OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Recovery of Low-Dimensional Structure from High-Dimensional Data
Authors: Shyjan Mahamud, Martial Hebert
Venue: ICCV
Year: 1999

Many modeling tasks in computer vision, e.g. structure from motion, shape/reflectance from shading, filter synthesis have a low-dimensional intrinsic structure even though the dimension of the input data can be relatively large. We propose a simple but surprisingly effective iterative randomized algorithm that drastically cuts down the time required for recovering the intrinsic structure. The computational cost depends only on the intrinsic dimension of the structure of the task. It is based on the recently proposed Cascade Basis Reduction (CBR) algorithm that was developed in the context of steerable filters. A key feature of our algorithm compared with CBR is that an arbitrary a priori basis for the task is not required. This allows us to extend the applicability of the algorithm to tasks beyond steerable filters such as structure from motion. We prove the convergence for the new algorithm. In practice the new algorithm is much faster than CBR for the same modeling error. We demonstrate this speed-up for the construction of a steerable basis for Gabor filters. We also demonstrate the generality of the new algorithm by applying it to to an example from structure from motion without missing data.
Segmentation of Salient Closed Contours from Real Images
Authors: Shyjan Mahamud, Karvel K. Thornber, Lance R. Williams
Venue: ICCV
Year: 1999

Using a saliency measure based on the global property of contour closure, we have developed a method that reliably segments out salient contours bounding unknown objects from real edge images. The measure also incorporates the Gestalt principles of proximity and smooth continuity that previous methods have exploited. Unlike previous measures, we incorporate contour closure by finding the eigen-solution associated with a stochastic process that models the distribution of contours passing through edges in the scene. The segmentation algorithm utilizes the saliency measure to identify multiple closed contours by finding strongly-connected components on an induced graph. The determination of strongly-connected components is a direct consequence of the property of closure. We report for the first time, results on large real images for which segmentation takes an average of about 10 secs per object on a general-purpose workstation. The segmentation is made efficient for such large images by exploiting the inherent symmetry in the task.
Correlation-based Estimation of Ego-Motion and Structure from Motion and Stereo
Authors: Robert Mandelbaum, Garbis Salgian, Harpreet S. Sawhney
Venue: ICCV
Year: 1999

This paper describes a correlation-based, iterative, multi-resolution algorithm which estimates both scene structure and the motion of the camera rig through an environment from the stream(s) of incoming images. Both single-camera rigs and multiple-camera rigs can be accommodated. The use of multiple synchronized cameras results in more rapid convergence of the iterative approach. The algorithm uses a global ego-motion constraint to refine estimates of inter-frame camera rotation and translation. It uses local window-based correlation to refine the current estimate of scene structure. All analysis is performed at multiple resolutions. In order to combine, in a straightforward way, the correlation surfaces from multiple viewpoints and from multiple pixels in a support region, each pixel's correlation surface is modeled as a quadratic. This parameterization allows direct, explicit computation of incremental refinements for ego-motion and structure using linear algebra. Batches can be of arbitrary size, allowing a trade-off between accuracy and latency. Batches can also be daisy-chained for extended sequences. Results of the algorithm are shown on synthetic and real outdoor image sequences.
Control in a 3D Reconstruction System using Selective Perception
Authors: Maurício Marengoni, Allen R. Hanson, Shlomo Zilberstein, Edward M. Riseman
Venue: ICCV
Year: 1999

This paper presents a control structure for general purpose image understanding that addresses both the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is performed by an independent subsystem that uses Bayesian networks and utility theory to compute the marginal value of information provided by alternative operators and selects the ones with the highest value. We have implemented and tested this control structure with several aerial image datasets. The results show that the knowledge base used by the system can be acquired using standard learning techniques and that the value-driven approach to the selection of vision algorithms leads to performance gains. Moreover, the modular system architecture simplifies the addition of both control knowledge and new vision algorithms.
Principal Manifolds and Bayesian Subspaces for Visual Recognition
Authors: Baback Moghaddam
Venue: ICCV
Year: 1999

We investigate the use of linear and nonlinear principal manifolds for learning low dimensional representations for visual recognition. Three techniques: principal component analysis (PCA), independent component analysis (ICA) and nonlinear PCA (NLPCA) are examined and tested in a visual recognition experiment using a large gallery of facial images from the "FERET" database. We compare the recognition performance of a nearest neighbour matching rule with each principal manifold representation to that of a maximum a posteriori (MAP) matching rule using a Bayesian similarity measure derived from probabilistic subspaces, and demonstrate the superiority of the latter.
Depth Estimation using Defocused Stereo Image Pairs
Authors: Uma Mudenagudi, Subhasis Chaudhuri
Venue: ICCV
Year: 1999

In this paper we propose a new method for estimating depth using a fusion of defocus and stereo, that relaxes the assumption of a pinhole model of the camera. It avoids the correspondence problem of stereo. The main advantage of this algorithm is simultaneous recovery of depth and image restoration. The depth (blur or disparity) in the scene and the intensity process in the focused image are individually modeled as Markov random fields (MRF). It avoids the windowing of data and allows incorporation of multiple observations in the estimation procedure. The accuracy of depth estimation and the quality of the restored image are improved compared to the depth from defocus method, and a dense depth map is estimated without correspondence and interpolation as in the case of stereo.
The Recovery of Object Shape and Camera Motion using a Sensing System with a Video Camera and a Gyro Sensor
Authors: Toshiharu Mukai, Noboru Ohnishi
Venue: ICCV
Year: 1999

Object shape and camera motion recovery from an image sequence is a crucial issue in computer vision and many methods have been proposed by researchers. Theoretically, these methods are perfect, but they are sensitive to noise, so that in many practical situations, satisfactory results cannot be obtained. To solve this problem, we propose a shape and motion recovery method using a gyro sensor attached an a video camera for compensating images. We made an experimental system with a CCD camera and a gyro sensor. Using this system, we have examined the accuracy of our method and obtained good results.
Gesture Recognition using Character Recognition Techniques on Two-Dimensional Eigenspace
Authors: Hiroshi Ohno, Masanobu Yamamoto
Venue: ICCV
Year: 1999

This paper describes a novel method for gesture recognition using character recognition techniques on two-dimensional eigenspace. An image-based approach can capture human body poses in 3D motion from multiple image sequences. The sequence of poses can be reduced into a trajectory on the two-dimensional eigenspace with preserving the main features in gesture, so that the gesture recognition equals the character recognition. Experiments for the gesture recognition using some character recognition techniques show our method is useful.
Fast Algorithms for Projective Multi-Frame Structure from Motion
Authors: John Oliensis, Yakup Genc
Venue: ICCV
Year: 1999

We describe new algorithms for multi-frame structure from motion from tracked point features. The algorithms are essentially linear and give accuracies similar to those of a maximum-likelihood estimate. For the common situations where the calibration is fixed and approximately known, we experimentally compare the fully projective versions of our algorithms to mixed projective/Euclidean strategies. Our theoretical results clarify the nature of dominant-plane compensation and the effect of calibration error on translation recovery.
New Algorithms for Two-Frame Structure from Motion
Authors: John Oliensis, Yakup Genc
Venue: ICCV
Year: 1999

We describe two new algorithms for two-frame structure from motion from tracked point features. One is the first fast algorithm for computing an exact least-squares estimate. It exploits our observation that the rotationally invariant least-squares error can be written in a simple form that depends just on the motion. The other is essentially as accurate as the least-squares estimate and is more efficient, probably faster, and potentially more robust than previous algorithms of comparable accuracy. We also analyze theoretically the accuracy of the optical-flow approximation to the least-squares error.
Measuring Convexity for Figure/Ground Separation
Authors: Hsing-Kuo Pao, Davi Geiger, Nava Rubin
Venue: ICCV
Year: 1999

In human perception, convex surfaces have a strong tendency to be perceived as the "figure". Convexity has a stronger influence on figural organization than other global shape properties, such as symmetry (G. Kanizsa, 1979), and yet there has been very little work on convexity properties in computer vision. We present a model for figure/ground segregation which exhibits a preference for convex regions as the figure (i.e., the foreground). The model also shows a preference for smaller regions to be selected as figures, which is also known to hold for human visual perception (K. Koffka, 1935). The model is based on the machinery of Markov random fields/random walks/diffusion processes, so that the global shape properties are obtained via local and stochastic computations. Experimental results demonstrate that our model performs well on ambiguous figure/ground displays which were not captured before. In particular in ambiguous displays where neither region is strictly convex, the model shows preference to the "more convex" region, thus offering a continuous measure of convexity in agreement with human perception.
A Pattern Classification Approach to Dynamical Object Detection
Authors: Constantine Papageorgiou, Tomaso A. Poggio
Venue: ICCV
Year: 1999

Current systems for object detection in video sequences rely on explicit dynamical models like Kalman filters or hidden Markov models. There is significant overhead needed in the development of such systems as well as the a priori assumption that the object dynamics can be described with such a dynamical model. This paper describes a new pattern classification technique for object detection in video sequences that uses a rich, overcomplete dictionary of wavelet features to describe an object class. Unlike previous work where a small subset of features was selected from the dictionary, this system does no feature selection and learns the model in the full 1,326 dimensional feature space. Comparisons using different sized sets of several types of features are given. We extend this representation into the time domain without assuming any explicit model of dynamics. This data driven approach produces a model of the physical structure and short-time dynamical characteristics of people from a training set of examples; no assumptions are made about the motion of people, just that short sequences characterize their dynamics sufficiently for the purposes of detection. One of the main benefits of this approach is that transient false positives are reduced. This technique compares favorably with the static detection approach and could be applied to other object classes. We also present a real-time version of one of our static people detection systems.
Geodesic Active Regions for Motion Estimation and Tracking
Authors: Nikos Paragios, Rachid Deriche
Venue: ICCV
Year: 1999

This paper proposes a new front propagation method to deal accurately with the challenging problem of tracking non-rigid moving objects. This is obtained by employing a geodesic active region model where the designed objective function is composed of boundary and region-based terms and optimizes the curve position with respect to motion and intensity properties. The main novelty of our approach is that we deal with the motion estimation (linear models are assumed) and the tracking problem simultaneously. In other words, the optimization problem contains a coupled set of unknown variables; the curve position and the corresponding motion model. The designed objective function is minimized using a gradient descent method; the curve is propagated towards the object boundaries under the influence of boundary, intensity and motion-based forces using a PDE, while given the curve position an incremental analytical solution is obtained for the motion model. Besides, this PDE is implemented using a level set approach where topological changes are naturally handled. Very promising experimental results are provided using real video sequences.
Geodesic Active Regions for Supervised Texture Segmentation
Authors: Nikos Paragios, Rachid Deriche
Venue: ICCV
Year: 1999

The paper presents a novel variational method for supervised texture segmentation. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.
Cluster-based Segmentation of Natural Scenes
Authors: Eric J. Pauwels, Greet Frederix
Venue: ICCV
Year: 1999

In cluster-based segmentation pixels are mapped into various feature spaces whereupon they are subjected to a grouping algorithm. In this paper we develop a robust and versatile non-parametric clustering algorithm that is able to handle the unbalanced and irregular clusters encountered in such segmentation applications. The strength of our approach lies in the definition and use of two cluster validity indices that are independent of the cluster topology. By combining them, an excellent clustering can be identified, and experiments confirm that the associated clusters do indeed correspond to perceptually salient image regions.
A Dynamic Bayesian Network Approach to Figure Tracking using Learned Dynamic Models
Authors: Vladimir Pavlovic 0001, James M. Rehg, Tat-Jen Cham, Kevin P. Murphy
Venue: ICCV
Year: 1999

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However most work on tracking and synthesizing figure motion has employed either simple, generic dynamic models or highly specific hand-tailored ones. Recently, a broad class of learning and inference algorithms for time-series models have been successfully cast in the framework of dynamic Bayesian networks (DBNs). This paper describes a novel DBN-based switching linear dynamic system (SLDS) model and presents its application to figure motion analysis. A key feature of our approach is an approximate Viterbi inference technique for overcoming the intractability of exact inference in mixed-state DBNs. We present experimental results for learning figure dynamics from video data and show promising initial results for tracking, interpolation, synthesis, and classification using learned models.
The PDAF based Active Contour
Authors: Natan Peterfreund
Venue: ICCV
Year: 1999

We present a new active contour model in spatio-velocity space which is based on the probability data association filter (PDAF) approach. It employs a directional-based measurement model of image potential and of optical-flow along the contour points. The proposed directional approach of measurements is the basis for the velocity-based discrimination between measurements of the object to that of image clutter. The model chooses the appropriate measurements which are most consistent with previous estimation of motion in the sense of the PDAF approach. In order to obtain reliable measurements of image motion and of gradient-based image potential, we propose a directional smoothing operator which is the basis for discriminating the objects measurements from that of image clutter. The method was applied to real world tracking problems such as a walking leg and a waving hand.
Empirical Evaluation of Dissimilarity Measures for Color and Texture
Authors: Jan Puzicha, Yossi Rubner, Carlo Tomasi, Joachim M. Buhmann
Venue: ICCV
Year: 1999

This paper empirically compares nine image dissimilarity measures that are based on distributions of color and texture features summarizing over 1,000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color and via an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, and segmentation tasks, and for a wide variety of dissimilarity measures. It is demonstrated how the selection of a measure, based on large scale evaluation, substantially improves the quality of classification, retrieval, and unsupervised segmentation of color and texture images.
Higher Order Statistical Learning for Vehicle Detection in Images
Authors: A. N. Rajagopalan 0001, Philippe Burlina, Rama Chellappa
Venue: ICCV
Year: 1999

The paper describes a scheme for detecting vehicles in images. The proposed method approximately models the unknown distribution of the images of vehicles by learning higher order statistics (HOS) information of the 'vehicle class' from sample images. Given a test image, statistical information about the background is learnt 'on the fly'. An HOS-based decision measure then classifies test patterns as vehicles or otherwise. When tested on real images of aerial views of vehicular activity, the method gives good results even on complicated scenes. It does not require any a priori information about the site. However, it is amenable to augmentation with contextual information. The method can serve as an important step towards building an automated roadway monitoring system.
A Cluster-based Statistical Model for Object Detection
Authors: Thomas D. Rikert, Michael J. Jones 0001, Paul A. Viola
Venue: ICCV
Year: 1999

This paper presents an approach to object detection which is based on recent work in statistical models for texture synthesis and recognition. Our method follows the texture recognition work of De Bonet and Viola (1998). We use feature vectors which capture the joint occurrence of local features at multiple resolutions. The distribution of feature vectors for a set of training images of an object class is estimated by clustering the data and then forming a mixture of Gaussian models. The mixture model is further refined by determining which clusters are the most discriminative for the class and retaining only those clusters. After the model is learned, test images are classified by computing the likelihood of their feature vectors with respect to the model. We present promising results in applying our technique to face detection and car detection.
Classification of Human Body Motion
Authors: Jens Rittscher, Andrew Blake 0001
Venue: ICCV
Year: 1999

The classification of human body motion is a difficult problem. In particular, the automatic segmentation of image sequences containing more than one class of motion is challenging. An effective approach is to use mixed discrete/continuous states to couple perception with classification. A spline contour is used to track the outline of the person. We show that, for a quasi-periodic human body motion, an autoregressive process is a suitable model for the contour dynamics. This can then be used as a dynamical model for mixed-state "condensation" filtering, switching automatically between different motion classes. We have developed "partial importance sampling" to enhance the efficiency of the mixed-state condensation filter. It is also shown that the importance sampling can be done in linear time, instead of the previous quadratic algorithm. "Tying" of discrete states is used to obtain further efficiency improvements. Automatic segmentation is demonstrated on video sequences of aerobic exercises. The performance is promising, but there remains a residual misclassification rate, and possible explanations for this are discussed.
Texture-based Image Retrieval without Segmentation
Authors: Yossi Rubner, Carlo Tomasi
Venue: ICCV
Year: 1999

Image segmentation is not only hard and unnecessary for texture-based image retrieval, but can even be harmful. Images of either individual or multiple textures are best described by distributions of spatial frequency descriptors, rather than single descriptor vectors over presegmented regions. A retrieval method based on the earth movers distance with an appropriate ground distance is shown to handle both complete and partial multi-textured queries. As an illustration, different images of the same type of animal are easily retrieved together. At the same time, animals with subtly different coats, like cheetahs and leopards, are properly distinguished.
Corner Detection in Textured Color Images
Authors: Mark A. Ruzon, Carlo Tomasi
Venue: ICCV
Year: 1999

Corner models in the literature have lagged behind edge models with respect to color and shading. We use both a region model, based on distributions of pixel colors, and an edge model, which removes false positives, to perform corner detection on color images whose regions contain texture. We show results on a variety of natural images at different scales that highlight the problems that occur when boundaries between regions have curvature.
Coupled Lighting Direction and Shape Estimation from Single Images
Authors: Dimitris Samaras, Dimitris N. Metaxas
Venue: ICCV
Year: 1999

This paper presents a new method for the simultaneous estimation of lighting direction and shape from shading. The method estimates the shape and the lighting direction using a two step iterative process. We assume an initial (possibly incorrect) estimate of the lighting position. A stiff deformable model is then fitted to the image, assuming this lighting position. Next, a least-squares estimate of the lighting position is derived from the model using the Levenberg-Marquart method. The two steps-model fitting and lighting-position estimation-are iterated. Once the light direction has converged to a stable solution the deformable model stiffness is lowered and the model fits accurately given the lighting model. In addition, we show how the method can be used with either orthographic or perspective projection assumptions. In a variety of experiments on real and synthetic data, the method is robust to errors both to the initial light position and shape estimates.