OpenRaiser/Intern-Atlas huggingface dataset:
Robust Visual Servoing based on Relative Orientation
Authors: Camillo J. Taylor, James P. Ostrowski, Sang-Hack Jung
Venue: CVPR
Year: 1999

In this paper the problem of controlling the spatial position and orientation of a robotic platform based on the image data obtained from a video camera mounted on that platform is considered. More specifically, we propose control laws that generate translational and angular velocities that will cause the robot to achieve and maintain a fixed position and orientation with respect to a set of feature points in the scene. The proposed control schemes make use of well established techniques for computing estimates for the relative orientation of two camera positions from a set of feature correspondences. An important advantage of these control schemes is that it is possible to demonstrate analytically that they are globally convergent even in the presence of large calibration errors in both the intrinsic parameters of the camera and in the extrinsic parameters which relate the frame of reference of the camera to the body frame of the robot platform which is being controlled. Furthermore no a priori knowledge about the structure of the scene is assumed.
Perceptual Organization via the Symmetry Map and Symmetry Transforms
Authors: Hüseyin Tek, Benjamin B. Kimia
Venue: CVPR
Year: 1999

Variations in the projection of objects on a 2D image, e.g., due to occlusion and articulation, lead to edge maps which are noisy, contain gaps and spurious elements, and which are deformed. These variations in turn cause variations in the edge map which are typically regularized by the use of a salient measure for each edge element. The use of edge salience, however, typically faced with two drawback. First, salience measures take advantage of boundary continuity, but not of shape continuity, which includes continuity of the interior. Second, while each edge element can only belong to one object boundary, in the computation of salience measures, it often freely contributes to the salience of edges in competing grouping hypotheses as well. We identify both drawbacks with the lack of an explicit intermediate representation between the edge map and grouped object boundaries. We propose that (i) a symmetry map can fully represent the initial edge map so that both boundary and regional continuities can be represented via skeletal/shock continuity; (ii) a re-organization of the edge map in the form of completing gaps, discarding spurious elements, smoothing, and partitioning a contour (grouped set of edge elements) can be represented by transformations on the symmetry map; (iii) the optimal grouping corresponds to the least action path consisting of a sequence of symmetry transforms. The focus of this paper is to define transformations on the symmetry map and illustrate results for them. Specifically, we illustrate how spurious elements can be removed, gaps completed, and parts computed despite significant noise.
Using a Linear Subspace Approach for Invariant Subpixel Material Identification in Airborne Hyperspectral Imagery
Authors: Bea Thai, Glenn Healey
Venue: CVPR
Year: 1999

We present an algorithm for subpixel material identification that is invariant to the illumination and atmospheric conditions. The target material spectral reflectance is the only prior information required by the algorithm. A target material subspace model is constructed from the reflectance using a physical model and a background subspace model is estimated directly from the image. These two subspace models are used to compute maximum likelihood estimates for the target material component and the background component at each image pixel. These estimates form the basis of a generalized likelihood ratio test for subpixel material identification. We present experimental results using HYDICE imagery that demonstrate the utility of the algorithm for subpixel material identification under varying illumination and atmospheric conditions.
Spatial Filter Selection for Illumination-Invariant Color Texture Discrimination
Authors: Bea Thai, Glenn Healey
Venue: CVPR
Year: 1999

Color texture contains a large amount of spectral and spatial structure that can be exploited for recognition. Recent work has demonstrated that spatial filters offer a convenient means of extracting illumination invariant spatial information from a color image. In this paper, we address the problem of deriving optimal fillers for illumination-invariant color texture discrimination. Color textures are represented by a set of illumination-invariant features that characterize the color distribution of a filtered image region. Given a pair of color textures, we derive a spatial filter that maximizes the distance between these textures in feature space. We provide a method for using the pair-wise result to obtain a filter that maximizes discriminability among multiple classes. A set of experiments on a database of deterministic and random color textures obtained under different illumination conditions demonstrates the improved discriminatory power achieved by using an optimized filler.
The Tensors of Three Affine Views
Authors: Torfi Thórhallsson, David William Murray 0001
Venue: CVPR
Year: 1999

In this paper we specialize the projective unifocal, bifocal, and trifocal tensors to the affine case, and show how the tensors obtained relate to the registered tensors encountered in previous work. This enables us to obtain an affine specialization of known projective relations connecting points and lines across two or three views. In the simpler case of affine cameras we give neccessary and sufficient constraints on the components of the trifocal tensor together with a simple geometric interpretation. Finally, we show how the estimation of the tensors from point correspondences is achieved through factorization, and discuss the estimation from line correspondences.
Generic Object Detection using Model Based Segmentation
Authors: Zhiqian Wang, Jezekiel Ben-Arie
Venue: CVPR
Year: 1999

This paper presents a novel approach for detection and segmentation of generic shapes in cluttered images. The underlying assumption is that generic objects that are man made, frequently have surfaces which closely resemble standard model shapes such as rectangles, semi-circles etc. Due to the perspective transformations of optical imaging systems, a model shape may appear differently in the image with various orientations and aspect ratios. The set of possible appearances can be represented compactly by a few vectorial eigenbases that are derived from a small set of model shapes which are affine transformed in a wide parameter range. Instead of regular boundary of standard models, we apply a vectorial boundary which improves robustness to noise, background clutter and partial occlusion. The detection of generic shapes is realized by detecting local peaks of a similarity measure between the image edge map and an eigenspace combined set of the appearances. At each local maxima, a fast search approach based on a novel representation by an angle space is employed to determine the best matching between models and the underlying subimage. We find that angular representation in multidimensional search corresponds better to Euclidean distance than conventional projection and yields improved classification of noisy shapes. Experiments are performed in various interfering distortions, and robust detection and segmentation are achieved.
A Novel Bayesian Method for Fitting Parametric and Non-Parametric Models to Noisy Data
Authors: Michael Werman, Daniel Keren
Venue: CVPR
Year: 1999

We offer a simple paradigm for fitting models, parametric and non-parametric, to noisy data, which resolves some of the problems associated with classic MSE algorithms. This is done by considering each point on the model as a possible source for each data point. The paradigm also allows to solve problems which are not defined in the classical MSE approach, such as fitting a segment (as opposed to a line). It is shown to be non-biased, and to achieve excellent results for general curves, even in the presence of strong discontinuities. Results are shown for a number of fitting problems, including lines, circles, segments, and general curves, contaminated by Gaussian and uniform noise.
Q-Warping: Direct Computation of Quadratic Reference Surfaces
Authors: Yonatan Wexler, Amnon Shashua
Venue: CVPR
Year: 1999

We consider the problem of wrapping around an object, of which two views are available, a reference surface and recovering the resulting parametric flow using direct computations (via spatio-temporal derivatives). The well known examples are affine flow models and B-parameter flow models - both describing a flow field of a planar reference surface. We extend those classic flow models to deal with a quadric reference surface and work out the explicit parametric form of the flow field. As a result we derive a simple warping algorithm that maps between two views and leaves a residual flow proportional to the 30 deviation of the surface from a virtual quadric surface. The applications include image morphing, model building, image stabilization, and disparate view correspondence.
Subset Selection for Active Object Recognition
Authors: Jay Winkeler, B. S. Manjunath, Shivkumar Chandrasekaran
Venue: CVPR
Year: 1999

This paper presents an algorithm for constructing object representations suitable for recognition. The system automatically selects a representative subset of the views of the object while constructing the eigenspace basis. These views are actively located for object identification and pose determination. All processing is performed on-line. The camera is actively positioned during both representation and recognition. When tested with 240 views for each of seven objects, the system achieves 100% accurate object recognition and pose determination. These results are shown to degrade gracefully as conditions deteriorate.
Data-Driven Shape-from-Shading Using Curvature Consistency
Authors: Philip L. Worthington, Edwin R. Hancock
Venue: CVPR
Year: 1999

This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. Firstly, we provide a geometric update procedure which allows the image irradiance equation to be satisfied as a hard-constraint. This improves the data-closeness of the recovered needle-map. Secondly, we consider how topographic constraints can be lured to impose local consistency on the recovered needle-map. We present several alternative curvature consistency models, and provide an experimental assessment of the new shape-from-shading framework on both real-world images and synthetic images with known ground-truth surface-normals. The main conclusion drawn from our analysis is that the new framework allows rapid development of more appropriate constraints on the SFS problem.
Recognizing Hand Gesture Using Motion Trajectories
Authors: Ming-Hsuan Yang 0001, Narendra Ahuja
Venue: CVPR
Year: 1999

We present an algorithm for extracting and classifying two-dimensional motion in an image sequence based on motion trajectories. First, a multiscale segmentation is performed to generate homogeneous regions in each frame. Regions between consecutive frames are then matched to obtain 2-view correspondences. Affine transformations are computed from each pair of corresponding regions to define pixel matches. Pixels matches over consecutive images pairs are concatenated to obtain pixel-level motion trajectories across the image sequence. Motion patterns are learned from the extracted trajectories using a time-delay neural network. We apply the proposed method to recognize 40 hand gestures of American Sign Language. Experimental results show that motion patterns in hand gestures can be extracted and recognized with high recognition rate using motion trajectories.
Detection and Characterization of Multiple Motion Points
Authors: Weichuan Yu, Gerald Sommer, Steven S. Beauchemin, Konstantinos Daniilidis
Venue: CVPR
Year: 1999

The computation of optical flow is a well studied topic in biological and computational vision. However, the existence of multiple motions in dynamic imagery due to occlusion or even transparency still raises challenging questions. In this paper, we propose an approach for the detection and characterization of occlusion and transparency. We propose a theoretical framework for both types of multiple motions which explicitly shows the difference between occlusion and transparency in the frequency domain. Then, we employ an EM-algorithm for the computation of one or two image velocities and a simple test for the detection of occlusion. Our approach differs from other EM-approaches which blindly assume the superposition of two models in the spatial domain without providing with a separate formal model for occlusion. We test and compare the characterization performance on synthetic and real data.
High-Level and Generic Models for Visual Search: When Does High Level Knowledge Help?
Authors: Alan L. Yuille, James M. Coughlan
Venue: CVPR
Year: 1999

We analyze the problem of detecting a road target in background clutter and investigate the amount of prior (i.e. target specific) knowledge needed to perform this search task. The problem is formulated in terms of Bayesian inference and we define a Bayesian ensemble of problem instances. This formulation implies that the performance measures of different models depend on order parameters which characterize the problem. This demonstrates that if there is little clutter then only weak knowledge about the target is required in order to detect the target. However at a critical value of the order parameters there is a phase transition and it becomes effectively impossible to detect the target unless high-level target specific knowledge is used. These phase transitions determine different regimes within which different search strategies will be effective. These results have implications for bottom-up and top-down theories of vision.
Multi-Frame Alignment of Planes
Authors: Lihi Zelnik-Manor, Michal Irani
Venue: CVPR
Year: 1999

Traditional plane alignment techniques are typically performed between pairs of frames. In this paper we present a method for extending existing two-frame planar-motion estimation techniques into a simultaneous multi-frame estimation, by exploiting multi-frame geometric constraints of planar surfaces. The paper has three main contributions: (i) we show that when the camera calibration does not change, the collection of all parametric image motions of a planar surface in the scene across multiple frames is embedded in a low dimensional linear subspace; (ii) we show that the relative image motion of multiple planar surfaces across multiple frames is embedded in a yet lower dimensional linear subspace, even with varying camera calibration; and (iii) we show how these multi-frame constraints can be incorporated into simultaneous multi-frame estimation of planar motion, without explicitly recovering any 3D information, or camera calibration. The resulting multi-frame estimation process is more constrained than the individual two-frame estimations, leading to more accurate alignment, even when applied to small image regions.
Harmonic Maps and Their Applications in Surface Matching
Authors: Dongmei Zhang 0001, Martial Hebert
Venue: CVPR
Year: 1999

The surface-matching problem is investigated in this paper using a mathematical tool called harmonic maps. The theory of harmonic maps studies the mapping between different metric manifolds from the energy-minimization point of view. With the application of harmonic maps, a surface representation called harmonic shape images is generated to represent and match 3D freeform surfaces. The basic idea of harmonic shape images is to map a 3D surface patch with disc topology to a 2D domain and encode the shape information of the surface patch into the 2D image. This simplifies the surface-matching problem to a 2D image-matching problem. Due to the application of harmonic maps in generating harmonic shape images, harmonic shape images have the following advantages: they have sound mathematical background; they preserve both the shape and continuity of the underlying surfaces; and they are robust to occlusion and independent of any specific surface sampling scheme. The performance of surface matching using harmonic maps is evaluated using real data. Preliminary results are presented in the paper.
Fast, Robust, and Consistent Camera Motion Estimation
Authors: Tong Zhang 0001, Carlo Tomasi
Venue: CVPR
Year: 1999

Previous algorithms that recover camera motion from image velocities suffer from both bias and excessive variance in the results. We propose a robust estimator of camera motion that is statistically consistent when image noise is isotropic. Consistency means that the estimated motion converges in probability, to the true value as the number of image points increases. An algorithm based on reweighted Gauss-Newton iterations handles 100 velocity measurements in about 50 milliseconds on a workstation.
Extending Superquadrics with Exponent Functions: Modeling and Reconstruction
Authors: Lin Zhou, Chandra Kambhamettu
Venue: CVPR
Year: 1999

Superquadrics are a family of parametric shapes which can model a diverse set of objects. They have received significant attention because of their compact representation and robust methods for recovery of 3D models. However, their assumption of intrinsical symmetry fails in modeling numerous real-world examples such as human, body, animals, and other naturally occurring objects. In this paper, we present a novel approach, which is called extended superquadric to extend superquadric's representation power with exponent functions. An extended superquadric model can be deformed in any direction because it extends the exponents of superquadrics from constants to functions of the latitude and longitude angles in the spherical coordinate system. Thus extended superquadrics can model more complex shapes than superquadrics. It also maintains many desired properties of superquadrics such as compactness controllability, and intuitive meaning, which are all advantageous for shape modeling, recognition, and reconstruction. In this paper, besides the use of extended superquadrics for modeling, we also discuss our research into the recovery of extended superquadrics from 3D information (reconstruction). Experimental results of fitting extended superquadrics to 3D real data are presented. Our results are very encouraging and indicate that the use of extended superquadric is a promising paradigm for shape representation and recovery in computers vision and has potential benefits for the generation of synthetic images for computer graphics.
Extracting Nonrigid Motion and 3D Structure of Hurricanes from Satellite Image Sequences without Correspondences
Authors: Lin Zhou, Chandra Kambhamettu, Dmitry B. Goldgof
Venue: CVPR
Year: 1999

Image sequences capturing Hurricane Luis through meteorological satellites (GOES-8 and GOES-9) are used to estimate hurricane-top heights (structure) and hurricane winds (motion). This problem is difficult not only due to the absence of correspondence but also due to the lack of depth cues in the 2D hurricane images (scaled orthographic projection). In this paper, we present a structure and motion analysis system, called SMAS. In this system, the hurricane images are first segmented into small square areas. We assume that each small area is undergoing similar nonrigid motion. A suitable nonrigid motion model for cloud motion is first defined. Then, non-linear least-square method is used to fit the nonrigid motion model for each area in order to estimate the structure, motion model, and 3D nonrigid motion correspondences. Finally, the recovered hurricane-top heights and winds are presented along with an error analysis. Both structure and 3D motion correspondences are estimated to subpixel accuracy. Our results are very encouraging, and have many potential applications in earth and space sciences, especially in cloud models for weather prediction.
Panoramic EPI Generation and Analysis of Video from a Moving Platform with Vibration
Authors: Zhigang Zhu 0001, Guangyou Xu, Xueyin Lin
Venue: CVPR
Year: 1999

This paper presents a novel approach for generating and analyzing epipolar plane images (EPIs) from video sequences taken from a moving platform subject to vibration so that the 3D model of an arbitrary scene can be constructed. Two problems are solved in our approach: (1) how to generate EPIs from video under a more general motion than a pure translation; (2) how to analyze the huge amount of data in the EPIs robustly and efficiently. For the first problem, a 3D image stabilization method is proposed which decouples the vibration from the vehicle's motion so that good EPIs and panoramic view images (PVIs) can be generated. For the second problem, we propose an efficient panoramic EPI analysis (PEPIA) method in which only one scanline of each EPI is processed. The PEPIA combines advantages of PVIs and EPIs and consists of three important steps: locus orientation detection, motion boundary localization, and occlusion/resolution recovery. The output of the PEPIA-a layered 3D panorama, is very useful in visual navigation and virtual reality modeling. Since camera calibration, image segmentation, feature extraction and matching are avoided, all the proposed algorithms are fully automatic and rather general. Results on real image sequences are given.
A Region Extraction Method Using Multiple Active Contour Models
Authors: Toru Abe, Yuki Matsuzawa
Venue: CVPR
Year: 2000

A new method for extracting an object region in a complex scene image is proposed. For improving the accuracy and the robustness of region extraction, to the region of a single object, the proposed method applies multiple active contour models (ACMs) controlled by the statistical characteristics of image data. Around the object boundary, these ACMs compete with each other and each ACM extracts a subregion of uniform image properties. As a result of this competition, the entire region of an object is extracted as a set of several subregions. For the proposed method it is necessary to set many initial contours. To lighten this load, a procedure for making multiple initial contours from a single initial curve is also proposed. In this procedure, a few initial curves are set in an image, each initial curve is divided into segments at the optimal loci, and initial contours are made by dilating these segments.
ROR: Rejection of Outliers by Rotations in Stereo Matching
Authors: Amit Adam, Ehud Rivlin, Ilan Shimshoni
Venue: CVPR
Year: 2000

We address the problem of rejecting false matches of points between two perspective views. Even the best algorithms for image matching make some mistakes and output some false matches. We present an algorithm for identification of the false matches between the views. The algorithm exploits the possibility of rotating one of the images to achieve some common behaviour of the correct matches. Those matches that deviate from this common behaviour turn out to be false matches. The statistical tool we use is the mean shift mode estimator. Our algorithm does not use in any way the image characteristics of the matched features. In particular it avoids problems that cause the false matches in the first place. The algorithm may be run as a post processing step on output from any point matching algorithm. Use of the algorithm may significantly improve the ratio of correct matches to incorrect matches. On real images our algorithm has improved the percentage of correct matches from an initial 20%-30% to a final 70%-80%. For robust estimation algorithms which are later employed, this is a very desirable quality since it reduces significantly their computational cost. We present the algorithm, identify the conditions under which it works, and present results of testing it on both synthetic and real images.