OpenRaiser/Intern-Atlas huggingface dataset:
Understanding Object Motion of Tools and Vehicles
Authors: Zoran Duric, Ehud Rivlin, Azriel Rosenfeld
Venue: ICCV
Year: 1998

Many types of common objects, such as tools and vehicles, usually move in simple ways when they are wielded or driven. The natural axes of the object tend to remain aligned with the local trihedron defined by the object's trajectory. Based on this observation we use a model called Frenet-Serret motion which corresponds to the motion of a moving trihedron along a space curve. Knowing how the Frenet-Serret frame is changing relative to the observer gives us essential information for understanding the object's motion. This is illustrated here for four examples, involving tools (a wrench and a saw) and vehicles (an accelerating van, a turning taxi).
Learning to Identify and Track Faces in Image Sequences
Authors: Gareth J. Edwards, Christopher J. Taylor 0001, Timothy F. Cootes
Venue: ICCV
Year: 1998

We address the problem of robust face identification in the presence of pose, lighting, and expression variation. Previous approaches to the problem have assumed similar models of variation for each individual, estimated from pooled training data. We describe a method of updating a first order global estimate of identity by learning the class-specific correlation between the estimate and the residual variation during a sequence. This is integrated with an optimal tracking scheme, in which identity variation is decoupled from pose, lighting and expression variation. The method results in robust tracking and a more stable estimate of facial identity under changing conditions.
A Nonlinear Method for Estimating the Projective Geometry of Three Views
Authors: Olivier D. Faugeras, Théodore Papadopoulo
Venue: ICCV
Year: 1998

This article deals with the problem of recovering the three trifocal tensors between three views from a set of point correspondences. We give a new way of deriving the trifocal tensor based on Grassmann-Cayley algebra that sheds some new light on its structure and leads to a complete characterization of its geometric and algebraic properties which is fairly institute, i.e. geometric. We give a set of algebraic constraints satisfied by the 27 coefficients of the trifocal tensor which allow to parameterize it minimally with 18 coefficients. We then describe a robust method for estimating the trifocal tensor from point and line correspondences that uses this minimal parameterization. Experimental results show that this method as superior to the linear methods which had been previously published.
What Can Projections of Flow Fields Tell Us About Visual Motion
Authors: Sándor Fejes, Larry S. Davis
Venue: ICCV
Year: 1998

The dimensionality of visual motion analysis can be reduced by analyzing projections of flow vector fields. In contrast to motion vector fields, these projections exhibit simple geometric properties which are invariant to the scene structure and depend only on the camera motion. Using these properties, structure and motion can be either completely or partially decoupled. We estimate motion parameters from projections of flow fields by using robust techniques, implemented an a reclusive observer model. The model is applicable to general camera motion and to large field of view and requires no point correspondence. We demonstrate our projection method on the problem of detecting independently moving objects from a moving camera. Using the projection approach, the problem can be reduced to a one-dimensional optimization process which involves robust line-fitting and outlier detection. Instantaneous detection measurements are integrated temporally using tracking and spatially applying grouping of coherently moving points.
Sectored Snakes: Evaluating Learned-Energy Segmentations
Authors: Samuel D. Fenster, John R. Kender
Venue: ICCV
Year: 1998

We describe how to teach deformable models to maximize image segmentation correctness based on user-specified criteria, and we present a method for evaluating which criteria work best. We present sectored snakes, a formulation that demonstrably improves upon regular snakes. A traditional deformable model ("snake" in 2D) fails to find an object's boundary when the strongest nearby image edges are not the ones sought. But models can be trained to respond to other image features instead, by learning their probability distributions. The implementor must then decide on which of many image qualities to teach the model. To this end, we show how to evaluate the efficacy of any resulting deformable model, given a sampling of ground truth, a model of the range of shapes tried during optimization, and a measure of shape closeness. In the domain of abdominal CT images, we demonstrate such evaluation on a simple "sectoring" of a snake, in which intensity and perpendicular gradient are observed over equal-length segments. This specific set of qualities shows a measured improvement over an objective function that is uniform around the shape, and it follows naturally from examination of the latter's failures due to images variations around the organ boundary.
Which Shape from Motion?
Authors: Cornelia Fermüller, Yiannis Aloimonos
Venue: ICCV
Year: 1998

In a practical situation, the rigid transformation relating different views is recovered with errors. In such a case, the recovered depth of the scene contains errors, and consequently a distorted version of visual space is computed. What then are meaningful shape representations that can be computed from the images? The result presented in this paper states that if the rigid transformation between different views is estimated in a way that gives rise to a minimum number of negative depth values, then at the center of the image affine shape can be correctly computed. This result is obtained by exploiting properties of the distortion function. The distortion model turns out to be a very powerful tool in the analysis and design of 3D motion and shape estimation algorithms, and as a byproduct of our analysis we present a computational explanation of psychophysical results demonstrating human visual space distortion from motion information.
Building Qualitative Event Models Automatically from Visual Input
Authors: Jonathan H. Fernyhough, Anthony G. Cohn 0001, David C. Hogg
Venue: ICCV
Year: 1998

We describe an implemented technique for generating event models automatically based on qualitative reasoning and a statistical analysis of video input. Using an existing tracking program which generates labelled contours for objects in every frame, the view from a fixed camera is partitioned into semantically relevant regions based on the paths followed by moving objects. The paths are indexed with temporal information so objects moving along the same path at different speeds can be distinguished. Using a notion of proximity based on the speed of the moving objects and qualitative spatial reasoning techniques, event models describing the behaviour of pairs of objects can be built, again using statistical methods. The system has been tested on a traffic domain and learns various event models expressed in the qualitative calculus which represent human observable events. The system can then be used to recognise subsequent selected event occurrences or unusual behaviours.
Achieving a Fitts Law Relationship for Visual Guided Reaching
Authors: Nicola J. Ferrier
Venue: ICCV
Year: 1998

In order to take advantage of the top speed of manipulators, vision cannot be tightly integrated into the motion control loop. Past visual servo control systems have performed satisfactorily with this constraint, however it can be shown that the task execution time can be reduced if the vision system is de-coupled from the low level motor control system. For reaching, there is a trade-off between the accuracy of a motion and the time required to execute motion. In studies of human motor control this tradeoff is quantified by Fitts Law, a relationship between the motion time, the target distance, and the target width. These studies suggest that vision is not used tightly within the control loop, i.e. as a sensor that is servo-ed on, but rather vision is used to determine where the reaching target is and whether target has been reached successfully. Through a simple robotic example we demonstrate that a similar trade off exists between motion accuracy and the motion execution time for visual guided robot reaching motions.
3D Reconstruction with Projective Octrees and Epipolar Geometry
Authors: Blanca Garcia, Pere Brunet
Venue: ICCV
Year: 1998

In this paper, the problem of generating a 3D octree-like structure with the help of epipolar geometry within a projective framework is addressed. After a brief introduction on the basics of octrees and epipolar geometry, the new concept called "projective octree" is introduced together with an algorithm for building this projective structure. Finally, some results of the implementations are presented in the last section together with the conclusions and future work.
Parameterized Image Varieties: A Novel Approach to the Analysis and Synthesis of Image Sequences
Authors: Yakup Genc, Jean Ponce
Venue: ICCV
Year: 1998

This paper addresses the problem of characterizing the space formed by all images of a rigid set of n points observed by a weak perspective or paraperspective camera. By taking explicitly into account the Euclidean constraints associated with calibrated cameras, we show that this space is a six-dimensional variety embedded in R/sup 2n/, and parameterize it using the image positions of three reference points. This parameterization is constructed via linear least squares from point correspondences established across a sequence of images, and it is used to synthesize new pictures without any explicit three-dimensional model. Degenerate scene and camera configurations are analyzed, and experiments with real image sequences are presented.
Affine Invariant Medial Axis and Skew Symmetry
Authors: Peter J. Giblin, Guillermo Sapiro
Venue: ICCV
Year: 1998

Affine invariant medial axes and symmetry sets of planar shapes are introduced and studied in this paper. Two different approaches are presented. The first one is based on affine invariant distances, and defines the symmetry set, a set containing the medial axis; as the closure of the locus of points on (at least) two affine normals an affine-equidistant from the corresponding points on the curve. The second approach is based on affine bitangent conics. In this case the symmetry set is defined as the closure of the locus of centers of conics with (at least) three-point contact with two or more distinct points on the curve. This is equivalent to conic and curve having, at those points, the same affine tangent, or the same Euclidean tangent and curvature. Although the two analogous definitions for the classical Euclidean symmetry set (medial axis) are equivalent, this is not the case for the affine group. We then show how to use the symmetry set to detect affine skew symmetry, proving that the contact based symmetry set is a straight line if and only if the given shape is the affine transformation of a symmetric object.
Face Surveillance
Authors: Srinivas Gutta, Jeffrey Huang, Vishal Kakkad, Harry Wechsler
Venue: ICCV
Year: 1998

Most of the research on face recognition addresses the MATCH problem and it assumes a closed universe where there is no need for a REJECT ('false positive') option. The SURVEILLANCE problem is addressed indirectly, if at all, through the MATCH problem, where the size of the gallery rather than that of the probe set is very large. This paper addresses the proper surveillance problem where the size of the probe ('unknown image') set vs. gallery ('known image') set is 450 vs. 50 frontal images. We developed robust face ID verification ('classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the FERET face data base. The hybrid classifier architecture consists of an ensemble of connectionist networks-Radial Basis Functions (RBF) and inductive decision trees (DT). Experimental results prove the feasibility of our approach and yield 97% accuracy using the probe and gallery sets specified above.
Wormholes in Shape Space: Tracking Through Discontinuous Changes in Shape
Authors: Tony Heap, David C. Hogg
Venue: ICCV
Year: 1998

Existing object tracking algorithms generally use some form of local optimisation, assuming that an object's position and shape change smoothly over time. In some situations this assumption is not valid: the track able shape of an object may change discontinuously, for example if it is the 2D silhouette of a 3D object. In this paper we propose a novel method for modelling temporal shape discontinuities explicitly. Allowable shapes are represented as a union of (learned) bounded regions within a shape space. Discontinuous shape changes are described in terms of transitions between these regions. Transition probabilities are learned from training sequences and stored in a Markov model. In this way we can create 'wormholes' in shape space. Tracking with such models is via an adaptation, of the CONDENSATION algorithm.
Fast Stereovision with Subpixel-Precision
Authors: Rolf D. Henkel
Venue: ICCV
Year: 1998

A fast stereo algorithm based on aliasing effects of simple disparity estimators within a coherence detection scheme is presented. The algorithm calculates dense disparity maps with subpixel-precision by performing local spatial filter operations and simple arithmetic transformations. Performance similar to classical area-based approaches is achieved, but without the complicated hierarchical search structure typical for these approaches. The algorithm is completely parallel; the disparity valves are calculated independently for each pixel. In addition, local validation counts for the disparity estimates and a fused cyclopean view of the scene are available within the proposed network structure for coherence-based stereo.
Spatial Color Indexing and Applications
Authors: Jing Huang 0019, Ravi Kumar 0001, Mandar Mitra, Wei-Jing Zhu
Venue: ICCV
Year: 1998

We suggest the use of the color correlogram as a generic indexing tool to tackle various computer vision problems. Correlograms were shown to be very effective for content-based image retrieval. We adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. Experimental results suggest that the color correlogram is much more effective than the histogram for these applications, with insignificant additional computational, storage, or processing cost. We also provide a technique to cut down the storage requirement of correlograms so that it is the same as that of histograms, with only negligible performance penalty compared to the original correlogram.
Relational Histograms for Shape Indexing
Authors: Benoit Huet, Edwin R. Hancock
Venue: ICCV
Year: 1998

This paper is concerned with the retrieval of images from large databases based on their shape similarity to a query image. Our approach is based on two dimensional histograms that encode both the local and global geometric properties of the shapes. The pairwise attributes are the directed segment relative angle and directed relative position. The novelty of the proposed approach is to simultaneously use the relational and structural constraints, derived from an adjacency graph, to gate histogram contributions. We investigate the retrieval capabilities of the method for various queries. We also investigate the robustness of the method to segmentation errors. We conclude that a relational histogram of pairwise segment attributes presents a very efficient way of indexing into large databases. The optimal configuration is obtained when the local features are constructed from six neighbouring segments pairs. Moreover, a sensitivity analysis reveals that segmentation errors do not affect the retrieval performances.
Robust Multi-Sensor Image Alignment
Authors: Michal Irani, P. Anandan 0001
Venue: ICCV
Year: 1998

This paper presents a method for alignment of images acquired by sensors of different modalities (e.g., EO and IR). The paper has two main contributions: (i) It identifies an appropriate image representation, for multi-sensor alignment, i.e., a representation which emphasizes the common information between the two multi-sensor images, suppresses the non-common information, and is adequate for coarse-to-fine processing. (ii) It presents a new alignment technique which applies global estimation to any choice of a local similarity measure. In particular, it is shown that when this registration technique is applied to the chosen image representation with a local normalized-correlation similarity measure, it provides a new multi-sensor alignment algorithm which is robust to outliers, and applies to a wide variety of globally complex brightness transformations between the two images. Our proposed image representation does not rely on sparse image features (e.g., edge, contour, or point features). It is continuous and does not eliminate the detailed variations within local image regions. Our method naturally extends to coarse-to-fine processing, and applies even in situations when the multi-sensor signals are globally characterized by low statistical correlation.
Mixtures of Eigen Features for Real-Time Structure from Texture
Authors: Tony Jebara, Kenneth B. Russell, Alex Pentland
Venue: ICCV
Year: 1998

We describe a face modeling system which estimates complete facial structure and texture from a real-time video stream. The system begins with a face trading algorithm which detects and stabilizes live facial images into a canonical 3D pose. The resulting canonical texture is then processed by a statistical model to filter imperfections and estimate unknown components such as missing pixels and underlying 3D structure. This statistical model is a soft mixture of eigenfeature selectors which span the 3D deformations and texture changes across a training set of laser scanned faces. An iterative algorithm is introduced for determining the dimensional partitioning of the eigenfeatures to maximize their generalization capability over a cross-validation set of data. The model's abilities to filter and estimate absent facial components are then demonstrated over incomplete 3D data. This ultimately allows the model to span known and regress unknown facial information front stabilized natural video sequences generated by a face tracking algorithm. The resulting continuous and dynamic estimation of the model's parameters over a video sequence generates a compact temporal description of the 3D deformations and texture changes of the face.
Multidimensional Morphable Models
Authors: Michael J. Jones 0001, Tomaso A. Poggio
Venue: ICCV
Year: 1998

We describe a flexible model for representing images of objects of a certain class, known a priori, such as faces, and introduce a new algorithm for matching it to a novel image and thereby performing image analysis. We call this model a multidimensional morphable model or just a, morphable model. The morphable model is learned from example images (called prototypes) of objects of a class. In this paper we introduce an effective stochastic gradient descent algorithm that automaticaIly matches a model to a novel image by finding the parameters that minimize the error between the image generated by the model and the novel image. Two examples demonstrate the robustness and the broad range of applicability of the matching algorithm and the underlying morphable model. Our approach can provide novel solutions to several vision tasks, including the computation of image correspondence, object verification, image synthesis and image compression.
Motion Estimation in Image Sequences Using the Deformation of Apparent Contours
Authors: Fredrik Kahl, Kalle Åström
Venue: ICCV
Year: 1998

The problem of determining the camera motion from apparent contours or silhouettes of curved three-dimensional surfaces is considered. In a sequence of images is shown how to use the generalized epipolar constraint on apparent contours. One such constraint is obtained for each epipolar tangency point in each image pair. Thus in theory the motion can be calculated from the deformation of a single contour. A robust algorithm for computing the motion is presented based on the maximum likelihood estimate. It is shown how to generate initial estimates on the camera motion using only the tracked contours. It is also shown how to improve this estimate by maximizing the likelihood function. The algorithm has been tested on real image sequences. The result is compared to that of using only point features. The statistical evaluation shows that the technique gives accurate and stable results.
Using Conic Correspondence in Two Images to Estimate the Epipolar Geometry
Authors: Fredrik Kahl, Anders Heyden
Venue: ICCV
Year: 1998

In this paper it is shown hour corresponding conics in two images can be used to estimate the epipolar geometry in terms of the fundamental/essential matrix. The corresponding conics can, be images of either planar celtics or silhouettes of quadrics. It is shown that one conic correspondence gives two independent constraints on the fundamental matrix and a method to estimate the fundamental matrix from at least four corresponding conics is presented. Furthermore, a new type of fundamental matrix for describing conic correspondences is introduced. Finally, it is shown that the problem of estimating the fundamental matrix from 5 point correspondences and 1 conic correspondence in general has 10 different solutions. A method to calculate these solutions is also given together with an experimental validation.
Accurate, Real-Time, Unadorned Lip Tracking
Authors: Robert Kaucic, Andrew Blake 0001
Venue: ICCV
Year: 1998

Human speech is inherently multi-modal, consisting of both audio and visual components. Recently researchers have shown that the incorporation of information about the position of the lips into acoustic speech recognisers enables robust recognition of noisy speech. In the case of Hidden Markov Model-recognition, we show that this happens because the visual signal stabilises the alignment of states. It is also shown, that unadorned lips, both the inner and outer contours, can be robustly tracked in real time on general-purpose workstations. To accomplish this, efficient algorithms are employed which contain three key components: shape models, motion models, and focused colour feature detectors-all of which are learnt from examples.
Robotic Control with Partial Visual Information
Authors: Keisuke Kinoshita, Michael Lindenbaum
Venue: ICCV
Year: 1998

We consider a class of control tasks, which rely from partial visual information in a robotic setting. These tasks are hard in the sense that at every given moment, the available information is insufficient for the control task. Still, the amount of information collected throughout the control process is large and thus seems sufficient for carrying out the task. Such situations commonly arise when the object is frequently occluded from one of the cameras in a stereo pair or when only one moving camera is available. We propose a generic control rule for such tasks and characterize the conditions required for the success of the task. The analysis is based on the observation that mathematically, the behavior of such systems is related to a class of row-action (and POCS) optimization algorithms. In the second part of the paper we focus on one particular task from this class: position and orientation control with a single rotating camera. We show that this task may be carried out, in principle, for any camera movement, and suggest efficient control strategy and camera moving strategy. Interestingly, it seems that the advisable control law is not consistent with simple-minded intuition. We substantiate our claims by simulations and experiments on real data.