OpenRaiser/Intern-Atlas huggingface dataset:
Car Detection in Low Resolution Aerial Image
Authors: Tao Zhao 0001, Ramakant Nevatia
Venue: ICCV
Year: 2001

We present a system to detect passenger cars in aerial images where cars appear as small objects. We pose this as a 3D object recognition problem to account for the variation in viewpoint and the shadow. We started from psychological tests to find important features for human detection of cars. Based on these observations, we selected the boundary of the car body, the boundary of the front windshield and the shadow as the features. Some of these features are affected by the intensity of the car and whether or not there is a shadow along it. This information is represented in the structure of the Bayesian network that we use to integrate all features. Experiments show very promising results even on some very challenging images.
3D LAMP: A New Layered Panoramic Representation
Authors: Zhigang Zhu 0001, Allen R. Hanson
Venue: ICCV
Year: 2001

In this paper a compact representation, 3D Layered, Adaptive-resolution and Multi-perspective Panorama (LAMP), is proposed for representing large scale and 3D scenes with occlusion. Two kinds of 3D LAMP representations are constructed, i.e. the relief-like LAMP and the image-based LAMP, both of which concisely represent almost all the information from a long image sequence. The relief-like LAMP is basically a single extended multi-perspective panoramic view image with both texture and depth values, but each pixel has multiple values to represent results of occlusion recovery and resolution enhancement. The image-based LAMP, on the other hand, consists of a set of multi-perspective layers, each of which has both texture and depth maps, with adaptive time-sampling scales depending on depths of scene points. Several examples of 3D LAMP construction for real image sequences are given. The 3D LAMP is a concise and powerful representation for image-based rendering.
Parallel-Perspective Stereo Mosaics
Authors: Zhigang Zhu 0001, Edward M. Riseman, Allen R. Hanson
Venue: ICCV
Year: 2001

In this paper we present a novel method for automatically and efficiently generating stereoscopic mosaics by seamless registration of optical data collected by a video camera mounted on an airborne platform that undergoes dominant translational motion. There are four critical points discussed in this paper: (1) Using a parallel-perspective representation, a pair of geometrically registered stereo mosaics can be constructed before we explicitly recover any 3D information under rather general motion. (2) A PRISM (parallel ray interpolation for stereo mosaicing) technique is proposed to make stereo mosaics seamless in the presence of motion parallax and for rather arbitrary scenes. A fast PRISM algorithm is presented and issues on stitching point selection and occlusion handling are discussed. (3) The epipolar geometry of parallel-perspective stereo mosaics generated under constrained 6 DOF motion is formulated, which shows optimal baselines, easy search for correspondence and constant depth resolution. (4) The proposed methods for the generation of stereo mosaics and then the reconstruction of a 3D map are efficient in both computation and storage. Experimental results on long video sequences are given.
Omni-Rig: Linear Self-Recalibration of a Rig with Varying Internal and External Parameters
Authors: Assaf Zomet, Lior Wolf, Amnon Shashua
Venue: ICCV
Year: 2001

We describe the principles of building a moving vision platform (a Rig) that once calibrated can thereon self-adjust to changes in its internal configuration and maintain an Euclidean representation of the 3D world using only projective measurements. We term this calibration paradigm "Omni-Rig". We assume that after calibration the cameras may change critical elements of their configuration, including internal parameters and centers of projection. Theoretically we show that knowing only the rotations between a set of cameras is sufficient for Euclidean calibration even with varying internal parameters and unknown translations. No other information of the world is required.
Paracatadioptric Camera Calibration Using Lines
Authors: João Pedro Barreto 0001, Helder Araújo
Venue: ICCV
Year: 2003

Paracatadioptric sensors combine a parabolic shaped mirror and a camera inducing an orthographic projection. Such a configuration provides a wide field of view while keeping a single effective viewpoint. Previous work in central catadioptric sensors proved that a line projects into a conic curve and that three line images are enough to calibrate the system. However the estimation of the conic curves where lines are mapped is hard to accomplish. In general only a small arc of the conic is visible in the image and conventional conic fitting techniques are unable to correctly estimate the curve. The present work shows that a set of conic curves corresponds to paracatadioptric line images if, and only if, certain properties are verified. These properties are used to constraint the search space and correctly estimate the curves. The accurate estimation of a minimum of three line images allows the complete calibration of the paracatadioptric camera. If the camera is skewless and the aspect ratio is known then the conic fitting problem is solved naturally by an eigensystem. For the general situation the conic curves are estimated using non-linear optimization.
Globally Convergent Autocalibration
Authors: Arrigo Benedetti, Alessandro Busti, Michela Farenzena, Andrea Fusiello
Venue: ICCV
Year: 2003

Existing autocalibration techniques use numerical optimization algorithms that are prone to the problem of local minima. To address this problem, we have developed a method where an interval branch-and-bound method is employed for numerical minimization. Thanks to the properties of interval analysis this method is guaranteed to converge to the global solution with mathematical certainty and arbitrary accuracy, and the only input information it requires from the user is a set of point correspondences and a search box. The cost function is based on the Huang-Faugeras constraint of the fundamental matrix. A recently proposed interval extension based on Bernstein polynomial forms has been investigated to speed up the search for the solution. Finally, some experimental results on synthetic images are presented.
Recognising Panoramas
Authors: Matthew Brown 0001, David G. Lowe
Venue: ICCV
Year: 2003

The problem considered in this paper is the fully automatic construction of panoramas. Fundamentally, this problem requires recognition, as we need to know which parts of the panorama join up. Previous approaches have used human input or restrictions on the image sequence for the matching step. In this work we use object recognition techniques based on invariant local features to select matching images, and a probabilistic model for verification. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the images. It is also insensitive to 'noise' images which are not part of the panorama at all, that is, it recognises panoramas. This suggests a useful application for photographers: the system takes as input the images on an entire flash card or film, recognises images that form part of a panorama, and stitches them with no user input whatsoever.
Using prior shape and intensity profile in medical image segmentation
Authors: Yunmei Chen, Feng Huang 0001, Hemant D. Tagare, Murali Rao, David Clifford Wilson, Edward A. Geiser
Venue: ICCV
Year: 2003

In this note we present a coupled optimization model for boundary determination. One part of the model incorporates a prior shape into a geometric active contour model with a fixed parameter. The second part determines the 'best' parameter used in the first part by maximizing the mutual information of the image geometry between the prior and an aligned novel image over all the alignments that are the solutions of the first part corresponding to different parameters. We also present an alternative method, which generates an intensity model formed as the average of a set of aligned training images. Experimental results on cardiac ultrasound images are presented. These results indicate that the proposed model provides close agreement with expert traced borders, and the parameter determined in this model for one image can be used for images with similar properties. The existence of a solution to the proposed minimization problem is also discussed.
Robust Regression with Projection Based M-estimators
Authors: Haifeng Chen, Peter Meer
Venue: ICCV
Year: 2003

The robust regression techniques in the RANSAC family are popular today in computer vision, but their performance depends on a user supplied threshold. We eliminate this drawback of RANSAC by reformulating another robust method, the M-estimator, as a projection pursuit optimization problem. The projection based pbM-estimator automatically derives the threshold from univariate kernel density estimates. Nevertheless, the performance of the pbM-estimator equals or exceeds that of RANSAC techniques tuned to the optimal threshold, a value which is never available in practice. Experiments were performed both with synthetic and real data in the affine motion and fundamental matrix estimation tasks.
On-Line Selection of Discriminative Tracking Features
Authors: Robert T. Collins, Yanxi Liu 0001
Venue: ICCV
Year: 2003

We present a method for evaluating multiple feature spaces while tracking, and for adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. We develop an online feature selection mechanism based on the two-class variance ratio measure, applied to log likelihood distributions computed with respect to a given feature from samples of object and background pixels. This feature selection mechanism is embedded in a tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented to illustrate how the method adapts to changing appearances of both tracked object and scene background.
Variational Space-Time Motion Segmentation
Authors: Daniel Cremers, Stefano Soatto
Venue: ICCV
Year: 2003

We propose a variational method for segmenting image sequences into spatiotemporal domains of homogeneous motion. To this end, we formulate the problem of motion estimation in the framework of Bayesian inference, using a prior which favors domain boundaries of minimal surface area. We derive a cost functional which depends on a surface in space-time separating a set of motion regions, as well as a set of vectors modeling the motion in each region. We propose a multiphase level set formulation of this functional, in which the surface and the motion regions are represented implicitly by a vector-valued level set function. Joint minimization of the proposed functional results in an eigenvalue problem for the motion model of each region and in a gradient descent evolution for the separating interface. Numerical results on real-world sequences demonstrate that minimization of a single cost functional generates a segmentation of space-time into multiple motion regions.
Active Concept Learning for Image Retrieval in Dynamic Databases
Authors: Anlei Dong, Bir Bhanu
Venue: ICCV
Year: 2003

Concept learning in content-based image retrieval (CBIR) systems is a challenging task. We present an active concept learning approach based on mixture model to deal with the two basic aspects of a database system: changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we develop a novel model selection method based on Bayesian analysis that evaluates the consistency of hypothesized models with the available information. The analysis of exploitation vs. exploration in the search space helps to find optimal model efficiently. Experimental results on Corel database show the efficacy of our approach.
A Multi-scale Generative Model for Animate Shapes and Parts
Authors: Aleksandr Dubinskiy, Song Chun Zhu
Venue: ICCV
Year: 2003

We present a multiscale generative model for representing animate shapes and extracting meaningful parts of objects. The model assumes that animate shapes (2D simple dosed curves) are formed by a linear superposition of a number of shape bases. These shape bases resemble the multiscale Gabor bases in image pyramid representation, are well localized in both spatial and frequency domains, and form an over-complete dictionary. This model is simpler than the popular B-spline representation since it does not engage a domain partition. Thus it eliminates the interference between adjacent B-spline bases, and becomes a true linear additive model. We pursue the bases by reconstructing the shape in a coarse-to-fine procedure through curve evolution. These shape bases are further organized in a tree-structure, where the bases in each subtree sum up to an intuitive part of the object. To build probabilistic model for a class of objects, we propose a Markov random field model at each level of the tree representation to account for the spatial relationship between bases. Thus the final model integrates a Markov tree (generative) model over scales and a Markov random field over space. We adopt EM-type algorithm for learning the meaningful parts for a shape class, and show some results on shape synthesis.
Shape Representation via Harmonic Embedding
Authors: Alessandro Duci, Anthony J. Yezzi, Sanjoy K. Mitter, Stefano Soatto
Venue: ICCV
Year: 2003

We present a novel representation of shape for closed planar contours explicitly designed to possess a linear structure. This greatly simplifies linear operations such as averaging, principal component analysis or differentiation in the space of shapes. The representation relies upon embedding the contour on a subset of the space of harmonic functions of which the original contour is the zero level set.
An automatic drowning detection surveillance system for challenging outdoor pool environments
Authors: How-Lung Eng, Kar-Ann Toh, Alvin Harvey Kam, Junxian Wang, Wei-Yun Yau
Venue: ICCV
Year: 2003

Automatically understanding events happening at a site is the ultimate goal of visual surveillance system. We investigate the challenges faced by automated surveillance systems operating in hostile conditions and demonstrate the developed algorithms via a system that detects water crises within highly dynamic aquatic environments. An efficient segmentation algorithm based on robust block-based background modelling and thresholding-with-hysteresis methodology enables swimmers to be reliably detected amid reflections, ripples, splashes and rapid lighting changes. Partial occlusions are resolved using a Markov Random Field framework that enhances the tracking capability of the system. Visual indicators of water crises are identified based on professional knowledge of water crises detection, based on which a set of swimmer descriptors has been defined. Through seamlessly fusing the extracted swimmer descriptors based on a novel functional link network, the system achieves promising results for water crises detection. The developed algorithms have been incorporated into a live system with robust performance for different hostile environments faced by an outdoor swimming pool.
On the Epipolar Geometry of the Crossed-Slits Projection
Authors: Doron Feldman, Tomás Pajdla, Daphna Weinshall
Venue: ICCV
Year: 2003

The Crossed-Slits (X-Slits) camera is defined by two nonintersecting slits, which replace the pinhole in the common perspective camera. Each point in space is projected to the image plane by a ray which passes through the point and the two slits. The X-Slits projection model includes the pushbroom camera as a special case. In addition, it describes a certain class of panoramic images, which are generated from sequences obtained by translating pinhole cameras. In this paper we develop the epipolar geometry of the X-Slits projection model. We show an object which is similar to the fundamental matrix; our matrix, however, describes a quadratic relation between corresponding image points (using the Veronese mapping). Similarly the equivalent of epipolar lines are conics in the image plane. Unlike the pin-hole case, epipolar surfaces do not usually exist in the sense that matching epipolar lines lie on a single surface; we analyze the cases when epipolar surfaces exist, and characterize their properties. Finally, we demonstrate the matching of points in pairs of X-Slits panoramic images.
Gamut Constrained Illuminant Estimation
Authors: Graham D. Finlayson, Steven D. Hordley, Ingeborg Tastl
Venue: ICCV
Year: 2003

This paper presents a novel solution to the illuminant estimation problem: the problem of how, given an image of a scene taken under an unknown illuminant, we can recover an estimate of that light. The work is founded on previous gamut mapping solutions to the problem which solve for a scene illuminant by determining the set of diagonal mappings which take image data captured under an unknown light to a gamut of reference colours taken under a known light. Unfortunately a diagonal model is not always a valid model of illumination change and so previous approaches sometimes return a null solution. In addition, previous methods are difficult to implement. We address these problems by recasting the problem as one of illuminant classification: we define a priori a set of plausible lights thus ensuring that a scene illuminant estimate will always be found. A plausible light is represented by the gamut of colours observable under it and the illuminant in an image is classified by determining the plausible light whose gamut is most consistent with the image data. We show that this step (the main computational burden of the algorithm) can be performed simply, quickly, and efficiently by means of a non-negative least-squares optimisation. We report results on a large set of real images which show that it provides excellent illuminant estimation, outperforming previous algorithms.
Image-based rendering using image-based priors
Authors: Andrew W. Fitzgibbon, Yonatan Wexler, Andrew Zisserman
Venue: ICCV
Year: 2003

Given a set of images acquired from known viewpoints, we describe a method for synthesizing the image which would be seen from a new viewpoint. In contrast to existing techniques, which explicitly reconstruct the 3D geometry of the scene, we transform the problem to the reconstruction of colour rather than depth. This retains the benefits of geometric constraints, but projects out the ambiguities in depth estimation which occur in textureless regions. On the other hand, regularization is still needed in order to generate high-quality images. The paper's second contribution is to constrain the generated views to lie in the space of images whose texture statistics are those of the input images. This amounts to an image-based prior on the reconstruction which regularizes the solution, yielding realistic synthetic views. Examples are given of new view generation for cameras interpolated between the acquisition viewpoints - which enables synthetic steadicam stabilization of a sequence with a high level of realism.
SVM-based Nonparametric Discriminant Analysis, An Application to Face Detection
Authors: Rik Fransens, Jan De Prins, Luc Van Gool
Venue: ICCV
Year: 2003

Detecting the dominant normal directions to the decision surface is an established technique for feature selection in high dimensional classification problems. Several approaches have been proposed to render this strategy more amenable to practice, but they still show a number of important shortcomings from a pragmatic point of view. This paper introduces a novel such approach, which combines the normal directions idea with support vector machine classifiers. The two make a natural and powerful match, as SVs are located nearby, and fully describe the decision surfaces. The approach can be included elegantly into the training of performant classifiers from extensive datasets. The potential is corroborated by experiments, both on synthetic and real data, the latter on a face detection experiment. In this experiment we demonstrate how our approach can lead to a significant reduction of CPU-time, with neglectable loss of classification performance.
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
Authors: Bogdan Georgescu, Ilan Shimshoni, Peter Meer
Venue: ICCV
Year: 2003

Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are constrained to be spherically symmetric and their number has to be known a priori. In nonparametric clustering methods, like the one based on mean shift, these limitations are eliminated but the amount of computation becomes prohibitively large as the dimension of the space increases. We exploit a recently proposed approximation technique, locality-sensitive hashing (LSH), to reduce the computational complexity of adaptive mean shift. In our implementation of LSH the optimal parameters of the data structure are determined by a pilot learning procedure, and the partitions are data driven. As an application, the performance of mode and k-means based textons are compared in a texture classification study.
Fragmentation in the Vision of Scenes
Authors: Jan-Mark Geusebroek, Arnold W. M. Smeulders
Venue: ICCV
Year: 2003

Natural images are highly structured in their spatial configuration. Where one would expect a different spatial distribution for every image, as each image has a different spatial layout, we show that the spatial statistics of recorded images can be explained by a single process of sequential fragmentation. The observation by a resolution limited sensory system turns out to have a profound influence on the observed statistics of natural images. The power-law and normal distribution represent the extreme cases of sequential fragmentation. Between these two extremes, spatial detail statistics deform from power-law to normal through the Weibull type distribution as receptive field size increases relative to image detail size.
On the Use of Marginal Statistics of Subband Images
Authors: Joshua Gluckman
Venue: ICCV
Year: 2003

A commonly used representation of a visual pattern is the set of marginal probability distributions of the output of a bank of filters (Gaussian, Laplacian, Gabor etc.). This representation has been used effectively for a variety of vision tasks including texture classification, texture synthesis, object detection and image retrieval. We examine the ability of this representation to discriminate between an arbitrary pair of visual stimuli. Examples of patterns are derived that provably possess the same marginal statistical properties, yet are "visually distinct." These results suggest the need for either employing a large and diverse filter bank or incorporating joint statistics in order to represent a large class of visual patterns.
Fast Stereo Matching Using Reliability-Based Dynamic Programming and Consistency Constraints
Authors: Minglun Gong, Yee-Hong Yang
Venue: ICCV
Year: 2003

A method for solving binocular and multiview stereo matching problems is presented here. A weak consistency constraint is proposed, which expresses the visibility constraint in the image space. It can be proved that the weak consistency constraint holds for scenes that can be represented by a set of 3D points. As well, also proposed is a new reliability measure for dynamic programming techniques, which evaluates the reliability of a given match. A novel reliability-based dynamic programming algorithm is derived accordingly, which can selectively assign disparity values to pixels when the reliabilities of the corresponding matches exceed a given threshold. Consistency constraints and the new reliability-based dynamic programming algorithm can be combined in an iterative approach. The experimental results show that the iterative approach can produce dense (60-90%) and reliable (total error rate of 0.1-1.1%) matching for binocular stereo datasets. It can also generate promising disparity maps for trinocular and multiview stereo datasets.