OpenRaiser/Intern-Atlas huggingface dataset:
Learning epipolar geometry from image sequences
Authors: Yonatan Wexler, Andrew W. Fitzgibbon, Andrew Zisserman
Venue: CVPR
Year: 2003

We wish to determine the epipolar geometry of a stereo camera pair from image measurements alone. This paper describes a solution to this problem, which does not require a parametric model of the camera system, and consequently applies equally well to a wide class of stereo configurations. Examples in the paper range from a standard pinhole stereo configuration to more exotic systems combining curved mirrors and wide-angle lenses. The method described here allows epipolar curves to be learnt from multiple image pairs acquired by stereo cameras with fixed configuration. By aggregating information over the multiple image pairs, a dense map of the epipolar curves can be determined on the images. The algorithm requires a large number of images, but has the distinct benefit that the correspondence problem does not have to be explicitly solved. We show that for standard stereo configurations the results are comparable to those obtained from a state of the art parametric model method, despite the significantly weaker constraints on the non-parametric model. The new algorithm is simple to implement, so it may easily be employed on a new and possibly complex camera system.
Kernel Principal Angles for Classification Machines with Applications to Image Sequence Interpretation
Authors: Lior Wolf, Amnon Shashua
Venue: CVPR
Year: 2003

We consider the problem of learning with instances defined over a space of sets of vectors. We derive a new positive definite kernel f(A, B) defined over pairs of matrices A, B based on the concept of principal angles between two linear subspaces. We show that the principal angles can be recovered using only inner-products between pairs of column vectors of the input matrices thereby allowing the original column vectors of A, B to be mapped onto arbitrarily high-dimensional feature spaces. We apply this technique to inference over image sequences applications of face recognition and irregular motion trajectory detection.
Tracking Appearances with Occlusions
Authors: Ying Wu 0001, Ting Yu 0003, Gang Hua 0001
Venue: CVPR
Year: 2003

Occlusion is a difficult problem for appearance-based target tracking, especially when we need to track multiple targets simultaneously and maintain the target identities during tracking. To cope with the occlusion problem explicitly, this paper proposes a dynamic Bayesian network, which accommodates an extra hidden process for occlusion and stipulates the conditions on which the image observation likelihood is calculated. The statistical inference of such a hidden process can reveal the occlusion relations among different targets, which makes the tracker more robust against partial even complete occlusions. In addition, considering the fact that target appearances change with views, another generative model for multiple view representation is proposed by adding a switching variable to select from different view templates. The integration of the occlusion model and multiple view model results in a complex dynamic Bayesian network, where extra hidden processes describe the switch of targets' templates, the targets' dynamics, and the occlusions among different targets. The tracking and inferring algorithms are implemented by the sampling-based sequential Monte Carlo strategies. Our experiments show the effectiveness of the proposed probabilistic models and the algorithms.
Adaptive Pattern Discovery for Interactive Multimedia Retrieval
Authors: Yimin Wu, Aidong Zhang 0001
Venue: CVPR
Year: 2003

Relevance feedback has been an indispensable component for multimedia retrieval systems. In this paper, we present an adaptive pattern discovery method, which addresses relevance feedback by interactively discovering meaningful patterns of relevant objects. To facilitate pattern discovery, we first present a dynamic feature extraction method, which aims to alleviate the curse of dimensionality by extracting a feature subspace using balanced information gain. In the feature subspace, we train an online pattern classification method called adaptive random forests to classify multimedia objects as relevant or irrelevant. Our adaptive random forests adapts the traditional classification method known as random forests for relevance feedback. It improves the efficiency of pattern discovery by choosing the most-informative samples for online learning. Extensive experiments are carried out on a Corel image set (with 31,438 images) to evaluate the performance of our method as compared against the state-of-the-art approaches.
Robust Crease Detection in Fingerprint Images
Authors: Chenyu Wu, Jie Zhou 0001, Zhaoqi Bian, Gang Rong
Venue: CVPR
Year: 2003

We study a pattern in the fingerprint called a crease, a kind of stripe which irregularly crosses the normal fingerprint patterns (ridges and valleys). Creases will cause spurious minutiae when using a conventional feature detection algorithm, and therefore decreases the recognition rate of fingerprint identification. By representing the crease using a parameterized rectangle, we design an optimal filter as a detector. We employ a multi-channel filtering framework to detect creases in different orientations. In each channel, PCA is used to extract a rectangle's parameters from the raw detected results. Our algorithm is demonstrated by experiments.
Error Analysis of 3-D Motion Estimation Algorithms in the Differential Case
Authors: Tao Xiang 0002, Loong Fah Cheong
Venue: CVPR
Year: 2003

We put forth in this paper a geometrically motivated 3D (three-dimensional) motion error analysis, which is capable of supporting investigation of global effect such as inherent ambiguities. The error expression that we derive allows us to predict the exact conditions likely to cause ambiguities and how these ambiguities vary with motion types such as lateral or forward motion. Our formulation, though geometrically motivated, is employed to model the effect of noise.
Object Segmentation Using Graph Cuts Based Active Contours
Authors: Ning Xu 0005, Ravi Bansal, Narendra Ahuja
Venue: CVPR
Year: 2003

In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation problems. Our method is a combination of active contours and the optimization tool of graph cuts and differs fundamentally from traditional active contours in that it uses graph cuts to iteratively deform the contour. Consequently, it has the following advantages. (1) It has the ability to jump over local minima and provide a more global result. (2) Graph cuts guarantee continuity and lead to smooth contours free of self-crossing and uneven spacing problems. Therefore, the internal force, which is commonly used in traditional energy functions to control the smoothness, is no longer needed, and hence the number of parameters is greatly reduced. (3). Our approach easily extends to the segmentation of three and higher dimensional objects. In addition, the algorithm is suitable for interactive correction and is shown to always converge. Experimental results and analyses are provided.
Structure From Motion for Scenes Without Features
Authors: Anthony J. Yezzi, Stefano Soatto
Venue: CVPR
Year: 2003

We describe an algorithm for reconstructing the 3D (three-dimensional) shape of the scene and the relative pose of a number of cameras from a collection of images under the assumption that the scene does not contain photometrically distinct "features". We work under the explicit assumption that the scene is made of a number of smooth surfaces that radiate constant energy isotropically in all directions, and setup a region-based cost functional that we minimize using local gradient flow techniques.
General c-Means Clustering Model and Its Application
Authors: Jian Yu 0001
Venue: CVPR
Year: 2003

Many partitional clustering algorithms originated from the definition of mean.We propose a new clustering model - general c-means clustering algorithm (GCM). Generally, when the data set is clustered into c (c > 1) subsets, each subset is often expected to have a different prototype (or cluster center) than others. Therefore, we propose the definition of undesirable solution of clustering algorithms. As the GCM has undesirable solution under a mild condition, undesirable solution of the GCM is not expected to be stable. According to these assumptions, we obtain the necessary conditions for the GCM as a good clustering model. Fortunately, such conditions have offered a theoretical basis for selection of the parameters in many clustering algorithms, which is an open problem for such algorithms, for example, we get the theoretical rule for selection of the weighting exponent in the FCM, and explain why the weighting exponent should be greater than 1, etc. Moreover, we discover the relation between the GCM model and Occam's razor, which offers the deep reason behind many famous partitional clustering algorithms. Based on these results, we can study many objective function based clustering algorithms.
3D Model Retrieval With Morphing-Based Geometric and Topological Feature Maps
Authors: Meng Yu, Indriyati Atmosukarto, Wee Kheng Leow, Zhiyong Huang, Rong Xu
Venue: CVPR
Year: 2003

Recent advancement in 3D digitization techniques have prompted the need for 3D object retrieval. Our method of comparing 3D objects for retrieval is based on 3D morphing. It computes, for each 3D object, two spatial feature maps that describe the geometry and topology of the surface patches on the object, while preserving the spatial information of the patches in the maps. The feature maps capture the amount of effort required to morph a 3D object into a canonical sphere, without performing explicit 3D morphing. Fourier transforms of the feature maps are used for object comparison so as to achieve invariant retrieval under arbitrary rotation, reflection, and non-uniform scaling o the objects. Experimental results show that our method of retrieving 3D models is very accurate, achieving a precision of above .086 even at a recall rate of 1.0.
Object-Specific Figure-Ground Segregation
Authors: Stella X. Yu, Jianbo Shi
Venue: CVPR
Year: 2003

We consider the problem of segmenting an image into foreground and background, with foreground containing solely objects of interest known a priori. We propose an integration model that incorporates both edge detection and object part detection results. It consists of two parallel processes: low-level pixel grouping and high-level patch grouping. We seek a solution that optimizes a joint grouping criterion in a reduced space enforced by grouping correspondence between pixels and patches. Using spectral graph partitioning, we show that a near global optimum can be found by solving a constrained eigenvalue problem. We report promising experimental results on a dataset of 15 objects under clutter and occlusion.
Classification Based on Symmetric Maximized Minimal Distance in Subspace (SMMS)
Authors: Wende Zhang, Tsuhan Chen
Venue: CVPR
Year: 2003

We introduce a new classification algorithm based on the concept of symmetric maximized minimal distance in subspace (SMMS). Given the training data of authentic samples and imposter samples in the feature space, SMMS tries to identify a subspace in which all the authentic samples are close to each other and all the imposter samples are far away from the authentic samples. The optimality of the subspace is determined by maximizing the minimal distance between the authentic samples and the imposter samples in the subspace. We present a procedure to achieve such optimality and to identify the decision boundary. The verification procedure is simple since we only need to project the test sample to the subspace and compare it against the decision boundary. Using face authentication as an example, we show that the proposed algorithm outperforms several other algorithms based on support vector machines (SVM).
A Bayesian Framework for Fusing Multiple Word Knowledge Models in Videotext Recognition
Authors: DongQing Zhang, Shih-Fu Chang
Venue: CVPR
Year: 2003

Videotext recognition is challenging due to low resolution, diverse fonts/styles, and cluttered background. Past methods enhanced recognition by using multiple frame averaging, image interpolation and lexicon correction, but recognition using multi-modality language models has not been explored. In this paper, we present a formal Bayesian framework for videotext recognition by combining multiple knowledge using mixture models, and describe a learning approach based on Expectation-Maximization (EM). In order to handle unseen words, a back-off smoothing approach derived from the Bayesian model is also presented. We exploited a prototype that fuses the model from closed caption and that from the British National Corpus. The model from closed caption is based on a unique time distance distribution model of videotext words and closed caption words. Our method achieves a significant performance gain, with word recognition rate of 76.8% and character recognition rate of 86.7%. The proposed methods also reduce false videotext detection significantly, with a false alarm rate of 8.2% without substantial loss of recall.
Learning a discriminative classifier using shape context distances
Authors: Hao Zhang 0003, Jitendra Malik
Venue: CVPR
Year: 2003

For the purpose of object recognition, we learn one discriminative classifier based on one prototype, using shape context distances as the feature vector. From multiple prototypes, the outputs of the classifiers are combined using the method called "error correcting output codes". The overall classifier is tested on a benchmark dataset and is shown to outperform existing methods with far fewer prototypes.
Dynamic Depth Recovery from Unsynchronized Video Streams
Authors: Chunxiao Zhou, Hai Tao
Venue: CVPR
Year: 2003

We propose an algorithm for estimating dense depth information of dynamic scenes from multiple video streams captured using unsynchronized stationary cameras. We solve this problem by first imposing two assumptions about the scene motion and the temporal offset between cameras. The motion of a scene is described using a local constant velocity model and the camera temporal offset is assumed to be constant within a short of period of time. Based on these models, geometric relations between the images of moving scene points, the scene depth, the scene motions, and the camera temporal offset are investigated and an estimation method is developed to compute the camera temporal offset. The three main steps of the proposed algorithm are: 1) the estimation of the temporal offset between cameras, 2) the synthesis of synchronized image pairs based on the estimated camera temporal offset and optical flow fields computed in each view, and 3) the stereo computation based on the synthesized synchronous image pairs. The proposed algorithm has been tested on both synthetic data and real image sequences. Promising quantitative and qualitative experimental results are demonstrated in the paper.
Toward a Stratification of Helmholtz Stereopsis
Authors: Todd E. Zickler, Peter N. Belhumeur, David J. Kriegman
Venue: CVPR
Year: 2003

Helmholtz stereopsis has been previously introduced as a surface reconstruction technique that does not assume a model of surface reflectance. This technique relies on the use of multiple cameras and light sources, and it has been shown to be effective when the camera and source positions are known. Here, we take a stratified look at uncalibrated Helmholtz stereopsis. We derive a photometric matching constraint that can be used to establish correspondence without any knowledge of the cameras and sources (except that they are co-located), and we determine conditions under which we can obtain affine and metric reconstructions. An implementation and experimental results are presented.
Automatic Method for Correlating Horizons across Faults in 3D Seismic Data
Authors: Fitsum Admasu, Klaus D. Tönnies
Venue: CVPR
Year: 2004

Horizons are visible boundaries between certain sediment layers in seismic data, and a fault is a crack of horizons and it is recognized in seismic data by the discontinuities of horizons layers. Interpretation of seismic data is a time-consuming manual task which is only partially supported by computer methods. In this paper, we present an automatic method for horizon correlation across faults in 3D seismic data. As automating horizons correlations using only seismic data features is not feasible, we reformulated the correlation task as a non-rigid continuous point matching problem. Seismic features on both sides of the fault are gathered and an optimal match is found based on geological fault displacement model. One side of the fault is the floating image while the other side is the reference image. First, very prominent regions on both sides are automatically extracted and a match between them is found. Sparse fault displacements are then computed for these regions and they are used to calculate parameters for the fault displacement model. A multi-resolution simulated annealing optimization scheme is then used for the continuous point matching. The method was applied to real 3D seismic data, and has shown to produce geologically acceptable horizons correlations.
Window-Based, Discontinuity Preserving Stereo
Authors: Motilal Agrawal, Larry S. Davis
Venue: CVPR
Year: 2004

Traditionally, the problem of stereo matching has been addressed either by a local window-based approach or a dense pixel-based approach using global optimization. In this paper we present an algorithm which combines window-based local matching into a global optimization framework. Our local matching algorithm assumes that local windows can have at most two disparities. Under this assumption, the local matching can be performed very efficiently using graph cuts. The global matching is formulated as minimization of an energy term that takes into account the matching constraints induced by the local stereo algorithm. Fast, approximate minimization of this energy is achieved through graph cuts. The key feature of our algorithm is that it preserves discontinuities both during the local as well as global matching phase.
Tomographic Reconstruction of Piecewise Smooth Images
Authors: Christopher V. Alvino, Anthony J. Yezzi Jr.
Venue: CVPR
Year: 2004

In computed tomography, direct inversion of the Radon transform requires more projections than are practical due to constraints in scan time and image accessibility. Therefore, it is necessary to consider the estimation of reconstructed images when the problem is under-constrained, i.e., when a unique solution does not exist. To resolve ambiguities among solutions, it is necessary to place additional constraints on the reconstructed image. In this paper, we present a surface evolution technique to model the reconstructed image as piecewise smooth. We model the reconstructed image as two regions that are each smoothly varying in intensity and are separated by a smooth surface. We define a cost functional to penalize deviation from piecewise smoothness while ensuring that the projections of the estimated image match the measured projections. From this functional, we derive an evolution for the modeled image intensity and an evolution for the surface, thereby defining a variational tomographic estimation technique. We show example reconstructions to highlight the performance of the proposed method on real medical images.
Bayesian Video Matting Using Learnt Image Priors
Authors: Nicholas Apostoloff, Andrew W. Fitzgibbon
Venue: CVPR
Year: 2004

Video matting, or layer extraction, is a classic inverse problem in computer vision that involves the extraction of foreground objects, and the alpha mattes that describe their opacity, from a set of images. Modem approaches that work with natural backgrounds often require user-labelled "trimaps" that segment each image into foreground, background and unknown regions. For long sequences, the production of accurate trimaps can be time consuming. In contrast, another class of approach depends on automatic background extraction to automate the process, but existing techniques do not make use of spatiotemporal consistency, and cannot take account of operator hints such as trimaps. This paper presents a method inspired by natural image statistics that cleanly unifies these approaches. A prior is learnt that models the relationship between the spatiotemporal gradients in the image sequence and those in the alpha mattes. This is used in combination with a learnt foreground colour model and a prior on the alpha distribution to help regularize the solution and greatly improve the automatic performance of such systems. The system is applied to several real image sequences that demonstrate the advantage that the unified approach can afford.
BoostMap: A Method for Efficient Approximate Similarity Rankings
Authors: Vassilis Athitsos, Jonathan Alon, Stan Sclaroff, George Kollios
Venue: CVPR
Year: 2004

This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, ID embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures: In both systems, in quantitative experiments, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings.
Multigrid and Multi-Level Swendsen-Wang Cuts for Hierarchic Graph Partition
Authors: Adrian Barbu, Song Chun Zhu
Venue: CVPR
Year: 2004

Many vision tasks can be formulated as partitioning an adjacency graph through optimizing a Bayesian posterior probability p defined on the partition-space. In this paper two approaches are proposed to generalize the Swendsen-Wang cut algorithm [A. Barbu and S.C. Zhu 2003] for sampling p. The first method is called multigrid SW-cut which runs SW-cut within a sequence of local "attentional" windows and thus simulates conditional probabilities of p in the partition space. The second method is called multi-level SW-cut which projects the adjacency graph into a hierarchical representation with each vertex in the high level graph corresponding to a sub-graph at the low level, and runs SW-cut at each level. Thus it simulates conditional probabilities of p at the higher level. Both methods are shown to observe the detailed balance equation and thus provide flexibilities in sampling the posterior probability p. We demonstrate the algorithms in image and motion segmentation with three levels (see Fig.1), and compare the speed improvement of the proposed methods.