OpenRaiser/Intern-Atlas huggingface dataset:
A Fast Scheme for Altering Resolution in the Compressed Domain
Authors: Rakesh Dugad, Narendra Ahuja
Venue: CVPR
Year: 1999

Given a video frame or image in terms of its 8/spl times/8 block-DCT coefficients we wish to obtain a downsized (lower resolution) or upsized (higher resolution) version of this frame also in terms of 8/spl times/8 block -DCT coefficients. We propose an algorithm for achieving this directly in the compressed domain which is computationally much faster, produces visually sharper images and gives significant improvements in PSNR (typically 4 dB better compared to other compressed domain methods based on bilinear interpolation). The downsampling and upsampling schemes combined together preserve all the low-frequency DCT coefficients of the original signal. This implies tremendous savings for coding the difference between the original (unsampled image) and its prediction (the upsampled image). This is desirable for many applications based on scalable encoding of video.
Learning 2D Shape Models
Authors: Nicolae Duta, Anil K. Jain 0001, Marie-Pierre Dubuisson-Jolly
Venue: CVPR
Year: 1999

A new fully automated shape learning method is presented. It is based on clustering a set of training shapes in the original shape space (defined by the coordinates of the contour points) and performing a Procrustes analysis on each cluster to obtain cluster prototypes and information about shape variation. The main difference from previously reported methods is that the training set is first automatically clustered and those shapes considered to be outliers are discarded. The second difference is in the manner in which registered sets of points are extracted from each shape contour. As a direct application of our shape learning method, an 11-structure shape model of brain substructures was extracted from MR image data, an eigen-shape model was automatically trained, and employed to segment several MR brain images not present in the shape-training set. A quantitative analysis of our shape registration approach, within the main cluster of each structure, shows that our results compare very well to those achieved by manual registration; achieving an average rms error of about 1 pixel. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual shape registration and analysis.
The Customized-Queries Approach to CBIR Using EM
Authors: Jennifer G. Dy, Carla E. Brodley, Avinash C. Kak, Chi-Ren Shyu, Lynn S. Broderick
Venue: CVPR
Year: 1999

This paper makes two contributions. The first contribution is an approach called the "customized-queries" approach (CQA) to content-based image retrieval. The second is an algorithm called FSSEM that performs feature selection and clustering simultaneously. The customized queries approach first classifies a query using the features that best differentiate the major classes and then customizes the query to that class by using the features that best distinguish the images within the chosen major class. This approach is motivated by the observation that the features that are most effective in discriminating among images from different classes may not be the most effective for retrieval of visually similar images within a class. This occurs for domains in which not all pairs of images within one class have equivalent visual similarity, i.e., subclasses exists. Because we are not given subclass labels, we must simultaneously find the features that best discriminate the subclasses and at the same time find these subclasses. We use FSSEM to find these features. We apply this approach to content-based retrieval of high-resolution tomographic images of patients with lung disease and show that this approach radically improves the retrieval precision over the traditional approach that performs retrieval using a single feature vector.
Improving Identification Performance by Integrating Evidence from Sequences
Authors: Gareth J. Edwards, Christopher J. Taylor 0001, Timothy F. Cootes
Venue: CVPR
Year: 1999

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.
Motion Segmentation: A Synergistic Approach
Authors: Cornelia Fermüller, Tomás Brodský, Yiannis Aloimonos
Venue: CVPR
Year: 1999

Since estimation of camera motion requires knowledge of independent motion, and moving object detection and localization requires knowledge about the camera motion, the two problems of motion estimation and segmentation need to be solved together in a synergistic manner. This paper provides an approach to treating both these problems simultaneously. The technique introduced here is based on a novel concept, "scene ruggedness" which parameterizes the variation in estimated scene depth with the error in the underlying three-dimensional (3D) motion. The idea is that incorrect 3D motion estimates cause distortions in the estimated depth map, and as a result smooth scene patches are computed as rugged surfaces. The correct 3D motion can be distinguished, as it does not cause any distortion and thus gives rise to the background patches with the least depth variation between depth discontinuities, with the locations corresponding to independent motion being rugged. The algorithm presented employs a binocular observer whose nature is exploited in the extraction of depth discontinuities, a step that facilitates the overall procedure, but the technique can be extended to a monocular observer in a variety of ways.
Statistical Biases in Optic Flow
Authors: Cornelia Fermüller, Robert Pless, Yiannis Aloimonos
Venue: CVPR
Year: 1999

The computation of optical flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing optic flow from image derivatives even in regions of consistent flow can lead to a systematic bias dependent upon the direction of the optic flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing optical flow in the spatiotemporal frequency domain. The predicted bias in the optical flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted optic flow or undistorted normal flow in tasks requiring higher lever processing.
Sampling, Resampling and Colour Constancy
Authors: David A. Forsyth
Venue: CVPR
Year: 1999

We formulate colour constancy as a problem of Bayesian inference, where one is trying to represent the posterior on possible interpretations given image data. We represent the posterior as a set of samples, drawn from that distribution using a Markov chain Monte Carlo method. We show how to build an efficient sampler. This approach has the advantage that it unifies the constraints on the problem, and represents possible ambiguities. In turn, a good description of possible ambiguities means that new information, instead of producing contradictions, is easily incorporated by resampling existing samples. The method is demonstrated on the case where surfaces seen in two distinct images are later discovered to be the same. We show examples using images of real scenes.
Surface Reconstruction from Multiple Aerial Images in Dense Urban Area
Authors: M. Fradkin, Michel Roux, Henri Maître, Ugur M. Leloglu
Venue: CVPR
Year: 1999

Accurate 3D surface models of dense urban areas are essential for a variety of applications, such as cartography, urban planning and monitoring mobile communications, etc. Since manual surface reconstruction is very costly and time-consuming, the development of automated algorithms is of great importance. While most of existing algorithms focus on surface reconstruction either in rural or sub-urban areas, we present an approach dealing with dense urban scenes. The approach utilizes different image-derived cues, like multiview stereo and color information, as well as the general scene knowledge, formulated in data-driven reasoning and geometric constraints. Another important feature of our approach is simultaneous processing of 2D and 3D data. Our approach begins with two independent tasks: stereo reconstruction using multiple views and region-based image segmentation, resulting in generation disparity and segmentation maps, respectively. Then, the information derived from the both maps is utilized for generation of a dense elevation map, through robust verification of planar surface approximations for the detected regions and imposition of geometric constraints. The approach has been successfully tested on complex residential and industrial scenes.
Virtual Snakes for Occlusion Analysis
Authors: Ben Galvin, Brendan McCane, Kevin Novins
Venue: CVPR
Year: 1999

We introduce virtual snakes for generating occlusion hypotheses. Initially, snakes are clustered based on their motion to form object hypotheses-a type of motion segmentation. When two snakes intersect, four virtual snakes are generated-a background and a foreground snake for each of the original two. The two foreground virtual snakes are allowed to relax, while the two background virtual snakes move in accordance with their previous motion. The combined energies of the snakes in the two colliding objects are examined after the collision to determine the occlusion relationship, and the inconsistent virtual snakes are deleted. We show that this heuristic can be used to correctly track objects in the presence of strong occlusion.
Stochastic Image Segmentation by Typical Cuts
Authors: Yoram Gdalyahu, Daphna Weinshall, Michael Werman
Venue: CVPR
Year: 1999

We present a stochastic clustering algorithm which uses pairwise similarity of elements, based on a new graph theoretical algorithm for the sampling of cuts in graphs. The stochastic nature of our method makes it robust against noise, including accidental edges and small spurious clusters. We demonstrate the robustness and superiority of our method for image segmentation on a few synthetic examples where other recently proposed methods (such as normalized-cut) fail. In addition, the complexity of our method is lower. We describe experiments with real images showing good segmentation results.
Parameterized Image Varieties and Estimation with Bilinear Constraints
Authors: Yakup Genc, Jean Ponce, Yoram Leedan, Peter Meer
Venue: CVPR
Year: 1999

This paper addresses the problem of reliably estimating the coefficients of the parameterized image variety (PIV) associated with the set of weak perspective images of a rigid scene, with applications in image-based rendering. Exploiting the fact that the constraints defining the PIV are linear in its coefficients and bilinear in the image data, the estimation procedure is cast in the errors-in-variables framework and solved using the method proposed by Y. Leedan and P. Meer (1998) for this type of problems. The proposed approach has been implemented, and experiments with real data are shown to yield much better prediction power than the original method based on singular value decomposition. Extensions to the more difficult case of paraperspective projection are briefly discussed.
On the Intrinsic Reconstruction of Shape from its Symmetries
Authors: Peter J. Giblin, Benjamin B. Kimia
Venue: CVPR
Year: 1999

We address the issue of the use of symmetry-based representations, such as the medial axis and an augmented form of it, the shock structure, to regenerate shapes. First, we address pointwise reconstruction of the boundary from points of the medial axis. As classified into three generic types (A/sup 2//1 mid-branch, A/sub 3/ end point of a branch, and A/sub 1//sup 3/ junction). Second, we examine the intrinsic reconstruction of shape when differential properties of the axis are also available. We show the surprising result that the tangent and curvature of the medial axis, coupled with the speed and acceleration of the shock flowing along the's axis, i.e., first and second order properties, are sufficient to determine the boundary tangents and curvatures at corresponding points of the boundary. This implies that for a rather coarse sampling of the symmetry axis, the location together with its tangent, curvature: speed, and acceleration is sufficient to accurately regenerate a local neighborhood of shape at this point. Together with reconstruction properties at junction (A/sup 3//sub 1/) and end points (A/sub 3/), these results lead to the full intrinsic regeneration of a shape from a representation of it as a directed planar graph (where the links represent curvature and acceleration functions, and where the nodes contain tangent and speed information): a representation ideally suited for the design and manipulation of free-form shape.
Background Estimation and Removal Based on Range and Color
Authors: Gaile G. Gordon, Trevor Darrell, Michael Harville, John Woodfill
Venue: CVPR
Year: 1999

Background estimation and removal based on the joint use of range and color data produces superior results than can be achieved with either data source alone. This is increasingly relevant as inexpensive, real-time, passive range systems become more accessible through novel hardware and increased CPU processing speeds. Range is a powerful signal for segmentation which is largely independent of color and hence not effected by the classic color segmentation problems of shadows and objects with color similar to the background. However range alone is also not sufficient for the good segmentation: depth measurements are rarely available at all pixels in the scene, and foreground objects may be indistinguishable in depth when they are close to the background. Color segmentation is complementary in these cases. Surprisingly, little work has been done to date on joint range and color segmentation. We describe and demonstrate a background estimation method based on a multidimensional (range and color) clustering at each image pixel. Segmentation of the foreground in a given frame is performed via comparison with background statistics in range and normalized color. Important implementation issues such as treatment of shadows and low confidence measurements are discussed in detail.
Norm2-Based Face Recognition
Authors: Daniel B. Graham, Nigel M. Allinson
Venue: CVPR
Year: 1999

Increasingly the problems of recognising faces under a variety of viewing conditions, including depth rotations, is being considered in the field. The concept of norm-based coding in face recognition is not new but has been little investigated in machine models. Here we describe a norm-based face recognition system which is capable of generalising from a single training view to recognise novel views of target faces. The system is based upon the characteristic nature faces as they move through a pose-varying eigenspace of facial images and deviations from the norm of a gallery of face images. We illustrate the use of the technique for a large range of pose variation.
Gesture Localization and Recognition using Probabilistic Visual Learning
Authors: Raouf Hamdan, Fabrice Heitz, Laurent Thoraval
Venue: CVPR
Year: 1999

A generic approach for the extraction and recognition of gesture using raw grey-level images is presented. The probabilistic visual learning approach, a learning method recently proposed by B. Moghaddam and A. Pentland (1997), is used to create a set of compact statistical representations of gesture appearance on low dimensional eigenspaces. The same probabilistic modeling framework is used to extract and track gesture and to perform gesture recognition over long image sequences. Gesture extraction and tracking are based on maximum likelihood gesture detection in the input image. Recognition is performed by using the set of learned probabilistic appearance models as estimates of the emission probabilities of a continuous density hidden Markov model (CDHMM). Although the segmentation and CDHMM-based recognition use raw grey-level images, the method is fast, thanks to the data compression obtained by probabilistic visual learning. The approach is comprehensive and may be applied to other visual motion recognition tasks. It does not require application-tailored extraction of image features, the use of markers or gloves. A real-time implementation of the method on a standard PC-based vision system is under consideration.
A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values
Authors: Joon H. Han, Yoon K. Kim
Venue: CVPR
Year: 1999

In this paper, a new fuzzy K-nearest neighbor (K-NN) algorithm, called "Variance Weighted Fuzzy K-NN", is proposed. The main idea of this method is in giving weights to neighbors according to the standard deviation of their class membership values which reflect the value of a discriminant function. The classification results of 32 classes of complex images are given. Compared to the K-NN and fuzzy K-NN algorithms, our method shows an improved classification rate for various conditions.
Yet another Method for Pose Estimation: A Probabilistic Approach using Points, Lines, and Cylinders
Authors: Robert Hanek, Nassir Navab, Mirko Appel
Venue: CVPR
Year: 1999

In this work, we use points, fines, and the linear extremal contours of cylinders to estimate the position and orientation of the camera in the world coordinate system. Other line-based pose estimation methods use the correspondences between 3D lines in space and 2D image lines, although the model and its observation are finite line segments. We present a noise model describing the probabilistic relationship between 3D lines and cylinders and their noisy observations. The noise model takes the finite nature of the observation into account. Position and orientation of cameras are estimated using a maximum-likelihood approach. Covariance matrices, confidence limits, and standard translation and rotation errors are estimated by singular value decomposition of the Jacobian matrix. The method provides clear indications on the reliability of each of the estimated parameters, and enables the user to add appropriate information, in terms of feature correspondence, to improve the accuracy if necessary. Simulation results are used to compare this method with some of the previously published ones. The algorithm is currently being used on real data for the update of 3D CAD models of industrial environments.
Multiscale Image Registration Using Scale Trace Correlation
Authors: Bruce B. Hansen, Bryan S. Morse
Venue: CVPR
Year: 1999

This paper presents a method for registering images at different magnifications (scales) by treating the problem not only as one of scaling the image coordinates but also as one inherently involving multiresolution information. While some existing methods for multiresolution registration do consider the way the resolution (scale) affects the image, they often consider the image one scale at a time, using geometric properties within that scale. Others use multiscale information, usually to produce more robust results, but only to register same-magnification images (e.g., stereo). A scale trace is the set of values that a single pixel takes on as magnification decreases and the effective point specifications corresponding increases. As such, scale traces capture information across multiple scales. This paper presents a method that uses correlation of scale traces through multiresolution images to find correspondences between images of the same scene at differing magnifications. Because the method relies solely on the multiscale properties of the images themselves and not on spatial correspondence, it is less sensitive to discrete sampling of potential scale factors. Sample results demonstrate that the method is able to accurately identify both the relative position and relative magnification (scale) of the two images.
Invariant Recognition in Hyperspectral Images
Authors: Glenn Healey, David Slater
Venue: CVPR
Year: 1999

The spectral radiance measured for a material by an airborne hyperspectral sensor depends strongly on. The illumination environment and the atmospheric conditions. This dependence has limited the success of material identification algorithms that rely exclusively on the information contained in hyperspectral image data. In this paper we use a comprehensive physical model to show that the set of observed 0.4-2.5 /spl mu/m spectral radiance vectors for a material lies in a lour-dimensional subspace of the hyperspectral measurement space. The physical model captures the dependence of reflected sunlight, reflected skylight, and path radiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace model, we develop a local maximum likelihood algorithm for automated material identification that is invariant to illumination, atmospheric conditions, and the scene geometry. We demonstrate the invariant algorithm for the automated identification of material samples in HYDICE imagery acquired under different illumination and atmospheric conditions.
Joint Detection for Potsherds of Broken Earthenware
Authors: Kenta Hori, Masakazu Imai, Tsukasa Ogasawara
Venue: CVPR
Year: 1999

In this paper, we propose a new strategy for detecting the joint among two potsherds. Joint detection problems were studied in jigsaw puzzle assembling. However, the shape assumptions of a piece used in the past researches cannot be applied to joint detection to reconstruct broken earthenware. To detect the joint, the most similar section among two contours must be detected by partial verification. In our strategy, each contour is segmented for partial verification without making any assumption about the shape of a potsherd, unlike previous jigsaw puzzle assembling methods. Our strategy consists of five processes: the contour segmentation process, the segment description process, the segment verification process, the candidate extraction process, and the candidate verification process. We also present experimental results of our strategy with two-dimensional images of potsherds.
3D Geometric Invariant Alignment of Surfaces with Application in Brain Mapping
Authors: Walid S. Ibrahim, Fernand S. Cohen
Venue: CVPR
Year: 1999

This paper is concerned with the problem of full or partial alignment of surfaces in the presence of affine transformations, local deformation and noise. This work addresses many alignment problems in diverse areas such as face recognition, fusion of multi-modality (e.g., evoked potential and MRI) for in vivo alignment of metabolic with anatomical maps, and brain mapping. In this paper, we concentrate on brain mapping and consider the intra and inter-animal brain surface registration problems. Surface alignment is based on a set of local affine invariants derived from the set of ordered inflection point pairs that reside on the umbilical curves of the surface. These points are local intrinsic geometric surface landmarks that are sought after because they are preserved under the affine transformation. The method is illustrated for intra and inter-animal registration using 3D data sets obtained from a sequence of external contours of coronal sections.