OpenRaiser/Intern-Atlas huggingface dataset:
Corner Detection with Covariance Propagation
Authors: Qiang Ji, Robert M. Haralick
Venue: CVPR
Year: 1997

This paper presents a statistical approach for detecting corners from chain encoded digital arcs. An arc point is declared as a corner if the estimated parameters of the two fitted lines of the two arc segments immediately to the right and left of the are point are statistically significantly different. The corner detection algorithm consists of two steps: corner detection and optimization. While corner detection involves statistically identifying the most likely corner points along an arc sequence, corner optimization deals with improving the locational errors of the detected corners. The major contributions of this research include developing a method for analytically estimating the covariance matrix of the fitted line parameters and developing a hypothesis test statistic to statistically test the difference between the parameters of two fitted lines. Performance evaluation study showed that the algorithm is robust and accurate for complex images. It has an average misdetection rate of 2.5% and false alarm rate of 2.2% for the complex RADIUS images. This paper discusses the theory and performance characterization of the proposed corner detector.
Recognizing Objects by Matching Oriented Points
Authors: Andrew E. Johnson 0002, Martial Hebert
Venue: CVPR
Year: 1997

We present an approach to recognition of complex objects in cluttered 3-D scenes that does not require feature extraction or segmentation. Our object representation comprises descriptive images associated with each oriented point on the surface of an object. Using a single point basis constructed from an oriented point, the position of other points on the surface of the object can be described by two parameters. The accumulation of these parameters for many points on the surface of the object results in an image at each oriented point. These images, localized descriptions of the global shape of the object, are invariant to rigid transformations. Through correlation of images, point correspondences between a model and scene data are established and then grouped using geometric consistency. The effectiveness of our algorithm is demonstrated with results showing recognition of complex objects in cluttered scenes with occlusion.
Matching 3-D arcs
Authors: Behrooz Kamgar-Parsi, Behzad Kamgar-Parsi
Venue: CVPR
Year: 1997

We present a new algorithm for efficient matching of 3-D polygonal arcs. The algorithm is based on the decomposition of the arcs into sets of corresponding line segments with equal lengths. We derive a closed-form solution for the transformation that gives the best match between two sets of corresponding line segments (best in the sense of an L/sub 2/ norm distance measure), which enables the development of efficient arc matching algorithm. We apply this algorithm to the problem of finding a match between a short are and a piece of a long arc in real and synthetic images, and compare the results with alternative techniques in the literature.
Characterization of errors in compositing panoramic images
Authors: Sing Bing Kang, Richard S. Weiss
Venue: CVPR
Year: 1997

In this paper we describe the effect of errors in the intrinsic camera parameters on reconstructed panoramic images. A panoramic image is created by first capturing a sequence of images while rotating the camera about a vertical axis a full 360/spl deg/. The subsequent steps are projecting the original rectilinear images onto cylindrical surfaces and compositing them to form the panoramic image. Our analysis has led to a technique that allows simultaneous recovery of the camera focal length and properly composited panoramic images. The correct focal length can be determined by iterating the processes of projecting the original rectilinear images onto cylindrical surfaces given an estimate of the focal length and compositing the resulting images to yield an increasingly better estimate of the focal length. This paper shows that the convergence towards the correct focal length is exponential.
Content-based trademark retrieval system using visually salient features
Authors: Yong-Sung Kim, Whoi-Yul Kim
Venue: CVPR
Year: 1997

An ever increasing number of registered trademarks has created greater demand for an automatic trademark retrieval system. We present a method for such a system based on the image content using shape features. Zernike or pseudo-Zernike moments of the image are employed as a feature set. To retrieve similar shapes, we take into account visually salient features that dominantly affect the global shape of the trademarks and ignore their minor detail. Experimental results on a database of 3,000 trademark images demonstrate that the proposed method retrieves visually similar trademarks which agree well with human perception.
Facial Expression Recognition and Its Degree Estimatio
Authors: Satoshi Kimura, Masahiko Yachida
Venue: CVPR
Year: 1997

The purpose of this study is not only to recognize a facial expression which is associated with human emotion but also to estimate its degree. Our method is based on the idea that facial expression recognition can be achieved by extracting a variation from an expressionless face considering the face area as a whole pattern. For the purpose of extracting subtle changes in the face such as the degree of expressions, it is necessary to eliminate the individuality appearing in the facial image. Using an elastic net model, a variation of facial expression is represented as motion vectors of the deformed net from a facial edge image. Then, applying K-L expansion, the change of facial expression represented as the motion vectors of nodes is mapped into a low dimensional eigenspace, and estimation is achieved by projecting input images on to the emotion space. We have constructed three kinds of expression models; happiness, anger, surprise, and experimental results are evaluated.
Interaction with On-Screen Objects using Visual Gesture Recognition
Authors: Rick Kjeldsen, John R. Kender
Venue: CVPR
Year: 1997

This paper will review the design of a working system that visually recognizes hand gestures for the control of a window based user interface. After an overview of the system, it will explore one aspect of gestural interaction in depth, hand tracking, and what is needed for the user to be able to interact comfortably with on-screen objects. We describe how the location of the hand is mapped to a location on the screen, and how it is both necessary and possible to smooth the camera input using a non-linear physical model of the cursor. The performance of the system is examined, especially with respect to abject selection. We show how a standard HCI model of object selection (Fitts' Law) can be extended to model the selection performance of free-hand pointing.
Non-linear operators in image restoration
Authors: Pierre Kornprobst, Rachid Deriche, Gilles Aubert
Venue: CVPR
Year: 1997

We present a variational approach such that during image restoration, edges detected in the original image are being preserved. We compare the mathematical foundation of this method with respect to some of the well known methods recently proposed in the literature within the class of PDE based algorithms (anisotropic diffusion, mean curvature motion, min/max flow technique). The performance of our approach is carefully examined and compared to the classical methods. Experimental results on synthetic and real images illustrate the capabilities of all the studied approaches.
An image-based visual-motion-cue for autonomous navigatio
Authors: Sridhar R. Kundur, Daniel Raviv, Ernest Kent
Venue: CVPR
Year: 1997

This paper presents a novel time-based visual motion cue called the Hybrid Visual Threat Cue (HVTC) that provides some measure for a change in relative range as well as absolute clearances, between a 3D surface and a moving observer. It is shown that the HVTC is a linear combination of Time-To-Contact (TTC), visual looming and the Visual Threat Cue (VTC). The visual field associated with the HVTC can be used to demarcate the regions around a moving observer into safe and danger zones of varying degree, which may be suitable for autonomous navigation tasks. The HVTC is independent of the 3D environment and needs almost no a-priori information about it. It is rotation independent, and is measured in ~time/sup -1/\ units Several approaches to extract the HVTC, are suggested. Also a practical method to extract it from a sequence of images of a 3D textured surface obtained by a visually fixating, fixed-focus monocular camera in motion is presented. This approach of extracting the HVTC is independent of the type of 3D surface texture and needs no optical flow information, 3D reconstruction, segmentation, feature tracking.
What is a light source?
Authors: Michael S. Langer, Steven W. Zucker
Venue: CVPR
Year: 1997

Traditional light source modelling is concerned with specific types of light sources, the two most common of which are point sources and daylight. Little attempt has been made, however, to relate different types of sources to each other. For example, how may the lighting from an overcast sky be compared to that from a lamp? Having a theoretical framework to compare different types of light sources is important for computer vision, in particular for understanding shading and shadow cues. A vision system needs to take account of the light source in order to interpret these cues. In this paper, we present a framework for comparing types of light sources which is based on a dimensional analysis of the set of light rays in a free space. Specifically, we introduce a 4-D light source hypercube in which the different types of sources may be embedded and compared. We also present a novel definition for light sources which generalizes the standard definition of a source as an emitter.
Using Chromaticity Distributions and Eigenspace Analysis for Pose, Illumination, and Specularity-Invariant Recognition of 3D Objects
Authors: Stephen Lin, Sang Wook Lee
Venue: CVPR
Year: 1997

The distribution of object colors can be effectively utilized for recognition and indexing. Difficulties arise in the recognition of object color distributions when there are variations in illumination color changes in object pose with respect to illumination direction, and specular reflections. However, most of the recent approaches to color-based recognition focus mainly on illumination color invariance. The authors propose an approach that identifies object color distributions influenced by: (1) illumination pose, (2) illumination color and (3) specularity. They suggest the use of chromaticity distributions to achieve illumination pose invariance. To characterize changes in chromaticity distribution due to illumination color a set of chromaticity histograms of each object is generated for a range of lighting colors based on linear models of illumination and reflectance, and the histograms are represented using a small number of eigenbasis vectors constructed from principal components analysis. Since specular reflections may alter the chromaticity distributions of rest objects, a model-based specularity detection/rejection algorithm, called chromaticity differencing, is developed to reduce these effects.
Configuration based scene classification and image indexing
Authors: Pamela Lipson, Eric Grimson, Pawan Sinha
Venue: CVPR
Year: 1997

Scene classification is a major open challenge in machine vision. Most solutions proposed so far such as those based on color histograms and local texture statistics cannot capture a scene's global configuration, which is critical in perceptual judgments of scene similarity. We present a novel approach, "configural recognition", for encoding scene class structure. The approach's main feature is its use of qualitative spatial and photometric relationships within and across regions in low resolution images. The emphasis on qualitative measures leads to enhanced generalization abilities and the use of low-resolution images renders the scheme computationally efficient. We present results on a large database of natural scenes. We also describe how qualitative scene concepts may be learned from examples.
Edge Flow: A Framework of Boundary Detection and Image Segmentation
Authors: Wei-Ying Ma, B. S. Manjunath
Venue: CVPR
Year: 1997

A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By iteratively propagating the edge flow, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection.
Object tracking using affine structure for point correspondences
Authors: Gurmeet Singh Manku, Pankaj Jain, Amit Aggarwal, Lalit Kumar, Subhashis Banerjee
Venue: CVPR
Year: 1997

A new object tracking algorithm based on affine structure has been developed and it is shown that its performance is better than that of a Kalman filter based correlation tracker. The algorithm is fast, reliable, viewpoint invariant, and insensitive to occlusion and/or individual corner disappearance or reappearance. Detailed experimental analysis on a long real image sequence is also presented.
A class of probabilistic shape models
Authors: Jorge S. Marques, Arnaldo J. Abrantes
Venue: CVPR
Year: 1997

Deformable models are related to other data representation methods. It was recently proposed a class of models based on a fuzzy energy function which includes many well known algorithms (snakes, elastic nets, fuzzy and hard c-means and Kohonen maps). This paper describes a probabilistic extension of these algorithms in a Bayesian framework, using Gibbs-Boltzman distributions. It is shown that the new class of models minimizes an energy function with an additional term: the log partition function. The role of the log partition function in probabilistic versions of snakes, c-means and elastic nets is studied and analytic expressions are derived in the case of probabilistic snakes. The log partition function produces an additional force field which improves the performance of these algorithms in some applications.
Edge Localization in Surface Reconstruction Using Optimal Estimation Theory
Authors: Shailendra Mathur, Frank P. Ferrie
Venue: CVPR
Year: 1997

Many relaxation based smoothing methods used in surface reconstruction algorithms filter out the effect of noise in image data, but result in the elimination of important discontinuity information as well. In this paper the inter-pixel interaction during relaxation is shown to be equivalent to a multiple measurement fusion problem which can be solved using optimal estimation theory. Pixels in a given neighbourhood act as noisy information sources, combining their information to update the state of that neighbourhood. By formulating discontinuities as another "noise" source in the image, and by using the so-called Curvature Consistency reconstruction algorithm on range images, it is shown that optimal estimation theory offers a method for the automatic and adaptive localization of discontinuities while providing a smooth piece wise continuous surface description.
A Real-time Computer Vision System for Measuring Traffic Parameters
Authors: Philip F. McLauchlan, David Beymer, Benjamin Coifman, Jitendra Malik
Venue: CVPR
Year: 1997

For the problem of tracking vehicles on freeways using machine vision, existing systems work well in free-flowing traffic. Traffic engineers, however, are more interested in monitoring freeways when there is congestion, and current systems break down for congested traffic due to the problem of partial occlusion. We are developing a feature-based tracking approach for the task of tracking vehicles under congestion. Instead of tracking entire vehicles, vehicle sub-features are tracked to make the system robust to partial occlusion. In order to group together sub-features that come from the same vehicle, the constraint of common motion is used. In this paper we describe the system, a real-time implementation using a network of DSP chips, and experiments of the system on approximately 44 lane hours of video data.
Prediction Intervals for Surface Growing Range Segmentation
Authors: James V. Miller, Charles V. Stewart
Venue: CVPR
Year: 1997

The surface growing framework presented by P. Besl and R. Jain (1988) has served as the basis for many range segmentation techniques. It has been augmented with alternative fitting techniques, model selection criteria, and solid modelling components. All of these approaches, however require global thresholds and large isolated seed regions. Range scenes typically do not satisfy the global threshold assumption since it requires data noise characteristics to be constant throughout the scene. Furthermore, as scene complexity increases, the number of surfaces, discontinuities, and outliers increase, hindering the identification of large seed regions. We present statistical criteria based on multivariate regression to replace the traditional decision criteria used in surface growing. We use local estimates and their uncertainties to construct criteria which capture the uncertainty in extrapolating estimated fits. We restrict surface expansion to very localized extrapolations, increasing the sensitivity to discontinuities and allowing regions to refine their estimates and uncertainties. Our approach uses a small number of parameters which are either statistical thresholds or cardinality measures, i.e. we do not use thresholds defined by specific range distances or orientation angles.
Fast 3D Stabilization and Mosaic Construction
Authors: Carlos Morimoto, Rama Chellappa
Venue: CVPR
Year: 1997

The authors present a fast electronic image stabilization system that compensates for 3D rotation. The extended Kalman filter framework is employed to estimate the rotation between frames, which is represented using unit quaternions. A small set of automatically selected and tracked feature points are used as measurements. The effectiveness of this technique is also demonstrated by constructing mosaic images from the motion estimates, and comparing them to mosaics built from 2D stabilization algorithms. Two different stabilization schemes are presented. The first, implemented in a real-time platform based on a Datacube MV200 board, estimates the motion between two consecutive frames and is able to process gray level images of resolution 128/spl times/120 at 10 Hz. The second scheme estimates the motion between the current frame and an inverse mosaic; this allows better estimation without the need for indexing the new image frames. Experimental results for both schemes using real and synthetic image sequences are presented.
Registering Multiple Cartographic Models with the Hierarchical Mixture of Experts Algorithm
Authors: Simon Moss, Edwin R. Hancock
Venue: CVPR
Year: 1997

This paper describes an application of the hierarchical mixture of experts algorithm (HME) to the registration of multiple cartographic models to noisy radar data. According to the HME algorithm each model is represented by a set of maximum likelihood registration parameters together with a set of matching probabilities. This architecture can be viewed as providing simultaneous registration and hypothesis verification. The maps in the cartographic data-base compete to account for radar data through the imposed probability normalisation. The resulting matching algorithm can be regarded as a generic tool for model retrieval from a database. Our evaluation on radar images illustrates some of the characteristics of the algorithm. Our main conclusions are that the method is both robust to added image noise and poor initialisation.
Detection and Description of Buildings from Multiple Aerial Images
Authors: Sanjay Noronha, Ramakant Nevatia
Venue: CVPR
Year: 1997

A method for detection and description of rectangular buildings from two or more registered aerial intensity images is proposed. The output is a 3D description of the buildings, with an associated confidence measure for each building. Hierarchical perceptual grouping and matching across views is employed to increase the robustness of the system. Verification of selected building hypotheses is done using shadow and wall evidence of the buildings. The system is largely feature-based. Grouping and matching are performed in a hierarchical manner utilizing primitives of increasing complexity, starting with line segments and junctions, and proceeding to higher level features. Binocular and trinocular epipolar constraints are used to reduce the search space for matching features.
LAFTER: Lips and Face Real-Time Tracker
Authors: Nuria Oliver, Alex Pentland, François Bérard
Venue: CVPR
Year: 1997

This paper describes an active-camera real-time system for tracking, shape description, and classification of the human face and mouth using only an SGI Indy computer. The system is based on use of 2-D blob features, which are spatially-compact clusters of pixels that are similar in terms of low-level image properties. Patterns of behavior (e.g., facial expressions and head movements) can be classified in real-time using Hidden Markov Model (HMM) methods. The system has been tested on hundreds of users and has demonstrated extremely reliable and accurate performance. Typical classification accuracies are near 100%.
Training Support Vector Machines: an Application to Face Detection
Authors: Edgar Osuna, Robert M. Freund, Federico Girosi
Venue: CVPR
Year: 1997

We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs., 1985) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.
Image velocity estimation from trajectory surface in spatiotemporal space
Authors: Kazuhiro Otsuka, Tsutomu Horikoshi, Satoshi Suzuki
Venue: CVPR
Year: 1997

A new framework and method, based on image motion trajectories in spatiotemporal space (x-y-t space), are proposed to estimate image velocity from an image sequence. We focus on the surfaces of the trajectories in the x-y-t space formed by the edges and contours of moving objects and obtain image velocity from the orientation of the intersection line formed by tangent planes on the trajectories. The proposed method includes two Hough transforms to detect the most dominant orientation in all possible intersection lines and reliably produces the dominant translational image velocity semi-locally. Also, the confidence measure of estimates is defined to decide the optimal size of patch that suppresses the aperture problem. Experimental results from several synthetic and real image sequences are presented to verify the effectiveness of the method and to confirm its robustness against noise and occlusion.