OpenRaiser/Intern-Atlas huggingface dataset:
Subset Selection for Efficient SVM Tracking
Authors: Shai Avidan
Venue: CVPR
Year: 2003

We update the SVM (support vector machine) score of an object through a video sequence with a small and variable subset of support vectors. In the first frame we use all the support vectors to compute the SVM score of the object but in subsequent frames we use only a small and variable subset of support vectors to update the SVM score. In each frame we calculate the dot-products of the support vectors in the subset with the pattern of the object being tracked. The difference in the dot-products, between past and current frames, is used to update the SVM score. This is done at a fraction of the computational cost required to re-evaluate the SVM score from scratch in every frame. The two methods we develop are "cyclic subset selection", in which we break the set of all support vectors into subsets of equal size and use them cyclically, and "maximum variance subset selection", in which we choose the support vectors whose dot-product with the test pattern varied the most in previous frames. We combine these techniques together for the problem of maintaining the SVM score of objects through a video sequence. Results on real video sequences are shown.
A Novel Convergence Scheme for Active Appearance Models
Authors: Aziz Umit Batur, Monson H. Hayes
Venue: CVPR
Year: 2003

The active appearance model (AAM) algorithm is a powerful tool for modeling images of deformable objects. AAM combines a subspace-based deformable model of an object's appearance with a fast and robust method of fitting this model to a previously unseen image. The speed of this algorithm comes from the assumption that the gradient matrix is fixed around the optimal coefficients for all images. In this paper, we propose a convergence scheme for AAM that adapts this gradient matrix to the target image's texture during convergence by adding linear modes of change that are based on the texture eigenvectors of AAM. We show that this adaptive strategy for the gradient matrix provides a significant increase in the performance of the AAM algorithm.
Automatic Relighting of Overlapping Textures of a 3D Model
Authors: Etienne Beauchesne, Sébastien Roy 0001
Venue: CVPR
Year: 2003

The paper presents a new method to correct overlapping textures of a single 3D model where each texture was obtained under possibly different lighting conditions and color response of the camera. This situation arises frequently when a single object is digitized using multiple 3D scanners. Our goal is to remove any visible seam and improve color consistency between the textures in order to merge them afterwards. To achieve this, we propose an efficient algorithm to compute the "ratio lighting" of two textures, derive a common lighting from it, and use it to "relight" each texture. We illustrate our method by correcting textures of human faces acquired with several structured-light 3D scanners. Experimental results are realistic and demonstrate how this method can reduce the need to calibrate colors or explicitly solve for the illumination.
Hue fields and Color Curvatures: A Perceptual Organization Approach to Color Image Denoising
Authors: Ohad Ben-Shahar, Steven W. Zucker
Venue: CVPR
Year: 2003

The denoising of color images is an increasingly studied problem whose state-of-the-art solutions employ a variety of diffusion schemes. Specifying the correct diffusion is difficult, however, in part because of the subtleties of color interactions. We address this difficulty by proposing a perceptual organization approach to color denoising based on the principle of good continuation. We exploit the periodic chromatic (hue) component of the color in its representation as a frame field. We derive two hue curvatures and use them to construct a local model for the behavior of the color, which in turn specifies consistency constraints between nearby color measurements. These constraints are then used to replace noisy pixels by examining their spatial context. Such a contextual analysis (combined with standard methods to handle the scalar channels, saturation and lightness), results in a robust noise removal process that preserves discontinuities, singularities, and fine chromatic structures, including those that diffusion processes are prone to distort. We demonstrate our approach on a variety of synthetic and natural images.
Eye Gaze Tracking Using an Active Stereo Head
Authors: David Beymer, Myron Flickner
Venue: CVPR
Year: 2003

In the eye gaze tracking problem, the goal is to determine where on a monitor screen a computer user is looking, ie., the gaze point. Existing systems generally have one of two limitations: either the head must remain fixed in front of a stationary camera, or, to allow for head motion, the user must wear an obstructive device. We introduce a 3D eye tracking system where the head motion is allowed without the need for markers or worn devices. We use a pair of stereo systems: a wide angle stereo system detects the face and steers an active narrow FOV stereo system to track the eye at high resolution. For high resolution tracking, the eye is modeled in 3D, including the corneal ball, pupil and fovea. We discuss the calibration of the stereo systems, the eye model, eye detection and tracking, and we close with an evaluation of the accuracy of the estimated gaze point on the monitor.
Shape-based Recognition Of Wiry Objects
Authors: Owen T. Carmichael, Martial Hebert
Venue: CVPR
Year: 2003

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge cues. We first use example images of the desired object in typical backgrounds to train a classifier cascade which determines whether edge pixels in an image belong to an instance of the object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels. The features used for this classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of complex objects in cluttered indoor scenes under arbitrary out-of-image-plane rotation.
Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms
Authors: Ho-Ming Chan, Albert C. S. Chung, Simon C. H. Yu, Alexander Norbash, William M. Wells III
Venue: CVPR
Year: 2003

We present a new multimodal image registration method based on the a priori knowledge of the class label mappings between two segmented input images. A joint class histogram between the image pairs is estimated by assigning each bin value equal to the total number of occurrences of the corresponding class label pairs. The discrepancy between the observed and expected joint class histograms should be minimized when the transformation is optimal. Kullback-Leibler distance (KLD) is used to measure the difference between these two histograms. Based on the probing experimental results on a synthetic dataset as well as a pair of precisely registered 3D clinical volumes, we show that, with the knowledge of the expected joint class histogram, our method obtained longer capture range and fewer local optimal points as compared with the conventional mutual information (MI) based registration method. We also applied the proposed method to a 2D-3D rigid registration problems between DSA and MRA volumes. Based on manually selected markers, we found that the accuracies of our method and the MI-based method are comparable. Moreover, our method is more computationally efficient than the MI-based method.
Establishment Shot Detection Using Qualitative Motion
Authors: Loong Fah Cheong, Yong Wang, Hee Lin Wang
Venue: CVPR
Year: 2003

In this paper, we developed a technique to detect a special kind of shot, namely establishment shot, which is used to introduce a new scene to the audience or remind the audience of a known environment. The detection of establishment shot aids in various middle-level issues such as analyzing the story units of a movie. An establishment shot is usually realized by using camera motions such as panning, tilting or lateral translation. As it possesses similar motion features to those of another kind of shot called object-tracking shot, the two can be easily confused. An object-tracking shot is important for indexing on its own; it keeps the target of interest in the field of view. In this paper, we developed a technique to differentiate these two kinds of shots. The technique developed does not require accurate egomotion estimation, and can handle arbitrary camera movement including zoom as well as multiple independent motions.
Markerless Kinematic Model and Motion Capture from Volume Sequences
Authors: Chi-Wei Chu, Odest Chadwicke Jenkins, Maja J. Mataric
Venue: CVPR
Year: 2003

An approach for model-free markerless motion capture of articulated kinematic structures is presented. This approach is centered our method for generating underlying nonlinear axes (or a skeleton curve) from the volume of an arbitrary rigid-body model. We describe the use of skeleton curves for deriving a kinematic model and motion (in the form of joint angles over time) from a captured volume sequence. Our motion capture method uses a skeleton curve, found in each frame of a volume sequence, to automatically determine kinematic postures. These postures are then aligned to determine a common kinematic model for the volume sequence. The derived kinematic model is then reapplied to each frame in the volume sequence to find the motion suited to this model. We demonstrate our method for several types of motion from synthetically generated volume sequences with arbitrary kinematic topology and human volume sequences captured from a set of multiple calibrated cameras.
Tensor-based Brain Surface Modeling and Analysi
Authors: Moo K. Chung, Keith J. Worsley, Steve Robbins, Alan C. Evans
Venue: CVPR
Year: 2003

We present a unified computational approach to tensor-based morphometry in detecting the brain surface shape difference between two clinical groups based on magnetic resonance images. Our approach is novel in a sense that we combined surface modeling, surface data smoothing and statistical analysis in a coherent unified mathematical framework. The cerebral cortex has the topology of a 2D highly convoluted sheet. Between two different clinical groups, the local surface area and curvature of the cortex may differ. It is highly likely that such surface shape differences are not uniform over the whole cortex. By computing how such surface metrics differ, the regions of the most rapid structural differences can be localized. To increase the signal to noise ratio, diffusion smoothing based on the explicit estimation of Laplace-Beltrami operator has been developed and applied to the surface metrics. As an illustration, we demonstrate how this new tensor-based surface morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children.
Learning Bayesian Network Classifiers for Facial Expression Recognition using both Labeled and Unlabeled Data
Authors: Ira Cohen, Nicu Sebe, Fábio Gagliardi Cozman, Marcelo Cesar Cirelo, Thomas S. Huang
Venue: CVPR
Year: 2003

Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. In this paper, we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We use Bayesian network classifiers for classifying expressions from video. One of the motivating factor in using the Bayesian network classifiers is their ability to handle missing data, both during inference and training. In particular, we are interested in the problem of learning with both labeled and unlabeled data. We show that when using unlabeled data to learn classifiers, using correct modeling assumptions is critical for achieving improved classification performance. Motivated by this, we introduce a classification driven stochastic structure search algorithm for learning the structure of Bayesian network classifiers. We show that with moderate size labeled training sets and large amount of unlabeled data, our method can utilize unlabeled data to improve classification performance. We also provide results using the Naive Bayes (NB) and the Tree-Augmented Naive Bayes (TAN) classifiers, showing that the two can achieve good performance with labeled training sets, but perform poorly when unlabeled data are added to the training set.
Object Removal by Exemplar-Based Inpainting
Authors: Antonio Criminisi, Patrick Pérez, Kentaro Toyama
Venue: CVPR
Year: 2003

A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: (i) "texture synthesis" algorithms for generating large image regions from sample textures, and (ii) "inpainting" techniques for filling in small image gaps. The former work well for "textures" - repeating two dimensional patterns with some stochasticity; the latter focus on linear "structures" which can be thought of as one dimensional patterns, such as lines and object contours. This paper presents a novel and efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. Computational efficiency is achieved by a block-based sampling process. A number of examples on real and synthetic images demonstrate the effectiveness of our algorithm in removing large occluding objects as well as thin scratches. Robustness with respect to the shape of the manually selected target region is also demonstrated. Our results compare favorably to those obtained by existing techniques.
Object Segmentation in Videos from Moving Camera with MRFs on Color and Motion Features
Authors: Rita Cucchiara, Andrea Prati 0001, Roberto Vezzani
Venue: CVPR
Year: 2003

In this paper we address the problem of fast segmenting moving objects in video acquired by moving camera or more generally with a moving background. We present an approach based on a color segmentation followed by a region-merging on motion through Markov random fields (MRFs). The technique we propose is inspired by the work of Gelgon and Bouthemy (2000), that has been modified to reduce computational cost in order to achieve a fast segmentation (about ten frame per second). To this aim a modified region matching algorithm (namely partitioned region matching) and an innovative arc-based MRF optimization algorithm with a suitable definition of the motion reliability are proposed. Results on both synthetic and real sequences are reported to confirm validity of our solution.
Estimating the photorealism of images: Distinguishing paintings from photographs
Authors: Florin Cutzu, Riad I. Hammoud, Alex Leykin
Venue: CVPR
Year: 2003

Automatic classification of an image as a photograph of a real-scene or as a painting is potentially useful for image retrieval and Web site filtering applications. The main contribution of the paper is the proposition of several features derived from the color, edge, and gray-scale-texture information of the image that effectively discriminate paintings from photographs. For example, we found that paintings contain significantly more pure-color edges, and that certain gray-scale-texture measurements (mean and variance of Gabor filters) are larger for photographs. Using a large set of images (12000) collected from different Web sites, the proposed features exhibit very promising classification performance (over 90%). A comparative analysis of the automatic classification results and psychophysical data is reported, suggesting that the proposed automatic classifier estimates the perceptual photorealism of a given picture.
Video-Rate Stereo Depth Measurement on Programmable Hardware
Authors: Ahmad Darabiha, Jonathan Rose, W. James MacLean
Venue: CVPR
Year: 2003

This paper describes the implementation of a stereo depth measurement algorithm in hardware on field programmable gate arrays (FPGAs). This system generates 8 bit sub-pixel disparities on 256 by 360 pixel images at video rate (30 frames/sec). The algorithm implemented is a multi-resolution, multi-orientation phase-based technique called local weighted phase-correlation (Fleet, 1994). Hardware implementation speeds up the performance more than 300 times that of the same algorithm running in software. In this paper, we describe the programmable hardware platform, the base stereo vision algorithm and the design of the hardware. We include various trade-offs required to make the hardware small enough to fit on our system and fast enough to work at video rate. We also show sample outputs from the functioning hardware. Although this paper is specifically focused on phase-based stereo vision FPGA realizations, most of the design issues are common to other DSP and vision applications.
Simultaneous Pose and Correspondence Determination using Line Feature
Authors: Philip David, Daniel DeMenthon, Ramani Duraiswami, Hanan Samet
Venue: CVPR
Year: 2003

We present a new robust line matching algorithm for solving the model-to-image registration problem. Given a model consisting of 3D lines and a cluttered perspective image of this model, the algorithm simultaneously estimates the pose of the model and the correspondences of model lines to image lines. The algorithm combines softassign for determining correspondences and POSIT for determining pose. Integrating these algorithms into a deterministic annealing procedure allows the correspondence and pose to evolve from initially uncertain values to a joint local optimum. This research extends to line features the SoftPOSIT algorithm proposed recently for point features. Lines detected in images are typically more stable than points and are less likely to be produced by clutter and noise, especially in man-made environments. Experiments on synthetic and real imagery with high levels of clutter, occlusion, and noise demonstrate the robustness of the algorithm.
Spacetime Stereo: A Unifying Framework for Depth from Triangulation
Authors: James Davis 0001, Ravi Ramamoorthi, Szymon Rusinkiewicz
Venue: CVPR
Year: 2003

Depth from triangulation has traditionally been treated in a number of separate threads in the computer vision literature, with methods like stereo, laser scanning, and coded structured light considered separately. In this paper, we propose a common framework, spacetime stereo, which unifies many of these previous methods. Viewing specific techniques as special cases of this general framework leads to insights regarding the solutions to many of the traditional problems of individual techniques. Specifically, we discuss a number of innovative possible applications such as improved recovery of static scenes under variable illumination, spacetime stereo for moving objects, structured light and laser scanning with multiple simultaneous stripes or patterns, and laser scanning of shiny objects. To suggest the practical utility of the framework, we use it to analyze one of these applications: recovery of static scenes under variable, but uncontrolled, illumination. Based on our analysis, we show that methods derived from the spacetime stereo framework can be used to recover depth in situations in which existing methods perform poorly.
Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery
Authors: Huawu Deng, David A. Clausi
Venue: CVPR
Year: 2003

The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. The paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modeling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.
Homotopy-Based Computation of Defocus Blur and Affine Transform
Authors: François Deschênes, Djemel Ziou
Venue: CVPR
Year: 2003

This paper presents a homotopy-based algorithm for simultaneous recovery of defocus blur and the affine transformation between two images of the same scene. One of the images (and its partial derivatives) is expressed as a function of the second image, partial derivatives of the two images, blur difference, affine parameters and a continuous parameter derived from homotopy methods. All of these unknowns can thus be directly computed by resolving a system of equations. The proposed algorithm is tested using synthetic and real images. The results confirm that dense and accurate estimation can be obtained.
Flux Invariants for Shape
Authors: Pavel Dimitrov, James N. Damon, Kaleem Siddiqi
Venue: CVPR
Year: 2003

We consider the average outward flux through a Jordan curve of the gradient vector field of the Euclidean distance function to the boundary of a 2D shape. Using an alternate form of the divergence theorem, we show that in the limit as the area of the region enclosed by such a curve shrinks to zero, this measure has very different behaviors at medial points than at non-medial ones, providing a theoretical justification for its use in the Hamilton-Jacobi skeletonization algorithm of Siddiqi et al. (2002). We then specialize to the case of shrinking circular neighborhoods and show that the average outward flux measure also reveals the object angle at skeletal points. Hence, formulae for obtaining the boundary curves, their curvatures, and other geometric quantities of interest, can be written in terms of the average outward flux limit values at skeletal points. Thus this measure can be viewed as a Euclidean invariant for shape description: it can be used to both detect the skeleton from the Euclidean distance function, as well as to explicitly reconstruct the boundary from it. We illustrate our results with several numerical simulations.
A New Semi-Supervised EM Algorithm for Image Retrieval
Authors: Anlei Dong, Bir Bhanu
Venue: CVPR
Year: 2003

One of the main tasks in content-based image retrieval (CBIR) is to reduce the gap between low-level visual features and high-level human concepts. This paper presents a new semi-supervised EM algorithm (NSSEM), where the image distribution in feature space is modeled as a mixture of Gaussian densities. Due to the statistical mechanism of accumulating and processing meta knowledge, the NSS-EM algorithm with long term learning of mixture model parameters can deal with the cases where users may mislabel images during relevance feedback. Our approach that integrates mixture model of the data, relevance feedback and long term learning helps to improve retrieval performance. The concept learning is incrementally refined with increased retrieval experiences. Experiment results on Corel database show the efficacy of our proposed concept learning approach.