapt to changes in object orientation or illumination. This approach is novel since the Kalman filter has been used in tracking mainly for smoothing the object trajectory. The performance of the Kalman filter is further improved by employing a robust and adaptive filtering algorithm. Special attention is paid to occlusion handling.
Determining Reflectance Parameters and Illumination Distribution from a Sparse Set of Images for View-dependent Image Synthesis
Authors: Ko Nishino, Zhengyou Zhang, Katsushi Ikeuchi
Venue: ICCV
Year: 2001

A framework for photo-realistic view-dependent image synthesis of a shiny object from a sparse set of images and a geometric model is proposed. Each image is aligned with the 3D model and decomposed into two images with regards to the reflectance components based on the intensity variation of object surface points. The view-independent surface reflection (diffuse reflection) is stored as one texture map. The view-dependent reflection (specular reflection) images are used to recover the initial approximation of the illumination distribution, and then a two step numerical minimization algorithm utilizing a simplified Torrance-Sparrow reflection model is used to estimate the reflectance parameters and refine the illumination distribution. This provides a very compact representation of the data necessary to render synthetic images from arbitrary view points. We have conducted experiments with real objects to synthesize photorealistic view-dependent images within the proposed framework.
An EM Algorithm for Video SummarizationGenerative Model Approach
Authors: Xavier Orriols, Xavier Binefa
Venue: ICCV
Year: 2001

In this paper, we address the visual video summarization problem in a Bayesian framework in order to detect and describe the underlying temporal transformation symmetries in a video sequence. Given a set of time correlated frames, we attempt to extract a reduced number of image-like data structures which are semantically meaningful and that have the ability of representing the sequence evolution. To this end, we present a generative model which involves jointly the representation and the evolution of appearance. Applying Linear Dynamical System theory to this problem, we discuss how the temporal information is encoded yielding a manner of grouping the iconic representations of the video sequence in terms of invariance. The formulation of this problem is driven in terms of a probabilistic approach, which affords a measure of perceptual similarity taking both learned appearance and time evolution models into account.
Image Detection Under Varying Illumination and Pose
Authors: Margarita Osadchy, Daniel Keren
Venue: ICCV
Year: 2001

This paper focuses on the detection of objects with Lambertian surface under both varying illumination and pose. We offer to apply a novel detection method that proceeds by modeling the different illuminations from a small number of images in the training set; this automatically voids the illumination effects, allowing fast illumination invariant detection, without having to create a large training set. It is demonstrated that the method "fits in" nicely with previous work about the modeling of the set of object appearances under varying illumination. In the experiments, an object was correctly detected under image plane rotations in a 15-degrees range, and a wide variety of different illuminations.
Classifying and Solving Minimal Structure and Motion Problems with Missing Data
Authors: Magnus Oskarsson, Kalle Åström, Niels Chr. Overgaard
Venue: ICCV
Year: 2001

In this paper we investigate the structure and motion problem for calibrated one-dimensional projections of a two-dimensional environment. In a previous paper the structure and motion problem for all cases with non-missing data was classified and solved. Our aim is here to classify all structure and motion problems, even those with missing data, and to solve them. Although our focus here is on one-dimensional retina, the classification part works equally well for ordinary cameras, and we give some results for those as well.
Gradient Vector Flow Fast Geodesic Active Contours
Authors: Nikos Paragios, Olivier Mellina-Gottardo, Visvanathan Ramesh
Venue: ICCV
Year: 2001

This paper proposes a new front propagation flow for boundary extraction. The proposed framework is inspired by the geodesic active contour model and leads to a paradigm that is relatively free from the initial curve position. Towards this end, it makes use of a recently introduced external boundary force, the gradient vector field that refers to a spatial diffusion of the boundary information. According to the proposed flow, the traditional boundary attraction term is replaced with a new force that guides the propagation to the object boundaries from both sides. This new geometric flow is implemented using a level set approach, thereby allowing dealing naturally with topological changes and important shape deformations. Moreover the level set motion equations are implemented using a recently introduced numerical approximation scheme, the Additive Operator Splitting Schema (AOS) which has a fast convergence rate and stable behavior. Encouraging experimental results are provided using real images.
JetStream: Probabilistic Contour Extraction with Particles
Authors: Patrick Pérez, Andrew Blake 0001, Michel Gangnet
Venue: ICCV
Year: 2001

The problem of extracting continuous structures from noisy or cluttered images is a difficult one. Successful extraction depends critically on the ability to balance prior constraints on continuity and smoothness against evidence garnered from image analysis. Exact, deterministic optimisation algorithms, based on discretized functionals, suffer from severe limitations on the form of prior constraint that can be imposed tractably. This paper proposes a sequential Monte-Carlo technique, termed JetStream, that enables constraints on curvature, corners, and contour parallelism. To be mobilized, all of which are infeasible under exact optimization. The power of JetStream is demonstrated in two contexts: (1) interactive cut-out in photo-editing applications, and (2) the recovery of roads in aerial photographs.
Do Ambiguous Reconstructions Always Give Ambiguous Images?
Authors: Marc Pollefeys, Luc Van Gool
Venue: ICCV
Year: 2001

In many cases self-calibration is not able to yield a unique solution for the 3D reconstruction of a scene. This is due to the occurrence of critical motion sequences. If this is the case, an ambiguity is left on the reconstruction. In this paper we derive under which conditions correct novel views can be generated from ambiguous reconstructions. The problem is first approached from a theoretical point of view: It is proven that novel views are correct as long as the inclusion of the next view the sequence yields the same ambiguity on the reconstruction. The problem is therefore much related to the problem of critical motion sequences since the virtual camera can be arbitrarily moved within the smallest critical motion set that the recovered camera motion without distortions becoming visible. Based on these results a practical measure for the expected ambiguity on a novel view generated from the recovered structure and motion is derived. As an application a viewer was built that indicates if a specific novel view can be trusted or not by altering the background color.
Structure From Motion Using Sequential Monte Carlo Methods
Authors: Gang Qian, Rama Chellappa
Venue: ICCV
Year: 2001

In this paper the structure from motion (SfM) problem is addressed using sequential Monte Carlo methods. A new SfM algorithm based on random sampling is derived to estimate the posterior distributions of camera camera motion and scene structure for the perspective projection camera model. Experimental results show that challenging issues in solving the structure from motion problem including errors in feature tracking, feature occlusion, motion/structure ambiguity, processing mixed-domain sequences and handling mismatched features can be well modeled and effectively addressed using the proposed method.
Reducing Drift in Parametric Motion Tracking
Authors: Ali Rahimi, Louis-Philippe Morency, Trevor Darrell
Venue: ICCV
Year: 2001

We develop a class of differential motion trackers that automatically stabilize when in finite domains. Most differential trackers compute motion only relative to one previous frame, accumulating errors indefinitely. We estimate pose changes between a set of past frames, and develop a probabilistic framework for integrating those estimates. We use an approximation to the posterior distribution of pose changes as an uncertainty model for parametric motion in order to help arbitrate the use of multiple base frames. We demonstrate this framework on a simple 2D translational tracker and a 3D, 6-degree of freedom tracker.
Simultaneous Estimation of Super-Resolved Intensity and Depth Maps from Low Resolution Defocused Observations of a Scene
Authors: Deepu Rajan, Subhasis Chaudhuri
Venue: ICCV
Year: 2001

This paper presents a novel technique to simultaneously estimate the depth map and the focused image of a scene, both at a super-resolution, from its defocused observations. Given a sequence of low resolution, blurred and noisy observations of a static scene, the problem is to generate a dense depth map at a resolution higher than one that can be generated from the observations as well as to estimate the true focused, super-resolved image. Both the depth and the intensity maps are modeled as separate Markov random fields (MRF) and a maximum a posteriori estimation method is used to recover the high resolution fields. Since there is no relative motion between the scene and the camera, as is the case with most of the super-resolution and structure recovery techniques, we do away with the correspondence problem.
A Maximum Likelihood Framework for Iterative Eigendecomposition
Authors: Antonio Robles-Kelly, Edwin R. Hancock
Venue: ICCV
Year: 2001

This paper presents an iterative maximum likelihood framework for perceptual grouping. We pose the problem of perceptual grouping as one of pairwise relational clustering. The method is quite generic and can be applied to a number of problems including region segmentation and line-linking. The task is to assign image tokens to clusters in which there is strong relational affinity between token pairs. The parameters of our model are the cluster memberships and the link weights between pairs of tokens. Commencing from a simple probability distribution for these parameters, we show how they may be estimated using an EM-like algorithm. The cluster memberships are estimated using an eigendecomposition method. Once the cluster memberships are to hand, then the updated link-weights are the expected values of their pairwise products. The new method is demonstrated on region segmentation and line-segment grouping problems where it is shown to outperform a noniterative eigenclustering method.
Computationally Efficient Face Detection
Authors: Sami Romdhani, Philip H. S. Torr, Bernhard Schölkopf, Andrew Blake 0001
Venue: ICCV
Year: 2001

This paper describes an algorithm for finding faces within an image. The basis of the algorithm is to run an observation window at all possible positions, scales and orientation within the image. A non-linear support vector machine is used to determine whether or not a face is contained within the observation window. The non-linear support vector machine operates by comparing the input patch to a set of support vectors (which can be thought of as face and anti-face templates). Each support vector is scored by some nonlinear function against the observation window and if the resulting sum is over some threshold a face is indicated. Because of the huge search space that is considered, it is imperative to investigate ways to speed up the support vector machine. Within this paper we suggest a method of speeding up the non-linear support vector machine. A set of reduced set vectors (RVs) are calculated from the support vectors. By considering the RV's sequentially, and if at any point a face is deemed too unlikely to cease the sequential evaluation, obviating the need to evaluate the remaining RVs. The idea being that we only need to apply a subset of the RVs to eliminate things that are obviously not a face (thus reducing the computation). The key then is to explore the RVs in the right order and a method for this is proposed.
Linear Multi View Reconstruction and Camera Recovery
Authors: Carsten Rother, Stefan Carlsson
Venue: ICCV
Year: 2001

This paper presents a linear algorithm for the simultaneous computation of 3D points and camera positions from multiple perspective views, based on having four points on a reference plane visible in all views. The reconstruction and camera recovery is achieved, in a single step, by finding the null-space of a matrix using singular value decomposition. Unlike factorization algorithms, the presented algorithm does not require all points to be visible in all views. By simultaneously reconstructing points and views the numerically stabilizing effect of having wide spread cameras with large mutual baselines is exploited. Experimental results are presented for both finite and infinite reference planes. An especially interesting application of this method is the reconstruction of architectural scenes with the reference plane taken as the plane at infinity which is visible via three orthogonal vanishing points. This is demonstrated by reconstructing the outside and inside (courtyard) of a building on the basis of 35 views in one single SVD.
Recognizing Large 3-D Objects through Next View Planning using an Uncalibrated Camera
Authors: Sumantra Dutta Roy, Santanu Chaudhury, Subhashis Banerjee
Venue: ICCV
Year: 2001

We present a new on-line scheme for the recognition and pose estimation of a large isolated 3-D object, which may not entirely fit in a camera's field of view. We do not assume any knowledge of the internal parameters of the camera, or their constancy. We use a probabilistic reasoning framework for recognition and next view planning. We show results of successful recognition and pose estimation even in cases of a high degree of interpretation ambiguity associated with the initial view.
Shadow Carving
Authors: Silvio Savarese, Holly E. Rushmeier, Fausto Bernardini, Pietro Perona
Venue: ICCV
Year: 2001

The shape of an object may be estimated by observing the shadows on its surface. We present a method that is robust with respect to a conservative classification of shadow regions. Assuming that a conservative estimate of the object shape is available, we analyze images of the object illuminated with known point light sources taken from known camera locations. We adjust our surface estimate using the shadow regions to produce a refinement that is still a conservative estimate. A proof of correctness is provided. No assumptions about the object topology are made, although any tangent plane discontinuities over the object's surface are supposed to be detectable. An implementation and some experimental results are presented.