OpenRaiser/Intern-Atlas huggingface dataset:
Using Three-Dimensional Features to Improve Terrain Classification
Authors: Xiaoguang Wang 0013, Frank Stolle, Howard J. Schultz, Edward M. Riseman, Allen R. Hanson
Venue: CVPR
Year: 1997

Texture has long been regarded as spatial distributions of gray-level variation, and texture analysis has generally been confined to the 2-D image domain. Introducing the concept of "3-D world feature", this paper considers texture as a function of 3-D structures and proposes a set of "3-D textural features". The proposed 3-D features appear to have a great potential in terrain classification. Experiments have been carried out to compare the 3-D features with a popular traditional 2-D feature set. The results show that the 3-D features significantly outperform the 2-D features in terms of classification accuracy.
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation
Authors: Yair Weiss
Venue: CVPR
Year: 1997

Grouping based on common motion, or "common fate" provides a powerful cue for segmenting image sequences. Recently a number of algorithms have been developed that successfully perform motion segmentation by assuming that the motion of each group can be described by a low dimensional parametric model (e.g. affine). Typically the assumption is that motion segments correspond to planar patches in 3D undergoing rigid motion. Here we develop an alternative approach, where the motion of each group is described by a smooth dense flow field and the stability of the estimation is ensured by means of a prior distribution on the class of flow fields. We present a variant of the EM algorithm that can segment image sequences by fitting multiple smooth flow fields to the spatiotemporal data. Using the method of Green's functions, we show how the estimation of a single smooth flow field can be performed in closed form, thus making the multiple model estimation computationally feasible. Furthermore, the number of models is estimated automatically using similar methods to those used in the parametric approach. We illustrate the algorithm's performance on synthetic and real image sequences.
Using Local 3D Structure for Segmentation of Bone from Computer Tomography Images
Authors: Carl-Fredrik Westin, Abhir Bhalerao, Ron Kikinis, Hans Knutsson
Venue: CVPR
Year: 1997

In this paper we focus on using local 3D structure for segmentation. A tensor descriptor is estimated for each neighbourhood, i.e. for each voxel in the data set. The tensors are created from a combination of the outputs form a set of 3D quadrature filters. The shape of the tensors describe locally the structure of the neighbourhood in terms of how much it is like a plane, a line, and a sphere. We apply this to segmentation of bone from Computer Tomography data (CT). Traditional methods are based purely on gray-level value discrimination and have difficulties in recovering thin bone structures due to so called partial voluming, a problem which is present in all such sampled data. We illuminate the partial voluming problem by showing that thresholding creates complicated artifacts even if the signal is densely enough sampled and can be perfectly reconstructed. The unwanted effects of thresholding can be reduced by a change of the signal basis. We show that by using additional local structure information can significantly reduce the degree of sampling artifacts. Evaluation of the method on a clinical case is presented, the segmentation of a human skull from a CT volume. The method shows that many of the thin bone structures which disappear in a pure thresholding can be recovered.
Physically based fluid flow recovery from image sequences
Authors: Richard P. Wildes, Michael J. Amabile, Ann-Marie Lanzillotto, Tzong-Shyng Leu
Venue: CVPR
Year: 1997

This paper presents an approach to measuring fluid flow from image sequences. The approach centers around a motion recovery algorithm that is based on principles from fluid mechanics: The algorithm is constrained so that recovered flows observe conservation of mass as well as physically motivated boundary conditions. Results are presented from application of the algorithm to transmittance imagery of fluid flows, where the fluids contained a contrast medium. In these experiments, the algorithm recovered accurate and precise estimates of the flow. The significance of this work is two fold. First, from a theoretical point of view it is shown how information derived from the physical behavior of fluids can be used to motivate a flow recovery algorithm. Second, from an applications point of view the developed algorithm can be used to augment the tools that are available for the measurement of fluid dynamics; other imaged flows that observe compatible constraints might benefit in a similar fashion.
Hyper-patches for 3D model acquisition and tracking
Authors: Charles Wiles, Atsuto Maki, Natsuko Matsuda, Mutsumi Watanabe
Venue: CVPR
Year: 1997

Automatic 3D model acquisition and 3D tracking of simple objects under motion using a single camera is often difficult due to the sparsity of information from which to establish the model. We have developed an automatic scheme that first computes a simple pointalistic Euclidean model of the object and then enriches this model using hyper-patches. These hyper-patches contain information on both the orientation and intensity pattern variation of roughly planar patches on an object. This information allows both the spatial and intensity distortions of the projected patch to be modelled accurately under 3D object motion. We show that hyper-patches not only can be computed automatically during model acquisition from a monocular image sequence, but that they are also extremely appropriate for the task of visual tracking.
Rational Discrete Generalized Cylinders and their Application to Shape Recovery in Medical Images
Authors: James P. Williams 0002, Lawrence B. Wolff, John K. Johnstone
Venue: CVPR
Year: 1997

Generalized cylinders (GCs) are a popular representational tool in computer vision. In medical imaging, the curved axis GC is particularly applicable to a number of elongated physical structures such as vasculature, bone and bronchi. In many of these instances, it is necessary to recover curved-axis GCs with arbitrary cross-sections. It is also vital that these structures, once recovered, can be analyzed and visualized with off-the-shelf algorithms and software packages. Such tools are usually designed to operate on the domain of polynomial or rational surfaces. Unfortunately most extant, suitably versatile GC representations do not admit rational parameterizations. We develop an entirely rational B-spline representation for generalized cylinders with curved axes and arbitrary cross-section functions. We demonstrate how our representation can be used as a deformable model by extracting a rational GC from pre-segmented spinal data using a discrete dynamic surface fit.
Temporal Classification of Natural Gesture and Application to Video Coding
Authors: Andrew D. Wilson, Aaron F. Bobick, Justine Cassell
Venue: CVPR
Year: 1997

A method for the temporal classification of natural gesture from video imagery is presented. The work is motivated by recent developments in the theory of natural gesture which have identified several key temporal aspects of gesture important to communication. In particular gesticulation during conversation can be coarsely characterized as periods of bi-phasic or tri-phasic gesture separated by a rest state. We first present an automatic procedure for hypothesizing plausible rest state configurations of a speaker. Second, we develop a state-based parsing algorithm used to both select among candidate rest states and to parse an incoming video stream into bi-phasic and tri-phasic gestures. Finally, we demonstrate the use of the bi-phasic/tri-phasic labeling to select semantically significant static images for low bandwidth coding of video of story-telling speakers.
A Minimum-Variance Adaptive Surface Mesh
Authors: Richard C. Wilson 0001, Edwin R. Hancock
Venue: CVPR
Year: 1997

The main contribution of the paper is to describe a new class of the adaptive mesh. The mesh uses both split and merge operations to adapt itself to the structure of volumetric data-points. The adaptive behaviour is controlled by the variance of the data-point positions about maximum-likelihood quadric patches. The authors show that the density of control points on the mesh is regulated by the curvature of the underlying surface. Finally, they illustrate the effectiveness of the method on both real-world and simulated data-sets.
Error Analysis of a Real-Time Stereo Syste
Authors: Yalin Xiong, Larry H. Matthies
Venue: CVPR
Year: 1997

Correlation-based real-time stereo systems have been proven to be effective in applications such as robot navigation, elevation map building etc. This paper provides an in-depth analysis of the major error sources for such a real-time stereo system in the contest of cross-country navigation of an autonomous vehicle. Three major types of errors: foreshortening error, misalignment error and systematic error are identified. The combined disparity errors can easily exceed three-tenths of a pixel, which translates to significant range errors. Upon understanding these error sources, we demonstrate different approaches to either correct them or model their magnitudes without excessive additional computations. By correcting those errors, we show that the precision of the stereo algorithm can be improved by 50%.
Temporal Multi-scale Models for Flow and Acceleration
Authors: Yaser Yacoob, Larry S. Davis
Venue: CVPR
Year: 1997

A model for computing image flow in image sequences containing a very wide range of instantaneous flows is proposed. This model integrates the spatio-temporal image derivatives from multiple temporal scales to provide both reliable and accurate instantaneous flow estimates. The integration employs robust regression and automatic scale weighting in a generalized brightness constancy framework. In addition to instantaneous flow estimation the model supports recovery of dense estimates of image acceleration and can be readily combined with parameterized flow and acceleration models. A demonstration of performance on image sequences of typical human actions taken with a high frame-rate camera, is given.
Determining a Polyhedral Shape Using Interreflections
Authors: Jun Yang, Dili Zhang, Noboru Ohnishi, Noboru Sugie
Venue: CVPR
Year: 1997

We discuss the uniqueness of 2-D shape recovery of a polyhedron from a single shading image. First, we analytically show that multiple convex (and concave) shape solutions usually exist for a simple polyhedron if interreflections are not considered. Then we propose a new approach to uniquely determine the concave shape solution using interreflections as a constraint. A numerical example, in which two convex shapes and two concave shapes exist for a trihedral corner, has been shown by Horn. However, it is difficult to prove the uniqueness using constraint equations. We analytically show that multiple convex (and concave) shape solutions usually exist for a pyramid using a reflectance map, if interreflection distribution is not considered. However, if interreflection distribution is used as a constraint that limits the shape solution (for a concave polyhedron), the polyhedral shape can be uniquely determined. Interreflections are used as a constraint to determine the shape solution in our approach.
Identification of salient contours in cluttered images
Authors: Shih-Cheng Yen, Leif H. Finkel
Venue: CVPR
Year: 1997

We present a model of contour extraction in which the perceptual salience of contours arises from long-range interactions between orientation-selective filters. It has been previously shown that salient contours may be extracted from noisy images by using a number heuristic features. Our algorithm is based on cortical mechanisms, and simulations show close agreement with results from recent anatomical, physiological and psychophysical studies including recent results of D.J. Field et al. (1993), I. Kovacs et al., and M.K. Kapadia et al. (1995). The performance of the algorithm is demonstrated on a range of psychophysical stimuli and real images.
Robust Occluding Contour Detection Using the Hausdorff Distance
Authors: Xilin Yi, Octavia I. Camps
Venue: CVPR
Year: 1997

In this paper, a correlational approach for distinguishing occluding contours from object markings for 3D object modeling is presented. The proposed method is valid under weak perspective projection, does not require to search for correspondences between frames, can handle scaling between consecutive images. Thus can estimate the full Euclidean surface structure, and does not require camera calibration or camera motion measurement. Extensive experimental results show that the method is robust to the occlusion of feature points and image noise unlike previous affine-based approaches. Qualitative and quantitative results for the relation between the required minimum viewing angle change for the detection and the surface curvature are also presented.
Surface Shape from Warping
Authors: Alan L. Yuille, Mario Ferraro, Tony Zhang
Venue: CVPR
Year: 1997

We propose a variant of shape from shading which we call shape from image warping. The idea is that the three-dimensional shape of an object is estimated by determining how much the image of the object is warped with respect to the image of a known prototype shape. We demonstrate that, for a class of reflectance functions, there is a direct relationship between these image warps and geometric warps of the underlying three-dimensional shapes. Therefore detecting the image warp relative to a prototype of known shape allows us to reconstruct the shape of the imaged object. We derive properties of these shape warps and illustrate the results by recovering the shapes effaces. This relationship between image and shape warps helps us understand the relationship between image based models of object recognition and approaches based on three-dimensional object models.
Fully 3D Active Surface Models with Self-Inflation and Self-Deflation Forces
Authors: Zixin Zhang 0001, Michael Braun 0001
Venue: CVPR
Year: 1997

In this paper, we propose fully 3D active surface models for image segmentation. Our models are capable of fitting a diverse range of region shapes. They have low sensitivity to initial shape and position. We design self-inflation/deflation forces, which cooperate naturally with gradient forces. They permit the active surface to travel a long distance without the aid of any external forces. They are easily controlled in both their direction and magnitude. The models produce accurate segmentation when tested with synthetic and real images. They manifest robustness to image noise and imperfect image data. Importantly, they are capable of converging to the correct boundary even if the initial estimate is not close.
Self-Maintaining Camera Calibration Over Time
Authors: Zhengyou Zhang, Veit Schenk
Venue: CVPR
Year: 1997

The success of an intelligent robotic system depends on the performance of its vision-system which in turn depends to a great extend upon the quality of its calibration. During the execution of a task the vision-system is subject to external influences such as vibrations, thermal expansion etc. which affect and possibly render invalid the initial calibration. Moreover it is possible that the parameters of the vision-system like, for example, the zoom or the focus are altered intentionally in order to perform specific vision-tasks. This paper describes a technique for automatically maintaining calibration of stereovision systems over time without using again any particular calibration apparatus. It uses all available information, i.e. both spatial and temporal data. Uncertainty is systematically manipulated and maintained. Synthetical and real data are used to validate the proposed technique, and the results compare very favourably with those given by classical calibration methods.
Learning Generic Prior Models for Visual Computation
Authors: Song Chun Zhu, David Mumford
Venue: CVPR
Year: 1997

This paper presents a novel theory for learning generic prior models from a set of observed natural images based on a minimax entropy theory that the authors studied in modeling textures. We start by studying the statistics of natural images including the scale invariant properties, then generic prior models were learnt to duplicate the observed statistics. The learned Gibbs distributions confirm and improve the forms of existing prior models. More interestingly inverted potentials are found to be necessary, and such potentials form patterns and enhance preferred image features. The learned model is compared with existing prior models in experiments of image restoration.
Using geometric corners to build a 2D mosaic from a set of image
Authors: Imad Zoghlami, Olivier D. Faugeras, Rachid Deriche
Venue: CVPR
Year: 1997

The main problem for building a mosaic is the computation of the warping functions (homographies). In fact two cases are to be distinguished. The first is when the homography is mainly a translation (i.e. The rotation around the optical axis and the zooming factor are small). The second is the general case (when the rotation around the optical axis and zooming are arbitrary). Some efficient methods have been developed to solve the first case. But the second case is more difficult, in particular, when the rotation around the optical axis is very large (90 degrees or more). Often in this case human interaction is needed to provide a first approximation of the transformation that will bring one back to the first case. The authors present a method to solve this problem without human interaction for any rotation around the optical axis and fairly large zooming factors.
Ground from Figure Discrimination
Authors: Arnon Amir, Michael Lindenbaum
Venue: CVPR
Year: 1998

This paper proposes a new, efficient, figure from ground method. At every stage the data features are classified to either "background" or "unknown yet" classes, thus emphasizing the background detection task (and implying the name of the method). The sequential application of such classification stages creates a bootstrap mechanism which improves performance in very cluttered scenes. This method can be applied to many perceptual grouping cues, and an application to smoothness-based classification of edge points is given. A fast implementation using a kd-tree allows to work on large, realistic images.
Reliable Tracking of Human Arm Dynamics by Multiple Cue Integration and Constraint Fusion
Authors: Yusuf Azoz, Lalitha Devi, Rajeev Sharma
Venue: CVPR
Year: 1998

The use of hard gestures provides an attractive means of interacting naturally with a computer generated display. Using one or more video cameras, the hand movements can potentially be interpreted as meaningful gestures. One key problem in building such am interface without a restricted setup is the ability to localize and track the human arm robust in image sequences. This paper proposes a multiple cite-based localization scheme combined with a tracking framework to reliably track the human arm dynamics in unconstrained environments. The localization scheme integrates the multiple cues of motion, shape, and color for locating a set of key image features. Using constraint fusion, these features are tracked by a modified Extended Kalman Filter that exploits the articulated structure of the arm. We also propose an interaction scheme between tracking and localization for improving the estimation process while reducing the computational requirements. The performance of the frameworks is validated with the help of extensive experiments and simulations.
Real-Time 2-D Feature Detection on a Reconfigurable Computer
Authors: Arrigo Benedetti, Pietro Perona
Venue: CVPR
Year: 1998

We have designed and implemented a system for real-time detection of 2-D features on a reconfigurable computer based on Field Programmable Gate Arrays (FPGA's). We envision this device as the front-end of a system able to track image features in real-time control applications like autonomous vehicle navigation. The algorithm employed to select good features is inspired by Tomasi and Kanade's method. Compared to the original method, the algorithm that we have devised does not require any floating point or transcendental operations, and can be implemented either in hardware or in software. Moreover, it maps efficiently into a highly pipelined architecture, well suited to implementation in FPGA technology. We have implemented the algorithm on a low-cost reconfigurable computer and have observed reliable operation on an image stream generated by a standard NTSC video camera at 30 Hz.
Robust Recognition of Scaled Eigenimages through a Hierarchical Approach
Authors: Horst Bischof, Ales Leonardis
Venue: CVPR
Year: 1998

Recently, we have proposed a new approach to estimation of the coefficients of eigenimages, which is robust against occlusion, varying background, and other types of non-Gaussian noise. In this paper we show that our method for estimating the coefficients can be applied to convolved and subsampled images yielding the same value of the coefficients. This enables an efficient multiresolution approach, where the values of the coefficients can directly be propagated through the scales. This property is used to extend our robust method to the problem of scaled images. We performed extensive experimental evaluations to confirm our theoretical results.
Action Recognition Using Probabilistic Parsing
Authors: Aaron F. Bobick, Yuri A. Ivanov
Venue: CVPR
Year: 1998

A new approach to the recognition of temporal behaviours and activities is presented. The fundamental idea, inspired by work in speech recognition, is to divide the inference problem into two levels. The lower level is performed using standard independent probabilistic temporal event detectors such as hidden Markov models (HMMs) to propose candidate detections of low level temporal features. The outputs of these detectors provide the input stream for a stochastic context-free grammar parsing mechanism. The grammar and parser provide longer range temporal constraints, disambiguate uncertain low level detections, and allow the inclusion of a priori knowledge about the structure of temporal events in a given domain. To achieve such a system we provide techniques for generating a discrete symbol stream from continuous low level detectors and for enforcing temporal exclusion constraints during parsing. We demonstrate the approach in several experiments using both visual and other sensing data.
Texture Recognition Using a Non-Parametric Multi-Scale Statistical Model
Authors: Jeremy S. De Bonet, Paul A. Viola
Venue: CVPR
Year: 1998

We describe a technique for using the joint occurrence of local features at multiple resolutions to measure the similarity between texture images. Though superficially similar to a number of "Gabor" style techniques, which recognize textures through the extraction of multi-scale feature vectors, our approach is derived from an accurate generative model of texture, which is explicitly multiscale and non-parametric. The resulting recognition procedure is similarly non-parametric, and can model complex non-homogeneous textures. We report results on publicly available texture databases. In addition, experiments indicate that this approach may have sufficient discrimination power to perform target detection in synthetic aperture radar images (SAR).