OpenRaiser/Intern-Atlas huggingface dataset:
Rectified Mosaicing: Mosaics without the Curl
Authors: Shmuel Peleg, Assaf Zomet, Chetan Arora 0001
Venue: CVPR
Year: 2000

When images captured by a tilted camera are mosaiced into a panorama, the resulting mosaic is curled. This happens, for example, with a panning camera that is not perfectly horizontal, and with a translating camera facing a tilted planar surface. The tilt of the camera causes differences in image velocity between the top and bottom parts of the image, causing the curled mosaic. In rectified mosaicing these distortions are overcome by warping the strips into rectangles, while keeping some image feature invariant. This warping equalizes the image motion at the different image parts, and the resulting mosaic is straight. Mosaicing is done without camera calibration or knowledge of the scene, and the process adapts automatically to smooth changes in the scene and the imaging conditions.
Maintaining Valid Topology with Active Contours: Theory and Application
Authors: A. G. Amitha Perera, Chia-Ling Tsai, Robin Y. Flatland, Charles V. Stewart
Venue: CVPR
Year: 2000

We develop and prove correct an algorithm that enables active contours to correctly represent regions that undergo topology changes as the contours evolve. Using the incremental motion typical of active contours, we introduce the concept of motion regions to determine the new topology. When the topology changes (e.g. by contours intersecting), the motion regions are used to delete and and reconnect the contours to accurately describe the new region. Contour intersections can also occur without topology changes. These are also appropriately handled. The algorithm to perform this task is proved correct in a general framework that makes few assumptions about the contour representation. We describe how this algorithm is applied to a polygonal representation of the contours, and argue that it does not significantly affect execution time. Finally, this polygonal implementation is used in surface extraction and phytoplankton classification.
Calibration of Light Sources
Authors: Mark W. Powell, Sudeep Sarkar, Dmitry B. Goldgof
Venue: CVPR
Year: 2000

We present a methodology for calibrating multiple light source locations in 3D from images. The procedure involves the use of a novel calibration object that consists of either 2 or 3 spheres at known relative positions. There are two variants of the process: one which uses range and intensity imaging to find the positions of the light sources, and one that uses only the intensity image to locate the illuminants. We conducted experiments using both variations of the technique to locate light sources in 51 different positions in a laboratory setting. Our data shows that the vector from a point in the scene to a light source can be measured to within 3/spl deg/(6%) of its tote direction and within 0.13 m (9%) of its true magnitude compared to empirically measured ground truth. Finally, we demonstrate how light source information can be applied to burn scar color correction and color segmentation.
Conditioning Analysis of Missing Data Estimation for Large Sensor Array
Authors: Hairong Qi 0001, Wesley E. Snyder
Venue: CVPR
Year: 2000

Optimal missing data estimation algorithms including deblurring and denoising are designed to restore images captured from large CCD sensor arrays using a butting technique, where 1 to 2 columns of data are missed at the butting edge. We developed a consistency method with separable deblurring to estimate the missing data. This method converts an ill-posed restoration problem into a well-posed one by making few assumptions based on regularization theory. Under the condition that no noise is inserted, and the separable blur kernel is exactly known, the consistency method can deblur the original image and at the same time estimate the missing columns(s) exactly. However, this algorithm becomes unstable when large noise is inserted or inaccurate estimation of the blur kernel is made. Conditioning analysis is used to quantify the amount of ill condition of the blur kernel when the assumptions are relaxed to different levels, which provides a solid measurement on how stable the system will remain knowing the signal-to-noise ratio and the inaccuracy of the blur kernel estimation. Experimental results from different approaches are compared.
Gesture, Speech, and Gaze Cues for Discourse Segmentation
Authors: Francis K. H. Quek, David McNeill, Robert K. Bryll, Cemil Kirbas, Hasan Arslan, Karl E. McCullough, Nobuhiro Furuyama
Venue: CVPR
Year: 2000

Psycholinguistic evidence has established the complementary nature of the verbal and non-verbal aspects of human expression. We present our findings in the detection of these cites in interaction. We use the psycholinguistic device known as the 'catchment' as the locus of integration of gesture, speech and gaze components. We videotape conversation elicitation experiments in which subjects convey complex spatial plans to an interlocutor using a calibrated three-camera setup. We extract the gestural motion of both hands, gaze direction, and voiced units in the discourse and compare these with transcripts generated by expert microanalysis of the video. Our results show the complementary nature of these communicative modalities. Where there is ambiguity in the structure of one modality (such as in haptologies or owing to noise in the audio signal), other modalities provide evidence for correct segmentation.
Parameterization of Closed Surfaces for Parametric Surface Descriptio
Authors: Michael Quicke, Christian Brechbühler, Johannes Hug, Hans Blattman, Gábor Székely
Venue: CVPR
Year: 2000

A procedure for the parameterization of surface meshes of objects with spherical topology is presented. The generation of such a parameterisation has been formulated and solved as a large constrained optimization problem by C. Brechbuhler (1995), but the convergence of this algorithm becomes unstable for object meshes consisting of several thousand vertices. We propose a new more stable algorithm to overcome this problem using multi-resolution meshes. A triangular mesh is mapped to a sphere by harmonic mapping. Next, a mesh hierarchy is constructed. The coarsest level is then optimized using a modification of the original procedure to map object surfaces to the unit sphere. The result is used as a starting point for the mapping of the next finer mesh, a process which is repeated until the final result is obtained. The new approach is compared to the original one and some parameterized object surfaces are presented.
Perspective Pose from Spectral Voting
Authors: Eraldo Ribeiro, Edwin R. Hancock
Venue: CVPR
Year: 2000

Multiple vanishing point detection provides the key to recovering the perspective pose of textured planes. If vanishing points are to be detected from spectral information then there are two computational problems that need to be solved. Firstly, the search of the extended image plane is unbounded, and hence the location of vanishing points at or near infinity is difficult. Secondly, correspondences between local spectra need to be established so that vanishing points can be triangulated. In this paper we offer a way of overcoming these two difficulties. We overcome the problem of unbounded search by mapping the information provided by local spectral moments onto a unit-sphere. According to our representation, the position and direction of each local spectrum maps onto a great circle on the unit-sphere. The need for correspondences is overcome by accumulating the great circle intercepts. Vanishing points occur at local accumulator maxima on the unit-sphere. We experiment with the new shape-from-texture technique on planar textures in buildings.
Invariant Web Defect Detection and Classification System
Authors: Dominik R. Rohrmus
Venue: CVPR
Year: 2000

We present a live web defect detection and classification system that runs on a Laptop PC with an attached camera. Features, invariant with respect to rotation and translation based on local integration are extracted from the grabbed images. They are then presented to a neural network for classification.
Learning to Recognize Objects
Authors: Dan Roth 0001, Ming-Hsuan Yang 0001, Narendra Ahuja
Venue: CVPR
Year: 2000

A learning account for the problem of object recognition is developed within the PAC (Probably Approximately Correct) model of learnability. The proposed approach makes no assumptions on the distribution of the observed objects, but quantifies success relative to its past experience. Most importantly, the success of learning an object representation is naturally tied to the ability to represent at as a function of some intermediate representations extracted from the image. We evaluate this approach an a large scale experimental study in which the SNoW learning architecture is used to learn representations for the 100 objects in the Columbia Object Image Database (COIL-100). The SNoW-based method is shown to outperform other methods in terms of recognition rates; its performance degrades gracefully when the training data contains fewer views and in the presence of occlusion noise.
Variable Albedo Surface Reconstruction from Stereo and Shape from Shading
Authors: Dimitris Samaras, Dimitris N. Metaxas, Pascal Fua, Yvan G. Leclerc
Venue: CVPR
Year: 2000

We present a multiview method for the computation of object shape and reflectance characteristics based on the integration of shape from shading (SFS) and stereo, for nonconstant albedo and non-uniformly Lambertian surfaces. First we perform stereo fitting on the input stereo pairs or image sequences. When the images are uncalibrated, we recover the camera parameters using bundle adjustment. Eased on the stereo result, we can automatically segment the albedo map (which is taken to be piece-wise constant) using a minimum description length (MDL) based metric, to identify areas suitable for SFS (typically smooth textureless areas) and to derive illumination information. The shape and the illumination parameter estimates are refined using a deformable model SFS algorithm, which iterates between computing shape and illumination parameters. Our method takes into account the viewing angle dependent for shortening and specularity effects, and compensates as much as possible by utilizing information from more than one images. We demonstrate that we can extend the applicability of SFS algorithms to real world situations when some of its traditional assumptions are violated. We demonstrate our method by applying it to face shape reconstruction. Experimental results indicate a significant improvement over SFS-only or stereo-only based reconstruction. Model accuracy and detail are improved, especially in areas of low texture detail. Albedo information is retrieved and can be used to accurately re-render the model under different illumination conditions.
Blind Recovery of Transparent and Semireflected Scenes
Authors: Yoav Y. Schechner, Joseph Shamir, Nahum Kiryati
Venue: CVPR
Year: 2000

We present a method to recover scenes deteriorated by superposition of transparent and semi-reflected contributions, as appear in reflections of windows. Separating the superimposed contributions from the images in which either contribution is in focus is based on mutual blurring and subtraction of the perturbing components. This procedure requires the defocus blur kernels to be known. The use of uncalibrated kernels had previously led to contaminated results. We propose a method for self-calibration of the blur kernels from the raw images themselves. The kernels are sought to minimize the mutual information of the recovered layers. This relaxes the need for prior knowledge on the optical transfer function. Experimental results are presented.
Corner Guided Curve Matching and its Application to Scene Reconstruction
Authors: Ying Shan, Zhengyou Zhang
Venue: CVPR
Year: 2000

Corners and curves are important image features in many vision-based applications. Corners are usually more stable and easier to match than curves, while curves contain richer information of scene structure. In previously work, corners are often used to recover the epipolar geometry between two views, which is then used in curve matching to reduce the search space. However, information of the scene structure contained in this set of matched corners is ignored. In this paper we present a curve matching algorithm that is guided by a set of matched corners. Within a probabilistic framework, the role of the corner guidance is explicitly defined by a set of similarity-invariant unary measurements and by a similarity function. The similarity function provides stronger capability of resolving matching ambiguity than the epipolar constraint, and is integrated into a relaxation scheme to reduce computational complexity and improve accuracy of curve matching. Experimental results clearly demonstrate the benefit of integrating corner matches into the curve matching procedure.
Estimation and Prediction of Evolving Color Distributions for Skin Segmentation under Varying Illumination
Authors: Leonid Sigal, Stan Sclaroff, Vassilis Athitsos
Venue: CVPR
Year: 2000

A model approach for real time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and based on predictions of the Markov model. The evolution of the skin color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and re-sampling the histogram. The parameters of the discrete time dynamic Markov model are estimated using maximum likelihood estimation, and also evolve over time. Quantitative evaluation of the method was conducted on labeled ground-truth video sequences taken from popular movies.
Smooth Region Structure: Folds, Domes, Bowls, Ridges, Valleys and Slopes
Authors: David Sinclair
Venue: CVPR
Year: 2000

This paper shows that the structure of the isobrightness contours in 'smooth' regions of a segmented image may be used to quantify the variation in shading. Topographic terms are then appropriate for labelling the kinds of generic structure that arise in smooth region domes, bowls, ridges, valleys, slopes and folds are quantifiable. The method uses contour context rather that filter banks or a solution to the shape from shading problem to quality the form of a region's brightness function. Region segmentation (distinguishing smooth from textured regions) is also performed with a largely filter free segmentation scheme with a brief outline of the scheme being given.
Template Deformation Constrained by Shape Priors
Authors: Mark B. Skouson, Zhi-Pei Liang
Venue: CVPR
Year: 2000

This paper describes a technique for using prior knowledge of shape variations to help guide a volumetric deformation process. The volumetric transform maintains the topology of a template while matching the template to an image under study. A statistical model is used to describe inter- and intra-shape correlations in the template. The parameters for the shape model are learned by performing eigenshape analysis on a training set consisting of deformations of a single template to several typical segmentations. The shape model is used to guide the deformation by the inclusion of a term to the deformation cost functional that promotes the most likely deformations according to the shape priors. Some advantages of the proposed method are that it inherently conserves the topology between multiple shapes, and that prelabeling of corresponding points and point ordering of the training set is not needed. Results are presented for segmentation of magnetic resonance and cryosection images with varying contrasts. A qualitative analysis shows that the inclusion of shape priors can significantly improve the final deformation result.
Surface Growing from Stereo Images
Authors: Oskar M. Skrinjar, Hemant D. Tagare, James S. Duncan
Venue: CVPR
Year: 2000

We present a new theoretical result for the problem of surface reconstruction from stereo images. For a given initial seed point, i.e. for a pair of corresponding points in the left and right image, the proposed algorithm grows the surface without directly computing the point correspondences. The method assumes the Lambertian surface reflectance model. Our approach is based on a surface normal calculation from the left and right image gradients. Knowing the surface normal, the algorithm grows the surface in the directions defined by the tangent plane. The algorithm is independent of the camera model, and requires placement of an initial seed point for each surface to be reconstructed. Technical problems associated with errors in the image gradient estimates and camera calibration are discussed and a solution is suggested. In addition to this algorithm, we present a theoretical result that permits one to track surfaces deforming in time, which is often encountered in medical applications (e.g. brain surface deforms during the surgery). These methods of surface reconstruction and deformable surface tracking are applied to the particular problem of brain shift, commonly recognized as one of the main source of errors in surgical navigation systems used in neurosurgery. We also suggest a way to overcome problems associated with brain surface specularities caused by fluids on the brain surface and lights in the operating room. We conclude with experimental results on real brain images and show that the surface reconstruction algorithm is robust to the position of the initial seed point.
Bayesian Super-Resolved Surface Reconstruction from Images
Authors: Vadim N. Smelyansky, Peter C. Cheeseman, David A. Maluf, Robin D. Morris
Venue: CVPR
Year: 2000

Bayesian inference has been used successfully for many problems where the aim is to infer the parameters of a model of interest. In this paper we formulate the three dimensional reconstruction problem as the problem of inferring the parameters of a surface model from image data, and show how Bayesian methods can be used to estimate the parameters of this model given the image data. Thus we recover the three dimensional description of the scene. This approach also gives great flexibility. We can specify the geometrical properties of the model to suit our purpose, and can also use different models for how the surface reflects the light incident upon it. In common with other Bayesian inference problems, the estimation methodology requires that we can simulate the data that would have been recorded for any values of the model parameters. In this application this means that if we have image data we must be able to render the surface model. However it also means that we can infer the parameters of a model whose resolution can be chosen irrespective of the resolution of the images, and may be super-resolved. We present results of the inference of surface models from simulated aerial photographs for the case of super-resolution, where many surface elements project into a single pixel in the low-resolution images.
A Geometric Approach to Blind Deconvolution with Application to Shape from Defocu
Authors: Stefano Soatto, Paolo Favaro
Venue: CVPR
Year: 2000

We propose a solution to the generic "bilinear calibration-estimation problem" when using a quadratic cost function and restricting to (locally) translation-invariant imaging models. We apply the solution to the problem of reconstructing the three-dimensional shape and radiance of a scene from a number of defocused images. Since the imaging process maps the continuum of three-dimensional space onto the discrete pixel grid, rather than discretizing the continuum we exploit the structure of maps between (finite-and infinite-dimensional) Hilbert spaces and arrive at a principled algorithm that does not involve any choice of basis or discretization. Rather, these are uniquely determined by the data, and exploited in a functional singular value decomposition in order to obtain a regularized solution.
Line Net Global Vectorization: an Algorithm and Its Performance Evaluation
Authors: Jiqiang Song, Feng Su, Jibing Chen, Chiew-Lan Tai, Shijie Cai
Venue: CVPR
Year: 2000

In this paper, an efficient global algorithm for vectorizing line drawings is presented. It first extracts a seed segment of a graphic entity from a raster image to obtain its direction and width, then tracks the pixels under the guidance of the direction so that the tracking can track through functions and is not affected by noise and degradation of image quality. Thus, an entity will be vectorized in one step without postprocessing. The relations among lines are also used to realize the continuous vectorization of a line net. The speed and quality of vectorization are greatly improved with this algorithm. The performance evaluation is carried out both by theoretical analysis and by experiments. Comparisons with other vectorization algorithms are also made.
CueVideo: A System for Cross-Modal Search and Browse of Video Databases
Authors: Tanveer Fathima Syeda-Mahmood, Savitha Srinivasan, Arnon Amir, Dulce B. Ponceleon, Brian Blanchard, Dragutin Petkovic
Venue: CVPR
Year: 2000

The detection and recognition of events is a challenging problem in video databases. It involves cross-linking and combining information available in multiple modalities such as audio, video and associated text metadata. CueVideo is a system designed for the discovery and recognition of specific events called topics of discussion through advanced video summarization and cross-modal indexing. It supports search for relevant video content through several modes of video summarization including storyboards, moving storyboards and time-scale modified audio summarization. It also enables the recognition and indexing of topical events through cross-model search of audio and video content based on text and image queries respectively.
Arbitrary View Position and Direction Rendering for Large-Scale Scenes
Authors: Takuji Takahashi, Hiroshi Kawasaki, Katsushi Ikeuchi, Masao Sakauchi
Venue: CVPR
Year: 2000

This paper presents a new method for rendering views, especially those of large-scale scenes, such as broad city landscapes. The main contribution of our method is that we are able to easily render any view from an arbitrary point to an arbitrary direction on the ground in a virtual environment. Our method belongs to the family of work that employs plenoptic functions; however, unlike other works of this type, this particular method allows us to render a novel view from almost any point on the plane at which images are taken. Previous methods, on the other hand, have some restraints concerning their re-constructable area. Thus, when synthesizing a large-scale virtual environment such as a city, our method has a great advantage. One of the applications of our method is a driving simulator in the ITS domain. We can generate any view on any lane on the road from images taken by running along just one lane. Our method, using an omni-directional camera or a measuring device of a similar type, first captures panoramic images by running along a straight line, recording the capturing position of each image. When rendering, the method divides the stored panoramic images into vertical slits, selects some suitable ones based on our theory, and reassembles them for generating an image. The method can make a virtual city with walk-through capabilities. In that virtual city, people can move and look rather freely. In this paper, we describe the basic theory of a new plenoptic function, analyze the applicable areas of the theory and the characteristics of generated images, and demonstrate a complete working system using both indoor and outdoor scenes.