OpenRaiser/Intern-Atlas huggingface dataset:
Integrating and Employing Multiple Levels of Zoom for Activity Recognition
Authors: Paul Smith 0003, Mubarak Shah, Niels da Vitoria Lobo
Venue: CVPR
Year: 2004

To facilitate activity recognition, analysis of the scene at multiple levels of detail is necessary. Required prerequisites for our activity recognition are tracking objects across frames and establishing a consistent labeling of objects across cameras. This paper makes several innovative uses of the epipolar constraint in the context of activity recognition. We first demonstrate how we track heads and hands using the epipolar geometry. Next we show how the detected objects are labeled consistently across cameras and zooms by employing epipolar, spatial, trajectory, and appearance properties. Finally we show how our method, utilizing the multiple levels of detail, is able to answer activity recognition problems which are difficult to answer with a single level of detail.
Thermal Face Recognition in an Operational Scenario
Authors: Diego A. Socolinsky, Andrea Selinger
Venue: CVPR
Year: 2004

We present results on the latest advances in thermal infrared face recognition, and its use in combination with visible imagery. Previous research by the authors has shown high performance under very controlled conditions, or questionable performance under a wider range of conditions. This paper shows results on the use of thermal infrared and visible imagery for face recognition in operational scenarios. In particular, we show performance statistics for outdoor face recognition and recognition across multiple sessions. Our results support the conclusion that face recognition performance with thermal infrared imagery is stable over multiple sessions, and that fusion of modalities increases performance. As measured by the number of images and number of subjects, this is the largest ever reported study on thermal face recognition.
Reconstructing Open Surfaces from Unorganized Data Points
Authors: Jan Erik Solem, Anders Heyden
Venue: CVPR
Year: 2004

In this paper a method for fitting open surfaces to an unorganized set of data points is presented using a level set representation of the surface. This is done by tracking a curve, representing the boundary, on the implicitly defined surface. This curve is given as the intersection of the level set describing the surface and an auxiliary level set. These two level sets are propagated using the same motion vector field. Special care has to be taken in order for the surfaces not to intersect at other places than at the desired boundary. Methods for accomplishing this are presented and a novel fast scheme for finding good initial values is proposed. This novel method gives a piecewise linear approximation of the initial surface boundary using a partition of the convex hull. With the described method open surfaces can be fitted to point clouds obtained using structure from motion techniques. This paper solves an important practical problem since in many cases the surfaces in the scene are open or can only be viewed from certain directions. Successful experiments on several data sets support the method.
Self Shadowing and Local Illumination of Randomly Rough Surfaces
Authors: Yinlong Sun
Venue: CVPR
Year: 2004

This paper develops an analytic model for self shadowing and local illumination of rough surfaces. The surface is assumed homogeneous, isotropic, and smooth microscopically, with a Gaussian height field. The total reflection is decomposed into single and multiple scattering. A shadowing factor is derived as a function of surface roughness, and color variation due to multiple scattering is described. This model is compared to previous models and the effects of surface roughness are studied via rendered images.
Automatic Cascade Training with Perturbation Bias
Authors: Jie Sun 0004, James M. Rehg, Aaron F. Bobick
Venue: CVPR
Year: 2004

Face detection methods based on cascade architecture have demonstrated fast and robust performance. Cascade learning is aided by the modularity of the architecture in which nodes are chained together to form a cascade. In this paper we present two new cascade learning results which address the decoupled nature of the cascade learning task. First, we introduce a cascade indifference curve framework, which connects the learning objectives for a node to the overall cascade performance. We derive a new cost function for node learning, which yields fully-automatic stopping conditions and improved detection performance. Second, we introduce the concept of perturbation bias, which leverages the statistical differences between target and non-target classes in a detection problem to obtain improved performance and robustness. We derive necessary and sufficient conditions for the success of the method and present experimental results.
Modelling the Effects of Walking Speed on Appearance-Based Gait Recognition
Authors: Rawesak Tanawongsuwan, Aaron F. Bobick
Venue: CVPR
Year: 2004

Researchers in the gait community propose various features, either appearance or model based, which they believe encode certain individual traits. One of the main assumptions made in many gait recognition techniques is constant walking-speed. Even though the gait patterns are repeatable, changes in walking speed can influence the gait patterns themselves. In this work we explore how changes in walking speed affect gait parameters in terms of recognition performance. A speed-varying walking database was collected to allow us to investigate and quantify the impact of speed on gait recognition methodically. We develop a normalization procedure, which maps gait features across speeds, and demonstrate their utility in previously proposed appearance-based gait recognition methods.
Frame Synchronization and Multi-Level Subspace Analysis for Video Based Face Recognition
Authors: Xiaoou Tang, Zhifeng Li 0001
Venue: CVPR
Year: 2004

In this paper, we develop a new video-to-video face recognition algorithm. The major advantage of the video based method is that more information is available in a video sequence than in a single image. In order to take advantage of the large amount of information in the video sequence and at the same time overcome the processing speed and data size problems we develop several new techniques including temporal and spatial frame synchronization and multi-level subspace analysis for video cube processing. The method preserves all the spatial-temporal information contained in a video sequence. Near perfect classification results are obtained on the XM2VTS face video database.
Perspective Shape-from-Shading by Fast Marching
Authors: Ariel Tankus, Nir A. Sochen, Yehezkel Yeshurun
Venue: CVPR
Year: 2004

Shape-from-shading (SfS) is a fundamental problem in computer vision. At its basis lies the image irradiance equation. Recently, the authors proposed to base the image irradiance equation on the assumption of perspective projection rather than the common orthographic one. The current paper presents a greatly-improved reconstruction method based on the perspective formulation. The proposed model is solved efficiently via a modification of the fast marching method of Kimmel and Sethian. We compare the two versions of the fast marching method (orthographic vs. perspective) on medical images. The perspective algorithm outperformed the orthographic one. This shows that the more realistic hypothesis of perspective projection improves reconstruction significantly. The comparison also demonstrates the usability of perspective SfS for real-life applications such as medical endoscopy.
Efficient Graphical Models for Processing Images
Authors: Marshall F. Tappen, Bryan C. Russell, William T. Freeman
Venue: CVPR
Year: 2004

Graphical models are powerful tools for processing images. However, the large dimensionality of even local image data poses a difficulty. Representing the range of possible graphical model node variables with discrete states leads to an overwhelmingly large number of states for the model, often making both exact and approximate inference computationally intractable. We propose a representation that allows a small number of discrete states to represent the large number of possible image values at each pixel or local image patch. Each node in the graph represents the best regression function, chosen from a set of candidate functions, for estimating the unobserved image pixels from the observed samples. This permits a small number of discrete states to summarize the range of possible image values at each point in the image. Belief propagation is then used to find the best regressor to use at each point. To demonstrate the usefulness of this technique, we apply it to two problems: super-resolution and color demosaicing. In both cases, we find our method compares well against other techniques for these problems.
Uncalibrated and Unsynchronized Human Motion Capture: A Stereo Factorization Approach
Authors: Philip A. Tresadern, Ian D. Reid 0001
Venue: CVPR
Year: 2004

Human motion capture typically requires several high quality, synchronized and calibrated cameras in a studio environment and can be potentially costly and technically complex. Instead, we propose a system which combines and improves upon two existing techniques, yielding an efficient method that recovers maximum likelihood joint angles and anthropomorphic data of the subject by factorization. The first technique concerns using a rank constraint framework to synchronize sequences of non-rigid motions where we extend affine methods to perspective and homography projection models. The second is a self-calibration method for two affine cameras, using constraints derived from prior knowledge of the underlying structure. We propose a minimal parameterization of the system to obtain an initial solution then apply a full bundle adjustment over the free parameters based on a geometric error We demonstrate the efficacy of our method by comparing the recovered structure and motion with that from a commercial motion capture system.
Face Localization via Hierarchical CONDENSATION with Fisher Boosting Feature Selection
Authors: Jilin Tu, ZhenQiu Zhang, Zhihong Zeng, Thomas S. Huang
Venue: CVPR
Year: 2004

We formulate face localization as a maximum a posteriori probability (MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribution for intrinsic face configuration is defined by active shape model (ASM). The likelihood model for local facial features is parameterized as mixture of Gaussians in feature space. A hierarchical CONDENSATION framework is then proposed to estimate the face configuration parameter. In order to improve the discriminative power of likelihood distribution in feature space, a new feature subspace, Fisher boosting feature space, is proposed and compared against PCA subspace and biased PCA subspace. Experiments show that, Fisher boosting algorithm can generate strong classifier with less number of weaker classifiers comparing to conventional Adaboosting algorithm as illustrated in a toy problem, that the face localization with Fisher boosting feature subspace outperforms that with PCA feature subspaces in localization accuracy and convergence rate, and that the design of hierarchical CONDENSATION framework alleviates the local minima problem which is frequently encountered by previous ASM optimization algorithms.
Biventricular Myocardial Kinematics Based on Tagged MRI from Anatomical NURBS Models
Authors: Nicholas J. Tustison, Dana Abendschein, Amir A. Amini
Venue: CVPR
Year: 2004

We present current research in which both left and right ventricular deformation is estimated from tagged cardiac magnetic resonance imaging using volumetric deformable models with a nonuniform rational D-splines (NURBS) basis. From a set of short and long axis images, the initial NURBS model is constructed by fitting two surfaces with the same parameterization to the set of epicardial and endocardial contours from which a volumetric model is created. Using displacement information derived from tag line and contour data, we can solve for the optimal homogeneous coordinates, in a least-squares sense, of the NURBS model at a later time point using quadratic programming. After fitting to all time points of data, lofting the NURBS model for each time point creates a comprehensive 4-D NURBS model. From this model 3-D myocardial displacement fields and corresponding strain maps, which are local measures of nonrigid deformation, become available.
Color Alignment in Texture Mapping of Images under Point Light Source and General Lighting Condition
Authors: Hiroki Unten, Katsushi Ikeuchi
Venue: CVPR
Year: 2004

Recently, creating digital contents through measurements of the real world has become an important technique in the field of virtual reality, digital preservation of cultural heritages and so forth. One of the effective methods for creating such digital contents is texture mapping, which maps color images on a 3D geometric model. This method has two problems: geometric and photometric. The geometric problem lies mostly in how to estimate the camera position and rotation relative to the 3D position of the target object. The photometric problem deals with color consistencies between input textures. Most studies on texture mapping focus on the geometric problem, while only a few works deal with the photometric problem. In this paper we propose two novel methods for color alignment in texture mapping. One method is based on the fact that chromaticity of images is independent from their geometric data. The other method utilizes the basis image of a point light source on an illumination sphere. In both methods, the pseudo-albedo is estimated and the color consistency between the input images is accomplished.
Estimating Illumination Direction from Textured Images
Authors: Manik Varma, Andrew Zisserman
Venue: CVPR
Year: 2004

We study the problem of estimating the illuminant's direction from images of textured surfaces. Given an isotropic, Gaussian random surface with constant albedo, Koenderink and Pont (2003) developed a theory for recovering the illuminant's azimuthal angle from a single image of the texture formed under a Lambertian model. In this paper, we extend the theory to deal with cases of spatially varying albedo. First, we generalise the theory to explain why their method should work even for certain types of spatially varying albedo. Our generalisation also predicts that the coherence of the structure tensor should lie below 0.8 in such non-constant albedo cases and accurately predicts the "deviation" from the true value observed by Koenderink and Pont on the Columbia-Utrecht (CUReT) texture database. Next, we extend the theory to account for arbitrarily varying albedo. We also investigate local, rather than global, estimates of the direction, and demonstrate our theory on the CUReT and the Heriot-Watt TextureLab databases where estimated directions are compared to ground truth.
Role of Shape and Kinematics in Human Movement Analysis
Authors: Ashok Veeraraghavan, Amit K. Roy-Chowdhury, Rama Chellappa
Venue: CVPR
Year: 2004

Human gait and activity analysis from video is presently attracting a lot of attention in the computer vision community. In this paper we analyze the role of two of the most important cues in human motion-shape and kinematics. We present an experimental framework whereby it is possible to evaluate the relative importance of these two cues in computer vision based recognition algorithms. In the process, we propose a new gait recognition algorithm by computing the distance between two sequences of shapes that lie on a spherical manifold. In our experiments, shape is represented using Kendall's definition of shape. Kinematics is represented using a Linear Dynamical system We place particular emphasis on human gait. Our conclusions show that shape plays a role which is more significant than kinematics in current automated gait based human identification algorithms. As a natural extension we study the role of shape and kinematics in activity recognition. Our experiments indicate that we require models that contain both shape and kinematics in order to perform accurate activity classification. These conclusions also allow us to explain the relative performance of many existing methods in computer-based human activity modeling.
Probabilistic Parameter-Free Motion Detection
Authors: Thomas Veit, Frédéric Cao, Patrick Bouthemy
Venue: CVPR
Year: 2004

We propose an original probabilistic parameter-free method for the detection of independently moving objects in an image sequence. We apply a probabilistic perceptual principle, the Helmholtz principle, whose main advantage is the automatization of the detection decision, by providing a light control of the number of false alarms. Not only does this method localize the moving objects but it also answers the preliminary question of the presence of motion. In particular the method works even when no assumption on motion presence is made. The algorithm is composed of three independent steps: estimation of the dominant image motion, spatial segmentation of object boundaries and independent motion detection itself We emphasize that none of these steps needs any parameter tuning. Results on real image sequences are reported and validate the proposed approach.
What Image Information Is Important in Silhouette-Based Gait Recognition?
Authors: Galina V. Veres, Layla Gordon, John N. Carter, Mark S. Nixon
Venue: CVPR
Year: 2004

Gait recognition has recently gained significant attention, especially in vision-based automated human identification at a distance in visual surveillance and monitoring applications. Silhouette-based gait recognition is one of the most popular methods for recognising moving shapes. This paper aims to investigate the important features in silhouette-based gait recognition from point of view of statistical analysis. It is shown that the average silhouette includes a static component of gait (head and body) as the most important image part, while dynamic component of gait (swings of legs and arms) is ignored as the least important information. At the same time ignoring dynamic part of gait can result in loss in recognition rate in some cases, and the importance of better motion estimation is underlined.
Detection and Tracking of Objects in Underwater Video
Authors: Dirk B. Walther, Duane R. Edgington, Christof Koch
Venue: CVPR
Year: 2004

For oceanographic research, remotely operated underwater vehicles (ROVs) routinely record several hours of video material each day. Manual processing of such large amounts of video has become a major bottleneck for scientific research based on this data. We have developed an automated system that detects and tracks objects that are of potential interest for human video annotators. By pre-selecting salient targets for track initiation using a selective attention algorithm, we reduce the complexity of multi-target tracking, in particular of the assignment problem. Detection of low-contrast translucent targets is difficult due to variable lighting conditions and the presence of ubiquitous noise from high-contrast organic debris ("marine snow") particles. We describe the methods we developed to overcome these issues and report our results of processing ROV video data.
Hybrid Textons: Modeling Surfaces with Reflectance and Geometry
Authors: Jing Wang 0008, Kristin J. Dana
Venue: CVPR
Year: 2004

The appearance of surface texture as it varies with angular changes of view and illumination is becoming an increasingly important research topic. The bidirectional texture function (BTF) is used in surface modeling because it describes observed image texture as a function of imaging parameters. The BTF has no geometric information, as it is based solely on observed texture appearance. Computational tasks such as recognizing or rendering typically require projecting a sampled BTF to a lower dimensional subspace or clustering to extract representative textons. However, there is a serious drawback to this approach. Specifically, cast shadowing and occlusions are not fully captured. When recovering the full BTF from a sampled BTF with interpolation, the following two characteristics are difficult or impossible to reproduce: (1) the position and contrast of the shadow border, (2) the movement of the shadow border when the imaging parameters are changed continuously. For a textured surface, the nonlinear effects of cast shadows and occlusions are not negligible. On the contrary, these effects occur throughout the surface and are important perceptual cues to infer surface type. In this paper we present a texture representation that integrates appearance-based information from the sampled BTF with concise geometric information inferred from the sampled BTF The model is a hybrid of geometric and image-based models and has key advantages in a wide range of tasks, including texture prediction, recognition, and synthesis.
Shape Correspondence through Landmark Sliding
Authors: Song Wang, Toshiro Kubota, Theodor Richardson
Venue: CVPR
Year: 2004

Motivated by improving statistical shape analysis, this paper presents a novel landmark-based method for accurate shape correspondence, where the general goal is to align multiple shape instances by corresponding a set of given landmark points along those shapes. Different from previous methods, we consider both global shape deformation and local geometric features in defining the shape-correspondence cost function to achieve a consistency between the landmark correspondence and the underlying shape correspondence. According to this cost function, we develop a novel landmark-sliding algorithm to achieve optimal landmark-based shape correspondence with preserved shape topology. The proposed method can be applied to correspond various 2D shapes in the forms of single closed curves, single open curves, self-crossing curves, and multiple curves. We also discuss the practical issue of landmark initialization. The proposed method has been tested on various biological shapes arising from medical image analysis and validated in constructing statistical shape models.
Generalized Quotient Image
Authors: Haitao Wang 0007, Stan Z. Li, Yangsheng Wang
Venue: CVPR
Year: 2004

We present a unified framework for modeling intrinsic properties of face images for recognition. It is based on the quotient image (QI) concept, in particular on the existing works of QI, spherical harmonic, image ratio and retinex. Under this framework, we generalize these previous works into two new algorithms: (1) non-point light quotient image (NPL-QI) extends QI to deal with non-point light sources by modeling non-point light directions using spherical harmonic bases; (2) self-quotient image (S-QI) extends QI to perform illumination subtraction without the need for alignment and no shadow assumption. Experimental results show that our algorithms can significantly improve the performance of face recognition under varying illumination conditions.
Random Sampling LDA for Face Recognition
Authors: Xiaogang Wang 0001, Xiaoou Tang
Venue: CVPR
Year: 2004

Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Fisherface and null space LDA (N-LDA) are two conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. In this paper, by analyzing different overfitting problems for the two kinds of LDA classifiers, we propose an approach using random subspace and bagging to improve them respectively. By random sampling on feature vector and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed. The two kinds of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. We also apply this approach to the integration of multiple features. A robust face recognition system integrating shape, texture and Gabor responses is finally developed.