OpenRaiser/Intern-Atlas huggingface dataset:
A Methodology for Deriving Probabilistic Correctness Measures from Recognizers
Authors: Djamel Bouchaffra, Venu Govindaraju, Sargur N. Srihari
Venue: CVPR
Year: 1998

This paper describes the derivation of probability of correctness from scores assigned by most recognizers. Motivation for this research is three-fold: (i) probability values can be used to rerank the output of any recognizer by using a new set of training data; if the training data is sufficiently large and representative of the test data, the recognition rates are seen to improve significantly, (ii) derivation of probability values puts the output of different recognizers on the same scale; this makes comparison across recognizers trivial, and (iii) word recognition can be readily extended to phrase and sentence recognition because the integration of language models becomes straightforward. We have conducted an extensive set of experiments. The results show a reranking of recognition choices based on the derived probability values leading to an enhancement in performance.
Remote Reality Demonstration
Authors: Terrance E. Boult
Venue: CVPR
Year: 1998

Remote Reality is an approach to providing an immersive environment via omni-directional imaging. The system can use a live video-feed from a remote location or can use recorded data and be remote in both space and time. While less interactive than traditional VR, remote reality has an important advantage: there is little to no need for model building. In addition, the objects, the textures and the motions are not just realistic, they are remote views of reality.
Markov Random Fields with Efficient Approximations
Authors: Yuri Boykov, Olga Veksler, Ramin Zabih
Venue: CVPR
Year: 1998

Markov Random Fields (MRFs) can be used for a wide variety of vision problems. In this paper we focus on MRFs with two-valued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut problem on a graph. We develop efficient algorithms for computing good approximations to the minimum multiway, cut. The visual correspondence problem can be formulated as an MRF in our framework; this yields quite promising results on real data with ground truth. We also apply our techniques to MRFs with linear clique potentials.
Using Hierarchical Shape Models to Spot Keywords in Cursive Handwriting Data
Authors: Michael C. Burl, Pietro Perona
Venue: CVPR
Year: 1998

Different instances of a handwritten word consist of the same basic features (humps, cusps, crossings, etc.) arranged in a deformable spatial pattern. Thus, keywords in cursive text can be detected by looking for the appropriate features in the "correct" spatial configuration. A keyword can be modeled hierarchically as a set of word fragments, each of which consists of lower-level features. To allow flexibility, the spatial configuration of keypoints within a fragment is modeled using a Dryden-Mardia (DM) probability density over the shape of the configuration. In a writer-dependent test on a transcription of the Declaration of Independence (/spl sim/1300 words, /spl sim/7500 characters), the method detected all eleven instances of the keyword "government" with only four false positives.
Hierarchical Organization of Appearance-Based Parts and Relations for Object Recognition
Authors: Octavia I. Camps, Chien-Yuan Huang, Tapas Kanungo
Venue: CVPR
Year: 1998

Previously a new object representation using appearance-based parts and relations to recognize 3D objects from 2D images, in the presence of occlusion and background clutter, was introduced. Appearance-based parts and relations are defined in terms of closed regions and the union of these regions, respectively. The regions are segmented using the MDL principle, and their appearance is obtained from collection of images and compactly represented by parametric manifolds in the eigenspaces spanned by the parts and the relations. In this paper we introduce the discriminatory power of the proposed features and describe how to use it to organize large databases of objects.
Automatic Mosaicing with Super-Resolution Zoom
Authors: David P. Capel, Andrew Zisserman
Venue: CVPR
Year: 1998

We describe mosaicing for a sequence of images acquired by a camera rotating about its centre. The novel contributions are in two areas. First, in the automation and estimation of image registration: images
Head Tracking via Robust Registration in Texture Map Images
Authors: Marco La Cascia, John Isidoro, Stan Sclaroff
Venue: CVPR
Year: 1998

A novel method for 3D head tracking in the presence of large head rotations and facial expression changes is described. Tracking is formulated in terms of color image registration in the texture map of a 3D surface model. Model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. Parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows and specular highlights. The system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. Experimental results are reported.
A Statistical Framework for Long-Range Feature Matching in Uncalibrated Image Mosaicing
Authors: Tat-Jen Cham, Roberto Cipolla
Venue: CVPR
Year: 1998

The problem considered is that of estimating the projective transformation between two images in situations where the image motion is large and feature matching is not aided by a proximity heuristic. The overall algorithm designed is based on a multiresolution, multihypothesis scheme, and similarities between tracking and matching through multiple resolution levels are exploited. Two major tools are developed in this paper: (i) a Bayesian framework for incorporating similarity measures of feature correspondences in regression to specify the different levels of confidence in the correspondences; and (ii) a Bayesian version of RANSAC, which is able to utilise prior estimates and matching probabilities. The algorithm is tested on a number of real images with large image motion and promising results were obtained.
Anomaly Detection through Registration
Authors: Mei Chen, Takeo Kanade, Henry A. Rowley, Dean Pomerleau
Venue: CVPR
Year: 1998

We study an application of image registration in the medical domain. Based on a 3-D hierarchical deformable registration algorithm, we have developed a prototype system which automatically aligns a standard atlas to a subject's data to create a customized atlas. Combined with domain knowledge, the registration algorithm can also detect asymmetries and abnormal variations in the subject's data that indicate the existence and location of pathologies. We have conducted tests on 106 MRI scans of normal brains, 3 MRI and 1 CT scan of brains with pathologies, with results qualitatively comparable to manual segmentation.
Orientation Space Filtering for Multiple Orientation Line Segmentation
Authors: Jian Chen, Yoshinobu Sato, Shinichi Tamura
Venue: CVPR
Year: 1998

The goal of this paper is to present appropriate line segmentation for intersections (X-junctions) and branches (T-junctions). In the local regions of intersections and branches, multiple orientations occur. A novel representation called "orientation space" is proposed, which is derived by adding the orientation axis to the abscissa and the ordinate of the image. The orientation space representation is constructed by treating the orientation parameter, to which Gabor filters can be tuned, as a continuous variable. The problem of multiple orientation line segmentation is dealt with by thresholding 3D images of the orientation space and then detecting the connected components therein. In this way, X-junctions and T-junctions are able to be separated effectively. Experimental results are presented using synthesized and real biomedical images. In particular, overlapping vessels in an x-ray coronary angiogram were well segmented by orientation space filtering.
Retrieval of Commercials by Video Semantics
Authors: Carlo Colombo, Alberto Del Bimbo, Pietro Pala
Venue: CVPR
Year: 1998

Videos convey information through several planes of communication, encompassing what is represented in the images how the images are linked together and how the subject is imaged. This feature is stressed in commercials where colors, editing effects, rhythms, and object motion are exploited to influence human purchasing habits. In this paper, based on researches in the marketing field, a link is formalized between low level features of a commercial video and feelings that the video would inspire in the observer. This link is used to define high level indices capturing the main semantics of the video. These indices are embedded in a video retrieval system to support access to a database of video based on their semantics.
Efficient Optimization of a Deformable Template Using Dynamic Programming
Authors: James M. Coughlan, Alan L. Yuille, Camper English, Daniel Snow
Venue: CVPR
Year: 1998

A novel deformable template is presented which detects the boundary of an open hand in a grayscale image. A dynamic programming algorithm enhanced by pruning techniques finds the hand contour in the image in as little as 19 seconds without initialization by the user. The template is translation- and rotation-invariant and accommodates shape deformation, significant occlusion and background clutter, and the presence of multiple hands.
An Optimized Interaction Strategy for Bayesian Relevance Feedback
Authors: Ingemar J. Cox, Matthew L. Miller, Thomas P. Minka, Peter N. Yianilos
Venue: CVPR
Year: 1998

A new algorithm and systematic evaluation is presented for searching a database via relevance feedback. It represents a new image display strategy for the PicHunter system. The algorithm takes feedback in the form of relative judgments ("item A is more relevant than item B") as opposed to the stronger assumption of categorical relevance judgments ("item A is relevant but item B is not"). It also exploits a learned probabilistic model of human behavior to make better use of the feedback it obtains. The algorithm can be viewed as an extension of indexing schemes like the k-d tree to a stochastic setting, hence the name "stochastic-comparison search." In simulations, the amount of feedback required for the new algorithm scales like log/sub 2/ |D|, where |D| is the size of the database, while a simple query-by-example approach scales like |D|/sup /spl alpha//, where /spl alpha/<1 depends on the structure of the database. This theoretical advantage is reflected by experiments with real users on a database of 1500 stock photographs.
Histogram Model for 3D Textures
Authors: Kristin J. Dana, Shree K. Nayar
Venue: CVPR
Year: 1998

Image texture can arise not only from surface albedo variations (2D texture) but also from surface height variations (3D texture). Since the appearance of 3D texture depends on the illumination and viewing direction in a complicated manner, such image texture can be called a bidirectional texture function. A fundamental representation of image texture is the histogram of pixel intensities. Since the histogram of 3D texture also depends on the illumination and viewing directions in a complex fashion, we refer to it as a bidirectional histogram. In this work, we present a concise analytical model for the bidirectional histogram of Lambertian, isotropic, randomly rough surfaces, which are common in real-world scenes. We demonstrate the accuracy of the histogram model by fitting to several samples from the Columbia-Utrecht texture database. The parameters obtained from the model fits are roughness measures which can be used in texture recognition schemes. In addition, the model has potential application in estimating illumination direction in scenes where surfaces of known tilt and roughness are visible. We demonstrate the usefulness of our model by employing it in a novel 3D texture synthesis procedure.
A Radial Cumulative Similarity Transform for Robust Image Correspondence
Authors: Trevor Darrell
Venue: CVPR
Year: 1998

We develop a local image-correspondence algorithm which performs well near occluding boundaries. Unlike traditional robust methods, our method can find correspondences when the only contrast present is the occluding boundary itself an when the sign of contrast along the boundary is possibly reversed. We define a new image transform which characterizes local image homogeneity, defined as an attribute value in a central region and most generally a function describing the surrounding local similarity structure. In this paper we use radial similarity functions and color attributes; within each window we compute the central color and an image with the cumulative probability color is unchanged along a ray from the center to given point in the window: this representation is insensitive to structure outside an occluding boundary, but can model the boundary itself. We show comparative results tracking finger, mouth, and eye features.
Magic Morphin' Mirror: Person Detection and Tracking
Authors: Trevor Darrell, Gaile G. Gordon, Michael Harville, John Woodfill
Venue: CVPR
Year: 1998

The demo presents a virtual mirror interface which reacts to the viewer using robust, real-time face tracking. The display directly combines a user's face with various graphical distortions, performed only on the face region in the image. The face detection and tracking is done in real-time, so the graphical effect stays with the user and continues to adapt al; they move within the viewing space, increasing in intensity as the user approaches the display.The tracking system performs well in crowded environments with open and moving backgrounds. This robust performance is achieved using multi-modal integration, combining stereo, color, and grey-scale pattern matching modules into 2 single realtime system. Stereo processing is used to isolate the figure of a user from other objects and people in the background. Skinhue classification identifies and tracks likely body parts within the foreground region. Face pattern detection discriminates and localizes the face within the tracked body parts. A second set of displays, located to the side of the main viewing area, shows intermediate results of each processing module of the system.
Image Editing in the Contour Domain
Authors: James H. Elder, Richard M. Goldberg
Venue: CVPR
Year: 1998

Image editing systems are essentially pixel-based. In this paper we propose a novel method for image editing in which the primitive working unit is not a pixel but an edge. The feasibility of this proposal is suggested by recent work showing that a grey-scale image can be accurately represented by its edge map if a suitable edge model and scale selection method are employed. In particular, an efficient algorithm has been reported to invert such an edge representation to yield a high-fidelity reconstruction of the original image. We have combined these algorithms together with an efficient method for contour grouping and an intuitive user interface to allow users to perform image editing operations directly in the contour domain. Experimental results suggest that this novel combination of vision algorithms may lead to substantial improvements in the efficiency of certain classes of image editing operations.
Background Modeling for Segmentation of Video-Rate Stereo Sequences
Authors: Christopher K. Eveland, Kurt Konolige, Robert C. Bolles
Venue: CVPR
Year: 1998

Stereo sequences promise to be a powerful method for segmenting images for applications such as tracking human figures. We present a method of statistical background modeling for stereo sequences that improves the reliability and sensitivity of segmentation in the presence of object clutter. The dynamic version of the method, called gated background adaptation, can reliably learn background statistics in the presence of corrupting foreground motion. The method has been used with a simple head discriminator to detect and track people using a stereo head mounted on a pan/tilt platform. It runs at video rates using standard PC hardware.
Image Segmentation Using Local Variation
Authors: Pedro F. Felzenszwalb, Daniel P. Huttenlocher
Venue: CVPR
Year: 1998

We present a new graph-theoretic approach to the problem of image segmentation. Our method uses local criteria and yet produces results that reflect global properties of the image. We develop a framework that provides specific definitions of what it means for an image to be under- or over-segmented. We then present an efficient algorithm for computing a segmentation that is neither under- nor over-segmented according to these definitions. Our segmentation criterion is based on intensity differences between neighboring pixels. An important characteristic of the approach is that it is able to preserve detail in low-variability regions while ignoring detail in high-variability regions, which we illustrate with several examples on both real and synthetic images.
Scene Segmentation from 3D Motion
Authors: Xiaolin Feng, Pietro Perona
Venue: CVPR
Year: 1998

We propose an EM approach combined with the modified separation matrix scheme to perform 3D motion segmentation of the image sequence which contains multiple moving objects. We observe that, given the detected features and their 2D optical flow, in most cases the objects or their flow are separated very well from each other in space. The separation matrix method modified by using normalized cuts achieves expected grouping results for these cases. However, when the objects are overlapped spatially but undergoing independent motions, such as Ullman's co-axial transparent cylinder demonstration, there will be no proper affinity to perform segmentation by that way. Pure underlying 3D motion becomes the only cite to segment the scene. We exploit the EM algorithm to deal with such difficult cases. The scheme is tested on vast number of synthetic image sequences. Results with real image sequence are also given.
A Theory of Selection for Gamut Mapping Color Constancy
Authors: Graham D. Finlayson, Steven D. Hordley
Venue: CVPR
Year: 1998

Gamut mapping colour constancy attempts to determine the set of diagonal matrices taking the gamut of image colours under an unknown illuminant into the gamut of colours observed under a standard illuminant. Forsyth (1990) developed such an algorithm in rgb sensor space which Finlayson (1996) later modified to work in a 2-d chromaticity space. In this paper we prove that Forsyth's 3-d solution gamut is, when projected to 2-d, identical to the gamut recovered by the 2-d algorithm. Whilst this implies that there is no inherent disadvantage in working in chromaticity space, this algorithm has a number of problems; the 2-d solution set is distorted and contains practically non-feasible illuminants. These problems have been addressed separately in previous works; we address them together in this paper. Non-feasible illuminants are discarded by intersecting the solution gamut with a non-convex gamut of common illuminants. In 2-d this intersection is relatively simple, but to remove the distortion, both these sets should be represented as 3-d cones of mappings, and the intersection is more difficult. We present an algorithm which avoids performing this intersection explicitly and which is simple to implement. Tests of this algorithm on both real and synthetic images show that it performs significantly better than the best current algorithms.
Mixtures of Local Linear Subspaces for Face Recognition
Authors: Brendan J. Frey, Antonio Colmenarez, Thomas S. Huang
Venue: CVPR
Year: 1998

Traditional subspace methods for face recognition compute a measure of similarity between images after projecting them onto a fixed linear subspace that is spanned by some principal component vectors (a.k.a. "eigenfaces") of a training set of images. By supposing a parametric Gaussian distribution over the subspace and a symmetric Gaussian noise model for the image given a point in the subspace, we can endow this framework with a probabilistic interpretation so that Bayes-optimal decisions can be made. However, we expect that different image clusters (corresponding, say, to different poses and expressions) will be best represented by different subspaces. In this paper, we study the recognition performance of a mixture of local linear subspaces model that can be fit to training data using the expectation maximization algorithm. The mixture model outperforms a nearest-neighbor classifier that operates in a PCA subspace.
Performance Characterization and Comparison of Video Indexing Algorithms
Authors: Ullas Gargi, Rangachar Kasturi, Sameer K. Antani
Venue: CVPR
Year: 1998

Temporal segmentation of video is a necessary first step to indexing digital video for browsing and retrieval. A number of different video temporal segmentation algorithms have been published in the literature. There has been little effort to evaluate and characterize their performance so as to deliver a single (or set of) algorithms that may be used by other researchers for indexing video databases. The present results of evaluating a number of these algorithms and characterizing their performance, specifically with respect to robustness to encoder and bitrate changes. The lessons learnt have relevance to algorithm development and evaluation in general.
Automatic Hierarchical Classification of Silhouettes of 3D Objects
Authors: Yoram Gdalyahu, Daphna Weinshall
Venue: CVPR
Year: 1998

The organization of image databases can rely upon different aspects of image similarity. Here we extract silhouettes from images of three dimensional objects, and rely upon curve similarity for image classification. Our scheme avoids the embedding of images in a vector space. Instead, we propose a curve dissimilarity measure which relies upon a novel curve matching syntactic algorithm, and use it to represent the database as a complete graph, with nodes representing the images and dissimilarity values assigning weights to the edges. A robust clustering algorithm, which is based on a physical ferromagnet model, is used to find the hierarchical structure underlying the collection of images. We tested our scheme with a database of 90 real images of 6 objects, some of them very different, others rather similar. We get a perfect hierarchical classification of these images into 6 classes of objects belonging to 3 different families.
Salient and Multiple Illusory Surfaces
Authors: Davi Geiger, Hsing-Kuo Pao, Nava Rubin
Venue: CVPR
Year: 1998

All illusory surface figures yield a perception of a surface occluding another one or the background. Occluded surfaces yield completion, a phenomena known as amodal completions. It is intriguing that for some images illusory surfaces are perceived, but not for other images. Also, illusory surfaces may have portions occlude. We aim to understand these phenomena. Our approach detects intensity edges and junctions. From the junctions we seek to find an optimal image organization, i.e., multiple ordered surfaces with the ordering accounting for salience. The most salient being the figure, while the other surfaces are classified as background. A decision of which surface is the visible one (on rep) is made locally, at each pixel, allowing the salient surface (figure) to have portions occluded, i.e., with amodal completions. We account for a variety of imagery not explained before.