OpenRaiser/Intern-Atlas huggingface dataset:
Image Indexing and Retrieval Based on Human Perceptual Color Clustering
Authors: Yihong Gong, Guido Proietti, Christos Faloutsos
Venue: CVPR
Year: 1998

We propose a new image retrieval method based on human perceptual clustering of color images. This color clustering produces for each image a small set of representative colors which captures the color properties of the image, and a small set of sizable contiguous regions which captures the spatial/geometrical properties of the image. The proposed method outperforms the traditional histogram and its improved methods not only with its richer image retrieval capabilities which cover a wider spectrum of user requirements, but also with its powerful indexing scheme which is essential to cater for large scale image databases.
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition
Authors: Michael A. Greenspan
Venue: CVPR
Year: 1998

A method is presented for efficient and reliable object recognition within noisy, cluttered, and occluded range images. The method is based on a strategy which hypothesizes the intersection of the object with some selected image point, and searches for additional surface data at locations relative to that point. At each increment, the image is queried for the existence of surface data at a specific spatial location, and the set of possible object poses is further restricted. Eventually, either the object is identified and localized, or the initial hypothesis is refuted. The strategy is implemented in the discrete domain as a binary decision tree classifier. The tree leaf nodes represent individual voxel templates of the model. The internal tree nodes represent the union of the templates of their descendant leaf nodes. The union of all leaf node templates is the complete template set of the model over its discrete pose space. Each internal node also references a single voxel which is the most common element of its child node templates. Traversing the tree is equivalent to efficiently matching the large set of templates at a selected image seed location. The process is approximately 3 orders of magnitude more efficient than brute-force template matching. Experimental results are presented in which objects are reliably recognized and localized in 6 dimensions in less than 60 seconds within noisy and significantly occluded range images.
Using Adaptive Tracking to Classify and Monitor Activities in a Site
Authors: W. Eric L. Grimson, Chris Stauffer, R. Romano, L. Lee
Venue: CVPR
Year: 1998

We describe a vision system that monitors activity in a site over extended periods of time. The system uses a distributed set of sensors to cover the site, and an adaptive tracker detects multiple moving objects in the sensors. Our hypothesis is that motion tracking is sufficient to support a range of computations about site activities. We demonstrate using the tracked motion data to calibrate the distributed sensors, to construct rough site models, to classify detected objects, to learn common patterns of activity for different object classes, and to detect unusual activities.
Segmenting 3-D Surfaces Using Multicolored Illumination
Authors: Glenn Healey, Lizhi Wang 0006
Venue: CVPR
Year: 1998

We introduce a method for segmenting surfaces of three-dimensional objects using two images of the object obtained from the same viewpoint under different illumination conditions. The method allows surface spectral reflectance to vary from point to point and requires only weak conditions on the uncalibrated illumination configuration. The algorithm is based on the local recovery of an illumination change matrix that depends on surface geometry but not on the spectral reflectance of the surface. We show that for typical sensor noise levels, this technique can be used for the reliable detection of surface orientation changes of a few degrees. This approach can be generalized using a calibrated setup to recover a dense set of surface orientation estimates from two images. We present a set of experiments demonstrating the capability of the algorithm for the segmentation of planar surfaces in the presence of spatially varying spectral reflectance.
Fuzzy Convergence
Authors: Adam W. Hoover, Michael H. Goldbaum
Venue: CVPR
Year: 1998

This work considers the problem of discovering areas of convergence of line-like shapes in an image. The motivating application is to use the convergence of the blood vessel network to automatically locate the optic nerve in an ocular fundus image. A fuzzy segment model is proposed, based on a conjecture that line-like shapes only contribute to a perception of convergence in their near neighborhood. Using this model, a voting-type method is described to compute a convergence image, which can be searched for one absolute, or one or more relative, strongest points of convergence. Results are presented for twenty ocular fundus images, with a 65% success rate for finding the optic nerve.
Fuzzy Relational Distance for Large-Scale Object Recognition
Authors: Benoit Huet, Edwin R. Hancock
Venue: CVPR
Year: 1998

This paper presents a new similarity measure for object recognition from large libraries of line-patterns. The measure draws its inspiration from both the Hausdorff distance and a recently reported Bayesian consistency measure that has been successfully used for graph-based correspondence matching. The measure uses robust error-kernels to gauge the similarity of pair-wise attribute relations defined on the edges of nearest neighbour graphs. We use the similarity measure in a recognition experiment which involves a library of over 1000 line-patterns. A sensitivity study reveals that the method is capable of delivering a recognition accuracy of 98%. A comparative study reveals that the method is most effective when a Gaussian kernel or Huber's robust kernel is used to weight the attribute relations. Moreover, the method consistently outperforms Rucklidge's median Hausdorff distance (1995).
Segmentation by Grouping Junctions
Authors: Hiroshi Ishikawa 0002, Davi Geiger
Venue: CVPR
Year: 1998

We propose a method for segmenting gray-value images. By segmentation, we mean a map from the set of pixels to a small set of levels such that each connected component of the set of pixels with the same level forms a relatively large and "meaningful" region. The method finds a set of levels with associated gray values by first finding junctions in the image and then seeking a minimum set of threshold values that preserves the junctions. Then it finds a segmentation map that maps each pixel to the level with the closest gray value to the pixel data, within a smoothness constraint. For a convex smoothing penalty, we show the global optimal solution for an energy function that fits the data can be obtained in a polynomial time, by a novel use of the maximum-flow algorithm. Our approach is in contrast to a view in computer vision where segmentation is driven by intensity, gradient, usually not yielding closed boundaries.
From Parametric Warping to the Cooperation of Local Features and Global Models
Authors: Thomas Jelonek, Frank P. Ferrie
Venue: CVPR
Year: 1998

This paper addresses the question of how to integrate local and global information-the goal being a stable mechanism to partition parametric data into meaningful classes without injecting a priori information about the data. To do this we introduce a novel framework to represent both local and global information and their interactions. Where both types of information are represented together in parameter space and together define a self-organisation or warping of the data. An unsupervised clustering analysis is then performed to extract from the parametric data classes that are stable and meaningful. As an example of this paradigm we consider the problem of shape decomposition. Here we describe how image discontinuities (i.e. curves, edges or local curvature) can be integrated with global parametric models that represent the image. The resulting class clusters are then equivalent to the inferred part decomposition. An example of how this process can be used is demonstrated by applying it to the specific problem of determining the parts of 3-D objects. Results on real laser rangefinder images of complex objects are presented.
The Acquisition and Use of Interaction Behavior Models
Authors: Neil Johnson 0001, Aphrodite Galata, David C. Hogg
Venue: CVPR
Year: 1998

Providing a machine with the ability to learn and use models of natural interaction is a challenging and largely unaddressed problem. A framework is developed enabling both the acquisition of interaction behaviours from the observation of humans, and the use of the acquired behaviour models to simulate a plausible partner during interaction. Statistically based interaction behaviour models are acquired automatically from the observation of interacting humans. Interaction with a virtual human is achieved using the model together with a stochastic tracking algorithm. Experimental results demonstrate the generation and use of the model for a simple human interaction.
Computer Modeling, Analysis and Synthesis of Dressed Humans
Authors: Nebojsa Jojic, Jin Gu, Ivan Mak, Helen C. Shen, Thomas S. Huang
Venue: CVPR
Year: 1998

In this paper we present a method for 3-D reconstruction of human bodies with application in CAD systems for garment design. The reconstruction scheme uses image information from several arbitrary views and deformable superquadrics as the models of the body parts. Two visual cues are used: occluding contours and stereo (possibly aided by projected patterns). Our preliminary experiments show that the reconstruction is more complete than in purely stereo or structured light based methods and more precise than the reconstruction from occluding contours only. From the reconstructed human body, the body measurements can be taken automatically, and used in garment design. We give an example of draping of virtual garment over the photo-realistic 3D model of the imaged human. One can easily envision the use of the described algorithms in the development of custom-fit garment retail software over the Internet, which would include the possibility of trying the garment on in virtual reality.
Image Segmentation Based on the Integration of Pixel Affinity and Deformable Models
Authors: Timothy N. Jones, Dimitris N. Metaxas
Venue: CVPR
Year: 1998

This paper describes a general-purpose method we have developed for automatically segmenting objects of an unknown number and unknown locations in images. Our method integrates deformable models and statistics of image cues including intensity, gradient, color and texture. By using a combination of image features rather than a single feature such as gradient our method is more robust to noise and sparse data. To allow for the automated segmentation of an unknown number and locations of objects, we simultaneously segment objects initialized at uniformly distributed points in the image. A method is developed to automatically merge models corresponding to the same object. Results of the method are presented for several examples, including greyscale, color and noisy images.
Hierarchical Morphable Models
Authors: Michael J. Jones 0001, Tomaso A. Poggio
Venue: CVPR
Year: 1998

This paper presents a new technique for modelling object classes (such as faces) and matching the model to novel images from the object class. The technique can be used for a variety of image analysis applications including face recognition, object verification and facial expression analysis. The model, called a hierarchical morphable model, is "learned" from example images (partioned into components) and their correspondences. This is an extension to the work on morphable models described in previous papers. Hierarchical morphable models are shown to find good matches to novel face images and are also robust to partial occlusion.
Fingerprint Preselection Using Eigenfeatures
Authors: Toshio Kamei, Masanori Mizoguchi
Venue: CVPR
Year: 1998

In this paper we propose a new distance measure for an identification problem and describe experiments on fingerprint preselection using eigenfeatures of ridge direction patterns. The distance is defined by likelihood ratio of error distribution of feature vectors to the whole distribution of feature vector differences. In addition, we introduce "quality indexes" of feature vectors and make the distance adaptive to the quality indexes. Experiments on fingerprint preselection for ten-print cards revealed that our proposed distance is much more effective than the Mahalanobis distance. By combining the eigenfeatures and traditional classification features, 0.06% false acceptance rate at 2.0% false rejection rate and one million cards/sec preselection speed on a standard workstation have been achieved. This makes it possible to construct high performance fingerprint identification systems.
Video Scene Segmentation via Continuous Video Coherence
Authors: John R. Kender, Boon-Lock Yeo
Venue: CVPR
Year: 1998

In extended video sequences, individual frames are grouped into shots which are defined as a sequence taken by a single camera, and related shots are grouped into scenes which are defined as a single dramatic event taken by a small number of related cameras. This hierarchical structure is deliberately constructed, dictated by the limitations and preferences of the human visual and memory systems. We present three novel high-level segmentation results derived from these considerations, some of which are analogous to those involved in the perception of the structure of music. First and primarily, we derive and demonstrate a method for measuring probable scene boundaries, by calculating a short term memory-based model of shot-to-shot "coherence". The detection of local minima in this continuous measure permits robust and flexible segmentation of the video into scenes, without the necessity for first aggregating shots into clusters. Second, and independently of the first, we then derive and demonstrate a one-pass on-the-fly shot clustering algorithm. Third, we demonstrate partially successful results on the application of these two new methods to the next higher, "theme", level of video structure.
Correspondence between Different View Breast X-Rays Using a Simulation of Breast Deformation
Authors: Yasuyo Kita, Ralph Highnam, Michael Brady 0001
Venue: CVPR
Year: 1998

We develop a method to find correspondences between a Cranio-Caudal (CC) and a Medio-Lateral Oblique (MLO) X-ray image of the same breast. Matching between such pairs of images is considered essential by radiologists for more reliable diagnosis of early breast cancer. The two images are taken while the breast is compressed between the cassette and plate of the X-ray machine, but, almost always, to a different extent in each direction. The deformations of the breast caused by the different compressions in the different directions causes corresponding points to appear far from the straight "epipolar lines" familiar from binocular stereo vision. The method developed in this paper calculates the line in a MLO image corresponding to a point in the CC image through simulation of the deformation and the projection of a 3D line (curve) corresponding to the point. Experiments using actual images show that the method gives good predictions which can be used to find exact correspondences between points in the two images.
A Weighted Combination of Classifiers Employing Shared and Distinct Representations
Authors: Josef Kittler, S. A. Hojjatoleslami 0001
Venue: CVPR
Year: 1998

This paper presents a theoretical framework for the combination of soft decisions generated by experts employing mixed (some shared and some distinct) object representations. By taking the confidence of the individuals experts into account, weighted benevolent fusion strategies are derived. This provides a basis for combining classifiers and illustrates that a substantial gain in performance can be achieved by using the opinions of multiple experts. These strategies are experimentally tested and their effectiveness is considered.
Model-Based Multiscale Detection of 3D Vessels
Authors: Karl Krissian, Grégoire Malandain, Nicholas Ayache, Régis Vaillant, Yves Trousset
Venue: CVPR
Year: 1998

In this paper, we present a new approach to segment vessels from 3D angiography of the brain. Our approach is based on a vessel model and uses a multiscale analysis in order to extract the vessel network surrounding an aneurysm. Our model allows us to choose a criterion based on the eigenvalues of the Hessian matrix for selecting a subset of interesting points near the vessel center. It also allows us to choose a good parameter for a /spl gamma/-normalization of the single scale response. The response at one scale is obtained by integrating along a circle the first derivative of the intensity in the radial direction. Once the multiscale response is obtained, we create a smoothed skeleton of the vessels combined with a MIP or a volume rendering to enhance their visualization. The method has been tested on a large variety of 3-D images of the brain, with excellent results. Vessels of various size and contrast are detected with a remarkable robustness, and most junctions are preserved.
Direct Shape from Texture Using a Parametric Surface Model and an Adaptive Filtering Technique
Authors: Kyoung Mu Lee, C.-C. Jay Kuo
Venue: CVPR
Year: 1998

In this research, we propose a new iterative shape from texture (SFT) algorithm which extracts accurate surface depth information of a curved object covered with fairly homogeneous texture directly. The shape information can be inferred from the rate of texture distortion depicted in an image, and therefore the modeling of the projection and surface geometry as well as the estimation of local texture variation are crucial in obtaining accurate surface shape of an object. By introducing semi-perspective projection camera model and a parametric surface model, we establish a new SFT problem formulation called the textural irradiance equation which relates the local texture density called textural intensity to finite surface parameters. Moreover, by adopting an adaptive multiscale filtering scheme for local texture density estimation, in which the scale or frequency band of a local edge filter is chosen adaptively according to the local shape information, we greatly enhance the accuracy of the estimation of the projected local texture densities, and the final reconstructed shape. We demonstrate the performance of the proposed algorithm by the test with several synthetic and real texture images.
Inferring Segmented Surface Description from Stereo Data
Authors: Mi-Suen Lee, Gérard G. Medioni
Venue: CVPR
Year: 1998

We present an integrated approach to the derivation of scene description from binocular stereo images. By inferring the scene description directly from local measurements of both point and line correspondences, we address both the stereo correspondence problem and the surface reconstruction problem simultaneously. We introduce a robust computational technique called tensor voting for the inference of scene description in terms of surfaces, junctions, and region boundaries. The methodology is grounded in two elements: tensor calculus for representation, and non-linear voting for data communication. By efficiently and effectively collecting and analyzing neighborhood information, we are able to handle the tasks of interpolation, discontinuity detection, and outlier identification simultaneously. The proposed method is non-iterative, robust to initialization and thresholding in the preprocessing stage, and the only critical free parameter is the size of the neighborhood. We illustrate the approach with results on a variety of images.
Probablistic Affine Invariants for Recognition
Authors: Thomas K. Leung, Michael C. Burl, Pietro Perona
Venue: CVPR
Year: 1998

Under a weak perspective camera model, the image plane coordinates in different views of a planar object are related by an affine transformation. Because of this property, researchers have attempted to use affine invariants for recognition. However, there are two problems with this approach: (1) objects or object classes with inherent variability cannot be adequately treated using invariants; and (2) in practice the calculated affine invariants can be quite sensitive to errors in the image plane measurements. In this paper we use probability distributions to address both of these difficulties. Under the assumption that the feature positions of a planar object can be modeled using a jointly Gaussian density, we have derived the joint density over the corresponding set of affine coordinates. Even when the assumptions of a planar object and a weak perspective camera model do not strictly hold, the results are useful because deviations from the ideal can be treated as deformability in the underlying object model.
Generation of the Euclidean Skeleton from the Vector Distance Map by a Bisector Decision Rule
Authors: Hong Li, Albert M. Vossepoel
Venue: CVPR
Year: 1998

The Euclidean skeleton is essential for general shape representation. This paper provides an efficient method to extract a well-connected Euclidean skeleton by a neighbor bisector decision (NBD) rule on a vector distance map. The shortest vector which generates a pixel's distance is stored when calculating the distance map. A skeletal pixel is extracted by checking the vectors of the pixel and its 8 neighbors. This method succeeds in generating a well-connected Euclidean skeleton without any linking algorithm. A theoretical analysis and many experiments with images of different sizes also shows the NBD rule works excellent. The average complexity of the method with the NBD rule algorithm and the vector distance transform algorithm is linear in the number of the pixels.
Metric Rectification for Perspective Images of Planes
Authors: David Liebowitz, Andrew Zisserman
Venue: CVPR
Year: 1998

We describe the geometry constraints and algorithmic implementation for metric rectification of planes. The rectification allows metric properties, such as angles and length ratios, to be measured on the world plane from a perspective image. The novel contributions are: first, that in a stratified context the various forms of providing metric information, which include a known angle, two equal though unknown angles, and a known length ratio; can all be represented as circular constraints on the parameters of an affine transformation of the plane-this provides a simple and uniform framework for integrating constraints; second, direct rectification from right angles in the plane; third, it is shown that metric rectification enables calibration of the internal camera parameters; fourth, vanishing points are estimated using a Maximum Likelihood estimator; fifth, an algorithm for automatic rectification. Examples are given for a number of images, and applications demonstrated for texture map acquisition and metric measurements.