OpenRaiser/Intern-Atlas huggingface dataset:
Progressive Learning for Interactive Surveillance Scenes Retrieval
Authors: Jérôme Meessen, Xavier Desurmont, Jean-François Delaigle, Christophe De Vleeschouwer, Benoît Macq
Venue: CVPR
Year: 2007

This paper tackles the challenge of interactively retrieving visual scenes within surveillance sequences acquired with fixed camera. Contrarily to today's solutions, we assume that no a-priori knowledge is available so that the system must progressively learn the target scenes thanks to interactive labelling of a few frames by the user. The proposed method is based on very low-cost features extraction and integrates relevance feedback, multiple-instance SVM classification and active learning. Each of these 3 steps runs iteratively over the session, and takes advantage of the progressively increasing training set. Repeatable experiments on both simulated and real data demonstrate the efficiency of the approach and show how it allows reaching high retrieval performances.
A Blind Source Separation Perspective on Image Restoration
Authors: Lidan Miao, Hairong Qi 0001
Venue: CVPR
Year: 2007

This paper re-investigates the physical image formation process leading to a new interpretation of the classic image restoration problem from a blind source separation (BSS) perspective. The observed distorted image is considered as a linear combination of a set of shifted version of the point spread function (PSF) with the weight coefficients determined by the actual image. The new interpretation brings two immediate benefits to the practice of image restoration. First, we can utilize the rich set of BSS methods to solve the blind image restoration problem. Second, the new formulation in terms of matrix product has the equivalent merit as the conventional matrix-vector notation in theoretical study of restoration algorithms. We develop a smoothness and block-decorrelation constrained nonnegative matrix factorization method (termed CNMF) to blindly recover both the PSF and the actual image. The experimental results compared to one of the state-of-the-art methods demonstrate the merit of the proposed approach.
Improving Variance Estimation in Biometric Systems
Authors: Ross J. Micheals, Terrance E. Boult
Venue: CVPR
Year: 2007

Measuring system performance seems conceptually straightforward. However, the interpretation of the results and predicting future performance remain as exceptional challenges in system evaluation. Robust experimental design is critical in evaluation, but there have been very few techniques to check designs for either overlooked associations or weak assumptions. For biometric & vision system evaluation, the complexity of the systems make a thorough exploration of the problem space impossible - this lack of verifiability in experimental design is a serious issue. In this paper, we present a new evaluation methodology that improves the accuracy of variance estimator via the discovery of false assumptions about the homogeneity of cofactors - i.e., when the data is not ''well mixed". The new methodology is then applied in the context of a biometric system evaluation with highly influential cofactors.
Multi-Modal Biometrics Involving the Human Ear
Authors: Christopher Middendorff, Kevin W. Bowyer, Ping Yan
Venue: CVPR
Year: 2007

Due to its semi-rigid shape and robustness against change over time, the ear has become an increasingly popular biometric feature. It has been shown that combining individual biometric methods into multi-biometric systems improves recognition. What features should be used, how they should be captured, what algorithms should be used, and how they should be combined are all open questions. In this paper, we discuss several existing methods of combination and the recognition rates of each.
Displaying a Moving Image By Multiple Steerable Projectors
Authors: Ikuhisa Mitsugami, Norimichi Ukita, Masatsugu Kidode
Venue: CVPR
Year: 2007

This paper proposes a method for precise overlapping of projected images from multiple steerable projectors. When they are controlled simultaneously, two problems are revealed: (1) even a slight positional error of the projected image, which does not matter in the case of a single projector, causes misalignments of multiple projected images that can be perceived clearly when using multiple projectors; and (2) as the projectors usually do not have architectures for their synchronization it is impossible to display a moving image that is by tiling or overlaying precisely the multiple projected images. To overcome (1), a method is proposed that measures preliminarily the misalignments through every plane in the environment, and hence displays the image without the misalignment. For (2), a consideration and a new proposal for the synchronization of multiple projectors are also discussed.
Practical Online Active Learning for Classification
Authors: Claire Monteleoni, Matti Kääriäinen
Venue: CVPR
Year: 2007

We compare the practical performance of several recently proposed algorithms for active learning in the online classification setting. We consider two active learning algorithms (and their combined variants) that are strongly online, in that they access the data sequentially and do not store any previously labeled examples, and for which formal guarantees have recently been proven under various assumptions. We motivate an optical character recognition (OCR) application that we argue to be appropriately served by online active learning. We compare the practical efficacy, for this application, of the algorithm variants, and show significant reductions in label-complexity over random sampling.
High-Speed Measurement of BRDF using an Ellipsoidal Mirror and a Projector
Authors: Yasuhiro Mukaigawa, Kohei Sumino, Yasushi Yagi
Venue: CVPR
Year: 2007

Measuring BRDF (bi-directional reflectance distribution function) requires huge amounts of time because a target object must be illuminated from all incident angles and the reflected lights must be measured from all reflected angles. In this paper, we present a high-speed method to measure BRDFs using an ellipsoidal mirror and a projector. Our method makes it possible to change incident angles without a mechanical drive. Moreover, the omni-directional reflected lights from the object can be measured by one static camera at once. Our prototype requires only fifty minutes to measure anisotropic BRDFs, even if the lighting interval is one degree.
Accurately measuring human movement using articulated ICP with soft-joint constraints and a repository of articulated models
Authors: Lars Mündermann, Stefano Corazza, Thomas P. Andriacchi
Venue: CVPR
Year: 2007

A novel approach for accurate markerless motion capture combining a precise tracking algorithm with a database of articulated models is presented. The tracking approach employs an articulated iterative closest point algorithm with soft-joint constraints for tracking body segments in visual hull sequences. The database of articulated models is derived from a combination of human shapes and anthropometric data, contains a large variety of models and closely mimics variations found in the human population. The database provides articulated models that closely match the outer appearance of the visual hulls, e.g. matches overall height and volume. This information is paired with a kinematic chain enhanced through anthropometric regression equations. Deviations in the kinematic chain from true joint center locations are compensated by the soft-joint constraints approach. As a result accurate and a more anatomical correct outcome is obtained suitable for biomechanical and clinical applications. Joint kinematics obtained using this approach closely matched joint kinematics obtained from a marker based motion capture system.
Shape Representation and Registration using Vector Distance Functions
Authors: Hossam El Din Hassan Abd El Munim, Aly A. Farag
Venue: CVPR
Year: 2007

This paper introduces a new method for shape registration by matching vector distance functions. The vector distance function representation is more flexible than the conventional signed distance map since it enables us to better control the shapes registration process by using more general transformations. Based on this model, a variational frame work is proposed for the global and local registration of shapes which does not need any point correspondences. The optimization criterion can handle efficiently the estimation of the global registration parameters. A closed form solution is provided to handle an incremental free form deformation model for covering the local deformations. This is an advantage over the gradient descent optimization which is biased towards the initialization and is more time consuming. Results of real shapes registration will be demonstrated to show the efficiency of the proposed approach with small and large global/local deformations.
Cordless portable multi-view fringe projection system for 3D reconstruction
Authors: Christoph Munkelt, Ingo Schmidt, Christian Bräuer-Burchardt, Peter Kühmstedt, Gunther Notni
Venue: CVPR
Year: 2007

A newly devised lightweight sensor head, combining a digital LED projector and two cameras in a stereo arrangement with access to even complicated measurable object details, is presented. It uses a pre-calibrated, epipolar constrained, phase correlation based fringe projection approach. The mobile unit is battery powered and data transfer is done via WLAN to enable flexible use in complex measurement situations. Multi-view measurement is realized using the phasogrammetric approach with virtual landmarks. Thereby the system enables whole-body measurement without matching procedures or markers. The mobile character suggest application in arts, design, archaeology and criminology.
Free-Form Nonrigid Image Registration Using Generalized Elastic Nets
Authors: Andriy Myronenko, Xubo B. Song, Miguel Á. Carreira-Perpiñán
Venue: CVPR
Year: 2007

We introduce a novel probabilistic approach for non-parametric nonrigid image registration using generalized elastic nets, a model previously used for topographic maps. The idea of the algorithm is to adapt an elastic net (a constrained Gaussian mixture) in the spatial-intensity space of one image to fit the second image. The resulting net directly represents the correspondence between image pixels in a probabilistic way and recovers the underlying image deformation. We regularize the net with a differential prior and develop an efficient optimization algorithm using linear conjugate gradients. The nonparametric formulation allows for complex transformations having local deformation. The method is generally applicable to registering point sets of arbitrary features. The accuracy and effectiveness of the method are demonstrated on different medical image and point set registration examples with locally nonlinear underlying deformations.
A Bayesian algorithm for tracking multiple moving objects in outdoor surveillance video
Authors: Manjunath Narayana, Donna Haverkamp
Venue: CVPR
Year: 2007

Reliable tracking of multiple moving objects in video is an interesting challenge, made difficult in real-world video by various sources of noise and uncertainty. We propose a Bayesian approach to find correspondences between moving objects over frames. By using color values and position information of the moving objects as observations, we probabilistically assign tracks to those objects. We allow for tracks to be lost and then recovered when they resurface. The probabilistic assignment method, along with the ability to recover lost tracks, adds robustness to the tracking system. We present results that show that the Bayesian method performs well in difficult tracking cases and compare the probabilistic results to a Euclidean distance based method.
Analysis of Light Transport based on the Separation of Direct and Indirect Components
Authors: Osamu Nasu, Shinsaku Hiura, Kosuke Sato
Venue: CVPR
Year: 2007

The light transport is one of the useful representation of the optical phenomena inside a scene (Sen et al;, 2005 and Seitz et al., 2005). By using this concept, we can regard any complex scene as a simple linear system between incident light and luminance distribution. However, it is difficult to obtain enough sample under varied lighting condition, because the light source has large degrees of freedom. Therefore, in this paper, we propose a fast and efficient sampling method using characteristics of direct and indirect components. Our method can be divided to three stages: at first, we obtain geometric relationships between projector and camera using Gray code. Then repetive dot patterns are projected to acquire direct reflection component in parallel. Finally, stripe pattern in a small rectangular is projected to model the low-frequency indirect light distribution. The system enables to obtain light transport quickly, and dynamic range of indirect component is also improved. Separated representation of direct and indirect components in light transport allows further applications.
Integration of Motion Cues in Optical and Sonar Videos for 3-D Positioning
Authors: Shahriar Negahdaripour, Hamed Pirsiavash, Hicham Sekkati
Venue: CVPR
Year: 2007

Target-based positioning and 3-D target reconstruction are critical capabilities in deploying submersible platforms for a range of underwater applications, e.g., search and inspection missions. While optical cameras provide high-resolution and target details, they are constrained by limited visibility range. In highly turbid waters, target at up to distances of 10 s of meters can be recorded by high-frequency (MHz) 2-D sonar imaging systems that have become introduced to the commercial market in years. Because of lower resolution and SNR level and inferior target details compared to optical camera in favorable visibility conditions, the integration of both sensing modalities can enable operation in a wider range of conditions with generally better performance compared to deploying either system alone. In this paper, estimate of the 3-D motion of the integrated system and the 3-D reconstruction of scene features are addressed. We do not require establishing matches between optical and sonar features, referred to as opti-acoustic correspondences, but rather matches in either the sonar or optical motion sequences. In addition to improving the motion estimation accuracy, advantages of the system comprise overcoming certain inherent ambiguities of monocular vision, e.g., the scale-factor ambiguity, and dual interpretation of planar scenes. We discuss how the proposed solution provides an effective strategy to address the rather complex opti-acoustic stereo matching problem. Experiment with real data demonstrate our technical contribution.
Kernel-based Tracking from a Probabilistic Viewpoint
Authors: Quang Anh Nguyen, Antonio Robles-Kelly, Chunhua Shen
Venue: CVPR
Year: 2007

In this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixels in both, the target model and its candidate as random variables and make use of a generative model so as to cast the tracking task into a maximum likelihood framework. This, in turn, permits the use of the EM-algorithm to estimate a set of latent variables that can be used to update the target-center position. Once the latent variables have been estimated, we use the Kullback-Leibler divergence so as to minimise the mutual information between the target model and candidate distributions in order to develop a target-center update rule and a kernel bandwidth adjustment scheme. The method is very general in nature. We illustrate the utility of our approach for purposes of tracking on real-world video sequences using two alternative kernel functions.
Using Galois Theory to Prove Structure from Motion Algorithms are Optimal
Authors: David Nistér, Richard I. Hartley, Henrik Stewénius
Venue: CVPR
Year: 2007

This paper presents a general method, based on Galois theory, for establishing that a problem can not be solved by a 'machine' that is capable of the standard arithmetic operations, extraction of radicals (that is, m-th roots for any m), as well as extraction of roots of polynomials of degree smaller than n, but no other numerical operations. The method is applied to two well known structure from motion problems: five point calibrated relative orientation, which can be realized by solving a tenth degree polynomial [6], and L2-optimal two-view triangulation, which can be realized by solving a sixth degree polynomial [3]. It is shown that both these solutions are optimal in the sense that an exact solution intrinsically requires the solution of a polynomial of the given degree (10 or 6 respectively), and cannot be solved by extracting roots of polynomials of any lesser degree.
Learning Visual Similarity Measures for Comparing Never Seen Objects
Authors: Eric Nowak, Frédéric Jurie
Venue: CVPR
Year: 2007

In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled "same" or "different". This is far less informative than the commonly used individual image labels (e.g., "car model X"), but it is cheaper to obtain. The proposed algorithm learns the characteristic differences between local descriptors sampled from pairs of "same" and "different" images. These differences are vector quantized by an ensemble of extremely randomized binary trees, and the similarity measure is computed from the quantized differences. The extremely randomized trees are fast to learn, robust due to the redundant information they carry and they have been proved to be very good clusterers. Furthermore, the trees efficiently combine different feature types (SIFT and geometry). We evaluate our innovative similarity measure on four very different datasets and consistently outperform the state-of-the-art competitive approaches.
Weighted Substructure Mining for Image Analysis
Authors: Sebastian Nowozin, Koji Tsuda, Takeaki Uno, Taku Kudo, Gökhan H. Bakir
Venue: CVPR
Year: 2007

In Web-related applications of image categorization, it is desirable to derive an interpretable classification rule with high accuracy. Using the bag-of-words representation and the linear support vector machine, one can partly fulfill the goal, but the accuracy of linear classifiers is not high and the obtained features are not informative for users. We propose to combine item set mining and large margin classifiers to select features from the power set of all visual words. Our resulting classification rule is easier to browse and simpler to understand, because each feature has richer information. As a next step, each image is represented as a graph where nodes correspond to local image features and edges encode geometric relations between features. Combining graph mining and boosting, we can obtain a classification rule based on subgraph features that contain more information than the set features. We evaluate our algorithm in a web-retrieval ranking task where the goal is to reject outliers from a set of images returned for a keyword query. Furthermore, it is evaluated on the supervised classification tasks with the challenging VOC2005 data set. Our approach yields excellent accuracy in the unsupervised ranking task compared to a recently proposed probabilistic model and competitive results in the supervised classification task.
Robust Change-Detection by Normalised Gradient-Correlation
Authors: Robert O&apos, Callaghan, Tetsuji Haga
Venue: CVPR
Year: 2007

A novel algorithm for robustly segmenting changes between different images of a scene is presented. This computationally efficient algorithm is based on a non-linear comparison of gradient structure in overlapping image-regions and offers intrinsic invariance to changing illumination, without recourse to background-model adaptation. High accuracy is demonstrated on test video data with and without illumination changes. The technique is applicable to motion-segmentation as well as measuring longer-term object-changes.
Stereo Matching via Disparity Estimation and Surface Modeling
Authors: Jong Dae Oh, Siwei Ma 0001, C.-C. Jay Kuo
Venue: CVPR
Year: 2007

Two new techniques are proposed to improve stereo matching performance in this work. First, to address the disparity discontinuity problem in occluded regions, we present a disparity estimation procedure, which consists of two steps; namely, a greedy disparity filling algorithm and a least-squared-errors (LSE) fitting method. Second, it is observed that the existing fronto-parallel model with color segmentation is built upon the piecewise constant surface approximation, which is however not efficient in approximating slanted or curved objects. We use a piecewise linear surface model to represent 3-dimensional (3D) geometric structure for better surface modeling. The proposed stereo matching system with these two new components is evaluated with Middlebury data sets with excellent quantitative and qualitative results.
Variational Bayes Based Approach to Robust Subspace Learning
Authors: Takayuki Okatani, Koichiro Deguchi
Venue: CVPR
Year: 2007

This paper presents a new algorithm for the problem of robust subspace learning (RSL), i.e., the estimation of linear subspace parameters from a set of data points in the presence of outliers (and missing data). The algorithm is derived on the basis of the variational Bayes (VB) method, which is a Bayesian generalization of the EM algorithm. For the purpose of the derivation of the algorithm as well as the comparison with existing algorithms, we present two formulations of the EM algorithm for RSL. One yields a variant of the IRLS algorithm, which is the standard algorithm for RSL. The other is an extension of Roweis's formulation of an EM algorithm for PCA, which yields a robust version of the alternated least squares (ALS) algorithm. This ALS-based algorithm can only deal with a certain type of outliers (termed vector-wise outliers). The VB method is used to resolve this limitation, which results in the proposed algorithm. Experimental results using synthetic data show that the proposed algorithm outperforms the IRLS algorithm in terms of the convergence property and the computational time.
Estimating Scale of a Scene from a Single Image Based on Defocus Blur and Scene Geometry
Authors: Takayuki Okatani, Koichiro Deguchi
Venue: CVPR
Year: 2007

Using an imaging system in which the image plane can be tilted with respect to the optical axis of the lens, the image of a large-scale scene that appears to be a miniature to human eyes can be captured. This phenomenon suggests that the image contains information regarding the scale of the scene and that human vision can extract this information and recognize the scene scale from a single image. In this study, we consider how human vision can perform this single-view scale estimation. Although it is obvious that the existence of defocus blur in the image that simulates a shallow DOF plays an essential role in the scale estimation, we propose that this alone is not sufficient to explain the estimation mechanism. By incorporating a few assumptions, we theoretically show that scale estimation is made possible when (1) the 3D structure of the scene can be recovered from the image and furthermore, (2) the structure is combined with the defocus blur. Further, we present a simple algorithm for scale recognition and demonstrate its working using a real image.