OpenRaiser/Intern-Atlas huggingface dataset:
Linear Image Coding for Regression and Classification using the Tensor-rank Principle
Authors: Amnon Shashua, Anat Levin
Venue: CVPR
Year: 2001

Given a collection of images (matrices) representing a "class" of objects we present a method for extracting the commonalities of the image space directly from the matrix representations (rather than from the vectorized representation which one would normally do in a PCA approach, for example). The general idea is to consider the collection of matrices as a tensor and to look for an approximation of its tensor-rank. The tensor-rank approximation is designed such that the SVD decomposition emerges in the special case where all the input matrices are the repeatition of a single matrix. We evaluate the coding technique both in terms of regression, i.e., the efficiency of the technique for functional approximation, and classification. We find that for regression the tensor-rank coding, as a dimensionality reduction technique, significantly outperforms other techniques like PCA. As for classification, the tensor-rank coding is at is best when the number of training examples is very small.
Frame-Rate Spatial Referencing Based on Invariant Indexing and Alignment with Application to Laser Retinal Surgery
Authors: Hong Shen 0003, Charles V. Stewart, Badrinath Roysam, Gang Lin, Howard L. Tanenbaum
Venue: CVPR
Year: 2001

The paper describes a fast feature-based algorithm for accurately estimating the absolute location of a surgical tool (a laser), pointed at the curved human retina, from a series of image frames. The method is capable of a 91% success rate with just a third of the feature extraction computation, taking 37 ms overall per image on a 900 MHz Pentium III. The success rate approaches 100% when the feature extraction is allowed to run to completion. The median error is 0.92 pixels with 512/spl times/512 8-bit image frames. Making a significant break from prior incremental tracking-based efforts, we propose a framework that involves extensive offline precomputation to build a "spatial map" and high-speed online "spatial referencing" to rapidly register each surgical image with this spatial map. The spatial referencing technique is designed around the idea of quasi-invariant indexing. Similarity invariants, locally valid despite the curved nature of the retina, are computed from constellations of vascular landmarks. These are detected using a high-speed algorithm that recursively traces the blood vessel structure. Invariant indexing establishes initial correspondences between landmarks from the online image and landmarks stored in the spatial map. Alignment and verification steps gradually extend the similarity transformation computed from these initial correspondences to a global, high-order transformation. The spatial map is precomputed to contain mosaics, distance maps and an invariant database, all designed to make these spatial referencing computations extremely fast.
3-D Interpretation of Single Line Drawings Based on Entropy Minimization Principle
Authors: Kenji Shoji, Kazunori Kato, Fubito Toyama
Venue: CVPR
Year: 2001

The human visual system can interpret two-dimensional (2-D) line drawings like the Necker cube as three-dimensional (3-D) wire frames. On this human ability Thomas Marill presented two important papers. First one proposed the 3-D interpretation model based on the principle to minimize the standard deviation of the angles between line segments in 3-D wire frame (MSDA), and reported the results of simulation experiments. Second one proposed the principle to minimize the description length on the internal representation in visual system. Motivated by Marill's principle to minimize the description length, we propose a principle to minimize the entropy of angle distribution between line segments in a 3-D wire frame (MEAD), which is more general than the MSDA one. And we implement the principle MEAD using a genetic algorithm (GA) as a simulation program. The results of simulation experiments show that the proposed principle of MEAD is more appropriate than the MSDA and another principle.
Spatial Lesion Indexing for Medical Image Databases Using Force Histograms
Authors: Chi-Ren Shyu, Pascal Matsakis
Venue: CVPR
Year: 2001

It is often difficult to find a well-principled approach for the selection of a spatial indexing mechanism for medical image databases. Spatial information concerning lesions in medical images is critically important in disease diagnosis and plays an important role in image retrieval. Unfortunately, images are rarely indexed properly for clinically useful retrieval. One example is the well-known R-tree and its variants which index image objects based on their physical locations in an "absolute" way. However, such information is not meaningful in medical content-based image retrieval systems, and the approaches suffer from problems caused by variations in object size and shape, imprecise image centering, etc. A more appropriate approach, which does not require object registration, is to model the spatial relationships between lesions and anatomical landmarks. To convey diagnostic information, lesions must exist in certain locations with regard to landmarks. In this paper, we show that the histogram of forces (which represents the relative position between two objects) provides an efficient spatial indexing mechanism in the medical domain.
Learning Generative Models of Scene Features
Authors: Robert Sim, Gregory Dudek
Venue: CVPR
Year: 2001

We present a method for learning a set of generative models which are suitable for representing variations of selected image-domain features of the scene as a function of changes in the camera viewpoint. Such models are important for robotic tasks, such as probabilistic position estimation (i.e. localization), as well as visualization. Our approach entails the selection of image-domain features, as well as the synthesis of models of their visual behavior. The model we propose is capable of generating maximum likelihood views of automatically selected features, as well as a measure of the likelihood of a particular view from a particular camera position. Training the models involves regularizing observations of the features from known camera locations. The uncertainty of the model is evaluated using cross validation. The features themselves are initially selected automatically as salient points by a measure of visual attention, and are tracked across multiple views. While the motivation for this work is for robot localization, the results have implications for image interpolation, virtual scene reconstruction and object recognition. The paper presents a formulation of the problem and illustrative experimental results.
Investigation of Measures for Grouping by Graph Partitioning
Authors: Padmanabhan Soundararajan, Sudeep Sarkar
Venue: CVPR
Year: 2001

Grouping by graph partitioning is an effective engine for perceptual organization. This graph partitioning process, mainly motivated by computational efficiency considerations, is usually implemented as recursive bi-partitioning, where at each step the graph is broken into two parts based on a partitioning measure. We study four such measures, namely, the minimum cut, average cut, Shi-Malik normalized cut, and a variation of the Shi-Malik normalized cut. Using probabilistic analysis we show that the minimization of the average cut and the normalized cut measure, using recursive bi-partitioning will, on an average, result in the correct segmentation. The minimum cut and the variation of the normalized cut will, on an average, not result in the correct segmentation and we can precisely express the conditions. Based on a rigorous empirical evaluation, we also show that, in practice, the quality of the groups generated using minimum, average or normalized cuts are statistically equivalent for object recognition, i.e. the best, the mean, and the variation of the qualities are statistically equivalent. We also find that for certain image classes, such as aerial and scenes with man-made objects in man-made surroundings, the performance of grouping by partitioning is the worst, irrespective of the cut measure.
A Framework for Sensor Planning and Control with Applications to Vision Guided Multi-robot Systems
Authors: John R. Spletzer, Camillo J. Taylor
Venue: CVPR
Year: 2001

The paper presents an approach to the problem of controlling the configuration of a team of mobile agents equipped with cameras so as to optimize the quality of the estimates derived from their measurements. The issue of optimizing the robots' configuration is particularly important in the context of teams equipped with vision sensors since most estimation schemes of interest will involve some form of triangulation. We provide a theoretical framework for tackling the sensor planning problem and a practical computational strategy, inspired by work on particle filtering, for implementing the approach. The ideas have been demonstrated both in simulation and on actual robotic platforms. The results indicate that the framework is able to solve fairly difficult sensor planning problems online without requiring excessive amounts of computational resources.
A Framework for Multiple Snakes
Authors: Thitiwan Srinark, Chandra Kambhamettu
Venue: CVPR
Year: 2001

A framework for segmenting multiple objects in an image based on deformable contours is proposed. In this framework, multiple snakes are applied with a new kind of energy called the "group energy." The group energy is introduced to handle sharing of properties across multiple objects in the image. Our framework allows contours of "strong objects" to guide contours of "weak objects" by utilizing deformable templates. We also automatically generate the necessary weighting parameters for energy minimization. A new approach for multiple snake optimization which is based on dynamic programming is also proposed. We applied our framework to the problem of image analysis of gene expression in microarrays. Comprehensive experiments were performed and comparisons were made between the individual energy based method and the proposed group energy based method. Our results are highly encouraging and have many potential applications in a variety of tracking scenarios.
Three-Dimensional Medial Shape Representation Incorporating Object Variability
Authors: Martin Styner, Guido Gerig
Venue: CVPR
Year: 2001

The paper presents a novel processing scheme for the automatic computation of a medial shape model which is representative for an object population with shape variability. The sensitivity of medial descriptions to object variations and small boundary perturbations are fundamental problems of any skeletonization technique. These problems are approached with the computation of a model with common medial branching topology and grid sampling. This model is then used for a medial shape description of individual objects via a constrained model fit. The process starts from parametric 3D boundary representations with existing point-to-point homology between objects. The Voronoi diagram of each sampled object boundary is grouped into medial sheets and simplified by a pruning algorithm using a volumetric contribution criterion. Medial sheets are combined to form a common medial branching topology. Finally, the medial sheets are sampled and represented as meshes of medial primitives. We present new results on populations of up to 184 biological objects. For these objects, the common medial branching topology is described by a small number of sheets. Despite the coarse medial sampling, a close approximation of individual objects is achieved.
Dimension Recognition and Geometry Reconstruction in Vectorization of Engineering Drawings
Authors: Feng Su, Jiqiang Song, Chiew-Lan Tai, Shijie Cai
Venue: CVPR
Year: 2001

This paper presents a novel approach for recognizing and interpreting dimensions in engineering drawings. It starts by detecting potential dimension frames, each comprising only the line and text components of a dimension, then verifies them by detecting the dimension symbols. By removing the prerequisite of symbol recognition from detection of dimension sets, our method is capable of handling low quality drawings. We also propose a reconstruction algorithm for rebuilding the drawing entities based on the recognized dimension annotations. A coordinate grid structure is introduced to represent and analyze two-dimensional spatial constraints between entities; this simplifies and unifies the process of rectifying deviations of entity dimensions induced during scanning and vectorization.
Dynamic Shadow Elimination for Multi-Projector Displays
Authors: Rahul Sukthankar, Tat-Jen Cham, Gita Sukthankar
Venue: CVPR
Year: 2001

A major problem with interactive displays based on front-projection is that users cast undesirable shadows on the display surface. This situation is only partially addressed by mounting a single projector at an extreme angle and pre-warping the projected image to undo keystoning distortions. This paper demonstrates that shadows can be muted by redundantly illuminating the display surface using multiple projectors, all mounted at different locations. However, this technique alone does not eliminate shadows: multiple projectors create multiple dark regions on the surface (penumbral occlusions). We solve the problem by using cameras to automatically identify occlusions as they occur and dynamically adjust each projector's output so that additional light is projected onto each partially-occluded patch. The system is self-calibrating: relevant homographies relating projectors, cameras and the display surface are recovered by observing the distortions induced in projected calibration patterns. The resulting redundantly-projected display retains the high image quality of a single-projector system while dynamically correcting for all penumbral occlusions. Our initial two-projector implementation operates at 3 Hz.
A tool for decomposing 3D discrete objects
Authors: Stina Svensson, Gabriella Sanniti di Baja
Venue: CVPR
Year: 2001

We suggest a tool to decompose 3D discrete objects into simpler parts. Decomposition is guided by object thickness and is based on region growing. Region growing starts from a set of seeds suitably identified in the innermost regions of the object. Our tool originates a decomposition into nearly convex and elongated parts. The decomposition is not affected remarkably by object rotation and has a limited computational cost. Since different recent imaging techniques give easier and cheaper access to 3D images, it is likely to believe that voxel-based decomposition tools, like the one here introduced, are of interest for future applications.
A Representation of Image Structure and Its Application to Object Selection Using Freehand Sketches
Authors: Kar-Han Tan, Narendra Ahuja
Venue: CVPR
Year: 2001

We present an algorithm for computing a representation of image structure, or image segmentation, and use it for selecting objects in the image with freehand sketches drawn by the user over the image. The sketches are mapped onto image segments whose union forms the intended object. The mapping operation is performed with the aid of a simplicial decomposition of the image segmentation-a triangulation formed with vertices chosen to lie along the medial axes of the segments. Each edge of a triangle lies entirely inside the two segments that contains its vertices. This decomposition captures the adjacency information about the segments as well as the shape of the segment boundaries. Any object boundary is completely contained in a set of triangles. The triangles are also used to formulate the problem of estimating gradual photometric transition across an object boundary, called alpha channel estimation, as a set of local, intratriangle alpha channel estimation problems that can then be solved more accurately, independently, and in parallel. Experimental results are included to show how the algorithm allows selection of image objects with complex boundaries using roughly drawn simple sketches.
Scene-Consistent Detection of Feature Points in Video Sequences
Authors: Ariel Tankus, Yehezkel Yeshurun
Venue: CVPR
Year: 2001

Detection of feature points in images is an important preprocessing stage for many algorithms in computer vision. We address the problem of detection of feature points in video sequences of 3D scenes, which could be mainly used for obtaining scene correspondence. The main feature we use is the zero crossing of the intensity gradient argument. We analytically show that this local feature corresponds to specific constraints on the local 3D geometry of the scene, thus ensuring that the detected points are based on real 3D features. We present a robust algorithm that tracks the detected points along a video sequence, and suggest some criteria for quantitative evaluation of such algorithms. These criteria serve in a comparison of the suggested operator with two other feature trackers. The suggested criteria are generic and could serve other researchers as well for performance evaluation of stable point detectors.
Dynamic Depth Recovery from Multiple Synchronized Video Streams
Authors: Hai Tao, Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: CVPR
Year: 2001

This paper addresses the problem of extracting depth information of non-rigid dynamic 3D scenes from multiple synchronized video streams. Three main issues are discussed in this context. (i) temporally consistent depth estimation, (ii) sharp depth discontinuity estimation around object boundaries, and (iii) enforcement of the global visibility constraint. We present a framework in which the scene is modeled as a collection of 3D piecewise planar surface patches induced by color based image segmentation. This representation is continuously estimated using an incremental formulation in which the 3D geometric, motion, and global visibility constraints are enforced over space and time. The proposed algorithm optimizes a cost function that incorporates the spatial color consistency constraint and a smooth scene motion model.
Calibrated, Registered Images of an Extended Urban Area
Authors: Seth J. Teller, Matthew E. Antone, Zachary Bodnar, Michael Bosse, Satyan R. Coorg, Manish Jethwa, Neel Master
Venue: CVPR
Year: 2001

We describe a dataset of several thousand calibrated, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination in an outdoor region spanning hundreds of meters. All image, feature, calibration, and geo-referencing data are available at http://city.lcs.mit.edu/data. Calibrated imagery is of fundamental interest in a wide variety of applications. We have made this data available in the belief that researchers in computer graphics, computer vision, photogrammetry and digital cartography will find it useful in several ways: as a test set for their own algorithms; as a calibrated image set for applications such as image-based rendering, metric 3D reconstruction, and appearance recovery; and as controlled imagery for integration into existing GIS systems and applications. The Web-based interface to the data provides interactive viewing of high-dynamic-range images and mosaics; extracted edge and point features; intrinsic and extrinsic calibration, along with maps of the ground context in which the images were acquired; the spatial adjacency relationships among images; the epipolar geometry relating adjacent images; compass and absolute scale overlays; and quantitative consistency measures for the calibration data.
First Order Tensor Voting, and Application to 3-D Scale Analysis
Authors: Wai-Shun Tong, Chi-Keung Tang, Gérard G. Medioni
Venue: CVPR
Year: 2001

Many computer vision systems depend on reliable detection of 3D boundaries and regions in order to proceed. In the presence of outliers, missing data and orientation discontinuities due to occlusion, it is difficult to detect boundaries and interpolate data without over-smoothing important feature curves. The authora address these problems by incorporating first order tensor information into the tensor voting formalism, which is second-order based. To propagate an adaptive smoothness constraint at a preferred orientation non-iteratively, we vote for a first order tensor (or vector) to capture polarity and orientation information. To integrate first and second order tensors, we propose an algorithm for inferring the proper scale based on the continuity constraint, and preserving the finest details. Given a noisy 3D point set, the new and improved formalism can better localize boundary curves and orientation discontinuities. Unlike many approaches that over-smooth features, or delay the handling of boundaries and discontinuities until model misfit occurs, the interaction of smooth features, boundaries, discontinuities, outliers are encoded at the representation level. We present results from a variety of datasets to show the efficacy of the improved formalism.
Epipolar Geometry Estimation for Non-Static Scenes by 4D Tensor Voting
Authors: Wai-Shun Tong, Chi-Keung Tang, Gérard G. Medioni
Venue: CVPR
Year: 2001

In the presence of false matches and moving objects, image registration is challenging, as outlier rejection, matching and registration become interdependent. We present an efficient and robust method, 4D tensor voting to estimate epipolar geometries for non-static scenes, and identify matching points due to salient and independent motions. Unlike other optimization techniques, data communication in 4D tensor voting does not involve any iterative search. Thus, initialization, local optimum, convergence, and dimensionality of parameter space are not problematic. Like the 8D counterpart, the only assumption we make is the pinhole camera model. Two advancements are made in this work. First, we reduce the dimensionality, and the 4D joint image space is an isotropic and orthogonal one, validating the general assumptions of tensor voting. This improvement is evidenced by the facts that only two passes are needed, and that 4D tensor voting can tolerate an even larger noise/signal ratio (up to a ratio of five). Second, instead of discarding motion pixels as outliers, we successively extract the epipolar geometries contributed by the static background and by the matching points due to salient motions. Only two frames are needed, and no simplifying assumption (such as affine camera model or homographic model between images) is made. Our 4D algorithm consists of two stages: local continuity constraint propagation to remove outliers, and global consistency checking to localize a 4D topological point cone. Results on challenging datasets are presented.
Dynamic Coupled Component Analysis
Authors: Fernando De la Torre, Michael J. Black
Venue: CVPR
Year: 2001

We present a method for simultaneously learning linear models of multiple high dimensional data sets and the dependencies between them. For example, we learn asymmetrically coupled linear models for the faces of two different people and show how these models can be used to animate one face given a video sequence of the other. We pose the problem as a form of Asymmetric Coupled Component Analysis (ACCA) in which we simultaneously learn the subspaces for reducing the dimensionality of each dataset while coupling the parameters of the low dimensional representations. Additionally, a dynamic form of ACCA is proposed, that extends this work to model temporal dependencies in the data sets. To account for outliers and missing data, we formulate the problem in a statistically robust estimation framework. We review connections with previous work and illustrate the method with examples of synthesized dancing and the animation of facial avatars.