OpenRaiser/Intern-Atlas huggingface dataset:
Ill-Posed Problems in Surface and Surface Shape Recovery
Authors: George I. Kamberov, Gerda Kamberova
Venue: CVPR
Year: 2000

We present new theoretical results which have implications in answering one of the fundamental questions in computer vision: recognition of surfaces and surface shapes. We state the conditions under which: (i) a surface can be recovered, uniquely, from the tangent plane map, in particular from the Gauss map; (ii) a surface shape can be recovered from the metric and the deforming forces. In case where such conditions are not satisfied we classify all exceptions, i.e. the surfaces and surface shapes for which the recovery and registration problems are ill-posed.
Visual Tunnel Analysis for Visibility Prediction and Camera Planning
Authors: Sing Bing Kang, Peter-Pike J. Sloan, Steven M. Seitz
Venue: CVPR
Year: 2000

A sequence of images taken along a camera trajectory captures a subset of scene appearance. If visibility space is the space that encapsulates the appearance of the scene at every conceivable pose and viewing angle, then the act of acquiring the image sequence constitutes "carving a volume in visibility space." We call such a volume a visual tunnel. The analysis of the visual tunnel allows us to do the following: predict the range of virtual camera poses in which the images can be reconstructed totally using the captured rays, predict which parts of the image can be generated for a given virtual camera pose, and plan camera paths for scene visualization at desired locations. We describe our visual tunnel concept and provide illustrative examples in 2D and 3D.
Spatio-Temporal Analysis of Omni Image
Authors: Hiroshi Kawasaki, Katsushi Ikeuchi, Masao Sakauchi
Venue: CVPR
Year: 2000

This paper describes an efficient method to obtain 3D information by using spatio-temporal analysis of omni images for outdoor navigation and map-making in the intelligent transportation system (ITS) application. Two types of omni-directional cameras are employed to make a spatio-temporal volume, which is a sequence of omni images stacked in the spatio-temporal space. For the spatio-temporal analysis of an omni image, we define several different cross sections in such spatio-temporal volumes, and examine characteristics of the traces of image features on the cross sections. We determine that the vertical straight lines in the real world are preserved as straight lines on these cross sections and that the degree of this slope represents the quotient of the velocity of the camera motion and the depth of the object. To acquire 3D information using these characteristics, we propose a hybrid method of the epipolar-plane image (EPI) analysis and the model-based analysis. To demonstrate the effectiveness of this method, we present some experimental results and the ITS applications using an omni-directional video camera to obtain images in outdoor environments.
Camera Model Selection Based on Geometric AIC
Authors: Keisuke Kinoshita, Michael Lindenbaum
Venue: CVPR
Year: 2000

The problem of selecting a camera model is addressed here using the Geometric AIC (Akaike Information Criterion) proposed by Kanatani, which considers both the residual of the data fitting to the model as well as the complexity of the model. Camera models describe the geometrical relation between the 3D location of object points and the image location of their projections. The most commonly used camera models are the projective/perspective camera model and the affine camera model. Intuitively, the projective camera model, which is nonlinear and is characterized by more parameters, models the imaging geometry better, but also, is believed to lead to numerically less stable solutions. The affine camera model, which is an approximation to the projective camera model with less parameters, is recommended to be used when the object depth is much smaller than the object distance. However, there is no quantitative criterion for the decision: which camera model should be used, projective or affine? In this paper, the Geometric AIC criterion is used for deciding between the two camera models is the context of two tasks: estimating the projection matrix from 3D and corresponding 2D data, and estimating the fundamental matrix from two sets of 3D data. It is found that in most cases, it is the projective camera model which is more appropriate. Still, in the cases where the affine camera model is traditionally used, the measures of appropriateness of the two models are roughly the same (with a small advantage to the affine camera model).
Statistical cues for Domain Specific Image Segmentation with Performance Analysis
Authors: Scott Konishi, Alan L. Yuille
Venue: CVPR
Year: 2000

This paper investigates the use of colour and texture cues for segmentation of images within two specified domains. The first is the Sowerby dataset, which contains one hundred colour photographs of country roads in England that have been interactively segmented and classified into six classes-edge, vegetation, air, road, building, and other. The second domain is a set of thirty five-images, taken in San Francisco, which have been interactively segmented into similar classes. In each domain we learn the joint probability distributions of filter responses, based on colour and texture, for each class. These distributions are then used for classification. We restrict ourselves to a limited number of filters in order to ensure that the learnt filter responses do not overfit the training data (our region classes are chosen so as to ensure that there is enough data to avoid over fitting). We do performance analysis on the two datasets by evaluating the false positive and false negative error rates for the classification. This shows that the learnt models achieve high accuracy in classifying individual pixels into those classes for which the filter responses are approximately spatially homogeneous (i.e. road, vegetation, and air but not edge and building). A more sensitive performance measure, the Chernoff information, is calculated in order to quantify how well the cues for edge and building are doing. This demonstrates that statistical knowledge of the domain is a powerful tool for segmentation.
Retinal Thickness Measurements in Optical Coherence Tomography Using a Markov Boundary Model
Authors: Dara Koozekanani, Kim L. Boyer, Cynthia Roberts
Venue: CVPR
Year: 2000

We present a highly accurate, robust system for measuring retinal thickness in optical coherence tomography (OCT) images. OCT is a relatively new imaging modality giving cross sectional images that are qualitatively similar to ultrasound but with 10 /spl mu/m resolution. We begin with a 1-dimensional edge detection kernel to yield edge primitives, which are then selected, corrected, and grouped to form a coherent boundary by use of a Markov model of retinal structure. We have tested the system extensively, and only one of 650 evaluation images caused the algorithm to fail. The anticipated clinical application for this work is the automatic determination of retinal thickness. A careful quantitative evaluation of the system performance over a 1 mm region near the fovea reveals that in more than 99% of the cases, the automatic and manual measurements differed by less than 25 /spl mu/m (below clinical significance), and in 89% of the tests the difference was less than 10 /spl mu/m (near the resolution limit). Current clinical practice involves only a qualitative, visual assessment of retinal thickness. Therefore, a robust, quantitatively accurate system should significantly improve patient care.
Tracking Segmented Objects Using Tensor Voting
Authors: Pierre Kornprobst, Gérard G. Medioni
Venue: CVPR
Year: 2000

The paper presents a new approach to track objects in motion when observed by a fixed camera, with severe occlusions, merging/splitting objects and defects in the detection. We first detect regions corresponding to moving objects in each frame, then try to establish their trajectory. We propose to implement the temporal continuity constraint efficiently, and apply it to tracking problems in realistic scenarios. The method is based on a spatiotemporal (2D+t) representation of the moving regions, and uses the tensor voting methodology to enforce smoothness in space and table of the tracked objects. Although other characteristics may be considered, only the connected components of the moving regions are used, without further assumptions about the object being tracked. We demonstrate the performance of the system on several real sequences.
A Fast and Robust Approach to Recovering Structure and Motion from Live Video Frames
Authors: Takeshi Kurata, Jun Fujiki, Masakatsu Kourogi, Katsuhiko Sakaue
Venue: CVPR
Year: 2000

This paper describes a fast and robust approach to recovering structure and motion from video frames. It first describes a robust recursive factorization method for affine projection. Using the least median of squares (LMedS) criterion, the method estimates the dominant 3D affine motion and discards feature points regarded as outliers. The computational cost of the overall procedure is reduced by combining this robust statistics-based method with a recursive factorization method that can at each frame provide the updated 3D structure of an object at a fixed computational cost by using the principal component analysis. Experiments with synthetic data and with real image sequences demonstrate that the method can be used to estimate the dominant structure and the motion robustly and in real-time, on an off-the-shelf PC. Finally this paper describes preliminary online experiments on selecting feature points, which have the dominant 3D motion, from live video frames by using a PC-cluster system.
Detecting Changes in 3-D Shape Using Self-Consistency
Authors: Yvan G. Leclerc, Quang-Tuan Luong, Pascal Fua, Koji Miyajima
Venue: CVPR
Year: 2000

A method for reliably detecting change in the 3-D shape of objects that are well-modeled as single-value functions Z=f(x,y) is presented. It uses an estimate of the accuracy of the 3-D models derived from a set of images taken simultaneously. This accuracy estimate is used to distinguish between significant and insignificant changes in 3-D models derived from different image sets. The accuracy of the 3-D model is estimated using a general methodology, called self-consistency, for estimating the accuracy of computer vision algorithms, which does not require prior establishment of "ground truth". A novel image-matching measure based on Minimum Description Length (MDL) theory allows us to estimate the accuracy of individual elements of the 3-D model. Experiments to demonstrate the utility of the procedure are presented.
Detecting People in Cluttered Indoor Scenes
Authors: Mi-Suen Lee
Venue: CVPR
Year: 2000

Motion is an important visual cue for scene analysis. It is particularly useful when the scene is cluttered, such as in typical home or office environments. We present a motion segmentation algorithm that makes use of temporal differencing to detect moving people in cluttered indoor scenes. The algorithm is devised based on a couple of perceptual organization principles. To deal with missing data, noise and outliers, a robust segmentation and grouping technique called tensor voting is employed. The resulting real-time people detector can handle the presence of multiple persons, and varying body sizes and poses. It requires no initialization, uses subjective threshold, which defines the minimum saliency of "significant" motion, and the only two parameters are the scales (sizes) of the local neighborhood for region and contour analysis.
Statistical Shape Influence in Geodesic Active Contours
Authors: Michael E. Leventon, W. Eric L. Grimson, Olivier D. Faugeras
Venue: CVPR
Year: 2000

A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero level set of a higher dimensional surface, and evolves the surface such that the zero level set converges on the boundary of the object to be segmented. At each step of the surface evolution, we estimate the maximum a posteriori (MAP) position and shape of the object in the image, based on the prior shape information and the image information. We then evolve the surface globally; towards the MAP estimate, and locally based on image gradients and curvature. Results are demonstrated on synthetic data and medical imagery in 2D min 3D.
Visual Websearching Using Iconic Queries
Authors: Michael S. Lew, Nicu Sebe
Venue: CVPR
Year: 2000

Imagine a professional Web site designer who constantly has to come up with innovative looks for the client's homepages? How does he find the new content? The major search engines such as Hotbot (http://www.hotbot.com) allow us to find text on the Web, but typically have few or no capabilities for finding visual media. In this article we discuss methods for finding visual media on the WWW. The emphasis is on iconic queries which are essentially drag-and-drop visual concepts or simple semantics. We describe our method for finding static feature sets and then describe a novel method called active feature sets, which chooses a feature set based on the context in the image.
Improving Visual Matching
Authors: Michael S. Lew, Nicu Sebe, Thomas S. Huang
Venue: CVPR
Year: 2000

Many visual matching algorithms can be described in terms of the features and the inter-feature distance or metric. The most commonly used metric is the sum of squared differences (SSD), which is valid from a maximum likelihood perspective when the real noise distribution is Gaussian. Based on real noise distributions measured from international test sets, we have found experimentally that the Gaussian noise distribution assumption is often invalid. This implies that other metrics, which have distributions closer to the real noise distribution, should be used. In this paper we considered two different visual matching applications: content-based retrieval in image databases and stereo matching. Towards broadening the results, we also implemented several sophisticated algorithms from the research literature. In each algorithm we compared the efficacy of the SSD metric, the SAD (sum of the absolute differences) metric, the Cauchy metric, and the Kullback relative information. Furthermore, in the case where sufficient training data is available, we discussed and experimentally tested a new metric based directly on the real noise distribution, which we denoted the maximum likelihood metric.
Simultaneous Tracking and Verification via Sequential Posterior Estimation
Authors: Baoxin Li, Rama Chellappa
Venue: CVPR
Year: 2000

An approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given measurement Z and two hypothesis H/sub 1/ and H/sub 0/, we first estimate posterior probabilities P(H/sub 0/|Z) and P(H/sub 1/|Z); and choose the one with the larger posterior probability as the true hypothesis. Applications of the approach are illustrated with experiments devised to evaluated the performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented.
Color Channels Decorrelation by ICA Transformation in the Wavelet Domain for Color Texture Analysis and Synthesis
Authors: Yufeng Liang, Eero P. Simoncelli, Zhibin Lei
Venue: CVPR
Year: 2000

In this paper, we developed a color model to cancel the dependency between color channels, which enables us to separate spectral processing from, spatial processing. We introduced Independent Component Analysis (ICA) transformation in the wavelet domain to decorrelate the subband color joint statistics. The decorrelated joint color conditional histograms display scaling of variance. Gaussian Scale Mixture (GSM) was used to model the subband color statistics and a normalization scheme was adapted to cancel the pair-wise color subband statistical dependency. This color model was combined with the Portilla/Simoncelli texture model to construct the color texture model. Based on this model, features were extracted and the corresponding color texture synthesis scheme was developed.
An Appearance Representation for Multiple Reflection Components
Authors: Stephen Lin, Sang Wook Lee
Venue: CVPR
Year: 2000

Variability in object appearance due to illumination conditions has typically been modelled for only diffuse reflection. In this paper we present a method for determining multiple reflection components for a set of photometric images, and describe a linear representation of these reflection elements for enumerating the possible illumination appearances of an object. The proposed approach involves a simultaneous estimation of reflectance parameters that can be generalized to yield a separation for an arbitrary number of reflection components. We show that the separated components of photometric images can be combined to form their counterparts in images with different illumination. Results are presented to illustrate the performance of this method for uncalibrated lighting directions.
A Computational Model for Repeated Pattern Perception Using Frieze and Wallpaper Groups
Authors: Yanxi Liu 0001, Robert T. Collins
Venue: CVPR
Year: 2000

Humans have an innate ability to perceive symmetry, but it is not obvious how to automate this powerful insight. In this paper the mathematical theory of frieze and wallpaper groups is used to extract visually meaningful building blocks (motifs) from a repeated pattern. A novel peak detection algorithm based on "regions of dominance" is used to automatically detect the underlying translational lattice of a repeated pattern. Following automatic classification of the pattern's symmetry group, knowledge of the interplay between rotation, reflection, glide-reflection and translation in that group leads to a small set of candidate motifs that exhibit local symmetry consistent with the global symmetry of the entire pattern. Although other work has addressed detection of the translational lattice of a repeated pattern, ours is the first to seek a principled method for determining a representative motif. Experiments show that the-resulting pattern motifs conform well with human perception.
Codimension - Two Geodesic Active Contours for the Segmentation of Tubular Structures
Authors: Liana M. Lorigo, W. Eric L. Grimson, Olivier D. Faugeras, Renaud Keriven, Ron Kikinis, Arya Nabavi, Carl-Fredrik Westin
Venue: CVPR
Year: 2000

Curve evolution schemes for segmentation, implemented with level set methods, have become an important approach in computer vision. Previous work has modeled evolving contours which are curves in 2D or surfaces in 3D. Our objective is to explore recent mathematical work enabling the evolution of manifolds of higher co-dimension. We consider 1D curves in 3D (codimension-two) for the application of automatically, segmenting blood vessels in volumetric magnetic resonance angiography (MRA) images. This paper describes the theoretical foundations of our system, CURVES, then provides segmentation results compared against segmentation obtained interactively by a neurosurgeon. Segmentation of bronchi in lung computed tomography (CT) scans are also presented. The new experiments, comparisons to manual segmentation, and sample comparison to the use of a codimension-one regularization force are the primary contributions of this report.
Robust Snake Model
Authors: Hui Luo, Qiang Lu, Raj Acharya, Roger S. Gaborski
Venue: CVPR
Year: 2000

In this paper, we propose a new deformable model a robust snake model, which solves the primary problems suffered by the conventional snake, such as contour initialization, proper internal parameter setting and the limited capture range of the external energy. A reformulated internal energy is used to serve the smoothness of snake contour without a contraction of the contour. The external energy combines both region and edge information to enlarge the capture range, and also reduces the requirement of initial contour. Both synthetic and real gray-level images are selected to evaluate the performance of the proposed model. Its implementation show it robust, fast and accurate. Initial experimental results are encouraging.
On Measuring Low-Level Saliency in Photographic Images
Authors: Jiebo Luo 0001, Amit Singhal 0001
Venue: CVPR
Year: 2000

Measuring perceptual saliency of regions in a scene is important for determining regions of interest. Color, texture and shape cues are good low-level features for detecting saliency. While self saliency refers to intrinsic attributes of a region, relative saliency as used to measure how salient a region is relative to its surrounding and, thus, needs to be defined within a spatial context. A few spatial context models are investigated in this study. In particular, we propose an auto-scaled, amorphous neighborhood as the context model to obtain reliable measurements of relative saliency features. Comparison of three context models has shown that the proposed model is capable of generating predicates more consistent with perceived saliency.
A Novel Algorithm for Rotated Human Face Detection
Authors: Xiao-guang Lv, Jie Zhou 0001, Changshui Zhang
Venue: CVPR
Year: 2000

We present a framework to detect human faces rotated within the image plane. A novel orientation histogram of an image is constructed using local orientation analysis. From the view of an oriented pattern, a histogram of the human face shows symmetry with respect to the orientation, /spl beta/, of the principal axis, and a local peak also occurs at the orientation orthogonal to /spl beta/. We analyze the histogram to find the orientation of the face principal axis, with which conventional upright face detectors can be used to verify the supposed rotated face. Experimental results show that the algorithm is effective.
Region Correspondence by Global Configuration Matching and Progressive Delaunay Triangulation
Authors: Jianbo Ma, Narendra Ahuja
Venue: CVPR
Year: 2000

In this paper, we present a novel algorithm for establishing region correspondences across images by first matching global region configuration and then propagating the matches locally constrained by Delaunay triangulation. We exploit a global configuration constraint, which has not been explicitly used in existing matching algorithms. The proposed algorithm is comprised of two stages: first, stable regions are matched by enforcing the global configuration constraint. This yields a set of global matches corresponding to stable regions distributed over the images. In the second stage, these matches are used to guide the matching of the remaining unmatched regions in the intervening spaces. This is done by enforcing local positioning constraint, which starts with the Delaunay triangulation defined by the global matches and performs progressive Delaunay triangulation for local matching. Experiments on both stereo and motion images are presented to show the effectiveness of the proposed algorithm.