OpenRaiser/Intern-Atlas huggingface dataset:
Multi-class object tracking algorithm that handles fragmentation and grouping
Authors: Biswajit Bose, Xiaogang Wang 0001, Eric Grimson
Venue: CVPR
Year: 2007

We propose a framework for detecting and tracking multiple interacting objects, while explicitly handling the dual problems of fragmentation (an object may be broken into several blobs) and grouping (multiple objects may appear as a single blob). We use foreground blobs obtained by background subtraction from a stationary camera as measurements. The main challenge is to associate blob measurements with objects, given the fragment-object-group ambiguity when the number of objects is variable and unknown, and object-class-specific models are not available. We first track foreground blobs till they merge or split. We then build an inference graph representing merge-split relations between the tracked blobs. Using this graph and a generic object model based on spatial connectedness and coherent motion, we label the tracked blobs as whole objects, fragments of objects or groups of interacting objects. The outputs of our algorithm are entire tracks of objects, which may include corresponding tracks from groups during interactions. Experimental results on multiple video sequences are shown.
A Bayesian Non-Gaussian Mixture Analysis: Application to Eye Modeling
Authors: Nizar Bouguila, Djemel Ziou, Riad I. Hammoud
Venue: CVPR
Year: 2007

Many computer vision and pattern recognition problems involve the use of finite Gaussian mixture models. Finite mixture model using generalized Dirichlet distribution has been shown as a robust alternative of normal mixtures. In this paper, we adopt a Bayesian approach for generalized Dirichlet mixture estimation and selection. This approach, offers a solid theoretical framework for combining both the statistical model learning and the knowledge acquisition. The estimation of the parameters is based on the Monte Carlo simulation technique of Gibbs sampling mixed with a Metropolis-Hastings step. For the selection of the number of clusters, we used Bayes factors. We have successfully applied the proposed Bayesian framework to model IR eyes. Experimental results are shown to demonstrate the robustness, efficiency, and accuracy of the algorithm.
Revocable Fingerprint Biotokens: Accuracy and Security Analysis
Authors: Terrance E. Boult, Walter J. Scheirer, Robert Woodworth
Venue: CVPR
Year: 2007

This paper reviews the biometric dilemma, the pending threat that may limit the long-term value of biometrics in security applications. Unlike passwords, if a biometric database is ever compromised or improperly shared, the underlying biometric data cannot be changed. The concept of revocable or cancelable biometric-based identity tokens (biotokens), if properly implemented, can provide significant enhancements in both privacy and security and address the biometric dilemma. The key to effective revocable biotokens is the need to support the highly accurate approximate matching needed in any biometric system as well as protecting privacy/security of the underlying data. We briefly review prior work and show why it is insufficient in both accuracy and security. This paper adapts a recently introduced approach that separates each datum into two fields, one of which is encoded and one which is left to support the approximate matching. Previously applied to faces, this paper uses this approach to enhance an existing fingerprint system. Unlike previous work in privacy-enhanced biometrics, our approach improves the accuracy of the underlying svstem! The security analysis of these biotokens includes addressing the critical issue of protection of small fields. The resulting algorithm is tested on three different fingerprint verification challenge datasets and shows an average decrease in the Equal Error Rate of over 30% - providing improved security and improved privacy.
Minimal Solutions for Panoramic Stitching
Authors: Matthew A. Brown, Richard I. Hartley, David Nistér
Venue: CVPR
Year: 2007

This paper presents minimal solutions for the geometric parameters of a camera rotating about its optical centre. In particular we present new 2 and 3 point solutions for the homography induced by a rotation with 1 and 2 unknown focal length parameters. Using tests on real data, we show that these algorithms outperform the standard 4 point linear homography solution in terms of accuracy of focal length estimation and image based projection errors.
Combining local and global motion models for feature point tracking
Authors: Aeron Buchanan, Andrew W. Fitzgibbon
Venue: CVPR
Year: 2007

Accurate feature point tracks through long sequences are a valuable substrate for many computer vision applications, e.g. non-rigid body tracking, video segmentation, video matching, and even object recognition. Existing algorithms may be arranged along an axis indicating how global the motion model used to constrain tracks is. Local methods, such as the KLT tracker, depend on local models of feature appearance, and are easily distracted by occlusions, repeated structure, and image noise. This leads to short tracks, many of which are incorrect. Alone, these require considerable postprocessing to obtain a useful result. In restricted scenes, for example a rigid scene through which a camera is moving, such postprocessing can make use of global motion models to allow "guided matching " which yields long high-quality feature tracks. However, many scenes of interest contain multiple motions or significant non-rigid deformations which mean that guided matching cannot be applied. In this paper we propose a general amalgam of local and global models to improve tracking even in these difficult cases. By viewing rank-constrained tracking as a probabilistic model of 2D tracks rather than 3D motion, we obtain a strong, robust motion prior, derived from the global motion in the scene. The result is a simple and powerful prior whose strength is easily tuned, enabling its use in any existing tracking algorithm.
Fourier Methods for Nonparametric Image Registration
Authors: Nathan D. Cahill, J. Alison Noble, David J. Hawkes
Venue: CVPR
Year: 2007

Nonparametric image registration algorithms use deformation fields to define nonrigid transformations relating two images. Typically, these algorithms operate by successively solving linear systems of partial differential equations. These PDE systems arise by linearizing the Euler-Lagrange equations associated with the minimization of a functional defined to contain an image similarity term and a regularizer. Iterative linear system solvers can be used to solve the linear PDE systems, but they can be extremely slow. Some faster techniques based on Fourier methods, multigrid methods, and additive operator splitting, exist for solving the linear PDE systems for specific combinations of regularizers and boundary conditions. In this paper, we show that Fourier methods can be employed to quickly solve the linear PDE systems for every combination of standard regularizers (diffusion, curvature, elastic, and fluid) and boundary conditions (Dirichlet, Neumann, and periodic).
Handwritten Carbon Form Preprocessing Based on Markov Random Field
Authors: Huaigu Cao, Venu Govindaraju
Venue: CVPR
Year: 2007

This paper proposes a statistical approach to degraded handwritten form image preprocessing including binarization and form line removal. The degraded image is modeled by a Markov random field (MRF) where the prior is learnt from a training set of high quality binarized images, and the probabilistic density is learnt on-the-fly from the gray-level histogram of input image. We also modified the MRF model to implement form line removal. Test results of our approach show excellent performance on the data set of handwritten carbon form images.
Using Stereo Matching for 2-D Face Recognition Across Pose
Authors: Carlos Domingo Castillo, David W. Jacobs
Venue: CVPR
Year: 2007

We propose using stereo matching for 2-D face recognition across pose. We match one 2-D query image to one 2-D gallery image without performing 3-D reconstruction. Then the cost of this matching is used to evaluate the similarity of the two images. We show that this cost is robust to pose variations. To illustrate this idea we built a face recognition system on top of a dynamic programming stereo matching algorithm. The method works well even when the epipolar lines we use do not exactly fit the viewpoints. We have tested our approach on the PIE dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.
Efficient Sampling of Disparity Space for Fast And Accurate Matching
Authors: Jan Cech, Radim Sára
Venue: CVPR
Year: 2007

A simple stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. Unlike in known seed-growing algorithms, it guarantees matching accuracy and correctness, even in the presence of repetitive patterns. This success is based on the fact it solves a global optimization task. The algorithm can recover from wrong initial seeds to the extent they can even be random. The quality of correspondence seeds influences computing time, not the quality of the final disparity map. We show that the proposed algorithm achieves similar results as an exhaustive disparity space search but it is two orders of magnitude faster. This is very unlike the existing growing algorithms which are fast but erroneous. Accurate matching on 2-megapixel images of complex scenes is routinely obtained in a few seconds on a common PC from a small number of seeds, without limiting the disparity search range.
Non-linear IR Scene Prediction for Range Video Surveillance
Authors: Mehmet Celenk, James Graham, Kai-Jen Cheng
Venue: CVPR
Year: 2007

This paper describes a non-linear IR (infra-red) scene prediction method for range video surveillance and navigation. A Gabor-filter bank is selected as a primary detector for any changes in a given IR range image sequence. The detected ROI (region of interest) involving arbitrary motion is fed to a non-linear Kalman filter for predicting the next scene in time-varying 3D IR video. Potential applications of this research are mainly in indoor/outdoor heat-change based range measurement, synthetic IR scene generation, rescue missions, and autonomous navigation. Experimental results reported herein show that non-linear Kalman filtering-based scene prediction can perform more accurately than linear estimation of future frames in range and intensity driven sensing. The low least mean square error (LMSE), on the average of about 2% using a bank of 8 Gabor filters, also proves the reliability of the IR scene estimator (or predictor) developed in this work.
Autocalibration via Rank-Constrained Estimation of the Absolute Quadric
Authors: Manmohan Krishna Chandraker, Sameer Agarwal 0001, Fredrik Kahl, David Nistér, David J. Kriegman
Venue: CVPR
Year: 2007

We present an autocalibration algorithm for upgrading a projective reconstruction to a metric reconstruction by estimating the absolute dual quadric. The algorithm enforces the rank degeneracy and the positive semidefiniteness of the dual quadric as part of the estimation procedure, rather than as a post-processing step. Furthermore, the method allows the user, if he or she so desires, to enforce conditions on the plane at infinity so that the reconstruction satisfies the chirality constraints. The algorithm works by constructing low degree polynomial optimization problems, which are solved to their global optimum using a series of convex linear matrix inequality relaxations. The algorithm is fast, stable, robust and has time complexity independent of the number of views. We show extensive results on synthetic as well as real datasets to validate our algorithm.
Multiview normal field integration using level set methods
Authors: Ju Yong Chang, Kyoung Mu Lee, Sang Uk Lee
Venue: CVPR
Year: 2007

In this paper, we propose a new method to integrate multiview normal fields using level sets. In contrast with conventional normal integration algorithms used in shape from shading and photometric stereo that reconstruct a 2.5D surface using a single-view normal field, our algorithm can combine multiview normal fields simultaneously and recover the full 3D shape of a target object. We formulate this multiview normal integration problem by an energy minimization framework and find an optimal solution in a least square sense using a variational technique. A level set method is applied to solve the resultant geometric PDE that minimizes the proposed error functional. It is shown that the resultant flow is composed of the well known mean curvature and flux maximizing flows. In particular, we apply the proposed algorithm to the problem of 3D shape modelling in a multiview photometric stereo setting. Experimental results for various synthetic data show the validity of our approach.
Constrained Optimization for Retinal Curvature Estimation Using an Affine Camera
Authors: Thitiporn Chanwimaluang, Guoliang Fan
Venue: CVPR
Year: 2007

We study retinal curvature estimation from multiple images that provides the fundamental geometry of human retina. We use an affine camera model due to its simplicity, linearity, and robustness. Moreover, the affine camera is suitable in this research because (1) NIH's retinal imaging protocols specify a narrow 30degC field-of-view in each eye and (2) each field has small depth variation. A major challenge is that there is a series of optics involved in the imaging process, including an actual fundus camera, a digital camera, and the human cornea, all of which cause significant non-linear distortions in the retinal images. In this work, we develop a new constrained optimization procedure that considers both the geometric shape of human retina and lens distortions. Moreover, the constrained optimization is implemented in the affine space because it is computationally efficient and robust to noise. Specifically, we amend the affine bundle adjustment algorithm by including a quadratic surface fitting error and the lens distortion correction into the cost function for constrained optimization. The experiments on both synthetic data and real retinal images show the effectiveness and robustness of the proposed algorithm.
Shape Statistics for Image Segmentation with Prior
Authors: Guillaume Charpiat, Olivier D. Faugeras, Renaud Keriven
Venue: CVPR
Year: 2007

We propose a new approach to compute non-linear, intrinsic shape statistics and to incorporate them into a shape prior for an image segmentation task. Given a sample set of contours, we first define their mean shape as the one which is simultaneously closest to all samples up to rigid motions, and compute it in a gradient descent framework. We consider here a differentiable approximation of the Hausdorff distance between shapes. Statistics on the instantaneous deformation fields that the mean shape should undergo to move towards each sample lead to sensible characteristic modes of deformation that convey the shape variability. Contour statistics are turned into a shape prior which is rigid-motion invariant. Image segmentation results show the improvement gained by the shape prior.
PrivacyCam: a Privacy Preserving Camera Using uCLinux on the Blackfin DSP
Authors: Ankur Chattopadhyay, Terrance E. Boult
Venue: CVPR
Year: 2007

Considerable research work has been done in the area of surveillance and biometrics, where the goals have always been high performance, robustness in security and cost optimization. With the emergence of more intelligent and complex video surveillance mechanisms, the issue of "privacy invasion" has been looming large. Very little investment or effort has gone into looking after this issue in an efficient and cost-effective way. The process of PICO (privacy through invertible cryptographic obscuration) is a way of using cryptographic techniques and combining them with image processing and video surveillance to provide a practical solution to the critical issue of "privacy invasion". This paper presents the idea and example of a realtime embedded application of the PICO technique, using uCLinux on the tiny Blackfin DSP architecture, along with a small Omnivision camera. It demonstrates how the practical problem of "privacy invasion" can be successfully addressed through DSP hardware in terms of smallness in size and cost optimization. After review of previous applications of "privacy protection", and system components, we discuss the "embedded jpeg-space" detection of regions of interest and the real time application of encryption techniques to improve privacy while allowing general surveillance to continue. The resulting approach permits full access (violation of privacy) only by access to the private-key to recover the decryption key, thereby striking a fine trade-off among privacy, security, cost and space.
Robust Real-Time Visual SLAM Using Scale Prediction and Exemplar Based Feature Description
Authors: Denis Chekhlov, Mark Pupilli, Walterio W. Mayol-Cuevas, Andrew Calway
Venue: CVPR
Year: 2007

Two major limitations of real-time visual SLAM algorithms are the restricted range of views over which they can operate and their lack of robustness when faced with erratic camera motion or severe visual occlusion. In this paper we describe a visual SLAM algorithm which addresses both of these problems. The key component is a novel feature description method which is both fast and capable of repeat-able correspondence matching over a wide range of viewing angles and scales. This is achieved in real-time by using a SIFT-like spatial gradient descriptor in conjunction with efficient scale prediction and exemplar based feature representation. Results are presented illustrating robust realtime SLAM operation within an office environment.
Thermal Imaging of the Superficial Temporal Artery: An Arterial Pulse Recovery Model
Authors: Sergey Y. Chekmenev, Aly A. Farag, Edward A. Essock
Venue: CVPR
Year: 2007

We present a novel model for measurement of the arterial pulse from the superficial temporal artery (STA) using passive thermal infrared (IR) sensors. The proposed approach has a physical and physiological basis and as such is of fundamental nature. Thermal IR camera is used to capture the heat pattern from superficial arteries, and a blood vessel model is used to describe the pulsatile nature of the blood flow. A multiresolution wavelet-based signal analysis approach is used to extract the arterial pulse waveform, which lends itself to various physiological measurements. We validate the results using a traditional contact vital-sign monitor as a ground truth. Eight people of different age, race and gender have been tested in our study consistent with IRB approval. The resultant arterial pulse waveforms exactly matched the ground-truth readings. The essence of our approach is the automatic detection of region of arterial pulse measurement (ROM), from which the arterial pulse waveform is extracted. To the best of our knowledge, the correspondence between non-contact thermal IR imaging based measurements of the arterial pulse in the time domain and traditional contact approaches has never been reported in the literature.
High Distortion and Non-Structural Image Matching via Feature Co-occurrence
Authors: Xi Chen 0069, Tat-Jen Cham
Venue: CVPR
Year: 2007

We propose a novel approach for determining if a pair of images match each other under the effect of a high-distortion transformation or non-structural relation. The co-occurrence statistics between features across a pair of images are learned from a training set comprising matched and mismatched image pairs - these are expressed in the form of a cross-feature ratio table. The proposed method does not require feature-to-feature correspondences, but instead identifies and exploits feature co-occurrences that are able to provide discriminative result from the transformation. The method not only allows for the matching of test image pairs that have substantially different visual content as compared to those present in the training set, but also caters for transformations and relations that do not preserve image structure.
Online Spatial-temporal Data Fusion for Robust Adaptive Tracking
Authors: Jixu Chen, Qiang Ji
Venue: CVPR
Year: 2007

One problem with the adaptive tracking is that the data that are used to train the new target model often contain errors and these errors will affect the quality of the new target model. As time passes by, these errors will accumulate and eventually lead the tracker to drift away. In this paper, we propose a new method based on online data fusion to alleviate this tracking drift problem. Based on combining the spatial and temporal data through a dynamic Bayesian network, the proposed method can improve the quality of online data labeling, therefore minimizing the error associated with model updating and alleviating the tracking drift problem. Experiments show the proposed method significantly improves the performance of an existing adaptive tracking method.
A Minutiae-based Fingerprint Individuality Model
Authors: Jiansheng Chen 0001, Yiu Sang Moon
Venue: CVPR
Year: 2007

Fingerprint individuality study deals with the crucial problem of the discriminative power of fingerprints for recognizing people. In this paper, we present a novel fingerprint individuality model based on minutiae, the most commonly used fingerprint feature. The probability of the false correspondence among fingerprints from different fingers is calculated by combining the distinctiveness of the spatial locations and directions of the minutiae. To validate our model, experiments were performed using different fingerprint databases. The matching score distribution predicted by our model actually fits the observed experimental results satisfactorily. Comparing to most previous fingerprint individuality models, our model makes more reasonably conservative estimate of the fingerprint discriminative power, making it a powerful tool for studying the fingerprint individuality as well as the performance evaluation of fingerprint verification systems.
Spatio-Temporal Markov Random Field for Video Denoising
Authors: Jia Chen, Chi-Keung Tang
Venue: CVPR
Year: 2007

This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and the proper use of motion estimation in the denoising process. Unlike previous algorithms which estimate the level of noise, our method learns the full noise distribution nonparametrically which serves as the likelihood model in the MRF. Instead of using deterministic motion estimation to align pixels, we set up a temporal likelihood by combining a probabilistic motion field with the learned noise model. The prior of this MRF is modeled by piece-wise smoothness. The main advantage of the proposed spatio-temporal MRF is that it integrates spatial and temporal information adaptively into a statistical inference framework, where the posteriori is optimized using graph cuts with alpha expansion. We demonstrate the performance of the proposed approach on benchmark data sets and real videos to show the advantages of our algorithm compared with previous single frame and multi-frame algorithms.