OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Face Recognition from Image Sequences Based on Clustering with Attraction and Repulsion
Authors: Bisser Raytchev, Hiroshi Murase
Venue: CVPR
Year: 2001

We propose a new method for unsupervised face recognition from time-varying sequences of face images obtained in real-world environments. Two types of forces, attraction and repulsion, operate across the spatio-temporal facial manifolds, to autonomously organize the data without relying on any category-specific information provided in advance. Experiments with real-world data gathered over a period of several months and including both frontal and side-view faces were used to evaluate the method and encouraging results were obtained The proposed method can be used in video surveillance systems or for content-based information retrieval.
Using occlusions to aid position estimation for visual motion capture
Authors: Maurice Ringer, Tom Drummond, Joan Lasenby
Venue: CVPR
Year: 2001

A new method for estimating the pose of a person in a marker-based visual motion capture system is presented. In such systems, certain markers are not detected at each time frame due to occlusion by the subject. It is proposed to estimate the person's pose subject to the constraint that non-detected markers remain occluded and that detected markers remain visible. In this paper, the complete posterior PDF for the person's pose is developed using the constraints mentioned above, as is a least squares solution for determining its maximum. It is shown that the new technique is able to correctly estimate a variety of poses in which many marker occlusions occur and in which typical motion capture systems fail.
A Graph-Spectral Method for Surface Height Recovery from Needle-maps
Authors: Antonio Robles-Kelly, Edwin R. Hancock
Venue: CVPR
Year: 2001

The paper describes a graph-spectral method for 3D surface integration. The algorithm takes as its input a 2D field of surface normal estimates, delivered, for instance, by a shape-from-shading or shape-from-texture procedure. The method borrows ideas from routing theory. We exploit the well-known fact that the leading eigenvector of a Markov chain transition probability matrix is the steady-state random walk on the equivalent weighted graph. We use this property to find the minimum total curvature path through the available surface normals. To do this, we construct a transition probability matrix whose elements are related to the differences in surface normal direction. By threading the surface normals together along the path specified by the magnitude order of the components of the leading eigenvector, we perform surface integration. The height increments along the path are simply related to the traversed path length and the slope of the local tangent plane. The method is evaluated on data delivered by a shape-from-shading algorithm.
A Pure Learning Approach to Background-Invariant Object Recognition using Pedagogical Support Vector Learning
Authors: Danny Roobaert, Michael Zillich, Jan-Olof Eklundh
Venue: CVPR
Year: 2001

Pursuing the goals of absolute simplicity of a detection/recognition system, a pure learning approach to background-invariance and visual 3D object detection/recognition is proposed. The approach relies on learning from examples only, and does not encode any domain knowledge (e.g. in the form of intermediate representations, or by solving segmentation or correspondence problems). To make the pure learning approach practically feasible, we propose the BW training method for teaching an object recognition system background-invariance. The method consist of pedagogically training the system, once with a black background and once with a white background. The method is formulated within the framework of support vector learning. Evaluation is performed with the Columbia Image (COIL) database, that is extended with different classes of cluttered backgrounds. Using this pure learning approach, a system is proposed that is able to perform 3D object detection/recognition successfully in real-world scenes, with varying illuminations and backgrounds. The system is able to perform this task in real-time.
Pairwise Coupling for Machine Recognition of Hand-Printed Japanese Characters
Authors: Volker Roth 0001, Koji Tsuda
Venue: CVPR
Year: 2001

Machine recognition of hand-printed Japanese characters has been an area of great interest for many years. A major problem of this classification task is the huge number of different characters. Applying standard "state-of-the-art" techniques, such as SVM, to multi-class problems of this kind imposes severe problems of both a conceptual and technical nature: (i) separating one class from all others may be an unnecessarily hard problem; and (ii) solving these subproblems can impose unacceptably high computational costs. In this paper, a new approach to Japanese character recognition is presented that successfully overcomes these shortcomings. It is based on a pairwise coupling procedure for probabilistic two-class kernel classifiers. Experimental results for Hiragana recognition effectively demonstrate that our method attains an excellent level of prediction accuracy while imposing very low computational costs.
Better Proposal Distributions: Object Tracking Using Unscented Particle Filter
Authors: Yong Rui, Yunqiang Chen
Venue: CVPR
Year: 2001

Tracking objects involves the modeling of non-linear non-Gaussian systems. On one hand, variants of Kalman filters are limited by their Gaussian assumptions. On the other hand, conventional particle filter, e.g., CONDENSATION, uses transition prior as the proposal distribution. The transition prior does not take into account current observation data, and many particles can therefore be wasted in low likelihood area. To overcome these difficulties, unscented particle filter (UPF) has recently been proposed in the field of filtering theory. In this paper, we introduce the UPF framework into audio and visual tracking. The UPF uses the unscented Kalman filter to generate sophisticated proposal distributions that seamlessly integrate the current observation, thus greatly improving the tracking performance. To evaluate the efficacy of the UPF framework, we apply it in two real-world tracking applications. One is the audio-based speaker localization, and the other is the vision-based human tracking. The experimental results are compared against those of the widely used CONDENSATION approach and have demonstrated superior tracking performance.
A New Signature-Based Method for Efficient 3-D Object Recognition
Authors: Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila
Venue: CVPR
Year: 2001

The paper considers the problem of shape-based recognition and pose estimation of 3D free-form objects in scenes that contain occlusion and clutter. Our approach is based on a novel set of discriminating descriptors called spherical spin images, which encode the shape information conveyed by classes of distributions of surface points constructed with respect to reference points on the surface of an object. The key to this approach is the relationship that exists between the l/sub 2/ metric, which compares n-dimensional signatures in Euclidean space, and the metric of the compact space on which the class representatives (spherical spin images) are defined. The connection allows us to efficiently utilize the linear correlation coefficient to discriminate scene points which have spherical spin images that are similar to the spherical spin images of points on the object being sought. The paper also addresses the problem of compressed spherical-spin-image representation by means of a random projection of the original descriptors that reduces the dimensionality without a significant loss of recognition/localization performance. Finally, the efficacy of the proposed representation is validated in a comparative study of the two algorithms presented that use uncompressed and compressed spherical spin images versus two previous spin image algorithms reported previously (A.E. Johnson and M. Hebert, 1999). The results of 2012 experiments suggest that the performance of our proposed algorithms is significantly better with respect to accuracy and speed than the performance of the other algorithms tested.
Robust and Adaptive Integration of Multiple Range Images with Photometric Attributes
Authors: Ryusuke Sagawa, Ko Nishino, Katsushi Ikeuchi
Venue: CVPR
Year: 2001

Integration of multiple range images is important to make use of 3D data acquired from stereo systems, laser range finders, etc. We propose a new range image integration method based on volumetric representation. Unlike other volume-based integration methods, we adaptively subdivide voxels depending on the curvature of the surface to be reconstructed, providing efficient representation of the underlying geometry and efficient use of computational resources. In our range image merging framework, additional attributes, e.g., color, laser reflectance power, etc., can be taken into account as well as 3D geometric information. This ability allows us to generate 3D models preserving sharp edges around texture boundaries, thereby providing a good basis for efficient rendering and texture mapping. The overall framework is designed to be robust against noise, taking consensus carefully in both geometry and color, which could be suitable for 3D model reconstruction from noisy stereo images. In this paper, we describe the system, and present several results of applying our framework to real data. We also present some other future applications based on our framework.
Discrimination of Motion Based on Traces in the Space of Probability Functions over Feature Relations
Authors: Sudeep Sarkar, Isidro Robledo Vega
Venue: CVPR
Year: 2001

In this paper we demonstrate that it is possible to discriminate between high level motion types such as walking, jogging, or running based on just the change in the relational statistics among the detected image features, without the need for object models, perfect segmentation, or tracking. Instead of the statistics of the feature attributes themselves, we consider the distribution of the statistics of the relations among the features. We represent the observed distribution of feature relations in an image as a point in a space where the Euclidean distance is related to the Bhattacharya distance between probability functions. Different motion types sweep out different traces in this Space of Probability Functions (SoPF). We demonstrate the effectiveness of this representation on image sequences of human in motion, gathered using a digital video camera. We show that it is not only possible to distinguish between motion types but also to discriminate between persons based on the SoPF traces.
Stability Issues in Recovering Illumination Distribution from Brightness in Shadows
Authors: Imari Sato, Yoichi Sato 0001, Katsushi Ikeuchi
Venue: CVPR
Year: 2001

The paper describes a robust method for estimating, in a reliable manner, the illumination distribution of a real scene from shadows in a given image. In general, shadows in a scene are caused by the occlusion of incoming light; image brightness inside shadows have great potential for providing distinct clues to the illumination distribution of the scene. Taking advantage of this fact, we recently proposed to estimate the illumination distribution of a real scene from a single image of the scene. The proposed method has been applied successfully to real images with complex illumination distributions. Nevertheless, it was found that sometimes the method failed to provide a correct estimate of illumination distribution. Those failures stem from the fact that the method does not take into account several factors regarding the stability of illumination estimation. The study analyzes how much information is obtainable from a given image about the illumination distribution of the scene. In particular, we carefully examine the source of instability of using shadows obtained from a single image for the estimation in several aspects: blocked view of shadows by the object; limited sampling resolution for image brightness inside shadows; and the appropriate light model to approximate the illumination distribution of the scene. Based on this analysis, we propose a method that guarantees to reliably estimate the illumination distribution of a scene, regardless of the type of input image.
Depth Layers from Occlusions
Authors: Arno Schödl, Irfan A. Essa
Venue: CVPR
Year: 2001

We present a method to extract relative depth information from an uncalibrated monocular video sequence. Our method detects occlusions caused by an object moving in a static scene to infer relative depth relationships between scene parts. Our approach does not rely on any strong assumptions about the object or the scene to aid in this segmentation into layers. In general, the problem of building relative depth relationships from occlusion events is underconstrained, even in the absence of observation noise. A minimum description length algorithm is used to reliably calculate layer opacities and their depth relationships in the absence of hard constraints. Our approach extends previously published approaches that are restricted to work with a certain type of moving object or require strong image edges to allow for an a-priori segmentation of the scene. We also discuss ideas on how to extend our algorithm to make use of a richer set of observations.
Tracking Multiple Moving Objects with a Mobile Robot
Authors: Dirk Schulz 0001, Wolfram Burgard, Dieter Fox, Armin B. Cremers
Venue: CVPR
Year: 2001

One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can determine the positions of the humans in its surrounding. We introduce sample-based joint probabilistic data association filters to track multiple moving objects with a mobile robot. Our technique uses the robot's sensors and a motion model of the objects being tracked. A Bayesian filtering technique is applied to adapt the tracking process to the number of objects in the sensor range of the robot. Our approach to tracking multiple moving objects has been implemented and tested on a real robot. We present experiments illustrating that our approach is able to robustly keep track of multiple persons even in situations in which people are temporarily occluded. The experiments furthermore show that the approach outperforms other techniques developed so far.
Minimally Supervised Acquisition of 3D Recognition Models from Cluttered Images
Authors: Andrea Selinger, Randal C. Nelson
Venue: CVPR
Year: 2001

Appearance-based object recognition systems rely on training from imagery, which allows the recognition of objects without requiting a 3D geometric model. It has been little explored whether such systems can be trained from imagery that is unlabeled, and whether they can be trained from imagery that is not trivially segmentable. In this paper we present a method for minimally supervised training of a previously developed recognition system from unlabeled and unsegmented imagery. We show that the system can successfully extend an object representation extracted from one black background image to contain object features extracted from unlabeled cluttered images and can use the extended representation to improve recognition performance on a test set.
Appearance-Based Object Recognition Using Multiple Views
Authors: Andrea Selinger, Randal C. Nelson
Venue: CVPR
Year: 2001

Object recognition from a single view fails when the available features are not sufficient to determine the identity of a single object, either because of similarity with another object or because of feature corruption due to clutter and occlusion. Active object recognition systems have addressed this problem successfully, but they require complicated systems with adjustable viewpoints that are not always available. In this paper we investigate the performance gain available by combining the results of a single view object recognition system applied to imagery obtained from multiple fixed cameras. In particular, we address performance in cluttered scenes with varying degrees of information about relative camera pose. We argue that a property common to many computer vision recognition systems, which we term a weak target error, is responsible for two interesting limitations of multi-view performance enhancement: the lack of significant improvement in systems whose single-view performance is weak, and the plateauing of performance improvement as additional multi-view constraints are added.
Structure and Motion Estimation with Expectation Maximization and Extended Kalman Smoother for Continuous Image Sequences
Authors: Yongduek Seo, Ki-Sang Hong
Venue: CVPR
Year: 2001

This paper deals with the problem of estimating structure and motion from long continuous image sequences, applying the expectation maximization algorithm based on an extended Kalman smoother to impose time-continuity of the motion parameters. By repeatedly estimating the state transition matrix of the dynamic equation and the parameters of noise processes in dynamic and measurement equations, this optimization gives maximum likelihood estimates of the motion and structure parameters. Practically, this research is essential for dealing with a long video-rate image sequence with partially unknown system equation and noise. The algorithm is implemented and tested for a real image sequence.
A Linear Iterative Method for Auto-Calibration using the DAC Equation
Authors: Yongduek Seo, Anders Heyden, Roberto Cipolla
Venue: CVPR
Year: 2001

In this paper, an iterative algorithm for auto-calibration is presented. The proposed algorithm switches between linearly estimating the dual of the absolute conic and the intrinsic parameters, while also incorporating the rank-3 constraint on the intrinsic parameters. The most important property of the algorithm is that it is completely general in the sense that any type of constraint on the intrinsic parameters might be used. The proposed algorithm locates in-between of a non-linear optimization and initial linear computation, and provides robust and sufficiently accurate initial values for a bundle adjustment routine. The performance of the algorithm is shown for both simulated and real data, especially in the important case of natural (zero skew and unit aspect ratio) cameras.
Integrated Face and Gait Recognition From Multiple Views
Authors: Gregory Shakhnarovich, Lily Lee, Trevor Darrell
Venue: CVPR
Year: 2001

We develop a view-normalization approach to multi-view face and gait recognition. An image-based visual hull (IBVH) is computed from a set of monocular views and used to render virtual views for tracking and recognition. We determine canonical viewpoints by examining the 3D structure, appearance (texture), and motion of the moving person. For optimal face recognition, we place virtual cameras to capture frontal face appearance; for gait recognition we place virtual cameras to capture a side-view of the person. Multiple cameras can be rendered simultaneously, and camera position is dynamically updated as the person moves through the workspace. Image sequences from each canonical view are passed to an unmodified face or gait recognition algorithm. We show that our approach provides greater recognition accuracy than is obtained using the unnormalized input sequences, and that integrated face and gait recognition provides improved performance over either modality alone. Canonical view estimation, rendering, and recognition have been efficiently implemented and can run at near real-time speeds.
Decomposed Eigenface for Face Recognition under Various Lighting Conditions
Authors: Takeshi Shakunaga, Kazuma Shigenari
Venue: CVPR
Year: 2001

Face recognition under various lighting condition's is discussed to cover cases when too few images are available for registration. This paper proposes decomposition of an eigenface into two orthogonal eigenspaces for realizing robust face recognition under such conditions. The decomposed eigenfaces consisting of two eigenspaces are constructed for each person even if only one image is available. A universal eigenspace called the canonical space (CS) plays an important role in creating the eigenspaces by way of decomposition, where CS is constructed a priori by principal component analysis (PCA) over face images of many people under many lighting conditions. In the registration stage, an input face image is decomposed to a projection image in CS and the residual of the projection. Then two eigenspaces are created independently in CS and in the orthogonal complement CS/sup /spl perp//. Some refinements of the two eigenspaces are also discussed. By combining the two eigenspaces, we can easily realize face identification that is robust to illumination change, even when too few images are registered. Through experiments, we show the effectiveness of the decomposed eigenfaces as compared with conventional methods.
Image-Based Surface Detail Transfer
Authors: Ying Shan, Zicheng Liu 0001, Zhengyou Zhang
Venue: CVPR
Year: 2001

We present a novel technique, called Image-Based Surface Detail Transfer, to transfer geometric details from one surface to another with simple 2D image operations. The basic observation is that, without knowing its 3D geometry, geometric details (local deformations) can be extracted from a single image of an object in a way independent of its surface reflectance, and furthermore, these geometric details can be transferred to modify the appearance of other objects directly in images. We show examples including surface detail transfer between real objects, as well as between real and synthesized objects.
Segmentation and Boundary Detection Using Multiscale Intensity Measurements
Authors: Eitan Sharon, Achi Brandt, Ronen Basri
Venue: CVPR
Year: 2001

Image segmentation is difficult because objects may differ from their background by any of a variety of properties that can be observed in some, but often not all scales. A further complication is that coarse measurements, applied to the image for detecting these properties, often average over properties of neighboring segments, making it difficult to separate the segments and to reliably detect their boundaries. Below we present a method for segmentation that generates and combines multiscale measurements of intensity contrast, texture differences, and boundary integrity. The method is based on our former algorithm SWA, which efficiently detects segments that optimize a normalized-cut like measure by recursively coarsening a graph reflecting similarities between intensities of neighboring pixels. In this process aggregates of pixels of increasing size are gradually collected to form segments. We intervene in this process by computing properties of the aggregates and modifying the graph to reflect these coarse scale measurements. This allows us to detect regions that differ by fine as well as coarse properties, and to accurately locate their boundaries. Furthermore, by combining intensity differences with measures of boundary integrity across neighboring aggregates we can detect regions separated by weak, yet consistent edges.