OpenRaiser/Intern-Atlas huggingface dataset:
Further constraints on visual articulated motions
Authors: David Sinclair, K. Zesar
Venue: CVPR
Year: 1996

This paper derives what we term the Euclidean hinge constraint for projective reconstruction of objects displaying articulated motion. A Euclidean hinge is defined here to be an articulation axis with the proviso that any plane perpendicular to the articulation axis in link 1 has a coincident plane which is perpendicular to the articulation ads in link 2 and that these planes remain coincident under articulated motion. This constraint permits the independent projective reconstructions of two adjacent articulated links to be placed in a common frame. The constraint may be expressed mathematically by considering what we define as circular parallax. Additionally the existence of a Euclidean hinge permits the permits the projective frame to be brought nearer to a Euclidean frame for the reconstructed object. A brief reprise of the method of articulation axis estimation is given together with a more extensive series of experimental results.
Using a spectral reflectance model for the illumination-invariant recognition of local image structure
Authors: David Slater, Glenn Healey
Venue: CVPR
Year: 1996

We represent local spatial structure in a color image using feature matrices that are computed from an image region. Feature matrices contain significantly more information about local image structure than previous representations. Although feature matrices are useful for surface recognition, this representation depends on the spectral properties of the scene illumination. Using a finite dimensional linear model for surface spectral reflectance with the same number of parameters as the number of color bands, we show that illumination changes correspond to linear transformations of the feature matrices and that surface rotations correspond to circular shifts of the matrices. From these relationships we derive an algorithm for illumination and geometry invariant recognition of local surface structure. We demonstrate the algorithm with a series of experiments on images of real objects.
Motion from fixation
Authors: Stefano Soatto, Pietro Perona
Venue: CVPR
Year: 1996

We study the problem of estimating rigid motion from a sequence of monocular perspective images obtained by navigating around an object while fixating a particular feature point. We cast the problem in the framework of "epipolar geometry", and propose a filter based upon implicit dynamical model for recursively estimating motion under the fixation constraint. This allows us to compare the quality of the estimates directly against the ones obtained assuming a general rigid motion simply by changing the geometry of the parameter space, while maintaining the same structure of the recursive estimator. We also present a closed-form static solution from two views, and a recursive estimator of the relative pose between the viewer and the scene.
Towards Accurate Recovery of Shape from Shading under Diffuse Lighting
Authors: A. James Stewart, Michael S. Langer
Venue: CVPR
Year: 1996

We present a surface radiance model for diffuse lighting that incorporates shadows, interreflections, and surface orientation. We show that, for smooth surfaces, the model is an excellent approximation of the radiosity equation. We present a new data structure and algorithm that uses this model to compute shape-from-shading under diffuse lighting. The algorithm was tested on both synthetic and real images, and performs more accurately than the only previous algorithm for this problem. Various causes of error are discussed, including approximation errors in image modelling, poor local constraints at the image boundary, and ill-conditioning of the problem itself.
Runway Obstacle Detection by Controlled Spatiotemporal Image Flow Disparity
Authors: Sanghoon Sull, Banavar Sridhar
Venue: CVPR
Year: 1996

This paper proposes a method for detecting obstacles on a runway by controlling their expected flow disparities. The runway is modeled as a planar surface. By approximating the runway by a planar surface, the initial model flow field (MFF) corresponding to an obstacle-free runway is described by the data from on-board sensors (OBS). The initial residual flow field (RFF) is obtained after warping (or stabilizing) the image using the initial MFF. The error variance of the initial MFF is estimated. The initial RFF and the error variance are first used to identify the pixels corresponding to the obstacle-free runway and then to noniteratively estimate the MFF and RFF. Obstacles are detected by comparing the expected residual flow disparities with the RFF. Expected temporal and spatial residual disparities are obtained from the use of the OBS. This allows us to control the residual disparities by increasing the temporal baseline and/or by utilizing the spatial baseline if distant objects cannot be detected for a given temporal baseline. Experimental results for two real flight image sequences are presented.
Non-Rigid Matching Using Demons
Authors: Jean-Philippe Thirion
Venue: CVPR
Year: 1996

We present the concept of non-rigid matching based on demons, by reference to Maxwell's demons. We contrast this concept with the more conventional viewpoint of attraction. We show that demons and attractive points are clearly distinct for large deformations, but also that they become similar for small displacements, encompassing techniques close to optical flow. We describe a general iterative matching method based on demons, and derive from it three different non-rigid matching algorithms, one using all the image intensities, one using only contours, and one for already segmented images. At last, we present results with synthesized and real deformations, with applications to Computer Vision and Medical Image Processing.
Incremental Focus of Attention for Robust Visual Tracking
Authors: Kentaro Toyama, Gregory D. Hager
Venue: CVPR
Year: 1996

We present the Incremental Focus of Attention (IFA) architecture for adding robustness to software-based, real-time, motion trackers. The framework provides a structure which, when given the entire camera image to search, efficiently focuses the attention of the system into a narrow set of possible slates that includes the target state. IFA offers a means for automatic tracking initialization and reinitialization when environmental conditions momentarily deteriorate and cause the system to lose track of its target. Systems based on the framework degrade gracefully as various assumptions about the environment are violated. In particular, multiple tracking algorithms are layered so that the failure of a single algorithm causes another algorithm of less precision to take over, thereby allowing the system to return approximate feature state information.
Structure and motion of curved 3D objects from monocular silhouettes
Authors: B. Vijayakumar, David J. Kriegman, Jean Ponce
Venue: CVPR
Year: 1996

The silhouette of a smooth 3D object observed by a moving camera changes over time. Past work has shown how surface geometry can be recovered using the deformation of the silhouette when the camera motion is known. This paper addresses the problem of estimating both the full Euclidean surface structure and the camera motion from a dense set of silhouettes captured under orthographic or scaled orthographic projection. The approach relies on a viewpoint-invariant representation of curves swept by viewpoint-dependent features such as bitangents, inflections and contour points with parallel tangents. Feature points, which form stereo frontier points between non-consecutive images, are matched using this representation. The camera's angular velocity is computed from constraints derived from this correspondence along with the image velocity of these features. From the angular velocity, the epipolar geometry is ascertained, and infinitesimal motion frontier points can be detected. In turn, the motion of these frontier points constrains the translation component of camera motion. Finally, the surface is reconstructed using established techniques once the camera motion has been estimated.
Blurring Strategies for Image Segmentation Using a Multiscale Linking Model
Authors: Koen L. Vincken, Wiro J. Niessen, Max A. Viergever
Venue: CVPR
Year: 1996

Multiscale approaches are an invaluable tool for image segmentation. A vast amount of research has been devoted to the construction of different multiscale representations of an image. In this paper we use the hyperstack-a multiscale linking model for image segmentation-for an in-depth comparison of four different scale space generators with respect to segmentation results. We consider the linear (Gaussian) scale space both in the spatial and the Fourier domain, the variable conductance diffusion according to the Perona and Malik equation, and the Euclidean shortening flow. We have done experiments on MR images of the brain, for which a gold standard is available. The hyperstack proves to be rather insensitive to the underlying scale space generator.
Determining Correspondences and Rigid Motion of 3-D Point Sets with Missing Data
Authors: Xiaoguang Wang 0013, Yong-Qing Cheng, Robert T. Collins, Allen R. Hanson
Venue: CVPR
Year: 1996

This paper addresses the general 3-D rigid motion problem, where the point correspondences and the motion parameters between two sets of 3-D points are to be recovered. The existence of missing points in the two sets is the most difficult problem. We first show a mathematical symmetry in the solutions of rotation parameters and point correspondences. A closed-form solution based on the correlation matrix eigenstructure decomposition is proposed for correspondence recovery with no missing points. Using a heuristic measure of point pair affinity derived from the eigenstructure, a weighted bipartite matching algorithm is developed to determine the correspondences in general cases where missing points occur. The use of the affinity heuristic also leads to a fast outlier removal algorithm, which can be run iteratively to refine the correspondence recovery. Simulation results and experiments on real images are shown in both ideal and general cases.
Illumination and geometry invariant recognition of texture in color images
Authors: Lizhi Wang 0006, Glenn Healey
Venue: CVPR
Year: 1996

We develop a method for recognizing color texture independent of rotation, scale, and illumination. Color texture is modeled using spatial correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of sensor classes, we show that illumination and geometry changes in the scene correspond to a linear transformation of the correlation functions and a linear transformation of their coordinates. A several step algorithm which includes scale estimation and correlation moment computation is used to achieve the invariance. The key to the method is the new result that illumination and geometry changes in the scene correspond to a specific transformation of correlation function Zernike moment matrices. These matrices can be estimated from a color image. This relationship is used to derive an efficient algorithm for recognition. The algorithm is substantiated using classification results on over 200 images of color textures obtained under various illumination conditions and geometric configurations.
A Unified Mixture Framework for Motion Segmentation: Incorporating Spatial Coherence and Estimating the Number of Models
Authors: Yair Weiss, Edward H. Adelson
Venue: CVPR
Year: 1996

Describing a video sequence in terms of a small number of coherently moving segments is useful for tasks ranging from video compression to event perception. A promising approach is to view the motion segmentation problem in a mixture estimation framework. However, existing formulations generally use only the motion, data and thus fail to make use of static cues when segmenting the sequence. Furthermore, the number of models is either specified in advance or estimated outside the mixture model framework. In this work we address both of these issues. We show how to add spatial constraints to the mixture formulations and present a variant of the EM algorithm that males use of both the form and the motion constraints. Moreover this algorithm estimates the number of segments given knowledge about the level of model failure expected in the sequence. The algorithm's performance is illustrated on synthetic and real image sequences.
Nonlinear Shape Restoration For Document Images
Authors: Yun Weng, Qiuming Zhu
Venue: CVPR
Year: 1996

Previous researches on nonlinear shape restoration are based on the assumptions that the original shapes and distortions of the images have known formulations under certain conditions. The main contribution of this research is the development of a new restoration algorithm, called Multi-Steps Restoration. The algorithm is based on a linear interpolation theory that is able to detect and restore nonlinear shape distortions in any irregular quadrilateral-shaped patterns. The main idea of the algorithm is the use of two-dimensional spline functions in bicubic, biquadratic, and/or bilinear models to approximate the three-dimensional nonlinear distortion curves. The performance of the approach shown by experiment is promising.
Attention Control for Robot Vision
Authors: Carl-Fredrik Westin, Carl-Johan Westelius, Hans Knutsson, Gösta H. Granlund
Venue: CVPR
Year: 1996

Focus of attention mechanisms for robot vision are discussed. A new method for neglecting low level filter responses from already modelled structures is presented. The method is based on a filtering technique termed normalized convolution. In one experiment, the robot is continuously moving its arm in the scene while tracking other objects. It is shown how the arm can be made "invisible" so that only the moving object of interest is detected. This makes tracking of objects much simpler. In another experiment, the attention of the system is shifted between objects by simply cancelling the mask of the object to be attended to. With this strategy the low level processes do not need to know the difference between a new object entering the scene and a mask being cancelled, and thus a complex communication structure between high and low levels is avoided.
Local Parallel Computation of Stochastic Completion Fields
Authors: Lance R. Williams, David W. Jacobs
Venue: CVPR
Year: 1996

We describe a local parallel method for computing the stochastic completion field introduced in the previous article (Williams and Jacobs, 1997). The stochastic completion field represents the likelihood that a completion joining two contour fragments passes through any given position and orientation in the image plane. It is based on the assumption that the prior probability distribution of completion shape can be modeled as a random walk in a lattice of discrete positions and orientations. The local parallel method can be interpreted as a stable finite difference scheme for solving the underlying Fokker-Planck equation identified by Mumford (1994). The resulting algorithm is significantly faster than the previously employed method, which relied on convolution with large-kernel filters computed by Monte Carlo simulation. The complexity of the new method is O (n 3 m), while that of the previous algorithm was O(n 4 m 2 (for an n × n image with m discrete orientations). Perhaps most significant, the use of a local method allows us to model the probability distribution of completion shape using stochastic processes that are neither homogeneous nor isotropic. For example, it is possible to modulate particle decay rate by a directional function of local image brightnesses (i.e., anisotropic decay). The effect is that illusory contours can be made to respect the local image brightness structure. Finally, we note that the new method is more plausible as a neural model since (1) unlike the previous method, it can be computed in a sparse, locally connected network, and (2) the network dynamics are consistent with psychophysical measurements of the time course of illusory contour formation.
Gauging Relational Consistency and Correcting Structural Errors
Authors: Richard C. Wilson 0001, Edwin R. Hancock
Venue: CVPR
Year: 1996

The aim of this paper is to provide a comparative evaluation of a number of contrasting approaches to relational matching. Unique to this study is the way in which we show how a diverse family of algorithms relate to one-another using a common Bayesian framework. Broadly speaking there are two main aspects to this study. Firstly we focus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the same Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realisations of the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter.
Polarization Phase-Based Method for Material Classification and Object Recognition in Computer Vision
Authors: Lawrence B. Wolff
Venue: CVPR
Year: 1996

A robust and accurate polarization phase-based technique for material classification is presented. The novelty of this technique is three-fold In (i) its theoretical development, (ii) its application, and, (iii) its experimental implementation. The concept of phase of polarization of a light wave is introduced to computer vision for discrimination between materials according to their intrinsic electrical conductivity, such as distinguishing conducting metals, and poorly-conducting dielectrics. Previous work has used intensity, color and polarization component ratios. This new method is based on the physical principle that metals retard orthogonal components of light upon reflection while dielectrics do not. This method has significant complementary advantages with respect to existing techniques, is computationally efficient, and can be easily implemented with existing imaging technology. Experiments for real circuit board inspection, non-conductive and conductive glass, and, outdoor object recognition have been performed to demonstrate its accuracy and potential capabilities.
Combination of Multiple Classifiers Using Local Accuracy Estimates
Authors: Kevin S. Woods, Kevin W. Bowyer, W. Philip Kegelmeyer
Venue: CVPR
Year: 1996

This paper presents a method for combining classifiers that uses estimates of each individual classifier's local accuracy in small regions of feature space surrounding an unknown test sample. An empirical evaluation using five real data sets confirms the validity of our approach compared to some other combination of multiple classifiers algorithms. We also suggest a methodology for determining the best mix of individual classifiers.
FRAME: Filters, Random fields, and Minimax Entropy - Towards a Unified Theory for Texture Modeling
Authors: Song Chun Zhu, Ying Nian Wu, David Mumford
Venue: CVPR
Year: 1996

In this paper, a minimax entropy principle is studied, based on which a novel theory, called FRAME (Filters, Random fields And Minimax Entropy) is proposed for texture modeling. FRAME combines attractive aspects of two important themes in texture modeling: multi-channel filtering and Markov random field (MRF) modeling. It incorporates the responses of a set of well selected filters into the distribution over a random field and hence has a much stronger descriptive ability than the traditional MRF models. Furthermore, it interprets and clarifies many previous concepts and methods for texture analysis and synthesis from a unified point of view. Algorithms are proposed for probability inference, stochastic simulation and filter selection. Experiments on a variety of textures are described to illustrate our theory and to show the performance of our algorithms. These experiments demonstrate that many textures previously considered as different categories can be modeled and synthesized in a common framework.
Isotropic Processing for Gradient Estimation
Authors: Djemel Ziou, Shengrui Wang
Venue: CVPR
Year: 1996

This paper concerns the influence of edge direction on the estimation of edge contrast and orientation. We show that the gradient estimated using radial filters is not affected by edge orientation. For non-radial filters the gradient can be affected by edge orientation. For instance, we find that the estimated edge orientation using a non-radial filter may be biased, even if the signal is noise-free. However, there are non-radial filters for which gradient is unaffected by edge orientation as in the case of radial filters. The properties of these functions are given in this paper. The results are illustrated by the study of the Canny, Deriche, and Shen & Castan detectors. We take into account discretization errors. These results give a clear indication of the effect of the rotation invariance property of an edge detector on its response, thus providing a more precise meaning for this property in edge detection.
3D Reconstruction of the Human Jaw from A Sequence of Images
Authors: Moumen T. Ahmed, Sameh M. Yamany, Elsayed E. Hemayed, S. Ahmed, S. Roberts, Aly A. Farag
Venue: CVPR
Year: 1997

A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.
Modelling of single mode distributions of colour data using directional statistics
Authors: Daniel C. Alexander, Bernard F. Buxton
Venue: CVPR
Year: 1997

Three different statistical models of colour data for use in segmentation or tracking algorithms are proposed. The results of a performance comparison of a tracking algorithm, applied to two separate applications, using each of the three different types of underlying model of the data are presented. From these a comparison of the performance of the statistical colour models themselves is obtained.
Verifying model-based alignments in the presence of uncertainty
Authors: T. D. Alter, W. Eric L. Grimson
Venue: CVPR
Year: 1997

This paper introduces a unified approach to the problem of verifying alignment hypotheses in the presence of substantial amounts of uncertainty in the predicted locations of projected model features. Our approach is independent of whether the uncertainty is distributed or bounded, and, moreover, incorporates information about the domain in a formally correct manner. Information which can be incorporated includes the error model, the distribution of background features, and the positions of the data features near each predicted model feature. Experiments are described that demonstrate the improvement over previously used methods. Furthermore, our method is efficient in that the number of operations is on the order of the number of image features that lie nearby the predicted model features.