OpenRaiser/Intern-Atlas huggingface dataset:
Mean Shift Analysis and Applications
Authors: Dorin Comaniciu, Peter Meer
Venue: ICCV
Year: 1999

A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The proposed filtering method associates with each pixel in the image the closest local mode in the density distribution of the joint domain. Segmentation into a piecewise constant structure requires only one more step, fusion of the regions associated with nearby modes. The proposed technique has two parameters controlling the resolution in the spatial and range domains. Since convergence is guaranteed, the technique does not require the intervention of the user to stop the filtering at the desired image quality. Several examples, for gray and color images, show the versatility of the method and compare favorably with results described in the literature for the same images.
Resolution Invariant Surfaces for Panoramic Vision Systems
Authors: Tanya L. Conroy, John B. Moore
Venue: ICCV
Year: 1999

A family of axially symmetric mirror shapes are proposed for panoramic imaging. These shapes keep the resolution in the image invariant to changes in elevation in the scene. In other words, this family of shapes achieves solid angle pixel density invariance. An analysis of range finding using two coaxial axially symmetric resolution invariant mirrors in a coaxial pair is presented. The resolution invariance property of these mirrors mean that when the image captured is unwarped the resultant image will not suffer from variable image quality. This problem is present in unwarped images captured with any mirror shape not designed for resolution invariance. The resolution invariance of these mirrors is especially important in the case of stereo panoramic mirrors where one view of the scene is captured within the other and thus will have lower resolution. It is necessary to clearly identify two was of an object to undertake range-finding. The proposed mirror shapes will be useful for mobile robotics and machine vision. Other applications areas such as visual sensing for control of traffic lights at street intersections could be considered.
Single View Metrology
Authors: Antonio Criminisi, Ian Reid 0001, Andrew Zisserman
Venue: ICCV
Year: 1999

We describe how 3D affine measurements may be computed from a single perspective view of a scene given only minimal geometric information determined from the image. This minimal information is typically the vanishing line of a reference plane and a vanishing point for a direction not parallel to the plane. It is shown that affine scene structure may then be determined from the image, without knowledge of the camera's internal calibration (e.g. focal length), nor of the explicit relation between camera and world (pose). In particular we show how to: compute the distance between planes parallel to the reference plane (up to a common scale factor); compute area and length ratios on any plane parallel to the reference plane; determine the camera's (viewer's) location. Simple geometric derivations are given for these results. We also develop an algebraic representation which unifies the three types of measurement and, amongst other advantages, permits a first order error propagation analysis to be performed, associating an uncertainty with each measurement. We demonstrate the technique for a variety of applications, including height measurements in forensic images and 3D graphical modelling from single images.
Parallax Geometry of Smooth Surfaces in Multiple Views
Authors: Geoffrey Cross, Andrew W. Fitzgibbon, Andrew Zisserman
Venue: ICCV
Year: 1999

This paper investigates the multiple view geometry of smooth surfaces and a plane, where the plane provides a planar homography mapping between the views. Innovations are made in three areas: first, new solutions are given for the computation of epipolar and trifocal geometry for this type of scene. In particular it is shown that the epipole may be determined from bitangents between the homography registered occluding contours, and a new minimal solution is given for computing the trifocal tensor: Second, algorithms are demonstrated for automatically estimating the fundamental matrix and trifocal tensor from images of such scenes. Third, a method is developed for estimating camera matrices for a sequence of images of these scenes. These three areas are combined in a "freehand scanner" application where 3D texture-mapped graphical models of smooth objects are acquired directly from a video sequence of the object and plane.
Direct Identification of Moving Objects and Background from 2D Motion Models
Authors: Gabriela Csurka, Patrick Bouthemy
Venue: ICCV
Year: 1999

This paper presents the dynamic scene analysis part of an original and consistent framework to video partitioning into shots, camera motion estimation and multiple motion analysis with a view to content-based video indexing. All the information parts required to achieve these different goals result from handling the apparent motion within consecutive image pairs. Within each extracted shot, a binary segmentation of the image is performed into regions whose motion either conforms or not to the 2D estimated dominant motion represented by a quadratic motion model. This paper focuses on a low-cost method based on projective geometry criteria to distinguish non-conforming regions generated by really moving objects from static ones in the scene. The proposed algorithm is validated on a variety of real image sequences.
Correlation Model for 3D Texture
Authors: Kristin J. Dana, Shree K. Nayar
Venue: ICCV
Year: 1999

While an exact definition of texture is somewhat elusive, texture can be qualitatively described as a distribution of color, albedo or local normal on a surface. In the literature, the word texture is often used to describe a color or albedo variation on a smooth surface. We refer to such texture as 2D texture. In real world scenes, texture is often due to surface height variations and can be termed 3D texture. Because of local foreshortening and masking, oblique views of 3D texture are not simple transformations of the frontal view. Consequently, texture representations such as the correlation function or power spectrum are also affected by local foreshortening and masking. This work presents a correlation model for a particular class of 3D textures. The model characterizes the spatial relationship among neighboring pixels in an image of 3D texture and the change of this spatial relationship with viewing direction.
3D Articulated Models and Multi-View Tracking with Silhouettes
Authors: Quentin Delamarre, Olivier D. Faugeras
Venue: ICCV
Year: 1999

We propose a method to estimate the motion of a person filmed by two or more fixed cameras. The novelty of our technique is its ability to cope with fast movements, self-occlusions and noisy images. Our algorithms are based on the latest works on calibration and image segmentation developed in our lab. We compare the projections of a 3D model of a person on the images to the detected silhouettes of the person, and by creating forces that will move the 3D model towards the final estimation of the real pose. We developed a fast algorithm that computes the motion of the articulated 3D model. We show that our results are good, even if the cameras are not synchronized.
Tracking through Singularities and Discontinuities by Random Sampling
Authors: Jonathan Deutscher, Ben North, Benedicte Bascle, Andrew Blake 0001
Venue: ICCV
Year: 1999

Some issues in markerless tracking of human body motion are addressed. Extended Kalman filters have commonly been applied to kinematic variables, to combine predictions consistent with plausible motion, with the incoming stream of visual measurements. Kalman filtering is applicable only when the underlying distribution is approximately Gaussian. Often this assumption proves remarkably robust. There are two pervasive circumstances under which the Gaussianity assumption can break down. The first is kinematic singularity and the second is at joint endstops. Failure of Kalman filtering under these circumstances is illustrated. The non-Gaussian nature of the distributions is demonstrated experimentally by means of Monte Carlo simulation. Random simulation (particle filtering or Condensation) proves to provide a robust alternative algorithm for tracking that can also deal with these difficult conditions.
Learning-based Object Detection in Cardiac MR Images
Authors: Nicolae Duta, Anil K. Jain 0001, Marie-Pierre Dubuisson-Jolly
Venue: ICCV
Year: 1999

An automated method for left ventricle detection in MR cardiac images is presented. Ventricle detection is the first step in a fully automated segmentation system used to compute volumetric information about the heart. Our method is based on learning the gray level appearance of the ventricle by maximizing the discrimination between positive and negative examples in a training set. The main differences from previously reported methods are feature definition and solution to the optimization problem involved in the learning process. Our method was trained on a set of 1,350 MR cardiac images from which 101,250 positive examples and 123,096 negative examples were generated. The detection results on a test set of 887 different images demonstrate an excellent performance: 98% detection rate, a false alarm rate of 0.05% of the number of windows analyzed (10 false alarms per image) and a detection time of 2 seconds per 256/spl times/256 image on a Sun Ultra 10 for an 8-scale search. The false alarms ore eventually eliminated by a position/scale consistency check along all the images that represent the same anatomical slice.
Advances in Active Appearance Models
Authors: Gareth J. Edwards, Timothy F. Cootes, Christopher J. Taylor 0001
Venue: ICCV
Year: 1999

This paper presents advances in the construction and use of Active Appearance Models (AAMs) for image interpretation. AAMs are photo-realistic generative models of object appearance that can be used to rapidly locate deformable objects in images. We extend the AAM method to include coloured texture and present an enhanced search algorithm with the ability to locate partially occluded objects. Previously, AAMs have been limited by the need for good manual initialisation. In this paper, we describe a hierarchical search algorithm that overcomes this drawback. The extended AAM method provides a complete, unified scheme for model based image interpretation. We demonstrate the application of the scheme to the task of locating faces in images.
Texture Synthesis by Non-parametric Sampling
Authors: Alexei A. Efros, Thomas K. Leung
Venue: ICCV
Year: 1999

A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter. The method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.
Optimal Filters for Gradient-based Motion Estimation
Authors: Michael Elad, Patrick C. Teo, Yacov Hel-Or
Venue: ICCV
Year: 1999

Gradient based approaches for motion estimation (optical-flow) estimate the motion of an image sequence based on local changes in the image intensities. In order to best evaluate local changes in the intensities, specific filters are applied to the image sequence. These filters are typically composed of spatio-temporal derivatives. The design of these filters plays an important role in the estimation accuracy. This paper proposes a method for the design of these filters in an optimal manner. Unlike previous approaches that design optimal derivative filters in some sense, the proposed technique defines the optimality directly with respect to the motion estimation goal. The suggested approach takes into account prior knowledge on the motion distribution, the image characteristics, and the allocated filter length. Simulations demonstrate the advantage of the new design approach.
Estimation of Structure and Motion Parameters for a Roaming Robot that Scans the Space
Authors: Minoru Etoh, Toshimichi Aoki, Koichi Hata
Venue: ICCV
Year: 1999

We present a formulation for estimation of structure and motion parameters for a roaming robot that has translation on the floor and rotation along the vertical axis. To estimate the parameters, we use at least five feature correspondences among three viewpoints, where we ignore those vertical positioning. This is because the vertical changes in pointwise matching between two views is generally subtle in roaming robot environment, then it is practically hard for a traditional structure-from-motion estimation method (i.e., a pointwise matching method). Focusing on the horizontal changes, we use "directional feature" in panoramic images; we formulate the problem as the estimation of depth of the directional features and location of the viewpoints in the reference coordinate. Owing to the proposed method, more than 1000 panoramic images have been registered with calibrated location, and used for image-based rendering in computer graphics.
Colour by Correlation: A Simple, Unifying Approach to Colour Constancy
Authors: Graham D. Finlayson, Steven D. Hordley, Paul M. Hubel
Venue: ICCV
Year: 1999

In this paper we consider the problem of colour constancy; how given an image of a scene under an unknown illuminant can we recover an estimate of that light? Rather than recovering a single estimate of the illuminant as many previous authors have done, in the first instance we recover a measure of the likelihood that each possible illuminant was the scene illuminant. We do this by correlating image colours with the colours that can occur under each of a set of possible lights. We then recover an estimate of the scene illuminant based on these likelihoods. Computation is expressed and performed in a generic correlation framework which we develop in this paper. We develop a new probabilistic instantiation of this framework which delivers very good colour constancy on synthetic and real images. We show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it; e.g. the grey-world and gamut mapping algorithms. We explore too the relationship of these algorithms to other probabilistic and neural network approaches.
Bayesian Structure from Motion
Authors: David A. Forsyth, Sergey Ioffe, John A. Haddon
Venue: ICCV
Year: 1999

Formulates structure from motion as a Bayesian inference problem and uses a Markov-chain Monte Carlo sampler to sample the posterior on this problem. This results in a method that can identify both small and large tracker errors and yields reconstructions that are stable in the presence of these errors. Furthermore, the method gives detailed information on the range of ambiguities in structure given a particular data set and requires no special geometric formulation to cope with degenerate situations. Motion segmentation is obtained by a layer of discrete variables associating a point with an object. We demonstrate a sampler that successfully samples an approximation to the marginal on this domain, producing a relatively unambiguous segmentation.
A Subset Approach to Contour Tracking in Clutter
Authors: Daniel Freedman, Michael S. Brandstein
Venue: ICCV
Year: 1999

A new method for tracking contours of moving objects in clutter is presented. For a given object, a model of its contours is learned from training data in the form of a subset of contour space. Greater complexity is added to the contour model by analyzing rigid and non-rigid transformations of contours separately. In the course of tracking, multiple contours may be observed due to the presence of extraneous edges in the form of clutter; the learned model guides the algorithm in picking out the correct one. The algorithm, which is posed as a solution to a minimization problem, is made efficient by the use of several iterative schemes. Results applying the proposed algorithm to the tracking of a flexing finger and to a conversing individual's lips are presented.
Learning Low-Level Vision
Authors: William T. Freeman, Egon C. Pasztor
Venue: ICCV
Year: 1999

We show a learning-based method for low-level vision problems-estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images. We model that world with a Markov network, learning the network parameters from the examples. Bayesian belief propagation allows us to efficiently find a local maximum of the posterior probability for the scene, given the image. We call this approach VISTA-Vision by Image/Scene TrAining. We apply VISTA to the "super-resolution" problem (estimating high frequency details from a low-resolution image), showing good results. For the motion estimation problem, we show figure/ground discrimination, solution of the aperture problem, and filling-in arising from application of the same probabilistic machinery.
Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image Components
Authors: Brendan J. Frey, Nebojsa Jojic
Venue: ICCV
Year: 1999

A simple, effective way to model images is to represent each input pattern by a linear combination of "component" vectors, where the amplitudes of the vectors are modulated to match the input. This approach includes principal component analysis, independent component analysis and factor analysis. In practice, images are subjected to randomly selected transformations of a known nature, such as translation and rotation. Direct use of the above methods will lead to severely blurred components that tend to ignore the more interesting and useful structure. In previous work, we introduced a clustering algorithm that is invariant to transformations. In this paper, we propose a method called transformed component analysis, which incorporates a discrete, hidden variable that accounts for transformations and uses the expectation maximization algorithm to jointly extract components and normalize for transformations. We illustrate the algorithm using a shading problem, facial expression modeling and written digit recognition.
Accurate Motion Flow Estimation with Discontinuities
Authors: Lionel Gaucher, Gérard G. Medioni
Venue: ICCV
Year: 1999

We address the problem of motion flow estimation for a scene with multiple moving objects, observed from a possibly moving camera. We take as input a (possibly sparse) noisy velocity field, as obtained from local matching, produce a set of motion boundaries, and identify pixels with different velocities in overlapping layers. For a fixed observer, these overlapping layers capture occlusion information. For a moving observer, further processing is required to segment independent objects and infer structure. Unlike previous approaches, which generate layers by iteratively fitting data to a set of predefined parameters, we instead find boundaries first, then infer regions and address occlusion overlap relationships. All computational steps use a common framework of tensors to represent velocity information, together with saliency (confidence), and uncertainty. Communication between sites is performed by convolution-like tensor voting. The scheme is non-iterative, and the only free parameter is the scale, related to neighborhood size. We illustrate the approach with results obtained from synthetic sequences and from real images. The quantitative results compare favorably with those of other methods, especially in the presence of occlusion.
Real-Time Object Detection for "Smart" Vehicles
Authors: Dariu Gavrila, Vasanth Philomin
Venue: ICCV
Year: 1999

This paper presents an efficient shape-based object detection method based on Distance Transforms and describes its use for real-time vision on-board vehicles. The method uses a template hierarchy to capture the variety of object shapes; efficient hierarchies can be generated offline for given shape distributions using stochastic optimization techniques (i.e. simulated annealing). Online, matching involves a simultaneous coarse-to-fine approach over the shape hierarchy and over the transformation parameters. Very large speed-up factors are typically obtained when comparing this approach with the equivalent brute-force formulation; we have measured gains of several orders of magnitudes. We present experimental results on the real-time detection of traffic signs and pedestrians from a moving vehicle. Because of the highly time sensitive nature of these vision tasks, we also discuss some hardware-specific implementations of the proposed method as far as SIMD parallelism is concerned.
On the Local Form and Transitions of Symmetry Sets, Medial Axes, and Shocks
Authors: Peter J. Giblin, Benjamin B. Kimia
Venue: ICCV
Year: 1999

In this report we explore the local geometry of the medial axes (MA) and shocks (SH), and their structural changes under deformations, by viewing these symmetries as subsets of the symmetry set (SS) and present two results. First, we establish that the local form of the medial axes must generically be one of three cases: endpoints (A/sub 3/)/sup 1/, interior points (A/sub 1//sup 2/), and junctions (A/sub 1//sup 3/). The local form of shocks is a subclassification of these points. Second, we address the (classical) instability of the MA, i.e., abrupt changes in the representation with a slight changes in shape, as when a new branch appears with slight protrusion. The identification of these "transitions" is clearly crucial in robust object recognition. We show that for the medial axis only two such instabilities are possible: (i) when four branches come together (A/sub 1//sup 4/), and (ii) when a new branch grows out of an existing one (A/sub 1/A/sub 3/). Similarly, the six cases of shock instabilities are sub-classifications of these. The identification of these skeletal instabilities allows us to make equivalent structurally distinct skeletons arising from highly similar shapes, thus, regularizing the recognition process.
Recognition of Temporal Structures: Learning Prior and Propagating Observation Augmented Densities via Hidden Markov States
Authors: Shaogang Gong, Michael Walter 0004, Alexandra Psarrou
Venue: ICCV
Year: 1999

An algorithm is described for modelling and recognising temporal structures of visual activities. The method is based on (1) learning prior probabilistic knowledge using hidden Markov models, (2) automatic temporal clustering of hidden Markov states based on expectation maximisation and (3) using observation augmented conditional density distributions to reduce the number of samples required for propagation and therefore improve recognition speed and robustness.
Curve Finder Combining Perceptual Grouping and a Kalman like Fitting
Authors: Frédéric Guichard, Jean-Philippe Tarel
Venue: ICCV
Year: 1999

We present an algorithm that extracts curves from a set of edgels within a specific class in a decreasing order of their "length". The algorithm inherits the perceptual grouping approaches. But, instead of using only local cues, a global constraint is imposed on each extracted subset of edgels, that the underlying curve belongs to a specific class. In order to reduce the complexity of the solution, we work with a linearly parameterized class of curves, a function of one image coordinate. This first allows one to use recursive Kalman based fitting and, second, to cast the problem as an optimal path search in a directed graph. Experiments on finding lane-markings on roads demonstrate that real-time processing is achievable.