OpenRaiser/Intern-Atlas huggingface dataset:
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval
Authors: Jan Puzicha, Thomas Hofmann 0001, Joachim M. Buhmann
Venue: CVPR
Year: 1997

In this paper we propose and examine non-parametric statistical tests to define similarity and homogeneity measures for textures. The statistical tests are applied to the coefficients of images filtered by a multi-scale Gabor filter bank. We demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence offer a unified approach to these closely related tasks. We present results on Brodatz-like micro-textures and a collection of real-word images.
Object Detection Using Hierarchical MRF and MAP Estimation
Authors: Richard J. Qian, Thomas S. Huang
Venue: CVPR
Year: 1997

This paper presents a new scale, position and orientation invariant approach to object detection. The proposed method first chooses attention regions in an image based on the region detection result on the image. Within the attention regions, the method then detects targets using a novel object detection algorithm that combines template matching methods with feature-based methods via hierarchical MRF and MAP estimation. Hierarchical MRF and MAP estimation provide a flexible framework to incorporate various visual clues. The combination of template matching and feature detection helps to achieve robustness against complex backgrounds and partial occlusions in object detection. Experimental results are given in the paper.
Optimal Selection of Camera Parameters for Recovery of Depth from Defocused Images
Authors: A. N. Rajagopalan 0001, Subhasis Chaudhuri
Venue: CVPR
Year: 1997

In the depth from defocus (DFD) method two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. An arbitrary selection of the camera settings can result in observed images whose relative blurring is insufficient to yield a good estimate of the depth. In this paper, we study the effect of the degree of relative blurring on the accuracy of the estimate of the depth by addressing the DFD problem in a maximum likelihood-based framework. We propose a criterion for optimal selection of camera parameters to obtain an improved estimate of the depth. The optimality criterion is based on the Cramer-Rao bound of the variance of the error in the estimate of blur. Simulations as well as experimental results on real images are presented for validation.
Dynamic Appearance-Based Recognition
Authors: Rajesh P. N. Rao
Venue: CVPR
Year: 1997

We describe a hierarchical appearance-based method for learning, recognizing, and predicting arbitrary spatiotemporal sequences of images. The method, which implements a robust hierarchical form of the Kalman filter derived from the Minimum Description Length (MDL) principle, includes as a special case several well-known object encoding techniques including eigenspace methods for static recognition. Successive levels of the hierarchical filter implement dynamic models operating over successively larger spatial and temporal scales. Each hierarchical level predicts the recognition state at a lower level and modifies its own recognition state using the residual error between the prediction and the actual lower-level state. Simultaneously, on a longer time scale, the filter learns an internal model of input dynamics by adapting its generative and state transition matrices at each level to minimize prediction errors. The resulting prediction/learning scheme thereby implements an on-line form of the well-known Expectation-Maximization (EM) algorithm from statistics. We present experimental results demonstrating the method's efficacy in mediating robust spatiotemporal recognition in a variety of scenarios containing varying degrees of occlusions and clutter.
Automated Model Acquisition from Range Images with View Planning
Authors: Michael K. Reed, Peter K. Allen, Ioannis Stamos
Venue: CVPR
Year: 1997

We present an incremental system that builds accurate CAD models of objects from multiple range images. Using a hybrid of surface mesh and volumetric representations, the system creates a "water-tight" 3D model at each step of the modeling process, allowing reasonable models to be built from a small number of views. We also present a method that can be used to plan the next view and reduce the number of scans needed to recover the object. Results are presented for the creation of 3D models of a computer game controller, a hip joint prosthesis, and a mechanical strut.
Vision for a smart kiosk
Authors: James M. Rehg, Maria Loughlin, Keith Waters
Venue: CVPR
Year: 1997

We describe a novel computer vision application: vision-based human sensing for a Smart Kiosk interface. A Smart Kiosk is a free-standing information dispensing computer appliance capable of engaging in public interactions with multiple people. Vision sensing is a critical component of the kiosk interface, where it is used to determine the context for the interaction. We present a taxonomy of vision problems for a kiosk interface and describe a prototype kiosk which uses color stereo tracking and graphical output to interact with several users.
A general filter for measurements with any probability distribution
Authors: Yoav Rosenberg, Michael Werman
Venue: CVPR
Year: 1997

The Kalman filter is a very efficient optimal filter, however it has the precondition that the noises of the process and of the measurement are Gaussian. The authors introduce 'the general distribution filter' which is an optimal filter that can be used even where the distributions are not Gaussian. An efficient practical implementation of the filter is possible where the distributions are discrete and compact or can be approximated as such.
Velocity and Disparity Cues for Robust Real-Time Binocular Tracking
Authors: Sebastien Rougeaux, Yasuo Kuniyoshi
Venue: CVPR
Year: 1997

We have designed and implemented a real-time binocular tracking system which uses two independent cues commonly found in the primary functions of biological visual systems to robustly track moving targets in complex environments, without a-priori knowledge of the target shape or texture: a fast optical flow segmentation algorithm quickly locates independently moving objects for target acquisition and provides a reliable velocity estimate for smooth tracking. In parallel, target position is generated from the output of a zero-disparity filter where a phase-based disparity estimation technique allows dynamic control of the camera vergence do adapt the horopter geometry to the target location. The system takes advantage of the optical properties of our custom-designed foveated wide-angle lenses, which exhibit a wide field of view along with a high resolution fovea. Methods to cope with the distortions introduced by the space-variant resolution, and a robust real-time implementation on a high performance active vision head are presented.
Analyzing Articulated Motion Using Expectation-Maximization
Authors: Henry A. Rowley, James M. Rehg
Venue: CVPR
Year: 1997

We present a novel application of the Expectation-Maximization algorithm to the global analysis of articulated motion. The approach utilizes a kinematic model to constrain the motion estimates, producing a segmentation of the flow field into parts with different articulated motions. Experiments with synthetic and real images are described.
Cylindrical rectification to minimize epipolar distortion
Authors: Sébastien Roy 0001, Jean Meunier, Ingemar J. Cox
Venue: CVPR
Year: 1997

We propose anew rectification method for aligning epipolar lines of a pair of stereo images taken under any camera geometry. It effectively remaps both images onto the surface of a cylinder instead of a plane, which is used in common rectification methods. For a large set of camera motions, remapping to a plane has the drawback of creating rectified images that are potentially infinitely large and presents a loss of pixel information along epipolar lines. In contrast, cylindrical rectification guarantees that the rectified images are bounded for all possible camera motions and minimizes the loss of pixel information along epipolar line. The processes (e.g., stereo matching, etc.) subsequently applied to the rectified images are thus more accurate and general since they can accommodate any camera geometry.
On Occluding Contour Artifacts in Stereo Vision
Authors: Radim Sára, Ruzena Bajcsy
Venue: CVPR
Year: 1997

We study occluding contour artifacts in area-based stereo matching: they are false responses of the matching operator to the occlusion boundary and cause the objects extend beyond their true boundaries in disparity maps. Most of the matching methods suffer from these artifacts; the effect is so strong that it cannot be ignored. We show what gives rise to the artifacts and design a matching criterion that accommodates the presence of occlusions as opposed to methods that identify and remove the artifacts. This approach leads to the problem of measurement contamination studied in statistics. We show that such a problem is hard given finite computational resources, unless more independent measurements directly related to occluding contours is available. What can be achieved is a substantial reduction of he artifacts, especially for large matching templates. Reduced artifacts allow for easier hierarchical matching and for easy fusion of reconstructions from different viewpoints into a coherent whole.
True Multi-Image Alignment and its Application to Mosaicing and Lens Distortion Correction
Authors: Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: CVPR
Year: 1997

Multiple images of a scene are related through 2D/3D view transformations and linear and non-linear camera transformations. In all the traditional techniques to compute these transformations, especially the ones relying on direct intensity gradients, one image and its coordinate system have been assumed to be ideal and distortion free. In this paper, we present a formulation and an algorithm for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. For instance, in the presence of lens distortion, none of the images can be assumed to be ideal. In our formulation, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation. The goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images. Key advantages of the technique presented in this paper are: (i) no reliance on one distortion free image, (ii) ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence, and (iii) ability to handle linear and non-linear transformations within the same framework. The new algorithm is evaluated in the context of two applications: (i) correction of lens distortion, and (ii) creation of video mosaics.
Photorealistic Scene Reconstruction by Voxel Coloring
Authors: Steven M. Seitz, Charles R. Dyer
Venue: CVPR
Year: 1997

A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information. The method avoids image correspondence problems by working in a discretized scene space whose voxels are traversed in a fixed visibility ordering. This strategy takes full account of occlusions and allows the input cameras to be far apart and widely distributed about the environment. The algorithm identifies a special set of invariant voxels which together form a spatial and photometric reconstruction of the scene, fully consistent with the input images. The approach is evaluated with images from both inward- and outward-facing cameras.
A Bayesian Segmentation Framework for Textured Visual Images
Authors: Shishir Shah 0001, J. K. Aggarwal
Venue: CVPR
Year: 1997

This paper presents a new framework for segmentation of textured visual imagery. The proposed method consists of a Bayesian formulation for labeling similar regions. Similarity is defined via texture features obtained by Gabor Wavelets. Multivariate Gaussian distributions are employed to model the feature class-conditional densities, while the Markov process is used to characterize the distributions of the region labeling due to each feature. A coarse nearest neighbor clustering is performed over the feature space to estimate the initial labelings. An iterative solution to the Maximum A Posteriori (MAP) estimation is developed, where the parameters of the prior distribution of region labels are estimated using the Expectation-Maximization (EM) algorithm. Finally, for man-made object segmentation, a region-growing procedure is used to analyze the classified texture regions by incorporating measures of local shape characteristics to obtain smooth boundaries and region homogeneity. Results of the developed algorithm on real scene images are presented.
Completion Energies and Scale
Authors: Eitan Sharon, Achi Brandt, Ronen Basri
Venue: CVPR
Year: 1997

The detection of smooth curves in images and their completion over gaps are two important problems in perceptual grouping. In this paper we examine the notion of completion energy and introduce a fast method to compute the most likely completions in images. Specifically we develop two novel analytic approximations to the curve of least energy. In addition, we introduce a fast numerical method to compute the curve of least energy, and show that our approximations are obtained at early stages of this numerical computation. We then use our newly developed energies to find the most likely completions in images through a generalized summation of induction fields. Since in practice edge elements are obtained by applying filters of certain widths and lengths to the image, we adjust our computation to take these parameters into account. Finally, we show that, due to the smoothness of the kernel of summation, the process of summing induction fields can be run in time that is linear in the number of different edge elements in the image, or in O(N log N) where N is the number of pixels in the image, using multigrid methods.
Ridge's corner detection and correspondence
Authors: Erez Shilat, Michael Werman, Yoram Gdalyahu
Venue: CVPR
Year: 1997

Traditionally, corners are found along step edges. In this paper we present an alternative approach-corners along ridges/troughs and local minima points. These features seem to be more reliable for tracking. A new approach for sub-pixel localization of these corners is suggested, using a local approximation of the image surface.
Area and Length Minimizing Flows for Shape Segmentation
Authors: Kaleem Siddiqi, Steven W. Zucker, Yves Bérubé Lauzière, Allen R. Tannenbaum
Venue: CVPR
Year: 1997

Several active contour models have been proposed to unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes. The essential idea is to evolve a curve (in 2D) or a surface (in 3D) under constraints from image forces so that it clings to features of interest in an intensity image. Recently the evolution equation has been derived from first principles as the gradient flow that minimizes a modified length functional, tailored to features such as edges. However, because the flow may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction. The authors provide a justification for this term based on the gradient flow derived from a weighted area functional, with image dependent weighting factor. When combined with the earlier modified length gradient flow they obtain a PDE which offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area flow may be used on its own, with significant computational savings.
Object recognition using invariant profiles
Authors: David Slater, Glenn Healey
Venue: CVPR
Year: 1997

We derive a sensitivity analysis for moment invariants of multidimensional distributions. These invariants have many uses in computational systems and have recently been used for illumination-invariant recognition in color images. In this context, the sensitivity analysis predicts the response of moment invariants to partial occlusion. Using the results of the sensitivity analysis, we develop a novel surface representation called the invariant profile which captures color distribution and spatial information while remaining invariant to the spectral content of the scene illumination. Unlike previous representations, the recognition of invariant profiles does not require illumination correction. We demonstrate the sensitivity analysis and the use of invariant profiles for recognition with a set of experiments on color images.
Removing the bias from line detection
Authors: Carsten Steger
Venue: CVPR
Year: 1997

The extraction of curvilinear structures is an important low-level operation in computer vision. Most existing operators use a simple model for the line that is to be extracted, i.e., they do not take into account the surroundings of a line. Therefore, they will estimate a wrong line position whenever a line with different lateral contrast is extracted. In contrast, the algorithm proposed in this paper uses an explicit model for lines and their surroundings. By analyzing the scale-space behavior of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed. Thus, the algorithm is able to extract an unbiased line position and width, both with sub-pixel accuracy.
Model-based brightness constraints: on direct estimation of structure and motion
Authors: Gideon P. Stein, Amnon Shashua
Venue: CVPR
Year: 1997

We describe a new direct method for estimating structure and motion from image intensities of multiple views. We extend the direct methods of B.K.P. Horn and E.J. Weldon (1988) to three views. Adding the third view enables us to solve for motion, and compute a dense depth map of the scene, directly from image spatio-temporal derivatives in a linear manner without first having to find point correspondences or complete optical flow. We describe the advantages and limitations of this method which are then verified with experiments using real images.
Shocks from images: propagation of orientation elements
Authors: Hüseyin Tek, Perry A. Stoll, Benjamin B. Kimia
Venue: CVPR
Year: 1997

The extraction of figure symmetry from image contours faces a number of fundamental difficulties: object symmetries are distorted due to (i) gaps in the bounding contour of a shape due to figure-ground blending, weak contrast edges, highlights, noise, etc.; (ii) an introduction of parts and occluders, and (iii) spurious edge elements due to surface markings, texture, etc. A framework for extracting such symmetries from real images is proposed based on the propagation of contour orientation information and the detection of four types of singularities (shocks) arising from the collision of propagating elements. In this paper, we show that an additional labeling of shocks based on whether the colliding wavefronts carry true orientation information (regular vs. rarefaction waves) allows a division of shocks into three sets: regular shocks are the partial shocks of partial contours as they remain invariant to the completion of the contour; semi-degenerate and degenerate shocks depict potential parts and gaps. Finally, shocks altered due to spurious edges, occlusion, and gaps are recovered via a simulation of inter-penetrating waves generated at select shock groups which with the aid of the above shock labels leads to second and further generations of shocks.
A Computational Approach to Steerable Functions
Authors: Patrick C. Teo, Yacov Hel-Or
Venue: CVPR
Year: 1997

We present a computational, group-theoretic approach to steerable functions. The approach is group-theoretic in that the treatment involves continuous transformation groups for which elementary Lie group theory may be applied. The approach is computational in that the theory is constructive and leads directly to a procedural implementation. For functions that are steerable with n finite number of basis functions under a k-parameter group, the procedure is efficient and is guaranteed to return the minimum number of basis functions. If the function is not steerable, a numerical implementation of the procedure could also be used to compute basis functions that approximately steer the function over a range of transformation parameters. Examples of both applications are demonstrated.
Ego-Motion Estimation Using Optical Flow Fields Observed from Multiple Cameras
Authors: An-Ting Tsao, Chiou-Shann Fuh, Yi-Ping Hung, Yong-Sheng Chen
Venue: CVPR
Year: 1997

In this paper, we consider a multi-camera vision system mounted on a moving object in a static three-dimensional environment. By using the motion flow fields seen by all of the cameras, an algorithm which does not need to solve the point-correspondence problem among the cameras is proposed to estimate the 3D ego-motion parameters of the moving object. Our experiments have shown that using multiple optical flow fields obtained from different cameras can be very helpful for ego-motion estimation.
Feature Tracking from an Image Sequence Using Geometric Invariants
Authors: Hung-Tat Tsui, Zhong-Ying Zhang, Shao-Hua Kong
Venue: CVPR
Year: 1997

In this paper two new feature tracking algorithms are proposed. In the first algorithm, a perspective camera model is used. Making use of the projective invariant of Barrett, and assuming the image feature points corresponding to 8 general points in space are tracked by a conventional method in the image sequence, the other feature points in the sequence can be tracked using a Hough technique. Correspondence between two reference images as required by the original Barrett's invariant is not necessary. In the second algorithm, an affine camera model is assumed and the image feature points corresponding to 4 non-coplanar points in space are assumed tracked in the image sequence using a conventional method. These image points form the basis of affine coordinates in each image. After the correspondence of a fifth point is established between the first two images, the affine coordinates of all image points in the first images existence can be computed. As far as we know this is the only algorithm which can transfer a point knowing only a single image. Experiments showed that both algorithms gave highly accurate tracking results.
Extracting Surface Textures and Microstructures from Multiple Aerial Images
Authors: Xiaoguang Wang 0013, Allen R. Hanson
Venue: CVPR
Year: 1997

We present a surface texture and microstructure extraction system to provide added realism in visualization and virtual reality applications. The system uses multiple images, 3-D models and camera information, in addition to knowledge about man-made structures, to cope with problems such as perspective distortion, data deficiency, and corruption caused by shadows and occlusions. Combined with the Ascender site modeling system and scene rendering algorithms, the system is typically useful for urban site model refinement and visualization.