OpenRaiser/Intern-Atlas huggingface dataset:
Passive Depth from Defocus Using a Spatial Domain Approach
Authors: Djemel Ziou
Venue: ICCV
Year: 1998

This paper presents an algorithm for a dense computation of the difference in blur between two images. The two images are acquired by varying the intrinsic parameters of the camera. The image formation system is assumed to be passive. Estimation of depth from the blur difference is straightforward. The algorithm is based on a local image decomposition technique using the Hermite polynomial basis. We show that any coefficient of the Hermite polynomial computed using the more blurred image is a function of the partial derivatives of the other image and the blur difference. Hence, the blur difference can be computed by resolving a system of equations. All computations required are local and carried out in the spatial domain. An algorithm is presented for estimation of the blur in 1D and 2D cases and its behavior is studied for constant images, step edges, line edges and junctions. The algorithm is tested using synthetic and real images. The results obtained are very encouraging.
Shape Similarity Retrieval under Affine Transform: Application to Multi-View Object Representation and Recognition
Authors: Sadegh Abbasi, Farzin Mokhtarian
Venue: ICCV
Year: 1999

The maxima of curvature scale space (CSS) image have already been used to represent 2-D shapes in different applications under similarity transforms. We also consider affine transforms and examine their effect on the representation. Since the CSS image employs the arc length parametrisation which is not affine invariant, we expect some deviation in the maxima of the CSS image under affine transforms. We first examine the arc length deviation and then show that the locations of the maxima of the CSS image do not change dramatically even under large affine transformations. Applying affine transforms to every object boundary of our database of 1100 images, we construct a large database of 5500 boundary contours. The CSS representation is then used to find similar shapes from this prototype database. The results provide substantial evidence of stability of the the CSS image and its contour maxima under affine transformation. Our system for shape-based retrieval from large image databases is then applied to multi-view 3-D object representation and recognition. Our collection of 3-D objects consists of 10 aircraft of different shapes. Each object is modelled using a small number of silhouette contours obtained from different view points (four contours in one experiment and nine contours in another). Three silhouette contours corresponding to random views are separately used as input for each object. Results indicate that robust and efficient 3-D free-form object recognition through multi-view representation can be achieved using the CSS representation.
Neurocalibration: A Neural Network That Can Tell Camera Calibration Parameters
Authors: Moumen T. Ahmed, Elsayed E. Hemayed, Aly A. Farag
Venue: ICCV
Year: 1999

Camera calibration is a primary crucial step in many computer vision tasks. We present a new neural approach for camera calibration. Unlike some existing neural approaches, our calibrating network can tell the perspective-projection-transformation matrix between the world 3D points and the corresponding 2D image pixels. Starting from random initial weights, the net can specify the camera model parameters satisfying the orthogonality constraints on the rotational transformation. The neurocalibration technique is shown to solve four different types of calibration problems that are found in computer vision applications. Moreover, it can be extended to the more difficult problem of calibrating cameras with automated active lenses. The validity and performance of our technique are tested with both synthetic data under different noise conditions and with real images. Experiments have shown the accuracy and the efficiency of our neurocalibration technique.
Recovery and Tracking of Continuous 3D Surfaces from Stereo Data using a Deformable Dual-Mesh
Authors: Yusuf Sinan Akgul, Chandra Kambhamettu
Venue: ICCV
Year: 1999

We propose a novel method for continuous 3D depth recovery and tracking using calibrated stereo. The method integrates stereo correspondence, surface reconstruction and tracking by using a new single deformable dual mesh optimization, resulting in simplicity, robustness and efficiency. In order to combine stereo correspondence and structure recovery, the method introduces an external energy function defined for a 3D volume based on cross-correlation between the stereo pairs. The internal energy functional of the deformable dual mesh imposes smoothness on the surfaces and it serves as a communication tool between the two meshes. Under the forces produced by the energy terms, the dual mesh deforms to recover and track the 3D surface. The newly introduced dual-mesh model, which is one of the main contributions of this paper, makes the system robust against local minima and yet it is efficient. A coarse-to-fine minimization approach makes the system even more efficient. Tracking is achieved by using the recovered surface as an initial position for the next time frame. Although the system can effectively utilize initial surface positions and disparity data, they are not needed for a successful operation, which makes this system applicable to a wide range of areas. We present the results of a number of experiments on stereo human face and cloud images, which proves that our new method is very effective.
Spectral Gradient: A Material Descriptor Invariant to Geometry and Incident Illumination
Authors: Elli Angelopoulou, Sang Wook Lee, Ruzena Bajcsy
Venue: ICCV
Year: 1999

The light reflected from a surface depends on the scene geometry, the incident illumination and the surface material. A novel methodology is presented which extracts reflectivity information of the various materials in the scene independent of incident light and scene geometry. A scene is captured under different narrow-band color filters and the spectral derivatives of the scene are computed. The resulting spectral derivatives form a spectral gradient at each pixel. This spectral gradient is a material descriptor which is invariant to scene geometry and incident illumination for smooth diffuse surfaces. Spectral gradients can discriminate among smooth dielectrics with different reflectance properties independent of viewing conditions.
Photometric Surface Analysis in a Tri-Luminal Environment
Authors: Elli Angelopoulou, James P. Williams 0002
Venue: ICCV
Year: 1999

Methods for the analysis of images of the same scene taken under three different lighting conditions are illustrated. A technique that separates the effects of geometry and surface coloration/texture in this "tri-luminal" environment is developed and experimental results are shown. Exploiting this technique to isolate geometric information, two methods which extract differential geometric properties of surfaces (the sign of Gaussian curvature and its magnitude to within a multiplicative factor) directly from tri-luminal photometric data are derived and demonstrated.
Structure and Motion from Lines under Affine Projections
Authors: Kalle Åström, Anders Heyden, Fredrik Kahl, Magnus Oskarsson
Venue: ICCV
Year: 1999

In this paper we investigate the geometry and algebra of multiple projections of lines with affine cameras. Previously, the case of seven lines in three images has been studied. It was thought that this was the minimal data necessary for recovering affine structure and motion and that there are in general two solutions. It was also thought that these two solutions persist with more than seven lines. In this paper it is shown that the minimal cases are six lines in three images and five lines in four images. These cases are solved and it is shown that there are in general four solutions in both problems. Two almost minimal cases (seven lines in three images and six lines in four images) are solved using linear methods. Furthermore, it is shown that the solution is in general unique in these almost minimal cases. Finally, experiments are conducted on both simulated and real data in order to show the applicability of the theory.
On the Evolution of the Skeleton
Authors: Jonas August, Allen R. Tannenbaum, Steven W. Zucker
Venue: ICCV
Year: 1999

It is commonly held that skeleton variation due to noise is unmanageable. It is also believed that smoothing, invoked to combat noise, creates no new structures, as in the causality principle for smoothing images. We demonstrate that both views are incorrect. We characterize how smooth points of the skeleton evolve under a general boundary evolution, with the corollary that, when the boundary is smoothed by a geometric heat equation, the skeleton evolves according to a related geometric heat equation. The surprise is that, while certain aspects of the skeleton simplify, as one would expect, others can behave wildly, including the creation of new skeleton branches. Fortunately such sections can be flagged as ligature, or those portions of the skeleton related to shape concavities. Our analysis also includes junctions and an explicit model for boundary noise. Provided a smoothness condition is met, the skeleton can often reduce noise. However when the smoothness condition is violated, the skeleton can change violently, which, we speculate, corresponds to situations in which "parts" are created, e.g., when the handle appears on a rotating cup.
Densities and Maximum Likelihood Estimation of Matching Constraints
Authors: Rikard Berthilsson
Venue: ICCV
Year: 1999

In this paper we present a theory for obtaining densities that are important for computer vision. As a result of the theory we compute the exact and novel density of the slope of a line fitted to image points. This density makes it possible to obtain confidence intervals for the slope or to make hypothesis testing about if two intersecting lines form a corner or not. The theory also lets us derive a novel technique for maximum likelihood estimation, that can be used for computing the fundamental matrix, conics, or any other constraint that can be expressed by polynomials of degree 2. We present exact and novel densities for the fundamental matrix and conic constraints, that are needed for the estimation. Experiments show how the results can be used in practise to compute maximum likelihood estimates of the fundamental matrix.
Finding Correspondences of Patches by Means of Affine Transformations
Authors: Rikard Berthilsson
Venue: ICCV
Year: 1999

We present a novel method for finding the optimal affine transformation for matching of images. The method requires no feature points, does not rely on normalization of images and can be tuned to highlight interesting parts in the images. Furthermore, the method does not need any derivatives for obtaining the affine transformation and it has a computational cost proportional to n/sup 2/logn for n/spl times/n images. The problem of finding the optimal affine transformation is solved by an iterative algorithm. In each step a global optimization is performed by the use of FFT. This global characteristic helps the algorithm from getting trapped in a local optimum. Novel theoretical results are presented that show under what restrictions the algorithm can be expected to work properly. Its intended primary use is for reconstruction problems in computer vision. These rely heavily on the establishment of point correspondences in the images. Since the method makes no assumptions on the images it can be used when feature points are difficult to detect. Experiments on real images are included and it is shown that the algorithm is robust and performs well even in difficult situations, with occlusions.
Reconstruction of Curves in R3, using Factorization and Bundle Adjustment
Authors: Rikard Berthilsson, Kalle Åström, Anders Heyden
Venue: ICCV
Year: 1999

In this paper we extend the notion of affine shape, introduced by Sparr (1995, 1996), from finite point sets to curves. The extension makes it possible to reconstruct 3D-curves up to projective transformations, from a number of their 2D-projections. We also extend the bundle adjustment technique from point features to curves. The first step of the curve reconstruction algorithm is based on affine shape, is independent of choice of coordinates, robust, does not rely on any preselected parameters and works for an arbitrary number of images. In particular this means that a solution is given to the aperture problem of finding point correspondences between curves. The second step takes advantage of any knowledge of measurement errors in the images. This is possible by extending the bundle adjustment technique to curves. Finally, experiments are performed on both synthetic and real data to show the performance and applicability of the algorithm.
Real-Time Epipolar Geometry Estimation and Disparity
Authors: Mårten Björkman, Jan-Olof Eklundh
Venue: ICCV
Year: 1999

Many visual tasks, such as recognition and visually guided manipulation, benefit from the use of binocular stereo with dynamic vergence. In order to achieve this the epipolar geometry of the two cameras has to be known. In this article we present a system capable of estimating the epipolar geometry of a stereo-head and calculating the binocular disparities in real-time producing dense disparity maps that, for example, can be used for figure-ground segmentation. The number of degrees of freedom has been minimized, without affecting the flexibility of the stereo-head, speeding up the process considerably.
Statistical Tracking in Video Traffic Surveillance
Authors: Jeffrey E. Boyd, Jean Meloche, Yehuda Vardi
Venue: ICCV
Year: 1999

Applications that interpret video data need to track objects as they move in a scene. Tracking methods that estimate the state trajectories of objects as they change over time (e.g. Kalman filter) have difficulty as the number of objects and clutter increase. We present an alternative, called statistical tracking, that is based on the concept of network tomography. A scene is modeled as a network of interconnected regions. Statistical tracking estimates the number of trips made from one region to another based on inter-region boundary traffic counts accumulated over time. It is not necessary to track an object through a scene, just to determine when an object crosses a boundary, something that is generally easier than estimating a continuous trajectory. In achieving this simplicity, statistical tracking gives up the ability to determine an object's state as the motion occurs. Instead, it determines mean traffic intensities based on statistics accumulated over a period of time. In spite of this limitation, there are several applications for which statistical tracking is useful. We demonstrate the application of the method to a large sample of video traffic surveillance data. The method does not require any data association which has some important implications concerning personal privacy.
Shadow Puppetry
Authors: Matthew Brand
Venue: ICCV
Year: 1999

The mapping between 3D body poses and 2D shadows is fundamentally many-to-many and defeats regression methods, even with windowed context. We show how to learn a function between paths in the two systems, resolving ambiguities by integrating information over the entire length of a sequence. The basis of this function is a configural and dynamical manifold that summarizes the target system's behaviour. This manifold can be modeled from data with a hidden Markov model having special topological properties that we obtain via entropy minimization. Inference is then a matter of solving for the geodesic on the manifold that best explains the evidence in the cue sequence. We give a closed-form maximum a posteriori solution for geodesics through the learned density space, thereby obtaining optimal paths over the dynamical manifold. These methods give a completely general way to perform inference over time-series; in vision they support analysis, recognition, classification and synthesis of behaviours in linear time. We demonstrate with a prototype that infers 3D from monocular monochromatic sequences (e.g., back-subtractions), without using any articulatory body model. The framework readily accommodates multiple cameras and other sources of evidence such as optical flow or feature tracking.
Optimum Fiducials under Weak Perspective Projection
Authors: Alfred M. Bruckstein, Robert J. Holt, Thomas S. Huang, Arun N. Netravali
Venue: ICCV
Year: 1999

We investigate how a given fixed number of points should be located in space so that the pose of a camera viewing them from unknown locations can be estimated with the greatest accuracy. We show that optimum solutions are obtained when the points form concentric complete regular polyhedra. For the case of optimal configurations, we provide a worst-case error analysis and use it to analyze the effects of weak perspective approximation to true perspective viewing. Comprehensive computer simulations validate the theoretical results.
Dynamic Feature Ordering for Efficient Registration
Authors: Tat-Jen Cham, James M. Rehg
Venue: ICCV
Year: 1999

Existing sequential feature based registration algorithms involving search typically either select features randomly (e.g. the RANSAC approach (M. Fischler and R. Bolles, 1981)) or assume a predefined, intuitive ordering for the features (e.g. based on size or resolution). The paper presents a formal framework for computing an ordering for features which maximizes search efficiency. Features are ranked according to matching ambiguity measure, and an algorithm is proposed which couples the feature selection with the parameter estimation, resulting in a dynamic feature ordering. The analysis is extended to template features where the matching is non discrete and a sample refinement process is proposed. The framework is demonstrated effectively on the localization of a person in an image, using a kinematic model with template features. Different priors are used on the model parameters and the results demonstrate nontrivial variations in the optimal feature hierarchy.
New Calibration-free Approach for Augmented Reality based on Parameterized Cuboid Structure
Authors: Chu-Song Chen, Chi-Kuo Yu, Yi-Ping Hung
Venue: ICCV
Year: 1999

A new method called PCS (parameterized cuboid structure) is presented for augmented reality. In particular, our method can insert animated virtual objects into a static scene, with geometric consistency, and also allow the user to interactively position and rotate the virtual objects with respect to a world coordinate system in a physically (or intuitively) meaningful way. Such capability cannot be achieved by using the existing calibration-free methods. To achieve this goal, we develop a new method for estimating camera parameters, which uses a cuboid structure (or more generally, a parallelepiped structure) as the reference object. The reference cuboid structure can be either explicit or implicit-implicit in the sense that the cuboid structure can be inferred by human perception even though it does not appear explicitly in the image. This method can determine the sizes of the cuboid (or parallelepiped) as well as the intrinsic and extrinsic parameters of the camera. To insert a virtual object into a single uncalibrated image, some human interaction is unavoidable. We have implemented an AR authoring system based on the proposed PCS method which provides an auxiliary line and a refinement criterion to assist human interaction. Experimental results have demonstrated that our method can successfully insert virtual objects into both static and dynamic scenes with highly convincing geometric consistency.
A Bidirectional Matching Algorithm for Deformable Pattern Detection with Application to Handwritten Word Retrieval
Authors: Kwok-Wai Cheung 0001, Dit-Yan Yeung, Roland T. Chin
Venue: ICCV
Year: 1999

A Bayesian framework for deformable pattern classification was proposed by K.W. Cheung et al. (1998), with promising results for isolated handwritten character recognition. Its performance, however degrades significantly when it is applied to detect deformable patterns in complex scenes, where the amount of outliers due to other neighboring objects or the background is usually large. Also, the fact that the associated evidence measure does not penalize models resting on white space results in a high false alarm rate. Another Bayesian framework for deformable pattern detection is proposed. The framework possesses the intrinsic property of matching with only part of an image (segmentation) and its associated evidence measure can penalize white space implicitly. However, limited data exploration capability is the major trade-off. By properly combining the two frameworks, a new matching algorithm called bidirectional matching is proposed. This combined approach possesses the advantages of the two frameworks and gives robust results for non-rigid shape extraction. To evaluate the performance of the proposed approach, we have applied it to shape-based handwritten word retrieval. Using a subset of the bb dataset in the CEDAR database, we can achieve a recall rate of 59% and a precision rate of 43%.
Shape from Texture through Deformations
Authors: Maureen Clerc, Stéphane Mallat
Venue: ICCV
Year: 1999

This paper is a contribution to the recovery of shape from texture under perspective projection. We regard shape from texture as a statistical estimation problem, the texture being the realization of a stochastic process. There are two minimal conditions in order for the problem to be solvable: the first is a stationarity condition on the texture with respect to the surface; the second is the regularity of the surface. Information about the surface is obtained by estimating a deformation map. We prove that at a fine scale, the wavelet decomposition of the image obeys a transport PDE, the coefficients of which can be estimated, and related to the deformation map. We show how the global surface shape can then be integrated.
The Earth Mover's Distance under Transformation Sets
Authors: Scott D. Cohen, Leonidas J. Guibas
Venue: ICCV
Year: 1999

The Earth Mover's Distance (EMD) is a distance measure between distributions with applications in image retrieval and matching. We consider the problem of computing a transformation of one distribution which minimizes its EMD to another. The applications discussed here include estimation of the size at which a color pattern occurs in an image, lighting-invariant object recognition, and point feature matching in stereo image pairs. We present a monotonically convergent iteration which can be applied to a large class of EMD under transformation problems, although the iteration may converge to only a locally optimal transformation. We also provide algorithms that are guaranteed to compute a globally optimal transformation for a few specific problems, including some EMD under translation problems.
Generalized Bounds for Time to Collision from First-Order Image Motion
Authors: Carlo Colombo, Alberto Del Bimbo
Venue: ICCV
Year: 1999

This paper addresses the problem of estimating time to collision from focal motion field measurements in the case of unconstrained relative rigid motion and surface orientation. It is first observed that, as long as time to collision is regarded as a scaled depth, the above problem does not admit a solution unless a narrow camera field of view is assumed. By a careful generalization of the time to collision concept, it is then expounded how to compute novel solutions which hold however wide the field of view. The formulation, which reduces to known literature approaches in the narrow field of view case, extends the applicability range of time to collision based techniques in areas such as mobile robotics and visual surveillance. The experimental validation of the main theoretical results includes a comparison of narrow- and wide-field of view time to collision approaches using both dense and sparse motion estimates.