OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Query Modification for Image Retrieval
Authors: Gaurav Aggarwal, Sugata Ghosal, Pradeep K. Dubey
Venue: CVPR
Year: 2000

Content-based Image Retrieval (CBIR) involves retrieving images similar to an example query image in terms of some features extracted from the image. However, inherent subjectivity in user perception of an image results in retrieved images that are largely irrelevant to the user. We propose a novel methodology for efficient understanding of user perception from the query image itself. Our system automatically generates a set of modified images, after the user selects object(s) of interest from the segmented query image. Our goal is to learn the retrieval parameters by modifying the segment-level description of the query image. Segment-level description includes individual segment properties as well as the inter-segment relationships. The user perception is then learnt on the basis of user feedback on this set of modified images. We demonstrate the feasibility and advantages of the proposed approach with examples. The proposed methodology of intra-query learning saves the cost of repeated database search incurred in existing relevance feedback based approaches.
A Neural Optimization Framework for Zoom Lens Camera Calibration
Authors: Moumen T. Ahmed, Aly A. Farag
Venue: CVPR
Year: 2000

Camera systems with zoom lenses are inherently more useful than those with passive lenses due to their flexibility and controllability. However, calibration techniques for active-cameras, still, lag behind those developed for calibration of passive-lens cameras. In this paper, we present a neural framework for zoom-lens camera calibration based on our proposed neurocalibration approach, which maps the classical problem of geometric camera calibration into a learning problem of a multi-layered feedforward neural network (MLFN). After discussing the features and advantages of the neurocalibration network, we present how this neural framework can capture the complex variations in the camera model parameters, both intrinsic and extrinsic, while minimizing the calibration error over all the calibration data across continuous ranges in the lens control space. The framework consists of a number of MLFNs learning concurrently, independently and cooperatively, the perspective projection transformation of the camera over its optical setting ranges. The calibration results of this technique applied to Hitachi CCD cameras with H10x11E Fujinon active lenses are reported.
Recursive Estimation of Motion and Planar Structure
Authors: Jonathan Alon, Stan Sclaroff
Venue: CVPR
Year: 2000

A specialized formulation of Azarbayejani and Pentland's (1995) framework for recursive recovery of motion, structure and focal length from feature correspondences tracked through an image sequence is presented. The specialized formulation addresses the case where all tracked points lie on a plane. This planarity constraint reduces the dimension of the original state vector, and consequently the number of feature points needed to estimate the state. Experiments with synthetic data and real imagery illustrate the system performance. The experiments confirm that the specialized formulation provides improved accuracy, stability to observation noise, and rate of convergence in estimation for the case where the tracked points lie on a plane.
Limits on Super-Resolution and How to Break Them
Authors: Simon Baker, Takeo Kanade
Venue: CVPR
Year: 2000

We analyze the super-resolution reconstruction constraints. In particular we derive a sequence of results which all show that the constraints provide far less useful information as the magnification factor increases. It is well established that the use of a smoothness prior may help somewhat, however for large enough magnification factors any smoothness prior leads to overly smooth results. We therefore propose an algorithm that learns recognition-based priors for specific classes of scenes, the use of which gives far better super-resolution results for both faces and text.
A Novel Approach to Depth Ordering in Monocular Image Sequences
Authors: Lothar Bergen, Fernand Meyer
Venue: CVPR
Year: 2000

Object oriented representation of image sequences requires accurate motion segmentation and depth ordering techniques. Unfortunately, the lack of precise motion estimates at the object boundaries makes these two tasks very difficult. We present a detailed analysis of the behaviour of dense motion estimation techniques at object boundaries which reveals the systematic nature of the motion estimation error; the motion of the occluding surface is observed in a small neighbourhood on the occluded side. We then show how the joint use of still image segmentation and robust regression can eliminate this error. Furthermore we present a novel technique which uses the position of the error as a depth cue. The validity of this technique, which requires only sub-pixel motion and which is capable of distinguishing between different types of intensity discontinuities, such as object boundaries, surface marks and illumination discontinuities, is then demonstrated on several synthetic and real image sequences.
A Generalized Optical Flow Constraint and its Physical Interpretation
Authors: Dominique Béréziat, Isabelle Herlin, Laurent Younes
Venue: CVPR
Year: 2000

This paper addresses the issue of motion estimation on image sequences. The standard motion equation used to complete the apparent motion of image irradiance patterns is an invariance brightness based hypothesis called the optical flow constraint. Other equations can be used, in particular the extended optical flow constraint, which is a variant of the optical flow constraint, inspired by the fluid mechanic mass conservation principle. In this paper we propose a physical interpretation of this extended optical flow equation and a new model unifying the optical flow and the extended optical flow constraints. We present results obtained for synthetic and meteorological images.
A Real-Time System for Epipolar Geometry and Ego-Motion Estimation
Authors: Mårten Björkman, Jan-Olof Eklundh
Venue: CVPR
Year: 2000

A visually guided mobile platform acting in a dynamic environment needs information, delivered from cues such as stereo and motion. In this paper a binocular stereo system, capable of dynamic vergence and ego-motion estimation, will be presented. The ability to dynamically verge the cameras is essential for visual tasks such as tracking, recognition and manipulation, since the computation of 3D geometry will be considerably simplified at fixation. We will show that the epipolar geometry of the presented system can be estimated in real-time. The number of degrees of freedom has been minimized, without affecting the flexibility of the system. Reconstructed 3D data is used in order to determine the ego-motion of the platform, at a very low computational cost. Finally, it will be shown how disparity maps and estimated ego-motion can facilitate the localization of independently moving objects.
A Bayesian Framework for Radar Shape-from-Shading
Authors: Adrian G. Bors, Edwin R. Hancock, Richard C. Wilson 0001
Venue: CVPR
Year: 2000

This paper introduces a Bayesian approach to shape-from-shading ($F$) which is applied to terrain recovery in Synthetic Aperture Radar ($AR) images. The Bayesian model relates the recovery of 3-D shape information to the original 2-D radar intensity and to edges separating different topographic regions. First, we model the image amplitude distribution and the reflection function in $AR images. Using a maximum log-likelihood feature detector derived from the image statistics we identify the ridges and ravines in the terrain image. These topographic features are used to constrain the recovery of suace normals in the shapefrom -shading process. Finally, the suace normals are smoothed using rvbust statistics operators.
Adaptive Bayesian Recognition in Tracking Rigid Objects
Authors: Yuri Boykov, Daniel P. Huttenlocher
Venue: CVPR
Year: 2000

We present a framework for tracking rigid objects based on an adaptive Bayesian recognition technique that incorporates dependencies between object features. At each frame we find a maximum a posteriori (MAP) estimate of the object parameters that include positioning and configuration of non-occluded features. This estimate may be rejected based on its quality. Our careful selection of data points in each frame allows temporal fusion via Kalman filtering. Despite "unimodality" of our tracking scheme, we demonstrate fairly robust results in highly cluttered aerial scenes. Our technique forms a natural feedback loop between the recognition method and the filter that helps to explain such robustness. We study this loop and derive a number of interesting properties. First, the effective threshold for recognition in each frame is adaptive. It depends on the current level of noise in the system. This allows the system to identify partially occluded or distorted objects as long as the predicted locations are accurate. But requires a very good match if there is uncertainty as to the object location. Second, the search area for the recognition method is automatically pruned based on the current system uncertainty, yielding an efficient overall method.
Intel's Computer Vision Library: Applications in Calibration, Stereo, Segmentation, Tracking, Gesture, Face and Object Recognition
Authors: Gary R. Bradski, Vadim Pisarevsky
Venue: CVPR
Year: 2000

Intel's Microcomputer Research Lab has been developing a highly optimized Computer Vision Library (CVLib) that automatically detects processor type and loads the appropriate MMX/sup TM/ technology assembly tuned module for that processor. MMX optimized functions are from 2 to 8 times faster than optimized C functions. We will be demonstrating various algorithms supported by CVLib and handing out CDs containing the library.
Recovering Non-Rigid 3D Shape from Image Streams
Authors: Christoph Bregler, Aaron Hertzmann, Henning Biermann
Venue: CVPR
Year: 2000

The paper addresses the problem of recovering 3D non-rigid shape models from image sequences. For example, given a video recording of a talking person, we would like to estimate a 3D model of the lips and the full face and its internal modes of variation. Many solutions that recover 3D shape from 2D image sequences have been proposed; these so-called structure-from-motion techniques usually assume that the 3D object is rigid. For example, C. Tomasi and T. Kanades' (1992) factorization technique is based on a rigid shape matrix, which produces a tracking matrix of rank 3 under orthographic projection. We propose a novel technique based on a non-rigid model, where the 3D shape in each frame is a linear combination of a set of basis shapes. Under this model, the tracking matrix is of higher rank, and can be factored in a three-step process to yield pose, configuration and shape. To the best of our knowledge, this is the first model free approach that can recover from single-view video sequences nonrigid shape models. We demonstrate this new algorithm on several video sequences. We were able to recover 3D non-rigid human face and animal models with high accuracy.
Visual Venture: Investigations with Images and Videos for Middle School Education
Authors: Lisa M. Brown
Venue: CVPR
Year: 2000

A software tool, which allows a middle school student to take measurements on images and videos, is presented. Using this software, students became scientists as they measure, collect and analyze data from a wide range of imagery. We demonstrate several novel techniques in image/video processing which enable students to take sophisticated measurements on a wide range of imagery. Routines which perform intelligent color segmentation and create visualizations of motion information are integrated into the software.
Distortion-Invariant Recognition via Jittered Querie
Authors: Michael C. Burl
Venue: CVPR
Year: 2000

This paper presents a new approach for achieving distortion-invariant recognition and classification. A test example to be classified is viewed as a query intended to find similar examples in the training set (or to find similar class models that represent a compression of the training set). The key idea is that instead of querying with a single pattern, we construct a more robust query, based on the family of patterns formed by distorting the test example. Although query execution is slower than if the invariances were successfully pre-compiled during training, there are significant advantages in several contexts: (i) providing invariances in memory-based learning, (ii) in model selection, where reducing training time at the expense of test time is a desirable trade-off, and (iii) in enabling robust, ad hoc searches based on a single example. Preliminary tests for memory-based learning on the NIST handwritten digit database with a limited set of shearing and translation distortions produced an error rate of 1.35%.
On the Space Requirements of Indexing 3D Models from 2D Perspective Images
Authors: Vincenzo Caglioti
Venue: CVPR
Year: 2000

The space requirements for indexing under perspective projections are addressed. It is known that the surface representing the set of possible images of a model point set within the index space must be three-dimensional (Jacobs, 1996). Under affine projections, the representing surface can be factorized as the cartesian product of lower-dimensional surfaces: these are obtained by projecting the representing surface onto orthogonal subspaces of the index space (Jacobs, 1992; Weinshall, 1993). This paper shows that, under perspective, such a factorization does not exist, yielding a negative answer to a question left open in (Jacobs, 1996). However, it is shown that there exist subspaces of the index space, onto which the representing surface projection is two-dimensional.
Object Recognition for an Intelligent Room
Authors: Richard Campbell, John Krumm
Venue: CVPR
Year: 2000

Intelligent rooms equipped with video cameras can exhibit compelling behaviors, many of which depend on object recognition. Unfortunately, object recognition algorithms are rarely written with a normal consumer in mind, leading to programs that would be impractical to use for a typical person. These impracticalities include speed of execution, elaborate training rituals, and setting adjustable parameters. We present an algorithm that can be trained with only a few images of the object, that requires only two parameters to be set, and that runs at 0.7 Hz on a normal PC with a normal color camera. The algorithm represents an object’s features as small, quantized edge templates, and it represents the object’s geometry with “Hough kernels”.
A Feature-Based Technique for Joint, Linear Estimation of High-Order Image-to-Mosaic Transformations: Application to Mosaicing the Curved Human Retina
Authors: Ali Can, Charles V. Stewart, Badrinath Roysam, Howard L. Tanenbaum
Venue: CVPR
Year: 2000

Methods are presented for increasing the coverage and accuracy of image mosaics constructed from multiple, uncalibrated, weak-perspective views of the human retina. Extending our previous algorithm for registering pairs of images using a non-invertible, 12-parameter, quadratic image transformation model and a hierarchical, robust estimation technique, two important innovations are presented. The first is a linear, non-iterative method for jointly estimating the transformations of all images onto the mosaic. This employs constraints derived from pairwise matching between the non-mosaic image frames. It allows the transformations to be estimated for images that do not overlap the mosaic anchor frame, and results in mutually consistent transformations for all images. This means the mosaics can cover a much broader area of the retinal surface, even though the transformation model is not closed under composition. This capability is particularly valuable for mosaicing the retinal periphery in the context of diseases such as AIDS/CMV. The second innovation is a method to improve the accuracy of the pairwise matches as well as the joint estimation by refining the feature locations and by adding new features based on the transformation estimates themselves. For matching image frames of size 1024/spl times/1024, this cuts the registration error from the range of 1 to 3 pixels to about 0.55 pixels. The overall transformation error in final mosaic construction is 0.80 pixels based on experiments over a large set of eyes.
Point Pattern Matching with Robust Spectral Correspondence
Authors: Marco Carcassoni, Edwin R. Hancock
Venue: CVPR
Year: 2000

This paper investigates the correspondence matching of point-sets using spectral graph analysis. In particular we are interested in the problem of how the modal analysis of point-sets can be rendered robust to contamination and drop-out. We make three contributions. First, we show how the modal structure of point-sets can be embedded within the framework of the EM algorithm. Second, we present several methods for computing the probabilities of point correspondences using the point proximity matrix. Third, we consider alternatives to the Gaussian proximity matrix. We evaluate the new method on both synthetic and real-world data. Here we show that the method can be used to compute useful correspondences even when the level of point contamination is as large as 50%.
Step towards Sequence-to-Sequence Alignment
Authors: Yaron Caspi, Michal Irani
Venue: CVPR
Year: 2000

The paper presents an approach for establishing correspondences in time and in space between two different video sequences of the same dynamic scene, recorded by stationary uncalibrated video cameras. The method simultaneously estimates both spatial alignment as well as temporal synchronization (temporal alignment) between the two sequences, using all available spatio-temporal information. Temporal variations between image frames (such as moving objects or changes in scene illumination) are powerful cues for alignment, which cannot be exploited by standard image-to-image alignment techniques. We show that by folding spatial and temporal cues into a single alignment framework, situations which are inherently ambiguous for traditional image-to-image alignment methods, are often uniquely resolved by sequence-to-sequence alignment. We also present a "direct" method for sequence-to-sequence alignment. The algorithm simultaneously estimates spatial and temporal alignment parameters directly from measurable sequence quantities, without requiring prior estimation of point correspondences, frame correspondences, or moving object detection. Results are shown on real image sequences taken by multiple video cameras.
Parallel Projections for Stereo Reconstruction
Authors: Jinxiang Chai, Heung-Yeung Shum
Venue: CVPR
Year: 2000

This paper proposes a novel technique to computing geometric information from images captured under parallel projections. Parallel images are desirable for stereo reconstruction because parallel projection significantly reduces foreshortening. As a result, correlation based matching becomes more effective. Since parallel projection cameras are not commonly available, we construct parallel images by rebinning a large sequence of perspective images. Epipolar geometry, depth recovery and projective invariant for both 1D and 2D parallel stereos are studied. From the uncertainty analysis of depth reconstruction, it is shown that parallel stereo is superior to both conventional perspective stereo and the recently developed multiperspective stereo for vision reconstruction, in that uniform reconstruction error is obtained in parallel stereo. Traditional stereo reconstruction techniques, e.g. multi-baseline stereo, can still be applicable to parallel stereo without any modifications because epipolar lines in a parallel stereo are perfectly straight. Experimental results further confirm the performance of our approach.
Robust Visual Recognition of Color Images Rozenn Dahyot, Pierre
Authors: Pierre Charbonnier, Fabrice Heitz
Venue: CVPR
Year: 2000

In this paper a robust pattern recognition system, using an appearance-based representation of colour images is described. Standard appearance-based approaches are not robust to outliers, occlusions or segmentation errors. The approach proposed here relies on robust M-estimators, involving non-quadratic and possibly non-convex energy functions. To deal with the minimisation of non-convex functions in a deterministic framework, we introduce an estimation scheme relying on M-estimators used in continuation, from convex functions to hard redescending nonconvex estimators. At each step of the robust estimation scheme, the non-quadratic criterion is minimized using the half-quadratic theory. This leads to a weighted least squares algorithm, which is easy to implement. The proposed robust estimation scheme does not require any user interaction because all necessary parameters are previously estimated. The method is illustrated on a road sign recognition application. Experiments show significant improvements with respect to standard estimation schemes.
In Search of Illumination Invariants
Authors: Hansen F. Chen, Peter N. Belhumeur, David W. Jacobs
Venue: CVPR
Year: 2000

We consider the problem of determining functions of an image of an object that are insensitive to illumination changes. We first show that for an object with Lambertian reflectance there are no discriminative functions that are invariant to illumination. This result leads as to adopt a probabilistic approach in which we analytically determine a probability distribution for the image gradient as a function of the surface's geometry and reflectance. Our distribution reveals that the direction of the image gradient is insensitive to changes in illumination direction. We verify this empirically by constructing a distribution for the image gradient from more than 20 million samples of gradients in a database of 1,280 images of 20 inanimate objects taken under varying lighting condition. Using this distribution we develop an illumination insensitive measure of image comparison and test it on the problem of face recognition.
Wide Area Camera Calibration Using Virtual Calibration Object
Authors: Xing Chen, James Davis 0001, Philipp Slusallek
Venue: CVPR
Year: 2000

The paper introduces a method to calibrate a wide area system of unsynchronized cameras with respect to a single global coordinate system. The method is simple and does not require the physical construction of a large calibration object. The user need only wave an identifiable point in front of all cameras. The method generates a rough estimate of camera pose by first performing pair-wise structure-from-motion on observed points, and then combining the pair-wise registrations into a single coordinate frame. Using the initial camera pose, the moving point can be tracked in world space. The path of the point defines a "virtual calibration object" which can be used to improve the initial estimates of camera pose. Iterating the above process yields a more precise estimate of both camera pose and the point path. Experimental results show that it performs as well as calibration from a physical target, in cases where all cameras share some common working volume. We then demonstrate its effectiveness in wide area settings by calibrating a system of cameras in a configuration where traditional methods cannot be applied directly.