OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Structure and Nonrigid Motion Recovery from 2D Monocular Views
Authors: Lin Zhou, Chandra Kambhamettu
Venue: CVPR
Year: 2000

Inferring both 3D structure and motion of nonrigid objects from monocular images is an important problem in computer vision. The challenges stem not only from the absence of point correspondences but also from the structure ambiguity. In this paper, a hierarchical method which integrates both local patch analysis and global shape descriptions is devised to solve the dual problem of structure and nonrigid motion recovery by using an elastic geometric model-extended superquadrics (ESQ). The nonrigid object of interest is segmented into many small areas and local analysis is performed to recover small details for each small area, assuming that each small area is undergoing similar nonrigid motion. Then, a recursive algorithm is proposed to guide and regularize local analysis with global information by using an appropriate global ESQ model. This local-global hierarchy enables us to capture both local and global deformations accurately and robustly. Experimental results on both simulation and real data are presented to validate and evaluate the effectiveness and robustness of the proposed approach.
Fluid Structure and Motion Analysis from Multi-spectrum 2D Cloud Image Sequences
Authors: Lin Zhou, Chandra Kambhamettu, Dmitry B. Goldgof
Venue: CVPR
Year: 2000

We present a novel approach to estimate and analyze 3D fluid structure and motion of clouds from multi-spectrum 2D cloud image sequences. Accurate cloud-top structure and motion are very important for a host of meteorological and climate applications. However, due to the extremely complex nature of cloud fluid motion, classical nonrigid motion analysis methods are insufficient for solving this particular problem. In this paper, two spectra of satellite cloud images are utilized. The high-resolution visible channel is first used to perform cloud tracking by using a recursive algorithm which integrates local motion analysis with a set of global fluid constraints, defined according to the physical fluid dynamics. Then, the infrared channel (thermodynamic information) is incorporated to post-process the cloud tracking results in order to capture the cloud density variations and small details of cloud fluidity. Experimental results on GOES (Geostationary Operational Environmental Satellite) cloud image sequences are presented in order to validate and evaluate both the effectiveness and robustness of our algorithm.
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
Authors: Song Chun Zhu, Xiuwen Liu
Venue: CVPR
Year: 2000

In this article, we present a unified framework for learning Gibbs models from training images. We identify two key factors that determine the accuracy and speed of learning Gibbs models: (1). Fisher information, and (2). The accuracy of Monte Carlo estimate for partition functions. We propose three new learning algorithms under the unified framework. (I). The maximum partial likelihood estimator. (II). The maximum patch likelihood estimator, and (III). The maximum satellite likelihood estimator. The first two algorithms can speed up the minimax entropy algorithm by about 2D times without losing much accuracy. The third one makes use of a set of known Gibbs models as references-dubbed "satellites" and can approximately estimate the minimax entropy model in the speed of 10 seconds.
Integrating Bottom-Up/Top-Down for Object Recognition by Data Driven Markov Chain Monte Carlo
Authors: Song Chun Zhu, Rong Zhang, Zhuowen Tu
Venue: CVPR
Year: 2000

This article presents a mathematical paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for object recognition. The objectives of this paradigm are two-fold. Firstly, it realizes traditional "hypothesis-and-test" methods through well-balanced Markov chain Monte Carlo (MCMC) dynamics, thus it achieves robust and globally optimal solutions. Secondly, it utilizes data-driven (bottom-up) methods in computer vision, such as Hough transform and data clustering, to design effective transition probabilities for Markov chain dynamics. This drastically improves the effectiveness of traditional MCMC algorithms in terms of two standard metrics: "burn-in" period and "mixing" rate. The article proceeds in three steps. Firstly, we analyze the structures of the solution space /spl Omega/ for object recognition. /spl Omega/ is decomposed into a large number of subspaces of varying dimensions in a hierarchy. Secondly, we use data-driven techniques to compute importance proposal probabilities in these spaces, each expressed in a non-parametric form using weighted samples or particles. Thirdly, Markov chains are designed to travel in such heterogeneous structured solution space, with both jump and diffusion dynamics. We use possibly the simplest objects-the "/spl Psi/-world" as an example to illustrate the concepts, and we briefly present results on an application of traffic sign detection.
Differential Methods for Nonmetric Calibration of Camera Lens Distortion
Authors: Moumen T. Ahmed, Aly A. Farag
Venue: CVPR
Year: 2001

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using non-linear search techniques to find the best distortion parameters that straighten these lines. Unlike other approaches, we also provide fast, closed-form solutions to the distortion coefficients. Experiments to evaluate the performance of this approach on synthetic and real data are reported.
Multispectral Skin Color Modeling
Authors: Elli Angelopoulou, Rana Molana, Kostas Daniilidis
Venue: CVPR
Year: 2001

The automated detection of humans in computer vision as well as the realistic rendering of people in computer graphics necessitates improved modeling of the human skin color. We describe the acquisition and modeling of skin reflectance data densely sampled over the entire visible spectrum. The data collected through a spectrograph allows us to explain skin color (and its variations) and to discriminate between human skin and dyes designed to mimic human skin. We study the approximation of these data using several sets of basis functions. Our study shows that skin reflectance data can best be approximated by a linear combination of Gaussians or their first derivatives. This result has a significant practical impact on optical acquisition devices: the entire visible spectrum of skin reflectance can now be captured with a few filters of optimally chosen central wavelengths and bandwidth.
Scalable, Absolute Position Recovery for Omni-Directional Image Networks
Authors: Matthew E. Antone, Seth J. Teller
Venue: CVPR
Year: 2001

We describe a linear-time algorithm that recovers absolute camera positions for networks of thousands of terrestrial images spanning hundreds of meters in outdoor urban scenes, under uncontrolled lighting. The algorithm requires no human input or interaction. For real data, it recovers camera pose globally consistent on average to roughly five centimeters, or about four pixels of epipolar alignment. The paper's principal contributions include an extension of Markov chain Monte Carlo estimation techniques to the case of unknown numbers of feature points, unknown occlusion and deocclusion, large scale (thousands of images, and hundreds of thousands of point features), and large dimensional extent (tens of meters of inter-camera baseline, and hundreds of meters of baseline overall). Also, a principled method is given to manage uncertainty on the sphere; a new use of the Hough transform is proposed; and a method for aggregating local baseline constraints into a globally consistent pose set is described.
Robot Homing based on Corner Tracking in a Sequence of Panoramic Images
Authors: Antonis A. Argyros, Kostas E. Bekris, Stelios C. Orphanoudakis
Venue: CVPR
Year: 2001

In robotics, homing can be defined as that behavior which enables a robot to return to its initial (home) position, after traveling a certain distance along an arbitrary path. Odometry has traditionally been used for the implementation of such a behavior, but it has been shown to be an unreliable source of information. In this work, a novel method for visual homing is proposed, based on a panoramic camera. As the robot departs from its initial position, it tracks characteristic features of the environment (corners). As soon as homing is activated, the robot selects intermediate target positions on the original path. These intermediate positions (IPs) are then visited sequentially, until the home position is reached. For the robot to move between two consecutive IPs, it is only required to establish correspondence among at least three corners. This correspondence is obtained through a feature tracking mechanism. The proposed homing scheme is based on the extraction of very low-level sensory information, namely the bearing angles of corners, and has been implemented on a robotic platform. Experimental results show that the proposed scheme achieves homing with a remarkable accuracy, which is not affected by the distance traveled by the robot.
Support Vector Tracking
Authors: Shai Avidan
Venue: CVPR
Year: 2001

Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using a homogeneous quadratic polynomial kernel-SVT for vehicle tracking in image sequences.
A Spherical Eye from Multiple Cameras (Makes Better Models of the World)
Authors: Patrick Baker, Cornelia Fermüller, Yiannis Aloimonos, Robert Pless
Venue: CVPR
Year: 2001

The paper describes an imaging system that has been designed specifically for the purpose of recovering egomotion and structure from video. The system consists of six cameras in a network arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system's own motion and consequently the estimation of shape models from the individual cameras. The reason is that inherent ambiguities of confusion between translation and rotation disappear. We provide algorithms for the calibration of the system and 3D motion estimation. The calibration is based on a new geometric constraint that relates the images of lines parallel in space to the rotation between the cameras. The 3D motion estimation uses a constraint relating structure directly to image gradients.
3D Reconstruction from 360 x 360 Mosaics
Authors: Hynek Bakstein, Tomás Pajdla
Venue: CVPR
Year: 2001

We are studying the geometry of a 360/spl times/360 mosaic image formation. A 360/spl times/360 mosaic camera model and a calibration procedure are proposed. It is shown that only one point correspondence is needed in order to acquire epipolar rectified images. The 360/spl times/360 mosaic camera model is therefore determined by only one intrinsic parameter It is shown that the relation between coordinates estimated with different values of intrinsic 360/spl times/360 mosaic camera parameters is a scaling of all scene point coordinates with additional nonlinear changes in the z coordinates of the scene points. Experimental results verifying the reconstruction of real scene points are presented.
Clustering Art
Authors: Kobus Barnard, Pinar Duygulu, David A. Forsyth
Venue: CVPR
Year: 2001

We extend a recently developed method (K. Barnard and D. Forsyth, 2001) for learning the semantics of image databases using text and pictures. We incorporate statistical natural language processing in order to deal with free text. We demonstrate the current system on a difficult dataset, namely 10000 images of work from the Fine Arts Museum of San Francisco. The images include line drawings, paintings, and pictures of sculpture and ceramics. Many of the images have associated free text which varies greatly from physical description to interpretation and mood. We use WordNet to provide semantic grouping information and to help disambiguate word senses, as well as emphasize the hierarchical nature of semantic relationships. This allows us to impose a natural structure on the image collection that reflects semantics to a considerable degree. Our method produces a joint probability distribution for words and picture elements. We demonstrate that this distribution can be used: (a) to provide illustrations for given captions, and (b) to generate words for images outside the training set. Results from this annotation process yield a quantitative study of our method. Finally, the annotation process can be seen as a form of object recognizer that has been learned through a partially supervised process.
Issues on the Geometry of Central Catadioptric Image Formation
Authors: João Pedro Barreto 0001, Helder Araújo
Venue: CVPR
Year: 2001

An imaging system with a single effective viewpoint is called a central projection system. The conventional perspective camera is an example of a central projection system. Systems using mirrors to enhance the field of view while keeping a unique center of projection are also examples of central projection systems. Perspective image formation can be described by a linear model with well known properties. In general central catadioptric imaging, the mapping between points in the world and in the image is highly nonlinear. The paper establishes a general model for central catadioptric image formation made up of three functions: a linear function mapping the world into an oriented projective plane, a nonlinear transformation between two oriented projective planes, and a collineation in the plane. The model is used to study issues in the projection of lines. The equations and geometric properties of general catadioptric imaging of lines are derived. The application of the results in auto-calibration of central catadioptric systems and reconstruction are discussed. A method to calibrate the system using three line images is presented.
Houghing the Hough: Peak Collection for Detection of Corners, Junctions and Line Intersections
Authors: William A. Barrett, Kevin D. Petersen
Venue: CVPR
Year: 2001

We exploit the Accumulator Array of the Hough Transform by finding collections of (2 or more) peaks through which a given sinusoid will pass. Such sinusoids identify points in the original image where lines intersect. Peak collection (or line aggregation) is performed by making a second pass through the edge map, but instead of laying points down in the accumulator array (as with the original Hough Transform), we compute the line integral over each sinusoid that corresponds to the current edge point. If a sinusoid passes through /spl ges/2 peaks, we deposit that sum/integral into a new accumulator array - an array that has a direct one-to-one correspondence with the original image. Thus, "Houghing the Hough" identifies points that correspond to corners, junctions or line intersections in image space. During initial peak collection, we include in the line integral only the most (locally) significant peaks while sifting out other (comparatively) weaker peaks from the current as well as subsequent peak collections. This "contextual peak sifting" greatly reduces computation, the effect of noise and the occurrence of false positives. Virtual line intersections (vanishing points, occluded corners, etc.) are detected as peaks without proximate edge support. Results in real-world images show the technique performs well in identifying corners, junctions and intersecting lines in a variety of scenes containing manmade objects such as buildings, documents, etc.
The 3D Line Motion Matrix and Alignment of Line Reconstructions
Authors: Adrien Bartoli, Peter F. Sturm
Venue: CVPR
Year: 2001

We study the problem of aligning two 3D line reconstructions expressed in Plucker line coordinates. We introduce the 6/spl times/6 3D line motion matrix that acts on Plucker coordinates in projective, affine or Euclidean space. We characterize its algebraic properties and its relation to the usual 4/spl times/4 point motion matrix, and propose various methods for estimating 3D motion from line correspondences, based on image-related and 3D cost functions. We assess the quality of the different estimation methods using simulated data and real images.
Photometric Stereo with General, Unknown Lighting
Authors: Ronen Basri, David W. Jacobs
Venue: CVPR
Year: 2001

Work on photometric stereo has shown how to recover the shape and reflectance properties of an object using multiple images taken with a fixed viewpoint and variable lighting conditions. This work has primarily relied on the presence of a single point source of light in each image. The authors show how to perform photometric stereo, assuming that all lights in a scene are isotropic and distant from the object but otherwise unconstrained. Lighting in each image may be an unknown and arbitrary combination of diffuse, point and extended sources. Our work is based on recent results showing that for Lambertian objects, general lighting conditions can be represented using low order spherical harmonics. Using this representation, we can recover shape by performing a simple optimization in a low-dimensional space. We also analyze the shape ambiguities that arise in such a representation.
View-Based Human Activity Recognition by Indexing & Sequencing
Authors: Jezekiel Ben-Arie, Purvin Pandit, Shyamsundar Rajaram
Venue: CVPR
Year: 2001

A novel method for view-based recognition of human activity is presented. The basic idea of our method is that activities can be positively identified from a sparsely sampled sequence of few body poses acquired from videos. In our approach, an activity is represented by a set of pose and velocity vectors for the major body parts (hands, legs and torso) and stored in a set of multidimensional hash tables. We show that robust recognition of a sequence of body pose vectors can be achieved by a method of indexing and sequencing and it requires only few vectors (i.e. sampled body poses in video frames). We find that the probability of false alarm drops exponentially with the increased number of sampled body poses. We also achieve speed invariant recognition by eliminating the time factor and replacing it with sequence information. Experiments performed with videos having 8 different activities show robust recognition even for different viewing directions.
On the Perceptual Organization of Texture and Shading Flows: From a Geometrical Model to Coherence Computation
Authors: Ohad Ben-Shahar, Steven W. Zucker
Venue: CVPR
Year: 2001

Locally parallel dense patterns - sometimes called texture flows define a perceptually coherent structure which is important to image segmentation, edge classification, shading analysis, and shape interpretation. This paper develops the notion of texture flow from a geometrical point of view to argue that local measurements of such structures must incorporate two curvatures. We show how basic theoretical considerations lead to a unique model for the local behavior of the flow and allow for the specification of consistency constraints between nearby measurements. The computation of globally coherent structure via neighborhood relationships is demonstrated on synthetic and natural images, and is compared to orientation diffusion.
3D Biplanar Reconstruction of Scoliotic Vertebrae Using Statistical Models
Authors: Said Benameur, Max Mignotte, Stefan Parent, Hubert Labelle, Wafa Skalli, Jacques A. de Guise
Venue: CVPR
Year: 2001

This paper presents a new 3D reconstruction method of the scoliotic vertebrae of a spine, using two conventional radiographic views (postero-anterior and lateral), and global prior knowledge on the geometrical structure of each vertebra. This geometrical knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in the Karhunen-Loeve expansion of the pathological deformations observed on a representative scoliotic vertebra population. The proposed reconstruction method consists in fitting the projections of this deformable template with the segmented contours of the corresponding vertebra on the two radiographic views. The 3D reconstruction problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. The reconstruction of the spine is then made vertebra by vertebra. This 3D reconstruction method has been successfully tested on several biplanar radiographic images, yielding very promising results.
Increasing the Discrimination Power of Spectral Spatial Gradients
Authors: Daniel Berwick, Sang Wook Lee
Venue: CVPR
Year: 2001

Spectral Spatial Gradients (SSGs) have been suggested as features invariant to illumination variation for color-based recognition and indexing. While SSGs have a greater degree of invariance than Spectral Gradients (SGs), they may have a reduced discrimination power between objects since they use only spatial changes of object reflectance. The approach presented is to use the framework of SGs and SSGs to create a recognition method which is invariant to varying illumination without discarding the reflectance distribution information. Two techniques are used to extract the varying illumination. The first is to assume that low-frequency SG variation is due to illumination. The second method uses regions with matching SSGs to get local estimates of illumination. Once the local illumination variation has been extracted, its effect on the SGs can be removed and the resulting Adapted Spectral Gradients (ASGs) have a greater power of discrimination for some objects than SSG features. Experimental results demonstrate cases where ASGs show improved performance.
A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition
Authors: J. Ross Beveridge, Kai She, Bruce A. Draper, Geof H. Givens
Venue: CVPR
Year: 2001

The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognition rates are statistically distinguishable subject to changes in test imagery. Nearest Neighbor classifiers using principal component and linear discriminant subspaces are compared using different choices of distance metric. Probability distributions for algorithm recognition rates and pairwise differences in recognition rates are determined using a permutation methodology. The principal component subspace with Mahalanobis distance is the best combination; using L2 is second best. Choice of distance measure for the linear discriminant subspace matters little, and performance is always worse than the principal components classifier using either Mahalanobis or L1 distance. We make the source code for the algorithms, scoring procedures and Monte Carlo study available in the hopes others will extend this comparison to newer algorithms.