OpenRaiser/Intern-Atlas huggingface dataset:
MUSE: Robust Surface Fitting using Unbiased Scale Estimates
Authors: James V. Miller, Charles V. Stewart
Venue: CVPR
Year: 1996

Despite many successful applications of robust statistics, they have yet to be completely adapted to many computer vision problems. Range reconstruction, particularly in unstructured environments, requires a robust estimator that not only tolerates a large outlier percentage but also tolerates several discontinuities, extracting multiple surfaces in an image region. Observing that random outliers and/or points from across discontinuities increase a hypothesized fit's scale estimate (standard deviation of the noise), our new operator; called MUSE (Minimum Unbiased Scale Estimator), evaluates a hypothesized fit over potential inlier sets via an objective function of unbiased scale estimates. MUSE extracts the single best fit from the data by minimizing its objective function over a set of hypothesized fits and can sequentially extract multiple surfaces from an image region. We show MUSE to be effective on synthetic data modelling small scale discontinuities and in preliminary experiments on complicated range data.
Bayesian face recognition using deformable intensity surfaces
Authors: Baback Moghaddam, Chahab Nastar, Alex Pentland
Venue: CVPR
Year: 1996

We describe a novel technique for face recognition based on deformable intensity surfaces which incorporates both the shape and texture components of the 2D image. The intensity surface of the facial image is modeled as a deformable 3D mesh in (z, y, I(x, y)) space. Using an efficient technique for matching two surfaces (in terms of the analytic modes of vibration), we obtain a dense correspondence field (or 3D warp) between two images. The probability distributions of two classes of warps are then estimated from training data: interpersonal and extrapersonal variations. These densities are then used in a Bayesian framework for image matching and recognition. Experimental results with facial data from the US Army FERET database demonstrate an increased recognition rate over the previous best methods.
Recovering Affine Motion and Defocus Blur Simultaneously
Authors: Zarina Myles, Niels da Vitoria Lobo
Venue: CVPR
Year: 1996

Motion in depth and/or zooming cause defocus blur. We show how the defocus blur in an image can be recovered simultaneously with affine motion. We introduce the theory, develop a solution method and demonstrate the validity of the theory and the solution by conducting experiments with real scenery.
Occlusion Detectable Stereo - Occlusion Patterns in Camera Matrix
Authors: Yuichi Nakamura 0001, Tomohiko Matsuura, Kiyohide Satoh, Yuichi Ohta
Venue: CVPR
Year: 1996

In stereo algorithms with more than two cameras, the improvement of accuracy is often reported since they are robust against noise. However, another important aspect of the polynocular stereo, that is the ability of occlusion detection, has been paid less attention. We intensively analyzed the occlusion in the camera matrix stereo (SEA) and developed a simple but effective method to detect the presence of occlusion and to eliminate its effect in the correspondence search. By considering several statistics on the occlusion and the accuracy in the SEA, we derived a few base masks which represent occlusion patterns and are effective for the detection of occlusion. Several experiments using typical indoor scenes showed quite good performance to obtain dense and accurate depth maps even at the occluding boundaries of objects.
Parametric Feature Detection
Authors: Shree K. Nayar, Simon Baker, Hiroshi Murase
Venue: CVPR
Year: 1996

We propose an algorithm to automatically construct feature detectors for arbitrary parametric features. To obtain a high level of robustness we advocate the use of realistic multi-parameter feature models and incorporate optical and sensing effects. Each feature is represented as a densely sampled parametric manifold in a low dimensional subspace of a Hilbert space. During detection, the brightness distribution around each image pixel is projected into the subspace. If the projection lies sufficiently close to the feature manifold, the feature is detected and the location of the closest manifold point yields the feature parameters. The concepts of parameter reduction by normalization, dimension reduction, pattern rejection, and heuristic search are all employed to achieve the required efficiency. By applying the algorithm to appropriate parametric feature models, detectors have been constructed for five features, namely, step edge, roof edge, line, corner, and circular disc. Detailed experiments are reported on the robustness of detection and the accuracy of parameter estimation.
Closest Point Search in High Dimensions
Authors: Sameer A. Nene, Shree K. Nayar
Venue: CVPR
Year: 1996

The problem of finding the closest point in high-dimensional spaces is common in computational vision. Unfortunately, the complexity of most existing search algorithms, such as k-d tree and R-tree, grows exponentially with dimension, making them impractical for dimensionality above 15. In nearly all applications, the closest point is of interest only if it lies within a user specified distance /spl epsiv/. We present a simple and practical algorithm to efficiently search for the nearest neighbor within Euclidean distance /spl epsiv/. Our algorithm uses a projection search technique along with a novel data structure to dramatically improve performance in high dimensions. A complexity analysis is presented which can help determine /spl epsiv/ in structured problems. Benchmarks clearly show the superiority of the proposed algorithm for high dimensional search problems frequently encountered in machine vision, such as real-time object recognition.
3-D Object Pose Estimation by Shading and Edge Data Fusion - Simulating virtual manipulation on mental images
Authors: Yoshihiko Nomura, Dili Zhang, Yuko Sakaida, Seizo Fujii
Venue: CVPR
Year: 1996

Human beings seem to recognize objects based on a kind of model-matching, i.e., a virtual manipulation on mental images. This paper presents a 3-D object pose estimation method simulating the human recognition scheme. Computer synthesizes not only an edge image but also a shading image from an object model. Then, it matches the two kinds of synthesized images with the inputted images individually by using a non-linear least-squares method, and estimates the pose parameter values. Finally, it chooses the better of the individually estimated poses. Thus, the fusion of the shading and the edge information is achieved. Since the two pieces of information complement each other, this method has the advantage of much higher robustness and accuracy of pose estimation than ordinary model-matching techniques which rely only on geometrical features such as vertices or edges.
Global Models with Parametric Offsets as Applied to Cardiac Motion Recovery
Authors: Thomas O&apos, Donnell, Terrance E. Boult, Alok Gupta
Venue: CVPR
Year: 1996

We introduce a new solid shape model formulation that includes built-in offsets from a base global component (e.g. an ellipsoid) which are functions of the global component's parameters. The offsets provide two features. First, they help to form an expected model shape which facilitates appropriate model data correspondences. Second, they scale with the base global model to maintain the expected shape even in the presence of large global deformations. We apply this model formulation to the recovery of 3-D cardiac motion from a volunteer dataset of tagged-MR images. The model instance is a variation of the hybrid volumetric ventriculoid (HVV), a deformable thick-walled ellipsoid model resembling the left ventricle (LV) of the heart. A unique aspect of of implementation is the employment of constant volume constraints when recovering the cardiac motion. In addition, we present a novel geodesic-like prismoidal tessellation of the model which provides for more stable fits.
Connectionist networks for feature indexing and object recognition
Authors: Clark F. Olson
Venue: CVPR
Year: 1996

Feature indexing techniques are promising for object recognition since they can quickly reduce the set of possible matches for a set of image features. This work exploits another property of such techniques. They have inherently parallel structure and connectionist network formulations are easy to develop. Once indexing has been performed, a voting scheme such as geometric hashing can be used to generate object hypotheses in parallel. We describe a framework for the connectionist implementation of such indexing and recognition techniques. With sufficient processing elements, recognition can be performed in a small number of time steps. The number of processing elements necessary to achieve peak performance and the fan-in/fan-out required for the processing elements is examined. These techniques have been simulated on a conventional architecture with good results.
Affine Invariant Detection: Edges, Active Contours, and Segments
Authors: Peter J. Olver, Guillermo Sapiro, Allen R. Tannenbaum
Venue: CVPR
Year: 1996

In this paper we undertake a systematic investigation of affine invariant object detection. Edge detection is first presented from the point of view of the affine invariant scale-space obtained by curvature based motion of the image level-sets. In this case, affine invariant edges are obtained as a weighted difference of images at different scales. We then introduce the affine gradient as the simplest possible affine invariant differential function which has the same qualitative behavior as the Euclidean gradient magnitude. These edge detectors are the basis both to extend the affine invariant scale-space to a complete affine flow for image denoising and simplification, and to define affine invariant active contours for object detection and edge integration. The active contours are obtained as a gradient flow in a conformally Euclidean space defined by the image on which the object is to be detected. That is, we show that objects can be segmented in an affine invariant manner by computing a path of minimal weighted affine distance, the weight being given by functions of the affine edge detectors. The geodesic path is computed via an algorithm which allows to simultaneously detect any number of objects independently of the initial curve topology.
A fast and flexible statistical method for text extraction in document pages
Authors: Pietro Parodi, Giulia Piccioli
Venue: CVPR
Year: 1996

This paper describes a fast and flexible method for extracting text regions from a document page containing text, graphics, and pictures. Such regions can be given as an input to an OCR system. The user fixes two parameters, the minimum width w of the text to be detected, and the precision /spl epsiv/ needed (both expressed as a percentage of the image width), according to the implementation needs. The method works by subdividing the page into overlapping columns whose width and inter-shift depend on w and /spl epsiv/, and by performing text lines extraction on each column separately. Successively, a statistical analysis of the text line elements found in each column is performed, and they are connected to form complete text lines. Finally, related pieces of text are merged into blocks so that a sensible reading order is provided for the OCR system. The algorithm is very fast, is able to work on low-resolution document pages and is robust against skew. The algorithm as also very flexible: no assumptions are made on the layout of the document, the shape of the text regions, and the font size and style; the main assumption is that the background is uniform and the text approximately horizontal. Despite the statistical nature of the method, a single line of text of a certain font size is generally sufficient to warrant detection. Experimental results are shown which demonstrate the effectiveness of the method on several different kinds of documents.
Closed-Loop Object Recognition Using Reinforcement Learning
Authors: Jing Peng, Bir Bhanu
Venue: CVPR
Year: 1996

Current computer vision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation parameters. This is accomplished by using the confidence level of model matching as a reinforcement signal for a team of learning automata to search for segmentation parameters during training. The use of the recognition algorithm as part of the evaluation function for image segmentation gives rise to significant improvement of the system performance by automatic generation of recognition strategies. The system is verified through experiments on sequences of indoor and outdoor color images with varying external conditions.
Epipolar Geometry and Linear Subspace Methods: A New Approach to Weak Calibration
Authors: Jean Ponce, Yakup Genc
Venue: CVPR
Year: 1996

This paper addresses the problem of estimating the epipolar geometry from point correspondences between two images taken by uncalibrated perspective cameras. It is shown that Jepson's and Heeger's linear subspace technique for infinitesimal motion estimation can be generalized to the finite motion case by choosing an appropriate basis for projective space. This yields a linear method for weak calibration. The proposed algorithm has been implemented and tested on both real and synthetic images, and it is compared to other linear and non-linear approaches to weak calibration.
Constrained Phase Congruency: Simultaneous Detection of Interest Points and of their Scales
Authors: Daniel Reisfeld
Venue: CVPR
Year: 1996

A novel feature detector-the Constrained Phase Congruency Transform (CPCT) is introduced. It simultaneously detects interest points as well as their scale in various orientations. The CPCT is especially important in registration applications: the local transformation between interest points can be determined based on their orientational scales. The CPCT detects the features in Mach bands and in sinusoidal waves. This cannot be done simply by looking for local maxima in intensity gradient nor by looking for local energy maxima. I conjecture that constraining the general phase congruency is sufficient for feature detection. The correct detection of features' location and of their scale is demonstrated. The robustness of the CPCT is achieved by constraining the local phase. Only four easy-to-detect phases are used in the computations. They correspond to symmetry and anti-symmetry in their neighborhood. The scale at any location and orientation is determined by the scale of the channel that conforms to the constraints and maximizes energy.
Modeling Clutter and Context for Target Detection in Infrared Images
Authors: Songnian Rong, Bir Bhanu
Venue: CVPR
Year: 1996

In order to reduce false alarms and to improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it is important to develop the models not only for man-made targets bat also of natural background clutters. Because of the high complexity of natural clutters, this clutter model can only be reliably built through learning from real examples. If available, contextual information that characterizes each training example can be used to further improve the learned clutter model. In this paper, we present such a clutter model aided target detection system. Emphases are placed on two topics: (1) learning the background clutter model from sensory data through a self-organizing process, (2) reinforcing the learned clutter model using contextual information.
Affine Structure and Photometry
Authors: Ruth Rosenholtz, Jan J. Koenderink
Venue: CVPR
Year: 1996

Motion of an observer relative to objects in a scene provides information about the structure of the scene. Changing patterns of shading due to motion relative to the light source provide information about surface structure, albedos, and light sources. One can stratify this photometric information into affine, unitary, and metric structure, much like the stratification of structure from motion. For Lambertian surfaces, if either motion or photometry give us more than affine structure, the two cues can be combined to yield full metric information. Edge constraints plus unitary photometry also give us full metric photometry. Affine structure alone contains much of the quantitative structure information, allowing us to judge such things as the ordinal relationships between the albedos.
Optimal servoing for active foveated vision
Authors: Héctor Rotstein, Ehud Rivlin
Venue: CVPR
Year: 1996

Foveated vision and two-mode tracking, as inspired by the human oculomotor system, are often used in active vision system. The purpose of this paper is to provide answers to the following basic questions which arise from implementations. First, is it beneficial to have foveated vision and what is the optimal size of the foveal window? Second, is there a need for two control mechanisms (smooth pursuit and saccade) for improved performance and how can one efficiently switch between them? In order to do so, a setup is proposed in which these strategies can be evaluated in a systematic manner. It is shown that the fovea appears as a compromise between the tightness of the tracking specifications and computational constraints. Introducing a model for the later and postulating some a priori knowledge of the target behavior, it is possible to compute the size of the fovea in an optimal way. As a by-product, "smooth-pursuit" can be defined in a natural way, and the use of a two-mode tracking scheme is justified. The second mode, i.e. "saccadic control", aims at re-centering the target on the fovea so that the smooth pursuit controller can continue to operate. It is shown that a control strategy can indeed be defined so that this objective can be met under appropriate operating conditions.
Neural Network-Based Face Detection
Authors: Henry A. Rowley, Shumeet Baluja, Takeo Kanade
Venue: CVPR
Year: 1996

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.
Complexity analysis of RBF networks for Pattern Recognition
Authors: Lucia Sardo, Josef Kittler
Venue: CVPR
Year: 1996

The problem of non-parametric probability density function (PDF) estimation using Radial Basis Function (RBF) Neural Networks is addressed here. We investigate two criteria, based on a modified Kullback-Leibler distance, that lead to an appropriate choice of the network architecture complexity. In the first criterion the modification consists in the addition of a term that penalizes complex architectures (MPL criterion). The second strategy, involves the regularization of the network through the imposition of lower bounds on the standard deviation derived from conditions of existence of rejection tests (LBSD criterion). Experimental results indicate that the MPL criterion outperforms the LBSD method.
Stereo Vision for View Synthesis
Authors: Daniel Scharstein
Venue: CVPR
Year: 1996

We propose a new method for view synthesis from real images using stereo vision. The method does not explicitly model scene geometry, and enables fast and exact generation of synthetic views. We also reevaluate the requirements on stereo algorithms for the application of view synthesis and discuss ways of dealing with partially occluded regions of unknown depth and with completely occluded regions of unknown texture. Our experiments demonstrate that it is possible to efficiently synthesize realistic new views even from inaccurate and incomplete depth information.
Combining greyvalue invariants with local constraints for object recognition
Authors: Cordelia Schmid, Roger Mohr
Venue: CVPR
Year: 1996

This paper addresses the problem of recognizing objects in large image databases. The method is based on local characteristics which are invariant to similarity transformations in the image. These characteristics are computed at automatically detected keypoints using the greyvalue signal. The method therefore works on images such as paintings for which geometry based recognition fails. Due to the locality of the method, images can be recognized being given part of an image and in the presence of occlusions. Applying a voting algorithm and semi-local constraints makes the method robust to noise, scene clutter and small perspective deformations. Experiments show an efficient recognition for different types of images. The approach has been validated on an image database containing 1020 images, some of them being very similar by structure, texture or shape.
Structure from Multiple 2D Affine Correspondences without Camera Calibration
Authors: Haim Schweitzer, Radha Krishnan
Venue: CVPR
Year: 1996

Image motion induced by camera or object motion can be approximated locally by an affine coordinate transformation. We extract 3D information directly from the affine parameters, without camera calibration. The derivation relies on the following assumptions: the object is rigid locally planar, and its local 3D motion is translation. These assumptions enable complete recovery of 3D structure, whereas it is impossible to compute the direction (and magnitude) of the motion. Still, it is possible to distinguish between objects moving differently. Explicit expressions for the structure and the motion indicators are given in terms of the 6 affine parameters, computed for each image patch. Results of experiments on data with known ground truth are described.
Coregistration of Range and Optical Images Using Coplanarity and Orientation Constraints
Authors: Anthony N. A. Schwickerath, J. Ross Beveridge
Venue: CVPR
Year: 1996

A least-squares method simultaneously solves for the model-to-sensor-suite pose and sensor-to-sensor registration. The development is for a sensor-suite containing separate range and optical sensors. To address outliers and, more generally, match finding, a statistical method (median filtering) and a search method (local search) are developed. Sensitivity to Gaussian noise and the choice of initial pose estimates is investigated on synthetic data. Both of the matching methods are demonstrated on real data.
On 3D Shape Similarity
Authors: Heung-Yeung Shum, Martial Hebert, Katsushi Ikeuchi
Venue: CVPR
Year: 1996

This paper addresses the problem of 3D shape similarity between closed surfaces. A curved or polyhedral 3D object of genus zero is represented by a mesh that has nearly uniform distribution with known connectivity among mesh nodes. A shape similarity metric is defined based on the L/sub 2/ distance between the local curvature distributions over the mesh representations of the two objects. For both convex and concave objects, the shape metric can be computed in time O(n/sup 2/), where n is the number of tessellations of the sphere or the number of meshes which approximate the surface. Experiments show that our method produces good shape similarity measurements.