OpenRaiser/Intern-Atlas huggingface dataset:
High Resolution Tracking of Non-Rigid 3D Motion of Densely Sampled Data Using Harmonic Maps
Authors: Yang Wang 0001, Mohit Gupta 0001, Song Zhang 0002, Sen Wang, Xianfeng Gu, Dimitris Samaras, Peisen Huang
Venue: ICCV
Year: 2005

We present a novel fully automatic method for high resolution, nonrigid dense 3D point tracking. High quality dense point clouds of nonrigid geometry moving at video speeds are acquired using a phase-shifting structured light ranging technique. To use such data for the temporal study of subtle motions such as those seen in facial expressions, an efficient nonrigid 3D motion tracking algorithm is needed to establish inter-frame correspondences. The novelty of this paper is the development of an algorithmic framework for 3D tracking that unifies tracking of intensity and geometric features, using harmonic maps with added feature correspondence constraints. While the previous uses of harmonic maps provided only global alignment, the proposed introduction of interior feature constraints guarantees that nonrigid deformations are accurately tracked as well. The harmonic map between two topological disks is a diffeomorphism with minimal stretching energy and bounded angle distortion. The map is stable, insensitive to resolution changes and is robust to noise. Due to the strong implicit and explicit smoothness constraints imposed by the algorithm and the high-resolution data, the resulting registration/deformation field is smooth, continuous and gives dense one-to-one inter-frame correspondences. Our method is validated through a series of experiments demonstrating its accuracy and efficiency.
Separating Reflections in Human Iris Images for Illumination Estimation
Authors: Huiqiong Wang, Stephen Lin 0001, Xiaopei Liu, Sing Bing Kang
Venue: ICCV
Year: 2005

A method is presented for separating corneal reflections in an image of human irises to estimate illumination from the surrounding scene. Previous techniques for reflection separation have demonstrated success in only limited cases, such as for uniform colored lighting and simple object textures, so they are not applicable to irises which exhibit intricate textures and complicated reflections of the environment. To make this problem feasible, we present a method that capitalizes on physical characteristics of human irises to obtain an illumination estimate that encompasses the prominent light contributors in the scene. Results of this algorithm are presented for eyes of different colors, including light colored eyes for which reflection separation is necessary to determine a valid illumination estimate.
Geometric Invariants and Applications under Catadioptric Camera Model
Authors: Yihong Wu 0002, Zhanyi Hu
Venue: ICCV
Year: 2005

This paper presents geometric invariants of points and their applications under central catadioptric camera model. Although the image has severe distortions under the model, we establish some accurate projective geometric invariants of scene points and their image points. These invariants, being functions of principal point, are useful, from which a method for calibrating the camera principal point and a method for recovering planar scene structures are proposed. The main advantage of using these in variants for plane reconstruction is that neither camera motion nor the intrinsic parameters, except for the principal point, is needed. The theoretical correctness of the established invariants and robustness of the proposed methods are demonstrated by experiments. In addition, our results are found to be applicable to some more general camera models other than the catadioptric one
8-Point Algorithm Revisited: Factorized 8-Point Algorithm
Authors: Fuchao Wu, Zhanyi Hu, Fuqing Duan
Venue: ICCV
Year: 2005

In this paper, a novel algorithm for the fundamental matrix estimation, called factorized 8-point algorithm, is presented. The factorized 8-point algorithm is composed of three steps: (1) The measurement matrix in the traditional 8-point algorithm is decomposed into two factor matrices; (2) By introducing some auxiliary variables, a new linear minimization problem is formed, where every element of its associated measurement matrix is simply either a measurement datum or a constant; (3) The fundamental matrix is determined by solving this minimization problem by a least squares method. Like the traditional 8-point algorithm and Hartley's normalized 8-point algorithm, the factorized 8-point algorithm is also completely linear. But unlike the normalized 8-point algorithm, the factorized 8-point algorithm does not need any pre-normalization step. Since every element of the measurement matrix in the factorized 8-point algorithm is a measurement datum or a constant, no amplification of measurement error is involved; the factorized 8-point algorithm can boost effectively the robustness of the estimation. Large numbers of experiments show that the factorized 8-point algorithm consistently outperforms the traditional 8-point algorithm. In addition, although the factorized 8-point algorithm is specially designed for fundamental matrix estimation, its basic principle can be generalized to other estimation problems in computer vision, such as camera projection matrix estimation, homography estimation, focus of expansion estimation, and trifocal tensor estimation.
Visual Learning Given Sparse Data of Unknown Complexity
Authors: Tao Xiang 0002, Shaogang Gong
Venue: ICCV
Year: 2005

This study addresses the problem of unsupervised visual learning. It examines existing popular model order selection criteria before proposes two novel criteria for improving visual learning given sparse data and without any knowledge about model complexity. In particular, a rectified Bayesian information criterion (BICr) and a completed likelihood Akaike's information criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for learning the dynamic structure of a visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from very small to large. Extensive experiments on learning a dynamic scene structure are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of BIC (Schwarz, 1978), AIC (Akaike, 1973), ICL (Biernacki, 2000) and a MML (Figueiredo and Jain, 2002) based criterion.
Video Behaviour Profiling and Abnormality Detection without Manual Labelling
Authors: Tao Xiang 0002, Shaogang Gong
Venue: ICCV
Year: 2005

A novel framework is developed for automatic behaviour profiling and abnormality sampling/detection without any manual labelling of the training dataset. Natural grouping of behaviour patterns is discovered through unsupervised model selection and feature selection on the eigenvectors of a normalised affinity matrix. Our experiments demonstrate that a behaviour model trained using an unlabelled dataset is superior to those trained using the same but labelled dataset in detecting abnormality from an unseen video.
Automatic 3D Face Modeling from Video
Authors: Le Xin, Qiang Wang 0023, Jianhua Tao 0001, Xiaoou Tang, Tieniu Tan, Harry Shum
Venue: ICCV
Year: 2005

In this paper, we develop an efficient technique for fully automatic recovery of accurate 3D face shape from videos captured by a low cost camera. The method is designed to work with a short video containing a face rotating from frontal view to profile view. The whole approach consists of three components. First, automatic initialization is performed in the first frame with approximately frontal face. Then, to handle the case of low quality image captured by low cost camera, the 2D feature matching, head poses and underlying 3D face shape are estimated and refined iteratively in an efficient way based on image sequence segmentation. Finally, to take advantage of the sparse structure of the proposed algorithm, sparse bundle adjustment technique is further employed to speed up the computation. We demonstrate the accuracy and robustness of the algorithm using a set of experiments
Face Recognition with MRC-Boosting
Authors: Xun Xu 0003, Thomas S. Huang
Venue: ICCV
Year: 2005

In this paper, a novel classification algorithm called MRC-Boosting is proposed. Through aggregating maximal-rejection-classifier features under boosting framework, this algorithm can deal with complicated two-class classification problem, especially for the category called target detection problem where a target class should be discriminated from tile surrounding clutter class. MRC-Boosting is efficient since unlike many other boosting based algorithms, at each iteration the optimal feature is computed in closed-form, with neither exhaustive search nor time-consuming numerical optimization. Furthermore, a variant of MRC-Boosting is derived and applied to face recognition. This variant MRC-Boosting algorithm is able to utilize large amount of training samples efficiently overcoming the difficulty faced by other algorithms like AdaBoost. The effectiveness of the proposed algorithm is validated by face recognition experiments on CMU-PIE database.
Integrating the Effects of Motion, Illumination and Structure in Video Sequences
Authors: Yilei Xu, Amit K. Roy-Chowdhury
Venue: ICCV
Year: 2005

Most work in computer vision has concentrated on studying the individual effect of motion and illumination on a 3D object. In this paper, we present a theory for combining the effects of motion, illumination, 3D structure, albedo, and camera parameters in a sequence of images obtained by a perspective camera. We show that the set of all Lambertian reflectance functions of a moving object, illuminated by arbitrarily distant light sources, lies close to a bilinear subspace consisting of nine illumination variables and six motion variables. This result implies that, given an arbitrary video sequence, it is possible to recover the 3D structure, motion and illumination conditions simultaneously using the bilinear subspace formulation. The derivation is based on the intuitive notion that, given an illumination direction, the images of a moving surface cannot change suddenly over a short time period. We experimentally compare the images obtained using our theory with ground truth data and show that the difference is small and acceptable. We also provide experimental results on real data by synthesizing video sequences of a 3D face with various combinations of motion and illumination directions.
Detection, Analysis and Matching of Hair
Authors: Yaser Yacoob, Larry S. Davis
Venue: ICCV
Year: 2005

We develop computational models for measuring hair appearance for comparing different people. The models and methods developed have applications to person recognition and face image indexing. An automatic hair detection algorithm is described and results reported. A multidimensional representation of hair appearance is presented and computational algorithms are described. Results on a dataset of 524 subjects are reported. Identification of people using hair attributes is compared to eigenface-based recognition along with a joint, eigenface-hair based identification.
Fast Multiple Object Tracking via a Hierarchical Particle Filter
Authors: Changjiang Yang, Ramani Duraiswami, Larry S. Davis
Venue: ICCV
Year: 2005

A very efficient and robust visual object tracking algorithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge orientation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. The first is the use of integral images for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.
Learning the Probability of Correspondences without Ground Truth
Authors: Qingxiong Yang, R. Matt Steele, David Nistér, Christopher O. Jaynes
Venue: ICCV
Year: 2005

We present a quality assessment procedure for correspondence estimation based on geometric coherence rather than ground truth. The procedure can be used for performance evaluation of correspondence extraction schemes developed by researchers, as well as for online learning and adaptation aimed at better system performance. A very important aspect of the proposed procedure is that it considers uncertainty in the correspondence extraction, and encourages the evaluated methods to deal correctly with uncertainty. Other important strengths of the procedure are that it does not use any manual work, and that it does not put any strong constraints on the scene, but rather relies on geometric coherence in the motion. Thanks to these strengths, it can therefore be used with large amounts of real, potentially application specific data, or even data acquired during system operation. In the evaluation the correspondence extractor is handled as a black box producing a probability distribution for the local motion vector between a pair of image patches. The procedure is therefore quite general. We are making the evaluation procedure available for public use.
Is ICA Significantly Better than PCA for Face Recognition?
Authors: Jian Yang 0003, David Zhang 0001, Jing-Yu Yang 0001
Venue: ICCV
Year: 2005

The standard PCA was always used as baseline algorithm to evaluate ICA-based face recognition systems in the previous research. In this paper, we examine the two architectures of ICA for image representation and find that ICA architecture I involves a PCA process by vertically centering (PCA I), while ICA architecture II involves a whitened PCA process by horizontally centering (PCA II). So, it is reasonable to use these two PCA versions as baseline algorithms to revaluate the ICA-based face recognition systems. The experiments were performed on the FERET face database. The experimental results show there is no significant performance differences between ICA architecture I (II) and PCA I (II), although ICA architecture II significantly outperforms the standard PCA. It can be concluded that the performance of ICA strongly depends on its involved PCA process. The pure ICA projection has little effect on the performance of face recognition.
Object Detection in Aerial Imagery Based on Enhanced Semi-Supervised Learning
Authors: Jian Yao 0003, Zhongfei (Mark) Zhang
Venue: ICCV
Year: 2005

Object detection in aerial imagery has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the background in a typical aerial image, a robust and efficient detection is still considered as an open and challenging problem. In this paper, we present the enhanced semi-supervised learning (ESL) framework and apply this framework to revising an object detection methodology we have developed in a previous effort. Theoretic analysis and experimental evaluation using the UCI machine learning repository clearly indicate the superiority of the ESL framework. The performance evaluations of the revised object detection methodology against the original one clearly demonstrate the promise and superiority of this approach.
Conformal Metrics and True "Gradient Flows" for Curves
Authors: Anthony J. Yezzi, Andrea Mennucci
Venue: ICCV
Year: 2005

We wish to endow the manifold M of smooth curves in /spl Ropf//sup n/ with a Riemannian metric that allows us to treat continuous morphs (homotopies) between two curves c/sub 0/ and c/sub 1/ as trajectories with computable lengths which are independent of the parameterization or representation of the two curves (and the curves making up the morph between them). We may then define the distance between the two curves using the trajectory of minimal length (geodesic) between them, assuming such a minimizing trajectory exists. At first we attempt to utilize the metric structure implied rather unanimously by the past twenty years or so of shape optimization literature in computer vision. This metric arises as the unique metric which validates the common references to a wide variety of contour evolution models in the literature as "gradient flows" to various formulated energy functionals. Surprisingly, this implied metric yields a pathological and useless notion of distance between curves. In this paper, we show how this metric can be minimally modified using conformal factors that depend upon a curve's total arclength. A nice property of these new conformal metrics is that all active contour models that have been called "gradient flows" in the past will constitute true gradient flows with respect to these new metrics under specific time reparameterizations.
Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras
Authors: Alper Yilmaz 0001, Mubarak Shah
Venue: ICCV
Year: 2005

Most work in action recognition deals with sequences acquired by stationary cameras with fixed viewpoints. Due to the camera motion, the trajectories of the body parts contain not only the motion of the performing actor but also the motion of the camera. In addition to the camera motion, different viewpoints of the same action in different environments result in different trajectories, which can not be matched using standard approaches. In order to handle these problems, we propose to use the multi-view geometry between two actions. However, well known epipolar geometry of the static scenes where the cameras are stationary is not suitable for our task. Thus, we propose to extend the standard epipolar geometry to the geometry of dynamic scenes where the cameras are moving. We demonstrate the versatility of the proposed geometric approach for recognition of actions in a number of challenging sequences.
Linear Approaches to Camera Calibration from Sphere Images or Active Intrinsic Calibration Using Vanishing Points
Authors: Xianghua Ying, Hongbin Zha
Venue: ICCV
Year: 2005

Spherical objects and vanish points are often used for camera calibration. An occluding contour of a sphere is projected to a conic in the perspective image, and using a moving active camera, the trajectory of a vanishing point in the perspective images is also a conic when the camera is rotated about a fixed 3D axis whereas the translation of the camera is arbitrary. In fact, the problems of camera calibration using conics from spheres or vanishing points can be described by same mathematic representations. Two linear approaches to the problems are proposed in this paper: one based on the geometric interpretation of the relation between image conics and the image of the absolute conic, and the other using the special structure of the problems in algebra. Only three such conics are needed for the two linear approaches, and the minimum number for previous nonlinear optimization methods is also three. All five intrinsic parameters are recovered linearly without making assumptions, such as, zero-skew or unitary aspect ratio which are often used in previous methods. The two linear algorithms have been tested in extensive experiments with respect to noise sensitivity and also made comparisons with recent calibration techniques.
Progressive Surface Reconstruction from Images Using a Local Prior
Authors: Gang Zeng, Sylvain Paris, Long Quan, François X. Sillion
Venue: ICCV
Year: 2005

This paper introduces a new method for surface reconstruction from multiple calibrated images. The primary contribution of this work is the notion of local prior to combine the flexibility of the carving approach with the accuracy of graph-cut optimization. A progressive refinement scheme is used to recover the topology and reason the visibility of the object. Within each voxel, a detailed surface patch is optimally reconstructed using a graph-cut method. The advantage of this technique is its ability to handle complex shape similarly to level sets while enjoying a higher precision. Compared to carving techniques, the addressed problem is well-posed, and the produced surface does not suffer from aliasing. In addition, our approach seamlessly handles complete and partial reconstructions: If the scene is only partially visible, the process naturally produces an open surface; otherwise, if the scene is fully visible, it creates a complete shape. These properties are demonstrated on real image sequences
A General Framework for Temporal Video Scene Segmentation
Authors: Yun Zhai, Mubarak Shah
Venue: ICCV
Year: 2005

Videos are composed of many shots caused by different camera operations, e.g., on/off operations and switching between cameras. One important goal in video analysis is to group the shots into temporal scenes, such that all the shots in a single scene are related to a particular physical setting, an on-going action or a theme. In this paper, we present a general framework for temporal scene segmentation for various video types. The proposed method is formulated in a statistical fashion and uses the Markov chain Monte Carlo (MCMC) technique to determine the boundaries between video scenes. In this approach, an arbitrary number of scene boundaries are randomly initialized and automatically updated using two types of updates: diffuse and jumps. The posterior probability on the number of scenes and their boundary locations is computed based on the model priors and the data likelihood. The updates of the model parameters are controlled by the hypothesis ratio test in the MCMC process. The proposed framework has been experimented on two types of videos, home videos and feature films, and accurate results have been obtained.
A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieva
Authors: Ruofei Zhang, Zhongfei (Mark) Zhang, Mingjing Li, Wei-Ying Ma, HongJiang Zhang
Venue: ICCV
Year: 2005

This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7,736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature.