OpenRaiser/Intern-Atlas huggingface dataset:
Visual Recognition of Multi-Agent Action Using Binary Temporal Relations
Authors: Stephen S. Intille, Aaron F. Bobick
Venue: CVPR
Year: 1999

A probabilistic framework for representing and visually recognizing complex multi-agent action is presented. Motivated by work in model-based object recognition and designed for the recognition of action from visual evidence, the representation has three components: (1) temporal structure descriptions representing the temporal relationships between agent goals, (2) belief networks for probabilistically representing and recognizing individual agent goals from visual evidence, and (3) belief networks automatically generated from the temporal structure descriptions that support the recognition of the complex action. We describe our current work on recognizing American football plays from noisy trajectory data.
Applying Perceptual Grouping to Content-Based Image Retrieval: Building Images
Authors: Qasim Iqbal, Jake K. Aggarwal
Venue: CVPR
Year: 1999

This paper presents an application of perceptual grouping rules for content-based image retrieval. The semantic interrelationships between different primitive image features are exploited by perceptual grouping to detect the presence of manmade structures. A methodology based on these principles in a Bayesian framework for the retrieval of building images, and the results obtained are presented. The image database consists of monocular grayscale outdoor images taken from a ground-level camera.
Projective Rectification Without Epipolar Geometry
Authors: Francesco Isgrò, Emanuele Trucco
Venue: CVPR
Year: 1999

We present a novel algorithm performing projective rectification which does not require explicit computation of the epipolar geometry and specifically of the fundamental matrix. Instead of finding the epipoles and computing two homographies mapping the epipoles to infinity, as done in recent work on projective rectification, we exploit the fact that the fundamental matrix of a pair of rectified images has a particular, known form. This allows us to set up a minimization that yields the rectifying, homographies directly from image correspondences. Experimental results show that our method works quite robustly even in the presence of noise, and with inaccurate point correspondences. The code of our implementation will be made available at the author's web site.
FingerCode: A Filterbank for Fingerprint Representation and Matching
Authors: Anil K. Jain 0001, Salil Prabhakar, Lin Hong, Sharath Pankanti
Venue: CVPR
Year: 1999

With the identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic positive personal identification applications, biometrics-based identification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a significant fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information, available in the fingerprints. The proposed filter-based algorithm uses a bank of Gabor filters to capture both the local and the global details in a fingerprint as a compact 640-byte fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. Our initial results show identification accuracies comparable to the best results of minutiae-based algorithms published in the open literature. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary fingerprint information.
Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length
Authors: David Jelinek, Camillo J. Taylor
Venue: CVPR
Year: 1999

This paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector, /spl lambda/. The reconstruction program takes as input a set of correspondences between features in the model and features in the image. From this information the program determines an appropriate projection model for the camera (scaled orthographic or perspective), the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. We demonstrate that this reconstruction task can be framed as an unconstrained optimization problem involving a small number of variables, no more than four, regardless of the number of parameters in the dimension vector.
Statistical Color Models with Application to Skin Detection
Authors: Michael J. Jones 0001, James M. Rehg
Venue: CVPR
Year: 1999

The existence of large image datasets such as photos on the World Wide Web make it possible to build powerful generic models for low-level image attributes like color using simple histogram learning techniques. We describe the construction of color models for skin and non-skin classes from a dataset of nearly 1 billion labeled pixels. These classes exhibit a surprising degree of separability which we exploit by building a skin pixel detector that achieves an equal error rate of 88%. We compare the performance of histogram and mixture models in skin detection and find histogram models to be superior in accuracy and computational cost. Using aggregate features computed from the skin detector we build a remarkably effective detector for naked people. We believe this work is the most comprehensive and detailed exploration of skin color models to date.
Detection and Removal of Line Scratches in Motion Picture Films
Authors: Laurent Joyeux, Olivier Buisson, Bernard Besserer, Samia Boukir
Venue: CVPR
Year: 1999

Line scratches are common degradations in motion picture films. This paper presents an efficient method for line scratches detection strengthened by a Kalman filter. A new interpolation technique, dealing with both low and high frequencies (i.e. film grain) around the line artifacts, is investigated to achieve a nearby invisible reconstruction of damaged areas. Our line scratches detection and removal techniques have been validated on several film sequences.
Automatic Aircraft Recognition: Toward Using Human Similarity Measure in a Recognition System
Authors: Behrooz Kamgar-Parsi, Behzad Kamgar-Parsi, Anil K. Jain 0001
Venue: CVPR
Year: 1999

The problem of screening images of the skies to determine whether they contain aircraft or not is both of theoretical and practical interest. After the most prominent visual signal in the infrared image of the sky is extracted, the question is whether the signal is a correct match of an aircraft. Common approaches calculate the degree of similarity of the shape of the signal with a model aircraft using a similarity measure such as Euclidean distance, and make a decision based on whether the degree of similarity exceeds a (pre-specified) threshold. Our approach avoids metric similarity measures and the use of thresholds as it attempts to employ similarity measures used by humans. In the absence of sufficient real data, the approach allows to specifically generate an arbitrarily large number of training exemplars projecting near classification boundary. Once trained on such a training set, the performance of the neural network was comparable to that of a human expert, and far better than a network trained only on the available real data. Furthermore, the results were considerably better than those obtained using a Euclidean discriminator.
A Dependency-based Framework of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
Authors: Hee-Joong Kang, Seong-Whan Lee
Venue: CVPR
Year: 1999

Although Behavior-Knowledge Space (BKS) method does not need any assumptions in combining multiple experts, it should build theoretically exponential storage spaces for storing and managing jointly observed K decisions from K experts. That is, combining K experts needs a (K+1)st-order probability distribution. However, it is well known that the distribution becomes unmanageable in storing and estimating, even for a small K. In order to overcome such weakness, it would be attractive to decompose the distribution into a number of component distributions and to approximate the distribution with a product of the component distributions. One of such previous works is to apply a conditional independence assumption to the distribution. Another work is to approximate the distribution with a product of only first-order tree dependencies or second-order distributions. In this paper, a dependency-based framework is proposed to optimality approximate a probability distribution with a product set of dth-order dependencies where 1<d<K, and to combine multiple experts based on the product set using the Bayesian formalism. This framework was experimented and evaluated with a standardized CEN-PARIMl data base.
Minimum-Entropy Models of Scene Activity
Authors: Vera M. Kettnaker, Matthew Brand
Venue: CVPR
Year: 1999

We show how to learn a concise, interpretable model of scene activity directly from optical flow. The model represents the principal routes and modes of movement in complex scenes such as pedestrian plazas and traffic intersections, and supports a variety of inferences about the observed activities, including annotation, prediction, and anomaly detection. The model takes the form of a novel hidden Markov model generalization that observes a variable number of datapoints per frame (time step). A monotonic entropy-optimizing algorithm determines the parameters and structure of this model, exploiting the duality between learning and compression to produce highly predictive and interpretable models. This approach discovers minimal models of coherent motions and their switching dynamics-without tracking or prior knowledge about the spatial or temporal structure of the scene.
A Convolver-Based Real-Time Stereo Machine (SAZAN)
Authors: Shigeru Kimura, Katsuyiki Nakano, Tetsuya Sinbo, Hiroyoshi Yamaguchi, Eiji Kawamura
Venue: CVPR
Year: 1999

For 3D reconstruction, polynocular stereo based on multiple image fusion is a promising method. We developed a convolver-based nine-eye stereo machine called SAZAN. It performs real-time acquisition of dense depth map at 20 MDPS (Million Depth-pixels Per Second). The reduction of matching ambiguities, which is the most crucial part in stereo matching, is effectively performed by filtering operations of 2D convolver LSI. Several new ideas and capabilities including a nonlinear data reduction of LoG outputs, an efficient geometric calibration and a subpixel disparity are also implemented in hardware. Considering the hardware size and the various factors that have an influence on the final processing quality, the computational performance is compared with existing stereo systems including the CMU stereo machine.
Fundamental Bounds on Edge Detection: An Information Theoretic Evaluation of Different Edge Cues
Authors: Scott Konishi, Alan L. Yuille, James M. Coughlan, Song Chun Zhu
Venue: CVPR
Year: 1999

We treat the problem of edge detection as one of statistical inference. Local edge cues, implemented by filters, provide information about the likely positions of edges which can be used as input to higher-level models. Different edge cues can be evaluated by the statistical effectiveness of their corresponding filters evaluated on a dataset of 100 presegmented images. We use information theoretic measures to determine the effectiveness of a variety of different edge detectors working at multiple scales on black and white and color images. Our results give quantitative measures for the advantages of multi-level processing, for the use of chromaticity in addition to greyscale, and for the relative effectiveness of different detectors.
Sensor Planning for a Trinocular Active Vision System
Authors: Peter Lehel, Elsayed E. Hemayed, Aly A. Farag
Venue: CVPR
Year: 1999

We present an algorithm to solve the sensor planning problem for a trinocular, active vision system. This algorithm uses an iterative optimization method to first solve for the translation between the three cameras and then uses this result to solve for parameters such as pan, tilt angles of the cameras and zoom setting.
Optimal Multi-Scale Matching
Authors: Michael S. Lew, Thomas S. Huang
Venue: CVPR
Year: 1999

The coarse-to-fine search strategy is extensively used in current reported research. However, it has the same problem as any hill climbing algorithm, most importantly, it often finds local instead of global minima. Drawing upon the artificial intelligence literature, we applied an optimal graph search, namely A*, to the problem. Using real stereo and video test sets, we compared the A* method to both template and hill climbing. Our results show that A* has greater accuracy than the ubiquitous coarse-to-fine hill climbing pyramidal search algorithm in both stereo matching and motion tracking.
An Efficient Recursive Factorization Method for Determining Structure from Motion
Authors: Yanhua Li, Michael J. Brooks
Venue: CVPR
Year: 1999

A recursive method is presented for recovering 3D object shape and camera motion under orthography from an extended sequence of video images. This may be viewed as a natural extension of both the original and the sequential factorization methods. A critical aspect of these factorization approaches is the estimation of the so-called shape space, and they may in part be characterized by the manner in which this subspace is computed. If P points are tracked through F frames, the recursive least-squares method proposed in this paper updates the shape space with complexity O(P) per frame. In contrast, the sequential factorization method updates the shape space with complexity O(P/sup 2/) per frame. The original factorization method is intended to be used in batch mode using points tracked across all available frames. It effectively computes the shape space with complexity O(FP/sup 2/) after F frames. Unlike other methods, the recursive approach does not require the estimation or updating of a large measurement or covariance matrix. Experiments with real and synthetic image sequences confirm the recursive method's low computational complexity and good performance, and indicate that it is well suited to real-time applications.
User Assisted Modeling of Buildings from Aerial Images
Authors: J. Li, Ramakant Nevatia, Sanjay Noronha
Venue: CVPR
Year: 1999

An approach that allows a user to assist an automatic system in modeling buildings is described. The approach is designed to be efficient in user time and effort while preserving the quality of the models created. Currently our system is able to handle the rectangular buildings with flat roof or symmetric gabled roof. Models can be created by only one or two clicks in many cases. Efficient editing of automatically derived models is also possible.
Adaptive Balloon Models
Authors: Xiaobo Li 0001, Jiankang Wang
Venue: CVPR
Year: 1999

The original snake models require a close initialization which in many situations are difficult to acquire. The balloon model presented by Cohen et al. to solve this problem suffers from the difficulty of choosing a constant inflating force due to variable internal shrinking forces and non-constant boundary intensity levels. Xu et al., on the other hand, proposed to use a pressure force to exactly offset the shrinking forces. The resulting model achieves better stability in terms of parameter insensitivity by sacrificing smoothness constraints, thus it would go through even small gaps on a boundary. We instead propose to compute an adaptive inflating force locally for each snaxel so that it is just enough to overcome the image force. A new smoothness constraint which can maintain smoothness without any shrinking side-effects is also presented, along with a new way to resample a balloon without significantly reducing its tension. The combined model is sensitive to weak and incomplete boundaries, and yet able to overcome noise edges. Experimental results are reported to support our statements.
Deformable Shape Detection and Description via Model-Based Region
Authors: Lifeng Liu, Stan Sclaroff
Venue: CVPR
Year: 1999

A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.
Face Recognition Using Shape and Texture
Authors: Chengjun Liu, Harry Wechsler
Venue: CVPR
Year: 1999

We introduce in this paper a new face coding and recognition method which employs the Enhanced FLD (Fisher Linear Discrimimant) Model (EFM) on integrated shape (vector) and texture ('shape-free' image) information. Shape encodes the feature geometry of a face while texture provides a normalized shape-free image by warping the original face image to the mean shape, i.e., the average of aligned shapes. The dimensionalities of the shape and the texture spaces are first reduced using Principal Component Analysis (PCA). The corresponding but reduced shape find texture features are then integrated through a normalization procedure to form augmented features. The dimensionality reduction procedure, constrained by EFM for enhanced generalization, maintains a proper balance between the spectral energy needs of PCA for adequate representation, and the FLD discrimination requirements, that the eigenvalues of the within-class scatter matrix should not include small trailing values after the dimensionality reduction procedure as they appear in the denominator.
Model-Guided Segmentation of Corpus Callosum in MR Images
Authors: Arvid Lundervold, Torfinn Taxt, Nicolae Duta, Anil K. Jain 0001
Venue: CVPR
Year: 1999

Magnetic resonance imaging (MRI) of the brain, followed by automated segmentation of the corpus callosum (CC) in midsagittal sections has important applications in neurology and neurocognitive research since the size and shape of the CC are shown to be correlated to sex, age, neurodegenerative diseases and various lateralized behavior in man. Moreover, whole head, multispectral 3D MRI recordings enable voxel-based tissue classification and estimation of total brain volumes, in addition to CC morphometric parameters. We propose a new algorithm that uses both multispectral MRI measurements (intensity values) and prior information about shape (CC template) to segment CC in midsagittal slices with very little user interaction. The algorithm has been successfully tested on a sample of 10 subjects scanned with multispectral 3D MRI, collected for a study of dyslexia. We conclude that the proposed method for CC segmentation is promising for clinical use when multispectral MR images are recorded.
Area-Based Computation of Stereo Disparity with Model-Based Window Size Selection
Authors: Robert Maas, Bart M. ter Haar Romeny, Max A. Viergever
Venue: CVPR
Year: 1999

In direct area-based stereo problems occur if there is a significant local deformation between the two images, and at borders of objects. We propose a correlation method with affine deformable windows in which an optimal window size is selected using model-based reasoning. Two criteria for the window size selection are studied and the resulting algorithms are applied to a random dot pair, an autostereogram and Hoff's fruit pair. The results are found to be both accurate and robust.
Interpolating View and Scene Motion by Dynamic View Morphing
Authors: Russell A. Manning, Charles R. Dyer
Venue: CVPR
Year: 1999

We introduce the problem of view interpolation for dynamic scenes. Our solution to this problem extends the concept of view morphing and retains the practical advantages of that method. We are specifically concerned with interpolating between two reference views captured at different times, so that there is a missing interval of time between when the views were taken. The synthetic interpolations produced by our algorithm portray one possible physically-valid version of what transpired in the scene during the missing time. It is assumed that each object in the original scene underwent a series of rigid translations. Dynamic view morphing can work with widely-spaced reference views, sparse point correspondences, and uncalibrated cameras. When the camera-to-camera transformation can be determined, the synthetic interpolation will portray scene objects moving along straight-line, constant-velocity trajectories in world space.
Optimal Rigid Motion Estimation and Performance Evaluation with Bootstrap
Authors: Bogdan Matei, Peter Meer
Venue: CVPR
Year: 1999

A new method for 3D rigid motion estimation is derived under the most general assumption that the measurements are corrupted by inhomogeneous and anisotropic, i.e., heteroscedastic noise. This is the case, for example, when the motion of a calibrated stereo-head is to be determined from image pairs. Linearization in the quaternion space transforms the problem into a multivariate, heteroscedastic errors-in-variables (HEIV) regression, from which the rotation and translation estimates are obtained simultaneously. The significant performance improvement is illustrated, for real data, by comparison with the results of quaternion, subspace and renormalization based approaches described in the literature. Extensive use as made of bootstrap, an advanced numerical tool from statistics, both to estimate the covariances of the 3D data points and to obtain confidence regions for the rotation and translation estimates. Bootstrap enables an accurate recovery of these information using only the two image pairs serving as input.
Constrained Self-Calibration
Authors: Jeffrey Mendelsohn, Konstantinos Daniilidis
Venue: CVPR
Year: 1999

This paper focuses on the estimation of the intrinsic camera parameters and the trajectory of the camera from an image sequence. Intrinsic camera calibration and pose estimation are the prerequisites for many applications involving navigation tasks, scene reconstruction, and merging of virtual and real environments. Proposed and evaluated is a technical solution to decrease the sensitivity of self-calibration by placing easily identifiable targets of known shape in the environment. The relative position of the targets need not be known a priori. Assuming an appropriate ratio of size to distance these targets resolve known ambiguities. Constraints on the target placement and the cameras' motions are explored. The algorithm is extensively tested in a variety of real-world scenarios.