OpenRaiser/Intern-Atlas huggingface dataset:
Scale-Space Vector Fields for Feature Analysis
Authors: Andrew D. J. Cross, Edwin R. Hancock
Venue: CVPR
Year: 1997

This paper describes a vectorial representation that can be used to assess the symmetry of objects in 2D images. The method exploits a magneto-static analogy. Commencing from the gradient-field extracted from filtered grey-scale images we construct a vector-potential. Our magneto-static analogy is that tangential gradient vectors represent the elements of a current distribution on the image plane. By embedding the image plane in an augmented 3-dimensional space, we compute the vector potential by performing volume integration over the current distribution. The associated magnetic field is computed by taking the curl of the vector-potential. The auxiliary spatial dimension provides a natural scale-space sampling of the generating current distribution; as the height above the image plane is increased, so the volume over which averaging is effected also increases. We extract edge and symmetry lines through a topographic analysis of the vector-field at various heights above the image plane. Symmetry axes are lines where the curl of the vector-potential vanishes; at edges the divergence of the vector-potential vanishes.
Character extraction of license plates from video
Authors: Yuntao Cui, Qian Huang
Venue: CVPR
Year: 1997

In this paper, we present a new approach to extract characters on a license plate of a moving vehicle given a sequence of perspective distortion corrected license plate images. We model the extraction of characters as a Markov random field (MRF). With the MRF modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function. Experiments and comparison study were conducted. It is shown that our approach provides better performance than other single frame methods.
The Representation and Recognition of Human Movement Using Temporal Templates
Authors: James W. Davis, Aaron F. Bobick
Venue: CVPR
Year: 1997

A new view-based approach to the representation and recognition of action is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using 18 aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: the first value is a binary value indicating the presence of motion, and the second value is a function of the recency of motion in a sequence. We then develop a recognition method which matches these temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on a standard platform. We recently incorporated this technique into the KIDSROOM: an interactive, narrative play-space for children.
MDL estimation for small sample sizes and its application to segmenting binary strings
Authors: Byron Dom
Venue: CVPR
Year: 1997

Minimum Description Length (MDL) estimation has proven itself of major importance in a large number of applications many of which are in the fields of computer vision and pattern recognition. A problem is encountered in applying the associated formulas, however, especially those associated with model cost. This is because most of these are asymptotic forms appropriate only for large sample sizes. J. Rissanen has recently derived sharper code-length formulas valid for much smaller sample sizes. Because of the importance of these results, it is our intent here to present a tutorial description of them. In keeping with this goal we have chosen a simple application whose relative tractability allows it to be explored more deeply than most problems: the segmentation of binary strings based on a piecewise Bernoulli assumption. By that we mean that the strings are assumed to be divided into substrings, the bits of which are assumed to have been generated by a single (within a substring) Bernoulli source.
Appearance Matching of Occluded Objects Using Coarse-to-fine Adaptive Masks
Authors: Jeff L. Edwards, Hiroshi Murase
Venue: CVPR
Year: 1997

In this paper, we discuss an appearance matching technique for the interpretation of color scenes containing occluded objects. Dealing with occlusions is very difficult, and we have explored the use of an iterative, coarse-to-fine correlation-based method that uses hypothesized occlusion events to modify the scene-to-template similarity measure at run-time. Specifically, a binary mask is used to adaptively exclude regions of the template image from the correlation computation. At each iteration, these masks are adjusted based on higher resolution scene data and the occluding interactions between multiple object hypotheses. We present results which demonstrate the technique is reasonably robust over a large database of color test scenes containing objects at a variety of scales, and tolerates minor object rotations and global illumination variations.
Efficient Regularity-Based Grouping
Authors: Jacob Feldman
Venue: CVPR
Year: 1997

This paper summarizes a novel logic-based approach to grouping and perceptual organization, and presents novel efficient methods for computing interpretations in this framework. Grouping interpretations are first defined as logical structures, built out of atomic premises ("regularities") that are derived from considerations of non-accidentalness. These interpretations can then be partially ordered by their degree of regularity or constraint (measured numerically by their codimension). The genericity constraint-the principle that interpretations should minimize coincidences in the observed configuration-dictates that the preferred interpretation will be the minimum in this partial order, i.e. the interpretation with maximum codimension. The preferred interpretation, called the qualitative parse, corresponds neatly to the interpretation intuitively preferred by human observers. As a side-effect, the "most salient" or most structured part of the scene can be identified, as the highest-codimension subtree of the qualitative parse. An efficient (O(n/sup 2/)) method for computing the maximum codimension interpretation is presented, along with examples.
The confounding of translation and rotation in reconstruction from multiple views
Authors: Cornelia Fermüller, Yiannis Aloimonos
Venue: CVPR
Year: 1997

If 3D rigid motion is estimated with some error a distorted version of the scene structure will in turn be computed. Of computational interest are these regions in space where the distortions are such that the depths become negative, because in order to be visible the scene has to lie in front of the image. The stability analysis for the structure-from-motion problem presented in this paper investigates the optimal relationship between the errors in the estimated translational and rotational parameters of a rigid motion, that results in the estimation of a minimum number of negative depth values. The input used is the value of the flow along some direction, which is more general than optic flow or correspondence. For a planar retina it is shown that the optimal configuration is achieved when the projections of the translational and rotational errors on the image plane are perpendicular. Furthermore, the projection of the actual and the estimated translation lie on a line passing through the image center. For a spherical retina given a rotational error, the optimal translation is the correct one, while given a translational error. The optimal rotational error is normal to the translational one at an equal distance from the real and estimated translations. The proofs, besides illuminating the confounding of translation and rotation in structure from motion, have an important application to ecological optics, explaining differences of planar and spherical eye or camera designs in motion and shape estimation.
A robust and convergent iterative approach for determining the dominant plane from two views without correspondence and calibration
Authors: Pär Fornland, Christoph Schnörr
Venue: CVPR
Year: 1997

A robust, iterative approach is introduced for finding the dominant plane in a scene using binocular vision. Neither camera calibration nor stereo correspondence is required. Recently L. Cohen (1996) formalized a framework guaranteeing (local) convergence of iterative two-step methods. In this paper, the framework is adopted, with a global step using tentative matches to estimate the planar projectivity, and a local step attempting to solve the stereo correspondence. A detected point in the first image is matched to an auxiliary point in the second image, on the line joining the transformed first image point, and its closest detected second image point. Convergence is assured, while achieving robustness to both mismatching and non-coplanar points.
Body plans
Authors: David A. Forsyth, Margaret M. Fleck
Venue: CVPR
Year: 1997

This paper describes a representation for people and animals, called a body plan, which is adapted to segmentation and to recognition in complex environments. The representation is an organized collection of grouping hints obtained from a combination of constraints on color and texture and constraints on geometric properties such as the structure of individual parts and the relationships between parts. Body plans can be learned from image data, using established statistical learning techniques. The approach is illustrated with two examples of programs that successfully use body plans for recognition: one example involves determining whether a picture contains a scantily clad human, using a body plan built by hand; the other involves determining whether a picture contains a horse, using a body plan learned from image data. In both cases, the system demonstrates excellent performance on large, uncontrolled test sets and very large and diverse control sets.
Efficient Stereo with Multiple Windowing
Authors: Andrea Fusiello, Vito Roberto, Emanuele Trucco
Venue: CVPR
Year: 1997

We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multi-window scheme using left-right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both robustness and efficiency.
Efficient Approximation of Range Images Through Data-Dependent Adaptive Triangulations
Authors: Miguel Ángel García, Angel Domingo Sappa, Luis Basañez
Venue: CVPR
Year: 1997

The paper presents an efficient algorithm for generating adaptive triangular meshes from dense range images. The proposed technique consists of two stages. First, a quadrilateral mesh is generated from the given range image. The points of this mesh adapt to the surface shapes represented in the range image by grouping in areas of high curvature and dispersing in low-variation regions. The second stage splits each quadrilateral cell obtained before into two triangles. Between the two possible flips, it is chosen the one whose diagonal's direction is closest to the orientation of the discontinuities present in that cell. Both stages avoid costly iterative optimization techniques. Results with real range images are presented. They show low CPU times and accurate triangular approximations of the given images.
Object-Based Video Indexing for Virtual Studio Productions
Authors: Bilge Günsel, A. Murat Tekalp, Peter J. L. van Beek
Venue: CVPR
Year: 1997

This paper introduces an object-based approach for temporal video partitioning and content-based indexing, where the basic indexing unit is "lifespan of a video object", rather than a "camera shot" or "story unit". We propose a system to extract content-based features of video objects (VOs), based on a compact 2D triangular mesh representation of them. An adaptive mesh-based video object tracking scheme is then employed to compute the motion trajectories of all node points. A set of "key snapshots" which constitute a visual summary of the lifespan of the object are automatically selected using motion and shape information. The system provides direct access to the VOs and gives the functionalities such as object-based search, manipulation, animation, and tracking.
Motion of Disturbances: Detection and Tracking of multi-Body non-Rigid Motion
Authors: Gilad Halevy, Daphna Weinshall
Venue: CVPR
Year: 1997

We present a new approach to the tracking of very non rigid patterns of motion, such as water flowing down a stream. The algorithm is based on a "disturbance map", which is obtained by linearly subtracting the temporal average of the previous frames from the new frame. Every local motion creates a disturbance having the form of a wave, with a "head" at the present position of the motion and a historical "tail" that indicates the previous locations of that motion. These disturbances serve as loci of attraction for "tracking particles" that are scattered throughout the image. The algorithm is very fast and can be performed in real time. We provide excellent tracking results on various complex sequences, using both stabilized and moving cameras, showing: a busy ant column, waterfalls, rapids and, flowing streams, shoppers in a mall, and cars in a traffic intersection.
Lip reading from scale-space measurements
Authors: Richard W. Harvey, Iain A. Matthews, J. Andrew Bangham, Stephen J. Cox
Venue: CVPR
Year: 1997

Systems that attempt to recover the spoken word from image sequences usually require complicated models of the mouth and its motions. Here we describe a new approach based on a fast mathematical morphology transform called the sieve. We form statistics of scale measurements in one and two dimensions and these are used as a feature vector for standard Hidden Markov Models (HMMs).
Hierarchical Recognition of Articulated Objects from Single Perspective Views
Authors: Alexa Hauck, Stefan Lanser, Christoph Zierl
Venue: CVPR
Year: 1997

This paper presents an approach to the recognition of articulated 3D objects in monocular video images. A hierarchical object representation models objects as a composition of rigid components which are explicitly connected by specific kinematic constraints, e.g., rotational and/or translational joints. The recognition task follows this tree-like structure by first estimating the 3D pose of the static component (root) and afterwards determining the relative 3D pose of the remaining components recursively. This method limits the search space for the actual correspondences between image and model features and copes with the problem of self-occlusion. Experiments in the context of autonomous, mobile robots show the practicability of this approach.
A Four-step Camera Calibration Procedure with Implicit Image Correction
Authors: Janne Heikkilä, Olli Silvén
Venue: CVPR
Year: 1997

In geometrical camera calibration the objective is to determine a set of camera parameters that describe the mapping between 3-D reference coordinates and 2-D image coordinates. Various methods for camera calibration can be found from the literature. However surprisingly little attention has been paid to the whole calibration procedure, i.e., control point extraction from images, model fitting, image correction, and errors originating in these stages. The main interest has been in model fitting, although the other stages are also important. In this paper we present a four-step calibration procedure that is an extension to the two-step method. There is an additional step to compensate for distortion caused by circular features, and a step for correcting the distorted image coordinates. The image correction is performed with an empirical inverse model that accurately compensates for radial and tangential distortions. Finally, a linear method for solving the parameters of the inverse model is presented.
Stereo-Motion that Complements Stereo and Motion Analyses
Authors: Pui-Kuen Ho, Ronald Chung
Venue: CVPR
Year: 1997

Recovering three-dimensional (3D) information of a scene from its images is a fundamental problem in computer vision. There exists two major multi-ocular cue for it, namely the motion cue and the stereo cue. This paper presents a new approach of integrating the two cues when two cameras that move through the scene while taking pictures repeatedly are available. The approach is based on the Singular Value Decomposition (SVD) technique, with which the 3D structure of the scene, the image projection parameters, the motion parameters, and the stereo geometry are all separated. The approach offers the advantages of both cues: simple correspondence as well as accurate reconstruction. It can also work with relatively short image sequences.
Object Recognition Using Appearance-Based Parts and Relations
Authors: Chien-Yuan Huang, Octavia I. Camps, Tapas Kanungo
Venue: CVPR
Year: 1997

The recognition of general three-dimensional objects in cluttered scenes is a challenging problem. In particular, the design of a good representation suitable to model large numbers of generic objects that is also robust to occlusion has been a stumbling block in achieving success. In this paper, we propose a representation using appearance-based parts and relations to overcome these problems. 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 two eigenspaces spanned by the parts and the relations.
Real-Time Estimation of Human Body Posture from Monocular Thermal Images
Authors: Shoichiro Iwasawa, Kazuyuki Ebihara, Jun Ohya, Shigeo Morishima
Venue: CVPR
Year: 1997

This paper introduces a new real-time method to estimate the posture of a human from thermal images acquired by an infrared camera regardless of the back-ground and lighting conditions. Distance transformation is performed for the human body area extracted from the thresholded thermal image for the. Calculation of the center of gravity. After the orientation of the upper half of the body is obtained by calculating the moment of inertia, significant points such as the top of the head, the tips of the hands and foot are heuristically located. In addition, the elbow and foot positions are estimated from the detected (significant) points using a genetic algorithm based learning procedure. The experimental results demonstrate the robustness of the proposed algorithm and real-time (faster than 20 frames per second) performance.
Linear Fitting with Missing Data: Applications to Structure-from-Motion and to Characterizing Intensity Images
Authors: David W. Jacobs
Venue: CVPR
Year: 1997

Several vision problems can be reduced to the problem of fitting a linear surface of low dimension to data, including the problems of structure-from-affine-motion, and of characterizing the intensity images of a Lambertian scene by constructing the intensity manifold. For these problems, one must deal with a data matrix with some missing elements. In structure-from-motion, missing elements will occur if some point features are not visible in some frames. To construct the intensity manifold missing matrix elements will arise when the surface normals of some scene points do not face the light source in some images. We propose a novel method for fitting a low rank matrix to a matrix with missing elements. We show experimentally that our method produces good results in the presence of noise. These results can be either used directly, or can serve as an excellent starting point for an iterative method.
3-D to 2-D Recognition with Regions
Authors: David W. Jacobs, Ronen Basri
Venue: CVPR
Year: 1997

This paper presents a novel approach to parts-based object recognition in the presence of occlusion. We focus on the problem of determining the pose of a 3-D object from a single 2-D image when convex parts of the object have been matched to corresponding regions in the image. We consider three types of occlusions: self-occlusion, occlusions whose locus is identified in the image, and completely arbitrary occlusions. We derive efficient algorithms for the first two cases, and characterize their performance. For the last case, we prove that the problem of finding valid poses is computationally hard, but provide an efficient, approximate algorithm. This work generalizes our previous work on region-based object recognition, which focused on the case of planar models.
Image Based View Synthesis of Articulated Agents
Authors: Martin Jägersand
Venue: CVPR
Year: 1997

Using a combination of techniques from visual representations, view synthesis, and visual-motor model estimation, we present a method for animating movements of an articulated agent (e.g. human or robot arm), without the use of any prior models or explicit 3D information. The information needed to generate simulated images can be acquired either on or off fine, by watching the agent doing an arbitrary, possibly unrelated task. We present experimental results synthesizing image sequences of the simulated movement of a human arm and a PUMA 760 robot arm. Control is in either image (camera), motor (joint), or Cartesian world coordinates. We have created a user interface, where a user can input a movement program, and then upon execution, view movies of the (simulated) agent executing the program, along with the instantaneous values of the dynamics variables.
Building Reconstruction from Optical and Range Images
Authors: Christopher O. Jaynes, Allen R. Hanson, Edward M. Riseman, Howard J. Schultz
Venue: CVPR
Year: 1997

A technique is introduced for extracting and reconstructing a wide class of building types from a registered range image and optical image. An attentional focus stage, followed by model indexing, allows top-down robust surface fitting to reconstruct the 3D nature of the buildings in the data. Because of the effectiveness of model selection, top-down processing of noisy range data still succeeds and the algorithm is capable of detecting and reconstructing several different building roof classes, including flat single level, flat multi-leveled, peaked, and curved rooftops. The algorithm is applicable to range data that may have been collected from several different range sensor types. We demonstrate reconstructions of different buildings classes in the presence of large amounts of noise. Our results underline the usefulness of range data when processed in the context of a focus-of-attention area derived from the monocular optical image.
Parametrized structure from motion for 3D adaptive feedback tracking of faces
Authors: Tony Jebara, Alex Pentland
Venue: CVPR
Year: 1997

A real-time system is described for automatically detecting, modeling and tracking faces in 3D. A closed loop approach is proposed which utilizes structure from motion to generate a 3D model of a face and then feed back the estimated structure to constrain feature tracking in the next frame. The system initializes by using skin classification, symmetry operations, 3D warping and eigenfaces to find a face. Feature trajectories are then computed by SSD or correlation-based tracking. The trajectories are simultaneously processed by an extended Kalman filter to stably recover 3D structure, camera geometry and facial pose. Adaptively weighted estimation is used in this filter by modeling the noise characteristics of the 2D image patch tracking technique. In addition, the structural estimate is constrained by using parametrized models of facial structure (eigen-heads). The Kalman filter's estimate of the 3D state and motion of the face predicts the trajectory of the features which constrains the search space for the next frame in the video sequence. The feature tracking and Kalman filtering closed loop system operates at 25 Hz.