OpenRaiser/Intern-Atlas huggingface dataset:
Adaptation For Multiple Cue Integration
Authors: Zhaohui Sun
Venue: CVPR
Year: 2003

Many classification tasks can be carried out by casting a domain-specific problem to general graph representation (with objects to be organized as graph nodes and pairwise similarities as graph edges) followed by a graph partition. In this paper, an adaptation scheme is proposed to integrate multiple graphs from various cues to a single graph, such that the distance between the ideal transition probability matrix to the one derived from cue integration is minimized. Four different distance measures, i.e., the Frobenius norm, the Kullback-Leibler directed divergence, the Jeffrey divergence and the cross entropy, are investigated to minimize the discrepancy. It is shown that the minimization leads to a closed-form nonlinear optimization that can be solved by the Levenberg-Marguardt method. Domain and task-specific knowledge is explored to facilitate the generic pattern classification task. Experimental results are demonstrated for image content description by multiple cue integration.
A Perspective on Distortions
Authors: Rahul Swaminathan, Michael D. Grossberg, Shree K. Nayar
Venue: CVPR
Year: 2003

A framework for analyzing distortions in non-single viewpoint imaging systems is presented. Such systems possess loci of viewpoints called caustics. In general, perspective (or undistorted) views cannot be computed from images acquired with such systems without knowing scene structure. Views computed without scene structure will exhibit distortions, which we call caustic distortions. We first introduce a taxonomy of distortions based on the geometry of imaging systems. Then, we derive a metric to quantify caustic distortions. We present an algorithm to compute minimally distorted views using simple priors on scene structure. These priors are defined as parameterized primitives such as spheres, planes and cylinders with simple uncertainty models for the parameters. To validate our method, we conducted extensive experiments on rendered and real images. In all cases our method produces nearly undistorted views even though the acquired images were strongly distorted. We also provide an approximation of the above method that warps the entire captured image into a quasi single viewpoint representation that can be used by any "viewer" to compute near-perspective views in real-time.
On The Fundamental Performance For Fingerprint Matching
Authors: Xuejun Tan, Bir Bhanu
Venue: CVPR
Year: 2003

Fingerprints have long been used for person authentication. However, there is not enough scientific research to explain the probability that two fingerprints, which are impressions of different fingers, may be taken as the same one. In this paper, we propose a formal framework to estimate the fundamental algorithm independent error rate of fingerprint matching. Unlike a previous work, which assumes that there is no overlap between any two minutiae uncertainty areas and only measures minutiae's positions and orientations. In our model, we do not make this assumption and measure the relations, i.e., ridge counts between different minutiae as well as minutiae's positions and orientations. The error rates of fingerprint matching obtained by our approach is significantly lower than that of previously published research. Results are shown using NIST-4 fingerprint database. These results contribute toward making fingerprint matching a science and settling the legal challenges to fingerprints.
Illumination Chromaticity Estimation using Inverse-Intensity Chromaticity Space
Authors: Robby T. Tan, Ko Nishino, Katsushi Ikeuchi
Venue: CVPR
Year: 2003

Existing color constancy methods cannot handle both uniform colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors, and become error prone when there are only few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces, but cannot be applied to highly textured surfaces since they require precise color segmentation. In this paper, we present a single integrated method to estimate illumination chromaticity from single/multi-colored surfaces. Unlike the existing dichromatic-based methods, the proposed method requires only rough highlight regions, without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in "inverse-intensity chromaticity space", a new two-dimensional space we introduce. In addition, by utilizing the Hough transform and histogram analysis in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface. Experimental results on real images show the effectiveness of the method.
Nearest Neighbor Search for Relevance Feedback
Authors: Jelena Tesic, B. S. Manjunath
Venue: CVPR
Year: 2003

We introduce the problem of repetitive nearest neighbor search in relevance feedback and propose an efficient search scheme for high dimensional feature spaces. Relevance feedback learning is a popular scheme used in content based image and video retrieval to support high-level concept queries. The paper addresses those scenarios in which a similarity or distance matrix is updated during each iteration of the relevance feedback search and a new set of nearest neighbors is computed. This repetitive nearest neighbor computation in high dimensional feature spaces is expensive, particularly when the number of items in the data set is large. In this context, we suggest a search algorithm that supports relevance feedback for the general quadratic distance metric. The scheme exploits correlations between two consecutive nearest neighbor sets thus significantly reducing the overall search complexity. Detailed experimental results are provided using 60 dimensional texture feature dataset.
Shape Context and Chamfer Matching in Cluttered Scenes
Authors: Arasanathan Thayananthan, Bjoern Stenger, Philip H. S. Torr, Roberto Cipolla
Venue: CVPR
Year: 2003

This paper compares two methods for object localization from contours: shape context and chamfer matching of templates. In the light of our experiments, we suggest improvements to the shape context: shape contexts are used to find corresponding features between model and image. In real images it is shown that the shape context is highly influenced by clutters; furthermore, even when the object is correctly localized, the feature correspondence may be poor. We show that the robustness of shape matching can be increased by including a figural continuity constraint. The combined shape and continuity cost is minimized using the Viterbi algorithm on features, resulting in improved localization and correspondence. Our algorithm can be generally applied to any feature based shape matching method. Chamfer matching correlates model templates with the distance transform of the edge image. This can be done efficiently using a coarse-to-fine search over the transformation parameters. The method is robust in clutter, however, multiple templates are needed to handle scale, rotation and shape variation. We compare both methods for locating hand shapes in cluttered images, and applied to word recognition in EZ-Gimpy images.
ROD-TV: Reconstruction on Demand by Tensor Voting
Authors: Wai-Shun Tong, Chi-Keung Tang
Venue: CVPR
Year: 2003

A "graphics for vision" approach is proposed to address the problem of reconstruction from a large and imperfect data set: reconstruction on demand by tensor voting, or ROD-TV. ROD-TV simultaneously delivers good efficiency and robustness, by adapting to a continuum of primitive connectivity, view dependence, and levels of detail (LOD). Locally inferred surface elements are robust to noise and better capture local shapes. By inferring per-vertex normals at sub-voxel precision on the fly, we can achieve interpolative shading. Since these missing details can be recovered at the current level of detail, our result is not upper bounded by the scanning resolution. By relaxing the mesh connectivity requirement, we extend ROD-TV and propose a simple but effective multiscale feature extraction algorithm. ROD-TV consists of a hierarchical data structure that encodes different levels of detail. The local reconstruction algorithm is tensor voting. It is applied on demand to the visible subset of data at a desired level of detail, by traversing the data hierarchy and collecting tensorial support in a neighborhood. We compare our approach and present encouraging results.
Resolution vs. tracking error: zoom as a gain controller
Authors: Ben Tordoff, David William Murray 0001
Venue: CVPR
Year: 2003

During tracking, lens zoom acts as a gain between scene dynamics and fixation errors, providing a trade-off between maximizing resolution and minimizing tracking error. Using a linear Kalman filter model, it is shown that when image measurement error scales with focal length, the filter is invariant to zoom. When the error is of fixed size, however, zooming alters the balance between process and measurement errors in a counter-intuitive manner. It is shown that this balance can be restored by appropriate adjustment of the process noise. With a zoom invariant filter, zoom can be used to ensure that fixation errors remain bounded. To this end, an error variance control method is proposed which gives high confidence that the target will not leave the image during tracking. To implement such a scheme, equipment delays and responses must be known, including those of the zoom lenses. Experiments to measure these are described, and overall results are presented for 30 Hz tracking of real scenes.
Properties and Applications of Shape Recipes
Authors: Antonio Torralba 0001, William T. Freeman
Venue: CVPR
Year: 2003

In low-level vision, the representation of scene properties such as shape, albedo, etc., are very high dimensional as they have to describe complicated structures. The approach proposed here is to let the image itself bear as much of the representational burden as possible. In many situations, scene and image are closely related and it is possible to find a functional relationship between them. The scene information can be represented in reference to the image where the functional specifies how to translate the image into the associated scene. We illustrate the use of this representation for encoding shape information. We show how this representation has appealing properties such as locality and slow variation across space and scale. These properties provide a way of improving shape estimates coming from other sources of information like stereo.
Surface Reconstruction via Helmholtz Reciprocity with a Single Image Pair
Authors: Peter H. Tu, Paulo R. S. Mendonça
Venue: CVPR
Year: 2003

This paper proposes a method for three-dimensional reconstruction of surfaces that takes advantage of the symmetry resulting from alternating the positions of a camera and a light source. This set up allows for use of the Helmholtz reciprocity principle to recover the shape of smooth surfaces with arbitrary bidirectional reflectance distribution functions without requiring the presence of texture, as well as for exploiting mutual occlusions between images. Different from previous methods, the technique works with as few as one reciprocal pair, and recovers surface depth and orientation simultaneously by finding the global minimum of an error function via dynamic programming. Since the error is a function not just of depth but of surface orientation as well, the reconstruction is subject to tighter geometric constraints. Given a current estimate of surface geometry and intensity measurements in one image, Helmholtz reciprocity is used to predict the pixel intensity values of the second image. The dynamic program finds the reconstruction that minimizes the total difference between the predicted and measured intensity values. This approach allows for the reconstruction of surfaces displaying specularities and regions of high curvature, which is a challenge commonly encountered in the optical inspection of industrial parts. Results with real data show the quality of the reconstruction obtained with the proposed algorithm.
3D Surface Modeling from Range Curves
Authors: Dragan Tubic, Patrick Hébert, Denis Laurendeau
Venue: CVPR
Year: 2003

Traditional approaches for surface reconstruction from range data require that the input data be either range images or unorganized sets of points. Since a large number of range sensors provide data along curvilinear patterns such as profiles, this paper presents an approach for reconstructing a surface from a set of unorganized curves. A strategy for updating the reconstructed surface during data acquisition is described as well. Curves are accumulated in a volumetric structure in which a vector field is built and updated. The information that is needed for efficient curve registration is also directly available in this vector field. This leads to a unified modeling approach combining surface reconstruction and curve registration. The algorithm implementing the approach is of linear complexity with respect to the number of input curves and makes it suitable for interactive modeling. Simulated data based on a set of six curvilinear patterns as well as data acquired with a range sensor are used to illustrate the various steps of the algorithm.
Multilinear Subspace Analysis of Image Ensembles
Authors: M. Alex O. Vasilescu, Demetri Terzopoulos
Venue: CVPR
Year: 2003

Multilinear algebra, the algebra of higher-order tensors, offers a potent mathematical framework for analyzing ensembles of images resulting from the interaction of any number of underlying factors. We present a dimensionality reduction algorithm that enables subspace analysis within the multilinear framework. This N-mode orthogonal iteration algorithm is based on a tensor decomposition known as the N-mode SVD, the natural extension to tensors of the conventional matrix singular value decomposition (SVD). We demonstrate the power of multilinear subspace analysis in the context of facial image ensembles, where the relevant factors include different faces, expressions, viewpoints, and illuminations. In prior work we showed that our multilinear representation, called TensorFaces, yields superior facial recognition rates relative to standard, linear (PCA/eigenfaces) approaches. We demonstrate factor-specific dimensionality reduction of facial image ensembles. For example, we can suppress illumination effects (shadows, highlights) while preserving detailed facial features, yielding a low perceptual error.
Activity Recognition Using the Dynamics of the Configuration of Interacting Objects
Authors: Namrata Vaswani, Amit K. Roy-Chowdhury, Rama Chellappa
Venue: CVPR
Year: 2003

Monitoring activities using video data is an important surveillance problem. A special scenario is to learn the pattern of normal activities and detect abnormal events from a very low resolution video where the moving objects are small enough to be modeled as point objects in a 2D plane. Instead of tracking each point separately, we propose to model an activity by the polygonal 'shape' of the configuration of these point masses at any time t, and its deformation over time. We learn the mean shape and the dynamics of the shape change using hand-picked location data (no observation noise) and define an abnormality detection statistic for the simple case of a test sequence with negligible observation noise. For the more practical case where observation (point locations) noise is large and cannot be ignored, we use a particle filter to estimate the probability distribution of the shape given the noisy observations up to the current time. Abnormality detection in this case is formulated as a change detection problem. We propose a detection strategy that can detect both 'drastic' and 'slow' abnormalities. Our framework can be directly applied for object location data obtained using any type of sensors - visible, radar, infrared or acoustic.
Constrained Subspace Modelling
Authors: Jaco Vermaak, Patrick Pérez
Venue: CVPR
Year: 2003

When performing subspace modeling of data using principal component analysis (PCA) it may be desirable to constrain certain directions to be more meaningful in the context of the problem being investigated. This need arises due to the data often being approximately isotropic along the lesser principal components, making the choice of directions for these components more-or-less arbitrary. Furthermore, constraining may be imperative to ensure viable solutions in problems where the dimensionality of the data space is of the same order as the number of data points available. This paper adopts a Bayesian approach and augments the likelihood implied by probabilistic principal component analysis (PPCA) (Tipping and Bishop, 1999) with a prior designed to achieve the constraining effect. The subspace parameters are computed efficiently using the EM algorithm. The constrained modeling approach is illustrated on two pertinent problems, one from speech analysis, and one from computer vision.
Bootstrapping SVM Active Learning by Incorporating Unlabelled Images for Image Retrieval
Authors: Lei Wang 0001, Kap Luk Chan, Zhihua Zhang
Venue: CVPR
Year: 2003

The performance of image retrieval with SVM active learning is known to be poor when started with few labeled images only. In this paper, the problem is solved by incorporating the unlabelled images into the bootstrapping of the learning process. In this work, the initial SVM classifier is trained with the few labeled images and the unlabelled images randomly selected from the image database. Both theoretical analysis and experimental results show that by incorporating unlabelled images in the bootstrapping, the efficiency of SVM active learning can be improved, and thus improves the overall retrieval performance.
Video Segmentation Based on Graphical Models
Authors: Yang Wang 0002, Tele Tan, Kia-Fock Loe
Venue: CVPR
Year: 2003

This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatiotemporal constraints. Given consecutive frames, an optimization method is proposed to maximize the conditional probability density of the three fields in an iterative way. Experimental results show that the approach is robust and generates spatiotemporally coherent segmentation results.
Simultaneous Smoothing and Estimation of the Tensor Field from Diffusion Tens or MRI
Authors: Zhizhou Wang, Baba C. Vemuri, Yunmei Chen, Thomas Mareci
Venue: CVPR
Year: 2003

Diffusion tensor magnetic resonance imaging (DT-MRI) is a relatively new imaging modality in the field of medical imaging. This modality of imaging allows one to capture the structural connectivity if any between functionally meaningful regions for example, in the brain. The data however can be noisy and requires restoration. In this paper, we present a unified model for simultaneous smoothing and estimation of diffusion tensor field from DT-MRI. The diffusion tensor field is estimated directly from the raw data with L/sup P/ smoothness and positive definiteness constraints. The data term we employ is from the original Stejskal-Tanner equation instead of the linearized version as usually done in literature. In addition, we use Cholesky decomposition to ensure positive definiteness of the diffusion tensor. The unified model is discretized and solved numerically using limited memory quasi-Newton method. Both synthetic and real data experiments are shown to depict the algorithm performance.
Learning Object Intrinsic Structure for Robust Visual Tracking
Authors: Qiang Wang 0023, Guangyou Xu, Haizhou Ai
Venue: CVPR
Year: 2003

In this paper, a novel method to learn the intrinsic object structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object intrinsic representation, the obtained non-rigid part of object state reduces even to 2 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker. We will show that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable. Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion. The proposed method also has the potential to solve other type of tracking problems.
Document Image Enhancement Using Directional Wavelet
Authors: Qian Wang, Tao Xia, Lida Li, Chew Lim Tan
Venue: CVPR
Year: 2003

This paper proposes a novel algorithm to clean up a large collection of historical handwritten documents kept in the National Archives of Singapore. Due to the seepage of ink over long period of storage, the front page of each document has been severely marred by the reverse side writing. Earlier attempts have been made to match both sides of a page to identify the offending strokes originating from the back so as to eliminate them with the aid of a wavelet transform. Perfect matching, however, is difficult due to document skews, differing resolutions, inadvertently missing out reverse side and warped pages during image capture. A new approach is now proposed to do away with double side mapping by using a directional wavelet transform that is able to distinguish the foreground and reverse side strokes much better than the conventional wavelet transform. Experiments have shown that the method indeed enhances the readability of each document significantly after the directional wavelet operation without the need for mapping with its reverse side. 1.
Constraint on Five Points in Two Images
Authors: Tomás Werner
Venue: CVPR
Year: 2003

It is well-known that epipolar geometry relating two uncalibrated images is determined by at least seven correspondences. If there are more than seven of them, their positions cannot be arbitrary if they are to be projections of any world points by any two cameras. Less than seven matches have been thought not to be constrained in any way. We show that there is a constraint even on five matches, i.e., that there exist forbidden configurations of five points in two images. The constraint is obtained by requiring orientation consistence points on the wrong side of rays are not allowed. For allowed configurations, we show that epipoles must lie in domains with piecewise-conic boundaries, and how to compute them. We present a concise algorithm deciding whether a configuration is allowed or forbidden.