OpenRaiser/Intern-Atlas huggingface dataset:
Jitter Camera: High Resolution Video from a Low Resolution Detector
Authors: Moshe Ben-Ezra, Assaf Zomet, Shree K. Nayar
Venue: CVPR
Year: 2004

Video cameras must produce images at a reasonable frame-rate and with a reasonable depth of field. These requirements impose fundamental physical limits on the spatial resolution of the image detector. As a result, current cameras produce videos with a very low resolution. The resolution of videos can be computationally enhanced by moving the camera and applying super-resolution reconstruction algorithms. However, a moving camera introduces motion blur, which limits super-resolution quality. We analyze this effect and derive a theoretical result showing that motion blur has a substantial degrading effect on the performance of super resolution. The conclusion is, that in order to achieve the highest resolution, motion blur should be avoided. Motion blur can be minimized by sampling the space-time volume of the video in a specific manner. We have developed a novel camera, called the jitter camera, that achieves this sampling. By applying an adaptive super-resolution algorithm to the video produced by the jitter camera, we show that resolution can be notably enhanced for stationary or slowly moving objects, while it is improved slightly or left unchanged for objects with fast and complex motions. The end result is a video that has a significantly higher resolution than the captured one.
On the Distribution of Saliency
Authors: Alexander Berengolts, Michael Lindenbaum
Venue: CVPR
Year: 2004

Detecting salient structures is a basic task in perceptual organization. Saliency algorithms typically mark edge-points with some saliency measure, which grows with the length and smoothness of the curve on which these edge-points lie. Here, we propose a modified saliency estimation mechanism that is based on probabilistically specified grouping cues and on curve length distributions. In this framework, the Shashua and Ullman saliency mechanism may be interpreted as a process for detecting the curve with maximal expected length. Generalized types of saliency naturally follow. We propose several specific generalizations (e.g., gray-level-based saliency) and rigorously derive the limitations on generalized saliency types. We then carry out a probabilistic analysis of expected length saliencies. Using ergodicity and asymptotic analysis, we derive the saliency distributions associated with the main curves and with the rest of the image. We then extend this analysis to finite-length curves. Using the derived distributions, we derive the optimal threshold on the saliency for discriminating between figure and background and bound the saliency-based figure-from-ground performance.
Improving Object Classification in Far-Field Video
Authors: Biswajit Bose, W. Eric L. Grimson
Venue: CVPR
Year: 2004

Object classification in far-field video sequences is a challenging problem because of low-resolution imagery and projective image distortion. Most existing far-field classification systems are trained to work well in a constrained set of scenes, but can fail dramatically when applied to new scenes, or even different views of the same scene. We identify discriminative object features for classifying vehicles and pedestrians and develop a scene-invariant classification system that is trained on a small number of labeled examples from a few scenes, but transfers well to a wide range of new scenes. Simultaneously, we demonstrate that use of scene-specific context features (such as image position and direction of motion of objects) can greatly improve classification in any given scene. To combine these ideas, we propose a new algorithm for adapting a scene-invariant classifier to scene-specific features by retraining with the help of unlabelled data in a novel scene. Experimental results demonstrate the effectiveness of our context features and scene-transfer/adaptation algorithm for multiple urban and highway scenes.
Algebraic Solution for the Visual Hull
Authors: Matthew Brand, Kongbin Kang, David B. Cooper
Venue: CVPR
Year: 2004

We introduce an algebraic dual-space method for reconstructing the visual hull of a three-dimensional object from occluding contours observed in 2D images. The method exploits the differential structure of the manifold rather than parallax geometry, and therefore requires no correspondences. We begin by observing that the set of 2D contour tangents determines a surface in a dual space where each point represents a tangent plane to the original surface. The primal and dual surfaces have a symmetric algebra: A point on one is orthogonal to its dual point and tangent basis on the other. Thus the primal surface can be reconstructed if the local dual tangent basis can be estimated. Typically this is impossible because the dual surface is noisy and riddled with tangent singularities due to self-crossings. We identify a directionally-indexed local tangent basis that is well-defined and estimable everywhere on the dual surface. The estimation procedure handles singularities in the dual surface and degeneracies arising from measurement noise. The resulting method has O(N) complexity for N observed contour points and gives asymptotically exact reconstructions of surfaces that are totally observable from occluding contours.
Motion Without Correspondence from Tomographic Projections by Bayesian Inversion Theory
Authors: Sami S. Brandt, Ville Kolehmainen
Venue: CVPR
Year: 2004

In conventional tomography, the interior of an object is reconstructed from tomographic projections such as X-ray or electron microscope images. All the current reconstruction methods assume that projection geometry of the imaging device is either known or solved in advance by using e.g. fiducial or non-fiducial feature points in the images. In this paper, we propose a novel approach where the imaging geometry is solved simultaneously with the volume reconstruction problem while no correspondence information is needed. Our approach is a direct application of Bayesian inversion theory and produces the maximum likelihood or maximum a posteriori estimates for the motion parameters under the selected noise and prior distributions. In this paper, we experiment the approach with synthetic and real data where 1D affine projections are assumed, this is also often a sufficient assumption since the reconstruction problem is frequently performed as a stack of 2D problems for computational reasons. However, the method can be directly extended to the general, projective 3D case which is left for the future research.
Graphical Models for Graph Matching
Authors: Tibério S. Caetano, Terry Caelli, Dante Augusto Couto Barone
Venue: CVPR
Year: 2004

This paper explores a formulation for attributed graph matching as an inference problem over a hidden Markov random field. We approximate the fully connected model with simpler models in which optimal inference is feasible, and contrast them to the well-known probabilistic relaxation method, which can operate over the complete model but does not assure global optimality. The approach is well suited for applications in which there is redundancy in the binary attributes of the graph, such as in the matching of straight line segments. Results demonstrate that, in this application, the proposed models have superior robustness over probabilistic relaxation under additive noise conditions.
Linear Sequence-to-Sequence Alignment
Authors: Rodrigo L. Carceroni, Flávio L. C. Pádua, Geraldo A. M. R. Santos, Kiriakos N. Kutulakos
Venue: CVPR
Year: 2004

In this paper, we consider the problem of estimating the spatiotemporal alignment between N unsynchronized video sequences of the same dynamic 3D scene, captured from distinct viewpoints. Unlike most existing methods, which work for N = 2 and rely on a computationally intensive search in the space of temporal alignments, we present a novel approach that reduces the problem for general N to the robust estimation of a single line in IR(N). This line captures all temporal relations between the sequences and can be computed without any prior knowledge of these relations. Considering that the spatial alignment is captured by the parameters of fundamental matrices, an iterative algorithm is used to refine simultaneously the parameters representing the temporal and spatial relations between the sequences. Experimental results with real-world and synthetic sequences show that our method can accurately align the videos even when they have large misalignments (e.g., hundreds of frames), when the problem is seemingly ambiguous (e.g., scenes with roughly periodic motion), and when accurate manual alignment is difficult (e.g., due to slow-moving objects).
Flexible Spatial Models for Grouping Local Image Features
Authors: Gustavo Carneiro 0001, Allan D. Jepson
Venue: CVPR
Year: 2004

A key step for the effective use of local image features (i.e., highly distinctive and robust features) for recognition or image matching is the appropriate grouping of feature matches. Spatial constraints are important in this grouping because, during a recognition process, they allow for the reduction of the number of hypotheses that must be verified and also reduce the number of false positives present in each of these hypotheses. A common choice for this grouping task is to use the Hough transform on the global spatial transformation parameters of the hypothesized matches. Here, instead, we use semi-local spatial constraints which allow for a greater range of shape deformations. A comparison with Hough transform shows that our method is more robust to both rigid and non-rigid deformations. Its functionality is demonstrated in an exemplar-based object recognition system that deals well with severe non-rigid deformations. We also show the efficacy of our flexible spatial grouping far long range motion problems.
Cyclic Articulated Human Motion Tracking by Sequential Ancestral Simulation
Authors: Cheng Chang, Rashid Ansari, Ashfaq A. Khokhar
Venue: CVPR
Year: 2004

Accurate tracking of cyclic human motion in video data helps in developing computer-aided applications such as gait analysis, visual surveillance, patient rehabilitation, etc. This paper presents a novel technique for tracking cyclic human motion based on decomposing complex cyclic motion into components and maintaining coupling between components. The decomposition reduces the dimensionality of the problem and enables a graphical modeling of the articulated human body. The coupling between components is modeled by their phase relationship and represented as directed edges in Bayesian networks and undirected edges in Markov random fields. Such coupling is maintained in tracking through ancestral simulation (AS) and Markov potentials in a sequential Monte Carlo tracking framework. We show that the approach handles severe self-occlusion and foreign body occlusion with improved accuracy and efficiency.
Probabilistic Expression Analysis on Manifolds
Authors: Ya Chang, Changbo Hu, Matthew Turk 0001
Venue: CVPR
Year: 2004

In this paper, we propose a probabilistic video-based facial expression recognition method on manifolds. The concept of the manifold of facial expression is based on the observation that the images of all possible facial deformations of an individual make a smooth manifold embedded in a high dimensional image space. An enhanced Lipschitz embedding is developed to embed the aligned face appearance in a low dimensional space while keeping the main structure of the manifold. In the embedded space, a complete expression sequence becomes a path on the expression manifold, emanating from a center that corresponds to the neutral expression. Each path consists of several clusters. A probabilistic model of transition between the clusters and paths is learned through training videos in the embedded space. The likelihood of one kind of facial expression is modeled as a mixture density with the clusters as mixture centers. The transition between different expressions is represented as the evolution of the posterior probability of the six basic paths. The experimental results demonstrate that the probabilistic approach can recognize expression transitions effectively. We also synthesize image sequences of changing expressions through the manifold model.
Estimation, Smoothing, and Characterization of Apparent Diffusion Coefficient Profiles from High Angular Resolution DWI
Authors: Yunmei Chen, Weihong Guo 0002, Qingguo Zeng, Xiaolu Yan, Feng Huang 0001, Hao Zhang 0030, Guojun He, Baba C. Vemuri, Yijun Liu
Venue: CVPR
Year: 2004

We present a new variational framework for recovery of apparent diffusion coefficient (ADC)from High Angular Resolution Diffusion-weighted (HARD) MRI. The model approximates the ADC profiles by a 4th order spherical harmonic series (SHS), whose coefficients are obtained by solving a constrained minimization problem. By minimizing the energy functional, the ADC profiles are estimated and regularized simultaneously across the entire volume. In this model, feature preserving smoothing is achieved by minimizing a non-standard growth functional, and the estimation is based on the original Stejskal-Tanner equation. The antipodal symmetry and positiveness of the ADC are also accommodated into the model. Furthermore, coefficients of the SHS and the variance of ADC profiles from its mean are used to characterize the diffusion anisotropy. The effectiveness of the proposed model is depicted via application to both simulated and HARD MRI human brain data. The characterization of non-Gaussian diffusion based on the proposed model showed consistency with known neuroanatomy.
Robust Color Object Detection Using Spatial-Color Joint Probability Functions
Authors: David J. Crandall, Jiebo Luo 0001
Venue: CVPR
Year: 2004

Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object. Consequently, it typically requires a large number of exemplars (for rigid objects) or a large amount of human intuition (for non-rigid objects) to develop a robust algorithm. We present a robust algorithm designed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniform, etc. Our approach is based on a particular type of spatial-color joint probability function called the color edge cooccurrence histogram (CECH). In addition, our algorithm employs perceptual color naming to handle color variation, and pre-screening to limit the search scope (i.e., size and location) of the object. Experimental results demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely related algorithm by a decisive margin.
The SPS Algorithm: Patching Figural Continuity and Transparency by Split-Patch Search
Authors: Antonio Criminisi, Andrew Blake 0001
Venue: CVPR
Year: 2004

This paper describes a novel algorithm for the efficient synthesis of high-quality virtual views from only two input images. The emphasis is on the recovery of continuity of objects boundaries (figural continuity) with faithful synthesis of transparency effects. The contribution of this paper is two-fold: (i) the split-patch search (SPS) technique is introduced for dense stereo which handles transparency effects by assigning multiple disparities to mixed pixels; (ii) an efficient extension of exemplar-based image synthesis to the case of two-camera stereo is proposed. Furthermore, this paper presents an approximate but effective solution to the challenging problem of layer estimation and compositing in the case of small image patches. The effectiveness of the proposed technique is demonstrated on a number of stereo image pairs taken from two camera video-conferencing setups, where the quality of the synthesized talking heads is of paramount importance. Moreover the improvement in the quality of image synthesis is quantified by comparing the output of the SPS algorithm with thirteen ground-truth images.
Approximation of Canonical Sets and Their Applications to 2D View Simplification
Authors: Trip Denton, Jeff Abrahamson, Ali Shokoufandeh
Venue: CVPR
Year: 2004

Given a set of patterns and a similarity measure between them, we would present an optimization framework to approximate a small subset, known as a canonical set, whose members closely resemble the members of the original set. We would present a combinatorial formulation of the canonical set problem in terms of quadratic optimization integer programming, present a relaxation through semidefinite programming, and propose a bounded performance rounding procedure for its approximation solution in polynomial time. Through a set of experiments we would investigate the application of canonical sets for computing a summary of views from a dense set of 2D views computed for a 3D object.
Separating Style and Content on a Nonlinear Manifold
Authors: Ahmed M. Elgammal, Chan-Su Lee
Venue: CVPR
Year: 2004

Bilinear and multi-linear models have been successful in decomposing static image ensembles into perceptually orthogonal sources of variations, e.g., separation of style and content. If we consider the appearance of human motion such as gait, facial expression and gesturing, most of such activities result in nonlinear manifolds in the image space. The question that we address in this paper is how to separate style and content on manifolds representing dynamic objects. In this paper we learn a decomposable generative model that explicitly decomposes the intrinsic body configuration (content) as a function of time from the appearance (style) of the person performing the action as time-invariant parameter. The framework we present in this paper is based on decomposing the style parameters in the space of nonlinear functions which map between a learned unified nonlinear embedding of multiple content manifolds and the visual input space.
Novel Region-Based Modeling for Human Detection within Highly Dynamic Aquatic Environment
Authors: How-Lung Eng, Junxian Wang, Alvin Harvey Kam, Wei-Yun Yau
Venue: CVPR
Year: 2004

One difficult challenge in autonomous video surveillance is on handling highly dynamic backgrounds. This difficulty is compounded if foreground objects of interest are partially hidden by specular reflections or glare. In this paper, we provide numerous insights into the technical difficulties faced in developing an automated video surveillance system within a hostile environment: an outdoor public swimming pool. For robust detection performance, we focused on two central aspects: (i) effective modeling of the dynamic outdoor aquatic background with rapid illumination changes, splashes and random spatial movements of background elements, owing to the movement of water ripples; and (ii) enhancing the visibility of swimmers that are partially hidden by specular reflections. Several innovations have been introduced from scratch in this paper. The first is the development of a scheme that models the background as regions of dynamic homogeneous processes. This model facilitates the implementation of an efficient spatial searching scheme for background subtraction that could exploit long-range spatial dependencies between pixels. The second is the implementation of a spatio-temporal filtering scheme that enhances the detection of swimmers that are partially hidden by specular reflections of artificial nighttime lighting, serving as a pre-processing module to foreground detection for nighttime operation. These various algorithms have been tightly integrated under a unified framework and demonstrated on a busy Olympic-sized outdoor public pool.
Efficient Belief Propagation for Early Vision
Authors: Pedro F. Felzenszwalb, Daniel P. Huttenlocher
Venue: CVPR
Year: 2004

Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical use. In this paper we present new algorithmic techniques that substantially improve the running time of the belief propagation approach. One of our techniques reduces the complexity of the inference algorithm to be linear rather than quadratic in the number of possible labels for each pixel, which is important for problems such as optical flow or image restoration that have a large label set. A second technique makes it possible to obtain good results with a small fixed number of message passing iterations, independent of the size of the input images. Taken together these techniques speed up the standard algorithm by several orders of magnitude. In practice we obtain stereo, optical flow and image restoration algorithms that are as accurate as other global methods (e.g., using the Middlebury stereo benchmark) while being as fast as local techniques.
Integrating Multiple Model Views for Object Recognition
Authors: Vittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
Venue: CVPR
Year: 2004

We present a new approach to appearance-based object recognition, which captures the relationships between multiple model views and exploits them to improve recognition performance. The basic building block is local, viewpoint invariant regions. We propose an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces (GAMs). During modeling, the model views are connected by a large number of region-tracks, each aggregating image regions of a single physical region across the views. At recognition time, GAMs are constructed matching a test image to each model view. The consistency of configurations of GAMs is measured by exploiting the model connections. A genetic algorithm finds covering the object as completely as possible the most consistent configuration. Introducing GAMs as an intermediate grouping level facilitates decision-making and improves discriminative power. As a complementary application, we introduce a novel GAM-based two-view filter and demonstrate its effectiveness in recovering correct matches in the presence of up to 96% mismatches.
Self-Normalized Linear Tests
Authors: Sachin Gangaputra, Donald Geman
Venue: CVPR
Year: 2004

Making decisions based on a linear combination L of features is of course very common in pattern recognition. For distinguishing between two hypotheses or classes, the test is of the form sign (L - /spl tau/) for some threshold /spl tau/. Due mainly to fixing /spl tau/, such tests are sensitive to changes in illumination and other variations in imaging conditions. We propose a special case, a "self-normalized linear test" (SNLT), hard-wired to be of the form sign (L/sub 1/ - L/sub 2/) with unit weights. The basic idea is to "normalize" L/sub 1/, which involves the usual discriminating features, by L/sub 2/, which is composed of non-discriminating features. For a rich variety of features (e.g., based directly on intensity differences), SNLTs are largely invariant to illumination and robust to unexpected background variations. Experiments in face detection are promising: they confirm the expected invariances and out-perform some previous results in a hierarchical framework.