OpenRaiser/Intern-Atlas huggingface dataset:
Light Field Rendering for Large-Scale Scenes
Authors: Hiroshi Kawasaki, Katsushi Ikeuchi, Masao Sakauchi
Venue: CVPR
Year: 2001

In this paper, we present an efficient method to synthesize large-scale scenes, such as broad city landscapes. To date, model based approaches have mainly been adopted for this purpose, and some fairly convincing polygon cities have been successfully generated. However, the shapes of real world objects are usually very complicated and it is infeasible to model an entire city realistically. On the other hand, image based approaches have been attempted only recently. Image based methods are effective for realistic rendering, but their huge data sets and restrictions on interactivity pose serious problems for an actual application. Thus, we propose a hybrid method, which uses simple shapes such as planes to model the city, and applies image based techniques to add realism. It can be performed automatically through a simple image capturing process. Further, we also analyze the relationship between error and number of needed images to reduce the data size.
Object Based Segmentation of Video Using Color, Motion and Spatial Information
Authors: Sohaib Khan, Mubarak Shah
Venue: CVPR
Year: 2001

Video segmentation is different from segmentation of a single image. While several correct solutions may exist for segmenting a single image, there needs to be a consistency among segmentations of each frame for video segmentation. Previous approaches of video segmentation concentrate on motion, or combine motion and color information in a batch fashion. We propose a maximum a posteriori probability (MAP) framework that uses multiple cues, like spatial location, color and motion, for segmentation. We assign weights to color and motion terms, which are adjusted at every pixel, based on a confidence measure of each feature. We also discuss the appropriate modeling of PDFs of each feature of a region. The correct modeling of the spatial PDF imposes temporal consistency among segments in consecutive frames. This approach unifies the strengths of both color segmentation and motion segmentation in one framework, and shows good results on videos that are not suited for either of these approaches.
Automatic Description of Buildings with Complex Rooftops from Multiple Images
Authors: Zu Whan Kim, Andres Huertas, Ramakant Nevatia
Venue: CVPR
Year: 2001

We present a model-based approach to detecting and describing compositions of buildings with complex rooftops. Previous approaches have dealt with either simpler models or models which lack geometric information. In spite of increasing model complexity, we maintain the computation affordable by effectively using multiple overlapping images. We obtain rooftop hypotheses in 3-D by using 3-D lines and junctions generated from multiple images. Image-derived unedited elevation data is used to assist feature matching, and to generate rough cues of the presence of 3-D structures. Experimental results are shown on complex buildings.
On Solving 2D and 3D Puzzles Using Curve Matching
Authors: Weixin Kong, Benjamin B. Kimia
Venue: CVPR
Year: 2001

We approach the problem of 2D and 3D puzzle solving by matching the geometric features of puzzle pieces three at a time. First, we define an affinity measure for a pair of pieces in two stages, one based on a coarse-scale representation of curves and one based on a fine-scale elastic curve matching method. This re-examination of the top coarse-scale matches at the fine scale results in an optimal relative pose as well as a matching cost which is used as the affinity measure for a pair of pieces. Pairings with overlapping boundaries are impossible and are removed from further consideration, resulting in a set of top valid candidate pairs. Second, triples arising from generic junctions are formed from this rank-ordered list of pairs. The puzzle is solved by a recursive grouping of triples using a best-first search strategy, with backtracking in the case of overlapping pieces. We also generalize aspects of this approach to matching of 3D pieces. Specifically, ridges of 3D fragments scanned using a laser range finder are detected using a dynamic programming method. A pair of ridges are matched using a generalization of the 2D curve matching approach to space curves by using an energy solution involving curvature and torsion, which are computed using a novel robust numerical method. The reconstruction of map fragments and broken tiles using this method is illustrated.
Tracking the Optic Nerve Head in OCT Video Using Dual Eigenspaces and an Adaptive Vascular Distribution Model
Authors: Dara Koozekanani, Kim L. Boyer, Cynthia Roberts, Steven Katz
Venue: CVPR
Year: 2001

Optical coherence tomography (OCT) is a novel ophthalmic imaging modality generating cross sectional views of the retina. OCT systems form images in 1.5 seconds by directing a superluminescent diode (SLD) beam over the retinal surface. Involuntary ocular motion may occur, however, causing incorrect locations to be imaged. This motion may leave no obvious artifacts and thus go undetected. For glaucoma monitoring especially, knowing the measurement location is crucial. The commercially available OCT system displays a near-IR video of the SLD beam traversing the retinal around the optic nerve head. We developed a prototype system to detect the nerve head and SLD beam position in this video, and report the actual scan path relative to the nerve head. This system must cope with low image contrast and few reliable retinal features. In its adaptive model generation phase, the system directly detects vasculature and the nerve head and builds an individual model of the vascular pattern. The nerve head identification is multi-tiered, using a novel, dual-eigenspace technique and a geometric comparison of detected vessel positions and nerve head hypotheses. In its operational phase, a correspondence is achieved between detected vasculature and the model. The system was evaluated on video of three subjects not used to form the eigenspaces. The system located the optic nerve head to within 5 pixels in 99% of 2800 video fields manually inspected, and was thus able to determine the true scan path relative to the nerve head.
Image-based Modeling and Rendering of Surfaces with Arbitrary BRDFs
Authors: Melissa L. Koudelka, Peter N. Belhumeur, Sebastian Magda, David J. Kriegman
Venue: CVPR
Year: 2001

A goal of image-based rendering is to synthesize as realistically as possible man made and natural objects. The paper presents a method for image-based modeling and rendering of objects with arbitrary (possibly anisotropic and spatially varying) BRDFs. An object is modeled by sampling the surface's incident light field to reconstruct a non-parametric apparent BRDF at each visible point on the surface, This can be used to render the object from the same viewpoint but under arbitrarily specified illumination. We demonstrate how these object models can be embedded in synthetic scenes and rendered under global illumination which captures the interreflections between real and synthetic objects. We also show how these image-based models can be automatically composited onto video footage with dynamic illumination so that the effects (shadows and shading) of the lighting on the composited object match those of the scene.
A Model-Based Road Sign Identification System
Authors: Yves Bérubé Lauzière, Denis Gingras, Frank P. Ferrie
Venue: CVPR
Year: 2001

A road sign (RS) recognition system poses a real challenge for machine vision. It must recognize a wide variety of RSs under considerable variations in illumination and imaging geometry-all in real-time. Such a system is presented, with emphasis on the system architecture and specific model-based techniques used in the different processing steps. Central to this are a unique physics-based color detection approach and a novel template matching scheme for planar objects. Since the approach strongly relies on modelling for both detection and recognition, it offers the advantage of being reconfigurable by changing only a few parameters. The system is modular with respect to the sensor and the recognition data structure is simple to extend and maintain, and is easily adaptable to different regulations, e.g. North American vs European RSs. The data needed for recognition is computed automatically by modelling image formation with a few geometrical parameters. Experimental results are presented which demonstrate the performance of the system in a real task environment with high overall performance.
Nine Points of Light: Acquiring Subspaces for Face Recognition under Variable Lighting
Authors: Kuang-Chih Lee, Jeffrey Ho, David J. Kriegman
Venue: CVPR
Year: 2001

Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. Basis images spanning this space are usually obtained in one of two ways: A large number of images of the object under different conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, a 3D model (perhaps reconstructed from images) is used to render virtual images under either point sources from which a subspace is derived using PCA or more recently under diffuse synthetic lighting based on spherical harmonics. In this paper we show that there exists a configuration of nine point light source directions such that by taking nine images of each individual under these single sources, the resulting subspace is effective at recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex intermediate steps such as PCA and 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or physically construct complex diffuse (harmonic) light fields. We provide both theoretical and empirical results to explain why these linear spaces should be good for recognition.
A Computer Vision System for On-Screen Item Selection by Finger Pointing
Authors: Mi-Suen Lee, Daphna Weinshall, Eric Cohen-Solal
Venue: CVPR
Year: 2001

Pointing at planar surfaces such as TV and computer monitors or projection screens can be a useful mode of interaction between humans and machines. To a large extent what seems to hinder the use of vision in such practical applications is the difficulty of the computational task, which is typically defined as 3-D reconstruction from uncalibrated 2-D images of a non-static scene. We describe below two designs where, using one or two cameras, the target of pointing on a flat monitor or screen is identified without 3-D inference, using only image morphing and line intersection. This is accomplished by registering the images with the target plane. When used to identify a pointing target on a surface hidden from the camera (e.g., a computer monitor which supports the camera itself as in most PC configurations), we add aperture(s) coplanar with the target surface in front of the camera(s). We describe experimental results showing a fully automated procedure for pointing target detection with high accuracy. The simplicity of our method and its robustness, as well as the relative accuracy of our results, can make pointing a practical means of human-machine interaction.
Adaptive Binning and Dissimilarity Measure for Image Retrieval and Classification
Authors: Wee Kheng Leow, Rui Li 0053
Venue: CVPR
Year: 2001

Color histogram is an important part of content-based image retrieval systems. It is a common understanding that histograms that adapt to images can represent their color distributions more efficiently than histograms with fixed binnings. However, among existing dissimilarity measures, only the Earth Mover's Distance can compare histograms with different binnings. This paper presents a detailed quantitative study of fixed and adaptive binnings and the corresponding dissimilarity measures. An efficient dissimilarity measure is proposed for comparing histograms with different binnings. Extensive test results show that adaptive binning and dissimilarity produce the best overall performance, in terms of good accuracy, small number of bins, no empty bin, and efficient computation, compared to existing fixed binning schemes and dissimilarity measures.
Time-varying Shape Tensors for Scenes with Multiply Moving Points
Authors: Anat Levin, Lior Wolf, Amnon Shashua
Venue: CVPR
Year: 2001

We derive single view indexing functions for dynamic scenes - where dynamic is defined as a scene consisting of multiply moving points each moving independently with constant velocity. The indexing functions we derive are view independent and form a generalization of the "shape tensors" associated with rigid scenes by introducing a time-varying parameter We derive those indexing functions under full 3D projective, 3D affine, and various reduced configurations. The indexing functions were implemented and tested for matching against objects for which their non-rigid motion is an intrinsic part of their character - human gait recognition and hand gesture identification are the two chosen application examples.
Constrained Minimum Cut for Classification Using Labeled and Unlabeled Data
Authors: C. H. Li
Venue: CVPR
Year: 2001

The use of unlabeled data has lead to an improvement in classification accuracy for a variety of classification problems via co-training approaches. In the co-training approach, the data has to be available in a dual view representation or two distinct classifiers are required. In this paper, a unified energy equation for classification combining labeled data and unlabeled data is introduced. This classification formulation is posed as a constrained minimum cut problem integrating labeling information on labeled data and cluster similarity information on unlabeled data for joint estimation. A novel constrained randomized contraction algorithm is proposed for finding the solution to the constrained minimum cuts problem. Experimental results on standard datasets and synthetic datasets are presented.
Robust Change Detection by Fusing Intensity and Texture Differences
Authors: Liyuan Li, Maylor K. H. Leung
Venue: CVPR
Year: 2001

The paper proposes a novel technique for robust change detection based upon the integration of intensity and texture differences between two frames. A new texture difference measure based on the relations between gradient vectors is described. The robustness of the measure with respect to noise and illumination changes has been analyzed. Two ways to integrate the intensity and texture differences are proposed. The first combines two measures according to the weightage of texture evidence, while the second takes into additional constraint of smoothness. The parameters of the algorithm are selected automatically. The computational complexity analysis indicates that the proposed technique can run in real-time. Experimental results show that by exploiting both intensity and texture differences for change detection, one can obtain much better segmentation results than using the intensity or structure difference alone.
Separation of Diffuse and Specular Reflection in Color Images
Authors: Stephen Lin 0001, Heung-Yeung Shum
Venue: CVPR
Year: 2001

The presence of specular reflections in images can lead many traditional computer vision algorithms to produce erroneous results. To address this problem, we propose a method based on the neutral interface reflection model for separating the diffuse and specular reflection components in color images. From two photometric images without calibrated lighting, the illuminant chromaticity is estimated, and the RGB intensities of the two reflection components are computed for each pixel using a linear model of surface reflectance. Unlike most previous methods, the presented technique does not assume any dependencies among pixels, such as regionally uniform surface reflectance.
On the Fundamental Limits of Reconstruction-Based Super-Resolution Algorithms
Authors: Zhouchen Lin, Heung-Yeung Shum
Venue: CVPR
Year: 2001

Super-resolution is a technique that produces higher resolution images from low resolution images (LRIs). In practice, the improvement in resolution is limited. The aim of this paper is to address the problem of whether fundamental limits exist for super-resolution? Specifically, this paper provides explicit limits for a major class of super-resolution algorithms, called reconstruction-based algorithms, under both real and synthetic conditions. Our analysis is based on perturbation theory of linear systems. We also show that a sufficient number of LRIs can be determined to reach the limit. Both real and synthetic experiments are carried out to verify our analysis.
A Vision System for Fast 3D Model Reconstruction
Authors: Huei-Yung Lin, Murali Subbarao
Venue: CVPR
Year: 2001

A desktop vision system is presented for complete 3D model acquisition. It is fast, low-cost, and accurate. Partial 3D shapes and texture information are acquired from multiple viewing directions using rotational stereo and shape from focus (SFF). The resulting range images are registered to a common coordinate system and a surface representation is created for each range image. The resulting surfaces are integrated using a new algorithm named Region-of-Construction. Unlike previous approaches, the Region-of-Construction algorithm directly exploits the structure of the raw range images. The algorithm determines regions in range images corresponding to non-redundant surfaces which can be stitched along the boundaries to construct the complete 3D surface model. The algorithm is computationally efficient and less sensitive to registration error. It also has the ability to construct complete 3D models of complex objects with holes. A photo realistic 3D model is obtained by mapping texture information onto the complete surface model representing 3D shape. Experimental results for several real objects are presented.
Relighting with the Reflected Irradiance Field: Representation, Sampling and Reconstruction
Authors: Zhouchen Lin, Tien-Tsin Wong, Heung-Yeung Shum
Venue: CVPR
Year: 2001

Image-based relighting (IBL) is a technique to change the illumination of an image-based object/scene. In this paper, we define a representation called the reflected irradiance field which records the reflection from an object surface irradiated by a point light source that moves on a plane. This representation is dual to that of the light field. It synthesizes a novel image under a different illumination by interpolating and superimposing appropriate recorded samples. Furthermore, we study the minimum sampling problem of the reflected irradiance field, i.e., how many point light sources are needed during sampling. We find that there exists a geometry-independent bound for the sampling interval whenever the second-order derivatives of the surface BRDF and the minimum depth of the scene are bounded. This bound ensures that the error in the reconstructed image is controlled by a given tolerance, regardless of the geometry. Experiments on both synthetic and real surfaces are conducted to verify our analysis.
Skewed Symmetry Groups
Authors: Yanxi Liu 0001, Robert T. Collins
Venue: CVPR
Year: 2001

We introduce the term skewed symmetry groups and provide a complete theoretical treatment for 2D wallpaper groups under affine transformations. For the first time, a given periodic pattern can be classified not simply by its Euclidean symmetry group but,by its highest "potential" symmetry group under affine deformation. A concise wallpaper group migration map is constructed that separates the 17 affinely deformed wallpaper groups into small, distinct orbits. The practical value of this result includes a novel indexing and retrieval scheme for regular patterns, and a maximal-symmetry-based method for estimating shape and orientation from texture under unknown views.
Time-constrained Dynamic Semantic Compression for Video Indexing and Interactive Searching
Authors: Tiecheng Liu, John R. Kender
Venue: CVPR
Year: 2001

We define and present a new model for semantic video compression and searching, and apply this model to several video genres, with special emphasis on instructional videos. In this model, a semantic distance based on the video genre is computed between adjacent frames in a dynamic video buffer of predetermined size, and redundant "unkey" frames leak from the buffer interior into a data structure "leakage history directed acyclic graph" (LHDAG) which records the relative importance of these frames. What exits the buffer forms a highly compressed video stream consisting of only the most semantically significant video frames, whereas the LHDAG permits efficient semantic exploration of the video interior. This novel hierarchical but context-sensitive data structure LHDAG permits the searching of the video at display rates that are proportional to visual significance, at levels of semantic density selectable by the user. The data structure is simple to create and query, and appears to be more psychologically plausible than more straightforward fixed sampling indexing schemes. We empirically display and mathematically analyze the relationships of the video buffer's leaking rate, buffer size, and exit delay, and demonstrate its performance on several extended videos. The flexibility of the method is indicated by its demonstration on two very different definitions of semantic significance: color similarity, and content ("ink pixel") similarity.
A Probabilistic Framework for Graph Clustering
Authors: Bin Luo 0001, Antonio Robles-Kelly, Andrea Torsello, Richard C. Wilson 0001, Edwin R. Hancock
Venue: CVPR
Year: 2001

The paper describes a probabilistic framework for graph clustering. We commence from a set of pairwise distances between graph structures. From this set of distances, we use a mixture model to characterize the pairwise affinity of the different graphs. We present an EM-like algorithm for clustering the graphs by iteratively updating the elements of the affinity matrix. In the M-step we apply eigendcomposition to the affinity matrix to locate the principal clusters. In the M-step we update the affinity probabilities. We apply the resulting unsupervised clustering algorithm to two practical problems. The first of these involves locating shape-categories using shock trees extracted from 2D silhouettes. The second problem involves finding the view structure of a polyhedral object using the Delaunay triangulation of corner features.
Provably-Convergent Iterative Methods for Projective Structure from Motion
Authors: Shyjan Mahamud, Martial Hebert, Yasuhiro Omori, Jean Ponce
Venue: CVPR
Year: 2001

The estimation of the projective structure of a scene from image correspondences can be formulated as the minimization of the mean-squared distance between predicted and observed image points with respect to the projection matrices, the scene point positions, and their depths. Since these unknowns are not independent, constraints must be chosen to ensure that the optimization process. is well posed. This paper examines three plausible choices, and shows that the first one leads to the Sturm-Triggs projective factorization algorithm, while the other two lead to new provably-convergent approaches. Experiments with synthetic and real data are used to compare the proposed techniques to the Sturm-Triggs algorithm and bundle adjustment.