OpenRaiser/Intern-Atlas huggingface dataset:
Learned Templates for Feature Extraction in Fingerprint Images
Authors: Bir Bhanu, Xuejun Tan
Venue: CVPR
Year: 2001

Most current techniques for minutiae extraction in fingerprint images utilize complex preprocessing and postprocessing. In this paper, we propose a new technique, based on the use of learned templates, which statistically characterize the minutiae. Templates are teamed from examples by optimizing a criterion function using Lagrange's method. To detect the presence of minutiae in test images, templates are applied with appropriate orientations to the binary image only at selected potential minutia locations. Several performance measures, which evaluate the quality and quantity of extracted features and their impact on identification, are used to evaluate the significance of learned templates. The performance of the proposed approach is evaluated on two sets of fingerprint images: one is collected by an optical scanner and the other one is chosen from NIST special fingerprint database 4. The experimental results show that learned templates can improve both the features and the performance of the identification system.
Recognition of Human Gaits
Authors: Alessandro Bissacco, Alessandro Chiuso, Yi Ma 0001, Stefano Soatto
Venue: CVPR
Year: 2001

We pose the problem of recognizing different types of human gait in the space of dynamical systems where each gait is represented Established techniques are employed to track a kinematic model of a human body in motion, and the trajectories of the parameters are used to learn a representation of a dynamical system, which defines a gait. Various types of distance between models are then computed These computations are non trivial due to the fact that, even for the case of linear systems, the space of canonical realizations is not linear.
Efficient Computation of Adaptive Threshold Surfaces for Image Binarization
Authors: Ilya Blayvas, Alfred M. Bruckstein, Ron Kimmel
Venue: CVPR
Year: 2001

The problem of binarization of gray level images acquired under nonuniform illumination is reconsidered. Yanowitz and Bruckstein (1989) proposed to use an adaptive threshold surface, determined by interpolation of the image gray levels at points where the image gradient is high. The rationale is that a high image gradient indicates probable object edges, and there the image values are between the object and background gray levels. The threshold surface was determined by successive overrelaxation as the solution of the Laplace equation. This work proposes a different method to determine an adaptive threshold surface. In this new method, inspired by multiresolution approximation, the threshold surface is constructed with considerably lower computational complexity and is smooth, yielding faster image binarizations and better visual performance.
Gait Recognition Using Static, Activity-Specific Parameters
Authors: Aaron F. Bobick, Amos Y. Johnson
Venue: CVPR
Year: 2001

A gait-recognition technique that recovers static body and stride parameters of subjects as they walk is presented. This approach is an example of an activity-specific biometric: a method of extracting identifying properties of an individual or of an individual's behavior that is applicable only when a person is performing that specific action. To evaluate our parameters, we derive an expected confusion metric (related to mutual information), as opposed to reporting a percent correct with a limited database. This metric predicts how well a given feature vector will filter identity in a large population. We test the utility of a variety of body and stride parameters recovered in different viewing conditions on a database consisting of 15 to 20 subjects walking at both an angled and frontal-parallel view with respect to the camera, both indoors and out. We also analyze motion-capture data of the subjects to discover whether confusion in the parameters is inherently a physical or a visual measurement error property.
Flexible flow for 3D nonrigid tracking and shape recovery
Authors: Matthew Brand, Rahul Bhotika
Venue: CVPR
Year: 2001

We introduce linear methods for model-based tracking of nonrigid 3D objects and for acquiring such models from video. 3D motions and flexions are calculated directly from image intensities without information-lossy intermediate results. Measurement uncertainty is quantified and fully propagated through the inverse model to yield posterior mean (PM) and/or mode (MAP) pose estimates. A Bayesian framework manages uncertainty, accommodates priors, and gives confidence measures. We obtain highly accurate and robust closed-form estimators by minimizing information loss from non-reversible (inner-product and least-squares) operations, and, when unavoidable, performing such operations with the appropriate error norm. For model acquisition, we show how to refine a crude or generic model to fit the video subject. We demonstrate with tracking, model refinement, and super-resolution texture lifting from low-quality low-resolution video.
Using an ICA Representation of High Dimensional Data for Object Recognition and Classification
Authors: Marco Bressan 0001, David Guillamet, Jordi Vitrià
Venue: CVPR
Year: 2001

This paper applies a Bayesian classification scheme to the problem of recognition through probabilistic modeling of high dimensional data. In this context, high dimensionality does not allow precision in the density estimation. We propose a local independent component analysis (ICA) representation of the data. The components can be assumed statistically independent and, in many cases, sparsity is observed. We show how these two characteristics can be used to simplify and add accuracy to the density estimation and develop bayesian decision within this representation. A first experiment illustrates that classification using an ICA representation is a technique that, even in low dimensions, performs comparably to standard classification techniques. The second experiment tests the ICA classification model on high dimensional data. Recognition was performed using local color histograms as salient features. It is also shown how our approach outperforms other techniques commonly used in the context of appearance-based recognition.
Registration via Direct Methods: A Statistical Approach
Authors: Jacques Bride, Peter Meer
Venue: CVPR
Year: 2001

The "direct methods" achieve global image registration without explicit knowledge of feature correspondences. We employ the motion gradient constraint as the relation between the motion parameters and the measured image gradients. While this relation appears as a linear system of equations, for any motion model (other than a translation) we show that the underlying noise process is data-dependent, i.e., heteroscedastic, a fact which must be taken into account in the parameter estimation process. The improvement obtained using the adequate procedure is confirmed for the 2D rigid motion model through comparison with the traditional total least square approach.
Super-Resolution from Multiple Views Using Learnt Image Models
Authors: David P. Capel, Andrew Zisserman
Venue: CVPR
Year: 2001

The objective of the work presented is the super-resolution restoration of a set of images, and we investigate the use of learnt image models within a generative Bayesian framework. It is demonstrated that restoration of far higher quality than that determined by classical maximum likelihood estimation can be achieved by either constraining the solution to lie on a restricted sub-space, or by using the sub-space to define a spatially varying prior. This sub-space can be learnt from image examples. The methods are applied to both real and synthetic images of text and faces, and results are compared to R.R. Schultz and R.L. Stevenson's (1996) MAP estimator. We consider in particular images of scenes for which the point-to-point mapping is a plane projective transformation which has 8 degrees of freedom. In the real image examples, registration is obtained from the images using automatic methods.
Bagging Is a Small-Data-Set Phenomenon
Authors: Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer
Venue: CVPR
Year: 2001

Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, disjoint partitions result in better performance than bootstrap aggregates (bags). Many applications (e.g., protein structure prediction) involve the use of datasets that are too large to handle in the memory of a typical computer. Our results indicate that, in such applications, the simple approach of creating a committee of classifiers from disjoint partitions is preferred over the more complex approach of bagging.
Robust Point Feature Matching In Projective Space
Authors: George Q. Chen
Venue: CVPR
Year: 2001

We present a robust method for matching point features across a set of images under full perspective projection. An expectation-maximization-like algorithm is developed to build an optimal potential match set (PMS) between each consecutive pair of views, by iteratively maximizing a heuristic objective function. All two-view matches are combined to form an M-view potential match set (MPMS) with a low contamination rate. Outliers in MPMS are removed incorporating the least-median-of-squares technique with projective reconstruction. The current work extends previous ones in two- or three-view matching, or under affine camera projection. Results on real imagery demonstrate the validity of the proposed method.
Multi-Object Tracking Using Dynamical Graph Matching
Authors: Hwann-Tzong Chen, Horng-Horng Lin, Tyng-Luh Liu
Venue: CVPR
Year: 2001

We describe a tracking algorithm to address the interactions among objects, and to track them individually and confidently via a static camera. It is achieved by constructing an invariant bipartite graph to model the dynamics of the tracking process, of which the nodes are classified into objects and profiles. The best match of the graph corresponds to an optimal assignment for resolving the identities of the detected objects. Since objects may enter/exit the scene indefinitely, or when interactions occur/conclude they could form/leave a group, the number of nodes in the graph changes dynamically. Therefore it is critical to maintain an invariant property to assure that the numbers of nodes of both types are kept the same so that the matching problem is manageable. In addition, several important issues are also discussed, including reducing the effect of shadows, extracting objects' shapes, and adapting large abrupt changes in the scene background. Finally, experimental results are provided to illustrate the efficiency of our approach.
Robust Regression for Data with Multiple Structures
Authors: Haifeng Chen, Peter Meer, David E. Tyler
Venue: CVPR
Year: 2001

In many vision problems (e.g., stereo, motion) multiple structures can occur in the data, in which case several instances of the same model need to be recovered from a single data set. However, once the measurement noise becomes significantly large relative to the separation between the structures, the robust statistical methods commonly used in the vision community tend to fail. In this paper, we show that all these techniques are special cases of the general class of M-estimators with auxiliary scale, and explain their failure in the presence of noisy multiple structures. To be able to cope with data containing multiple structures the techniques innate to vision (Hough and RANSAC) should be combined with the robust methods customary in statistics. The implications of our analysis are illustrated by introducing a simple procedure for 2D multistructured data problematic for all known current techniques.
JPDAF Based HMM or Real-Time Contour Tracking
Authors: Yunqiang Chen, Yong Rui, Thomas S. Huang
Venue: CVPR
Year: 2001

Tracking objects using multiple cues yields more robust results. The well-known hidden Markov model (HMM) provides a powerful framework to incorporate multiple cues by expanding its observation. However, a plain HMM does not capture the inter-correlation between measurements of neighboring states when computing the transition probabilities. This can seriously damage the tracking performance. To overcome this difficulty, we propose a novel HMM framework targeted at contour-based object tracking. A joint probability data association filter (JPDAF) is used to compute the HMM's transition probabilities, taking into account the intercorrelated neighboring measurements. To ensure real-time performance, we have further developed an efficient method to calculate the data association probability via dynamic programming, which allows the proposed JPDAF-HMM to run comfortably at 30 frames/sec. This new tracking framework can easily incorporate various image cues (e.g., edge intensity, foreground region color and background region color), and also offers an online learning process to adapt to changes in the scene. To evaluate its tracking performance, we have applied the proposed JPDAF-HMM in various real-world video sequences. We report promising tracking results in complex environments.
Sequential Knowledge-Driven Scene Recognition Model
Authors: Dimitri A. Chernyak, Lawrence W. Stark
Venue: CVPR
Year: 2001

Eye movements are an important aspect of human visual behavior. The temporal and space-variant nature of sampling a visual scene requires frequent attentional gaze shifts, saccades, to fixate onto different parts of an image. Experimental evidence suggests that fixations are often directed towards the most informative regions in the visual scene. We develop a model and its simulation that can select such regions based on prior knowledge of similar scenes. Having representations of scene categories as a probabilistic combination of hypothetical objects, i.e., prototypical regions with certain properties, it is possible to assess the likely contribution of each image region to the successive recognition process. Using conditional probabilities for each region given the scene category, the model can then predict its informative value and initiate a sequential spatial information-gathering algorithm, analogous to an eye movement saccade to a new fixation. This algorithm establishes the most likely scene category for a given image.
Evolving Image Segmentations for the Analysis of Video Sequences
Authors: A. A. Clark, Barry T. Thomas
Venue: CVPR
Year: 2001

This paper concerns the segmentation of successive frames of a video sequence. Traditional methods, treating each frame in isolation, are computationally expensive, ignore potentially useful information derived from previous frames, and can lead to instabilities over the sequence. The approach developed here, based on the Region Competition algorithm (Zhu and Yuille, IEEE Trans. PAMI, 1996), employs a mesh of active contour primitives supervised by an MDL energy criterion. Temporal extensions, namely Boundary Momentum, Region Memory, and Optical Boundary Flow, are developed to ease the transition between successive frames. Further enhancements are made by incorporating mechanisms to accommodate the topological discontinuities that can arise during the sequence. The algorithm is demonstrated using a number of synthetic and real video sequences and is shown to provide an efficient method of segmentation which encourages stability across frames and preserves the quality of the original segmentation over the sequence.
Grouping connected components using minimal path techniques. Application to reconstruction of vessels in 2D and 3D images
Authors: Laurent D. Cohen, Thomas Deschamps
Venue: CVPR
Year: 2001

We address the problem of finding a set of contour curves in a 2D or 3D image. We consider the problem of perceptual grouping and contour completion, where the data is an unstructured set of regions in the image. A new method to find complete curves from a set of edge points is presented. Contours, are found as minimal paths between connected components, using the fast marching algorithm. We find the minimal paths between each of these components, until the complete set of these "regions" is connected. The paths are obtained using backpropagation from the saddle points to both components. We then extend this technique to 3D. The data is a set of connected components in a 3D image. We find 3D minimal paths that link together these components. Using a potential based on vessel detection, we illustrate the capability of our approach to reconstruct tree structures in a 3D medical image dataset.
On Representing Edge Structure for Model Matching
Authors: Timothy F. Cootes, Christopher J. Taylor 0001
Venue: CVPR
Year: 2001

We show how a novel, non-linear representation of edge structure can be used to improve the performance of model matching algorithms and object verification/recognition tasks. Rather than represent the image structure using intensity values or gradients, we use a measure which indicates the orientation of structures at each pixel, together with an indication of how reliable the orientation estimate is. Orientations in flat, noisy regions tend to be penalised whereas those near strong edges are favoured. We demonstrate that this representation leads to more accurate and reliable matching between models and new images, and leads to better recognition/verification of faces in an access control task.
3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain
Authors: Andrew J. Davison, Nobuyuki Kita
Venue: CVPR
Year: 2001

Work in simultaneous localisation and map-building ("SLAM") for mobile robots has focused on the simplified case in which a robot is considered to move in two dimensions on a ground plane. While this is often a good approximation, a large number of real-world applications require robots to move around terrain which has significant slopes and undulations, and it is desirable that these robots too should be able to estimate their locations by building maps of natural features. We describe a real-time EKF-based-SLAM system permitting unconstrained 3D localisation, and in particular develop models for the motion of a wheeled robot in the presence of unknown slope variations. In a fully automatic implementation, our robot observes visual point features using fixating stereo vision and builds a sparse map on-the-fly. Combining this visual measurement with information from odometry and a roll/pitch accelerometer sensor, the robot performs accurate, repeatable localisation while traversing an undulating course.
Automatic Partitioning of High Dimensional Search Spaces Associated with Articulated Body Motion Capture
Authors: Jonathan Deutscher, Andrew J. Davison, Ian D. Reid 0001
Venue: CVPR
Year: 2001

Particle filters have proven to be an effective tool for visual tracking in non-Gaussian, cluttered environments. Conventional particle filters, however, do not scale to the problem of human motion capture (HMC) because of the large number of degrees of freedom involved. Annealed Particle Filtering (APF), introduced by J. Deutscher et al. (2000), tackled this by layering the search space and was shown to be a very effective tool for HMC. We improve upon and extend the APF in two ways. First we develop a hierarchical search strategy which automatically partitions the search space without any explicit representation of the partitions. Then we introduce a crossover operator (similar to that found in genetic algorithms) which improves the ability of the tracker to search different partitions in parallel. We present results for a simple example to demonstrate the new algorithm's implementation and then apply it to the considerably more complex problem of human motion capture with 34 degrees of freedom.
Reconstruction of 3-D Figure Motion from 2-D Correspondences
Authors: David E. DiFranco, Tat-Jen Cham, James M. Rehg
Venue: CVPR
Year: 2001

We present a method for computing the 3D motion of articulated models from 2D correspondences. An iterative batch algorithm is proposed which estimates the maximum a posteriori trajectory based on 2D measurements subject to a number of constraints. These include (i) kinematic constraints based on a 3D kinematic model, (ii) joint angle limits, (iii) dynamic smoothing, and (iv) 3D key frames which can be specified by the user. The framework handles any variation in the number of constraints as well as partial or missing data. This method is shown to obtain favorable reconstruction results on a number of complex human motion sequences.
Fast Focal Length Solution in Partial Panoramic Image Stitching
Authors: Kirk L. Duffin, William A. Barrett
Venue: CVPR
Year: 2001

Accurate estimation of effective camera focal length is crucial to the success of panoramic image stitching. Fast techniques for estimating the focal length exist, but are dependent upon a close initial approximation or the existence of a full circle panoramic image sequence. Numerical solutions of the focal length demonstrate strong coupling between the focal length and the angles used to position each component image about the common spherical center. This paper demonstrates that parameterizing panoramic image positions using spherical arc length instead of angles effectively decouples the focal length from the image position. This new parameterization does not require an initial focal length estimate for quick convergence, nor does it require a full circle panorama in order to refine the focal length. Experiments with synthetic and real image sets demonstrate the robustness of the method and a speedup of 5 to 20 times over angle based positioning.
Bending Invariant Representations for Surfaces
Authors: Asi Elad (Elbaz), Ron Kimmel
Venue: CVPR
Year: 2001

Isometric surfaces share the same geometric structure also known as the first fundamental form. For example, bending of a given surface, that includes length preserving deformations without tearing or stretching the surface, are considered to be isometric. We present a method to construct a bending invariant canonical form for such surfaces. This invariant representation is an embedding of the intrinsic geodesic structure of the surface in a finite dimensional Euclidean space, in which geodesic distances are approximated by Euclidean ones. The canonical representation is constructed by first measuring the intergeodesic distances between points on the surfaces. Next, multi-dimensional scaling (MDS) techniques are applied to extract a finite dimensional flat space in which geodesic distances are represented as Euclidean ones. The geodesic distances are measured by the efficient fast marching on triangulated domains numerical algorithm. Applying this transform to various objects with similar geodesic structures (similar first fundamental form) maps isometric objects into similar canonical forms. We show a simple surface classification method based on the bending invariant canonical form.