OpenRaiser/Intern-Atlas huggingface dataset:
Image Statistics and Anisotropic Diffusion
Authors: Hanno Scharr, Michael J. Black, Horst W. Haussecker
Venue: ICCV
Year: 2003

Many sensing techniques and image processing applications are characterized by noisy, or corrupted, image data. Anisotropic diffusion is a popular, and theoretically well understood, technique for denoising such images. Diffusion approaches however require the selection of an "edge stopping" function, the definition of which is typically ad hoc. We exploit and extend recent work on the statistics of natural images to define principled edge stopping functions for different types of imagery. We consider a variety of anisotropic diffusion schemes and note that they compute spatial derivatives at fixed scales from which we estimate the appropriate algorithm-specific image statistics. Going beyond traditional work on image statistics, we also model the statistics of the eigenvalues of the local structure tensor. Novel edge-stopping functions are derived from these image statistics giving a principled way of formulating anisotropic diffusion problems in which all edge-stopping parameters are learned from training data.
A Theory of Multiplexed Illumination
Authors: Yoav Y. Schechner, Shree K. Nayar, Peter N. Belhumeur
Venue: ICCV
Year: 2003

Imaging of objects under variable lighting directions is an important and frequent practice in computer vision and image-based rendering. We introduce an approach that significantly improves the quality of such images. Traditional methods for acquiring images under variable illumination directions use only a single light source per acquired image. In contrast, our approach is based on a multiplexing principle, in which multiple light sources illuminate the object simultaneously from different directions. Thus, the object irradiance is much higher. The acquired images are then computationally demultiplexed. The number of image acquisitions is the same as in the single-source method. The approach is useful for imaging dim object areas. We give the optimal code by which the illumination should be multiplexed to obtain the highest quality output. For n images corresponding to n light sources, the noise is reduced by /spl radic/(n)/2 relative to the signal. This noise reduction translates to a faster acquisition time or an increase in density of illumination direction samples. It also enables one to use lighting with high directional resolution using practical setups, as we demonstrate in our experiments.
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
Authors: Khurram Shafique, Mubarak Shah
Venue: ICCV
Year: 2003

We present a framework for finding point correspondences in monocular image sequences over multiple frames. The general problem of multiframe point correspondence is NP hard for three or more frames. A polynomial time algorithm for a restriction of this problem is presented, and is used as the basis of proposed greedy algorithm for the general problem. The greedy nature of the proposed algorithm allows it to be used in real time systems for tracking and surveillance etc. In addition, the proposed algorithm deals with the problems of occlusion, missed detections, and false positives, by using a single noniterative greedy optimization scheme, and hence, reduces the complexity of the overall algorithm as compared to most existing approaches, where multiple heuristics are used for the same purpose. While most greedy algorithms for point tracking do not allow for entry and exit of points from the scene, this is not a limitation for the proposed algorithm. Experiments with real and synthetic data show that the proposed algorithm outperforms the existing techniques and is applicable in more general settings.
Weighted and Robust Incremental Method for Subspace Learning
Authors: Danijel Skocaj, Ales Leonardis
Venue: ICCV
Year: 2003

Visual learning is expected to be a continuous and robust process, which treats input images and pixels selectively. In this paper, we present a method for subspace learning, which takes these considerations into account. We present an incremental method, which sequentially updates the principal subspace considering weighted influence of individual images as well as individual pixels within an image. This approach is further extended to enable determination of consistencies in the input data and imputation of the values in inconsistent pixels using the previously acquired knowledge, resulting in a novel incremental, weighted and robust method for subspace learning.
Tales of Shape and Radiance in Multi-view Stereo
Authors: Stefano Soatto, Anthony J. Yezzi, Hailin Jin
Venue: ICCV
Year: 2003

To what extent can three-dimensional shape and radiance be inferred from a collection of images? Can the two be estimated separately while retaining optimality? How should the optimality criterion be computed? When is it necessary to employ an explicit model of the reflectance properties of a scene? In this paper we introduce a separation principle for shape and radiance estimation that applies to Lambertian scenes and holds for any choice of norm. When the scene is not Lambertian, however, shape cannot be decoupled from radiance, and therefore matching image-to-image is not possible directly. We employ a rank constraint on the radiance tensor, which is commonly used in computer graphics, and construct a novel cost functional whose minimization leads to an estimate of both shape and radiance for nonLambertian objects, which we validate experimentally.
The Beltrami Flow over Implicit Manifolds
Authors: Nir A. Sochen, Rachid Deriche, Lucero Lopez Perez
Venue: ICCV
Year: 2003

In many medical computer vision tasks the relevant data is attached to a specific tissue such as the colon or the cortex. This situation calls for regularization techniques which are defined over surfaces. We introduce in this paper the Beltrami flow over implicit manifolds. This new regularization technique overcomes the over-smoothing of the L/sub 2/ flow and the staircasing effects of the L/sub 1/ flow, that were recently suggested via the harmonic map methods. The key of our approach is first to clarify the link between the intrinsic Polyakov action and the implicit harmonic energy functional and then use the geometrical understanding of the Beltrami flow to generalize it to images on implicitly defined non flat surfaces. It is shown that once again the Beltrami flow interpolates between the L/sub 2/ and L/sub 1/ flows on non flat surfaces. The implementation scheme of this flow is presented and various experimental results obtained on a set of various real images illustrate the performances of the approach as well as the differences with the harmonic map flows. This extension of the Beltrami flow to the case of non flat surfaces opens new perspectives in the regularization of noisy data defined on manifolds.
Spectral Partitioning for Structure from Motion
Authors: Drew Steedly, Irfan A. Essa, Frank Dellaert
Venue: ICCV
Year: 2003

We propose a spectral partitioning approach for large-scale optimization problems, specifically structure from motion. In structure from motion, partitioning methods reduce the problem into smaller and better conditioned subproblems which can be efficiently optimized. Our partitioning method uses only the Hessian of the reprojection error and its eigenvector. We show that partitioned systems that preserve the eigenvectors corresponding to small eigenvalues result in lower residual error when optimized. We create partitions by clustering the entries of the eigenvectors of the Hessian corresponding to small eigenvalues. This is a more general technique than relying on domain knowledge and heuristics such as bottom-up structure from motion approaches. Simultaneously, it takes advantage of more information than generic matrix partitioning algorithms.
Variational Frameworks for DT-MRI Estimation, Regularization and Visualization
Authors: David Tschumperlé, Rachid Deriche
Venue: ICCV
Year: 2003

We address three crucial issues encountered in DT-MRI (diffusion tensor magnetic resonance imaging): diffusion tensor estimation, regularization and fiber bundle visualization. We first review related algorithms existing in the literature and propose then alternative variational formalisms that lead to new and improved schemes, thanks to the preservation of important tensor constraints (positivity, symmetry). We illustrate how our complete DT-MRI processing pipeline can be successfully used to construct and draw fiber bundles in the white matter of the brain, from a set of noisy raw MRl images.
Image Parsing: Unifying Segmentation, Detection, and Recognition
Authors: Zhuowen Tu, Xiangrong Chen, Alan L. Yuille, Song Chun Zhu
Venue: ICCV
Year: 2003

In this chapter we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a “parsing graph”, in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches – generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this chapter, we focus on two types of visual patterns – generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation [48].). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions.
Plane-based Calibration Algorithm for Multi-camera Systems via Factorization of Homography Matrices
Authors: Toshio Ueshiba, Fumiaki Tomita
Venue: ICCV
Year: 2003

A new calibration algorithm for multicamera systems using a planar reference pattern is proposed. The algorithm is an extension of Sturm-Maybank-Zhang style plane-based calibration technique for use with multiple cameras. Rigid displacements between the cameras are recovered as well as the intrinsic parameters only by capturing with the cameras a model plane with known reference points placed at three or more locations. Thus the algorithm yields a simple calibration means for stereo vision systems with an arbitrary number of cameras while maintaining the handiness and flexibility of the original method. The algorithm is based on factorization of homography matrices between the model and image planes into the camera and plane parameters. To compensate for the indetermination of scaling factors, each homography matrix is rescaled by a double eigenvalue of a planar homology defined by two views and two model planes. The obtained parameters are finally refined by a nonlinear maximum likelihood estimation (MLE) process. The validity of the proposed technique was verified through simulation and experiments with real data.
Detecting Pedestrians Using Patterns of Motion and Appearance
Authors: Paul A. Viola, Michael J. Jones 0001, Daniel Snow
Venue: ICCV
Year: 2003

This paper describes a pedestrian detection system that integrates image intensity information with motion information. We use a detection style algorithm that scans a detector over two consecutive frames of a video sequence. The detector is trained (using AdaBoost) to take advantage of both motion and appearance information to detect a walking person. Past approaches have built detectors based on appearance information, but ours is the first to combine both sources of information in a single detector. The implementation described runs at about 4 frames/second, detects pedestrians at very small scales (as small as 20/spl times/15 pixels), and has a very low false positive rate. Our approach builds on the detection work of Viola and Jones. Novel contributions of this paper include: i) development of a representation of image motion which is extremely efficient, and ii) implementation of a state of the art pedestrian detection system which operates on low resolution images under difficult conditions (such as rain and snow).
Facial Expression Decomposition
Authors: Hongcheng Wang, Narendra Ahuja
Venue: ICCV
Year: 2003

In this paper, we propose a novel approach for facial expression decomposition - higher-order singular value decomposition (HOSVD), a natural generalization of matrix SVD. We learn the expression subspace and person subspace from a corpus of images showing seven basic facial expressions, rather than resort to expert-coded facial expression parameters. We propose a simultaneous face and facial expression recognition algorithm, which can classify the given image into one of the seven basic facial expression categories, and then other facial expressions of the new person can be synthesized using the learned expression subspace model. The contributions of this work lie mainly in two aspects. First, we propose a new HOSVD based approach to model the mapping between persons and expressions, used for facial expression synthesis for a new person. Second, we realize simultaneous face and facial expression recognition as a result of facial expression decomposition. Experimental results are presented that illustrate the capability of the person subspace and expression subspace in both synthesis and recognition tasks. As a quantitative measure of the quality of synthesis, we propose using gradient minimum square error (GMSE) which measures the gradient difference between the original and synthesized images.
Landmark-based Shape Deformation with Topology-Preserving Constraints
Authors: Song Wang, Jim Xiuquan Ji, Zhi-Pei Liang
Venue: ICCV
Year: 2003

This paper presents a novel approach for landmark-based shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression problem. To well describe nonrigid shape deformation, this paper measures the shape difference using a thin-plate spline model. The proposed approach is capable of preserving the topology of the template shape in the deformation. This property is achieved by inserting a set of additional points and imposing a set of linear equality and/or inequality constraints. The underlying optimization problem is solved using a quadratic programming algorithm. The proposed method has been tested using practical data in the context of shape-based image segmentation. Some relevant practical issues, such as missing detected landmarks and selection of the regularization parameter are also briefly discussed.
Variable Bandwidth QMDPE and Its Application in Robust Optical Flow Estimation
Authors: Hanzi Wang, David Suter
Venue: ICCV
Year: 2003

Robust estimators, such as least median of squared (LMedS) residuals, M-estimators, the least trimmed squares (LTS) etc., have been employed to estimate optical flow from image sequences in recent years. However, these robust estimators have a breakdown point of no more than 50%. We propose a novel robust estimator, called variable bandwidth quick maximum density power estimator (vbQMDPE), which can tolerate more than 50% outliers. We apply the novel proposed estimator to robust optical flow estimation. Our method yields better results than most other recently proposed methods, and it has the potential to better handle multiple motion effects.
Unified Subspace Analysis for Face Recognition
Authors: Xiaogang Wang 0001, Xiaoou Tang
Venue: ICCV
Year: 2003

We propose a face difference model that decomposes face difference into three components, intrinsic difference, transformation difference, and noise. Using the face difference model and a detailed subspace analysis on the three components we develop a unified framework for subspace analysis. Using this framework we discover the inherent relationship among different subspace methods and their unique contributions to the extraction of discriminating information from the face difference. This eventually leads to the construction of a 3D parameter space that uses three subspace dimensions as axis. Within this parameter space, we develop a unified subspace analysis method that achieves better recognition performance than the standard subspace methods on over 2000 face images from the FERET database.
Color Edge Detection by Photometric Quasi-Invariants
Authors: Joost van de Weijer 0001, Theo Gevers, Jan-Mark Geusebroek
Venue: ICCV
Year: 2003

Photometric invariance is used in many computer vision applications. The advantage of photometric invariance is the robustness against shadows, shading and illumination conditions. However, the drawbacks of photometric invariance are the loss of discriminative power and the inherent instabilities caused by the nonlinear transformations to compute the invariants. In this paper, we propose a new class of derivatives which we refer to as photometric quasi-invariants. These quasi-invariants share with full invariants the nice property that they are robust against photometric edges, such as shadows or specular edges. Further, these quasi-invariants do not have the inherent instabilities of full photometric invariants. We will apply these quasi-invariant derivatives in the context of photometric invariant edge detection and classification. Experiments show that the quasi-invariant derivatives are stable and they significantly outperform the full invariant derivatives in discriminative power.
Combinatorial Constraints on Multiple Projections of a Set of Points
Authors: Tomás Werner
Venue: ICCV
Year: 2003

Multiple projections of a scene cannot be arbitrary, the allowed configurations being given by matching constraints. This paper presents new matching constraints on multiple projections of a rigid point set by uncalibrated cameras, obtained by formulation in the oriented projective rather than projective geometry. They follow from consistency of orientations of camera rays and from the fact that the scene is the affine rather that projective space. For their non-parametric nature, we call them combinatorial. The constraints are derived in a unified theoretical framework using the theory of oriented matroids. For example, we present constraints on 4 point correspondences for 2D camera resectioning, on 3 correspondences in two 1D cameras, and on 4 correspondences in two 2D cameras.
Feature Selection for Unsupervised and Supervised Inference: the Emergence of Sparsity in a Weighted-based Approach
Authors: Lior Wolf, Amnon Shashua
Venue: ICCV
Year: 2003

The ability to reliably infer the nature of telephone conversations opens up a variety of applications, ranging from designing context-sensitive user interfaces on smartphones, to providing new tools for social psychologists and social scientists to study and understand social life of different subpopulations within different contexts. Using a unique corpus of everyday telephone conversations collected from eight residences over the duration of a year, we investigate the utility of popular features, extracted solely from the content, in classifying business-oriented calls from others. Through feature selection experiments, we find that the discrimination can be performed robustly for a majority of the calls using a small set of features. Remarkably, features learned from unsupervised methods, specifically latent Dirichlet allocation, perform almost as well as with as those from supervised methods. The unsupervised clusters learned in this task shows promise of finer grain inference of social nature of telephone conversations.
Combining Gradient and Albedo Data for Rotation Invariant Classification of 3D Surface Texture
Authors: Jiahua Wu, Mike J. Chantler
Venue: ICCV
Year: 2003

We present a new texture classification scheme which is invariant to surface-rotation. Many texture classification approaches have been presented in the past that are image-rotation invariant. However, image rotation is not necessarily the same as surface rotation. We have therefore developed a classifier that uses invariants that are derived from surface properties rather than image properties. Previously we developed a scheme that used surface gradient (normal) fields estimated using photometric stereo. In this paper we augment these data with albedo information and also employ an additional feature set: the radial spectrum. We used 30 real textures to test the new classifier. A classification accuracy of 91% was achieved when albedo and gradient 1D polar and radial features were combined. The best performance was also achieved by using 2D albedo and gradient spectra. The classification accuracy is 99%.
Automatically Labeling Video Data Using Multi-class Active Learning
Authors: Rong Yan, Jie Yang 0001, Alexander G. Hauptmann
Venue: ICCV
Year: 2003

Labeling video data is an essential prerequisite for many vision applications that depend on training data, such as visual information retrieval, object recognition, and human activity modelling. However, manually creating labels is not only time-consuming but also subject to human errors, and eventually, becomes impossible for a very large amount of data (e.g. 24/7 surveillance video). To minimize the human effort in labeling, we propose a unified multiclass active learning approach for automatically labeling video data. We include extending active learning from binary classes to multiple classes and evaluating several practical sample selection strategies. The experimental results show that the proposed approach works effectively even with a significantly reduced amount of labeled data. The best sample selection strategy can achieve more than a 50% error reduction over random sample selection.
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
Authors: Changjiang Yang, Ramani Duraiswami, Nail A. Gumerov, Larry S. Davis
Venue: ICCV
Year: 2003

Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. The quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. The fast Gauss transform (FGT) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. Unfortunately, the cost of a direct extension of the FGT to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. We develop an improved fast Gauss transform to efficiently estimate sums of Gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. The improved FGT has been applied to the mean shift algorithm achieving linear computational complexity. Experimental results demonstrate the efficiency and effectiveness of our algorithm.