OpenRaiser/Intern-Atlas huggingface dataset:
Viewpoint Invariant Texture Matching and Wide Baseline Stereo
Authors: Frederik Schaffalitzky, Andrew Zisserman
Venue: ICCV
Year: 2001

We describe and demonstrate a texture region descriptor which is invariant to affine geometric and photometric transformations, and insensitive to the shape of the texture region. It is applicable to texture patches which are locally planar and have stationary statistics. The novelty of the descriptor is that it is based on statistics aggregated over the region, resulting in richer and more stable descriptors than those computed at a point. Two texture matching applications of this descriptor are demonstrated: (1) it is used to automatically identify, regions of the same type of texture, but with varying surface pose, within a single image; (2) it is used to support wide baseline stereo, i.e. to enable the automatic computation of the epipolar geometry between two images acquired from quite separated viewpoints. Results are presented on several sets of real images.
Generalized Mosaicing
Authors: Yoav Y. Schechner, Shree K. Nayar
Venue: ICCV
Year: 2001

We present an approach that significantly enhances the capabilities of traditional image mosaicing. The key observation is that as a camera moves, it senses each scene point multiple times. We rigidly attach to the camera an optical filter with spatially varying properties, so that multiple measurements are obtained for each scene point under different optical settings. Fusing the data captured in the multiple images yields an image mosaic that includes additional information about the scene. This information can come in the form of extended dynamic range, high spectral quality, or enhancements to other dimensions of imaging. We refer to this approach as generalized mosaicing. The approach was tested using a filter with spatially varying transmittance and a standard 8-bit black/white video camera, to achieve image mosaicing with dynamic range comparable to imaging with a 16-bit camera. In another experiment, we attached a spatially varying spectral filter to the same camera to obtain mosaics that represent the spectral distribution (rather than the usual RGB measurements) of each scene point. We also discuss how generalized mosaicing can be used to explore other imaging dimensions.
Template Matching Approach to Content Based Image Indexing by Low Dimensional Euclidean Embedding
Authors: Haim Schweitzer
Venue: ICCV
Year: 2001

Content based indexing is computed from input that consists of matching values between images and templates. The key idea is to embed both images and templates in a low-dimensional Euclidean space so that matching between embedded images and embedded templates approximates the given input. It is shown that such embedding can be computed by means of a singular value decomposition of the input matrix. Classic principal component analysis is shown to be a special case of the proposed technique, corresponding to the case where the templates and the images are the same.
Recognition of Shapes by Editing Shock Graphs
Authors: Thomas B. Sebastian, Philip N. Klein, Benjamin B. Kimia
Venue: ICCV
Year: 2001

This paper presents a novel recognition framework which is based on matching shock graphs of 2D shape outlines, where the distance between two shapes is defined to be the cost of the least action path deforming one shape to another. Three key ideas render the implementation of this framework practical. First, the shape space is partitioned by defining an equivalence class on shapes, where two shapes with the same shock graph topology are considered to be equivalent. Second, the space of deformations is discretized by defining all deformations with the same sequence of shock graph transitions as equivalent. Shock transitions are points along the deformation where the shock graph topology changes. Third, we employ a graph edit distance algorithm that searches in the space of all possible transition sequences and finds the globally optimal sequence in polynomial time. The effectiveness of the proposed technique in the presence of a variety of visual transformations including occlusion, articulation and deformation of parts, shadow and highlights, viewpoint variation, and boundary perturbations is demonstrated. Indexing into two separate databases of roughly 100 shapes results in 100% accuracy for top three matches and 99.5% for the next three matches.
Model-Based Bundle Adjustment with Application to Face Modeling
Authors: Ying Shan, Zicheng Liu 0001, Zhengyou Zhang
Venue: ICCV
Year: 2001

We present a new model-based bundle adjustment algorithm to recover the 3D model of a scene/object from a sequence of images with unknown motions. Instead of representing scene/object by a collection of isolated 3D features (usually points), our algorithm uses a surface controlled by a small set of parameters. Compared with previous model-based approaches, our approach has the following advantages. First instead of using the model space as a regularizer we directly use it as our search space, thus resulting in a more elegant formulation with fewer unknowns and fewer equations. Second, our algorithm automatically associates tracked points with their correct locations on the surfaces, thereby eliminating the need for a prior 2D-to-3D association. Third, regarding face modeling, we use a very small set of face metrics (meaningful deformations) to parameterize the face geometry, resulting in a smaller search space and a better posed system. Experiments with both synthetic and real data show that this new algorithm is faster, more accurate and more stable than existing ones.
Multi-Frame Infinitesimal Motion Model for the Reconstruction of (Dynamic) Scenes with Multiple Linearly Moving Objects
Authors: Amnon Shashua, Anat Levin
Venue: ICCV
Year: 2001

We introduce new small-motion multi-frame equations applicable to the reconstruction of dynamic scenes in which points are allowed to move along straight-line paths with constant velocity. The motion equations apply to both static and dynamic points, thus prior segmentation is not necessary. We present a reconstruction algorithm of camera motion, scene structure, and point trajectories embedded into a multi-frame factorization principle which requires the minimum of 11 images and 7 points (out of which at feast 3 are dynamic).
Continuous Global Evidence-Based Bayesian Modality Fusion for Simultaneous Tracking of Multiple Objects
Authors: Jamie Sherrah, Shaogang Gong
Venue: ICCV
Year: 2001

Robust, real-time tracking of objects from visual data requires probabilistic fusion of multiple visual cues. Previous approaches have either been ad hoc or relied on a Bayesian network with discrete spatial variables which suffers from discretisation and computational complexity problems. We present a new Bayesian modality fusion network that uses continuous domain variables. The network architecture distinguishes between cues that are necessary or unnecessary for the object's presence. Computationally expensive and inexpensive modalities are also handled differently to minimise cost. The method provides a formal, tractable and robust probabilistic method for simultaneously tracking multiple objects. While instantaneous inference is exact, approximation is required for propagation over time.
Physics-based Model Acquisition and Identification in Airborne Spectral Images
Authors: David Slater, Glenn Healey
Venue: ICCV
Year: 2001

We consider the problem of acquiring models for unknown materials in airborne 0.4 /spl mu/m-2.5 /spl mu/m hyperspectral imagery and using these models to identify the unknown materials an image data obtained under significantly different conditions. The material models are generated using an airborne sensor spectrum measured under unknown conditions and a physical model for spectral variability. For computational efficiency, the material models are represented using low-dimensional spectral subspaces. We demonstrate the effectiveness of the material models using a set of material tracking experiments in HYDICE images acquired in a forest environment over widely varying conditions. We show that techniques based on the new representation significantly outperform methods based on direct spectral matching.
Dynamic Textures
Authors: Stefano Soatto, Gianfranco Doretto, Ying Nian Wu
Venue: ICCV
Year: 2001

Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include sea-waves, smoke, foliage, whirlwind but also talking faces, traffic scenes etc. We present a novel characterization of dynamic textures that poses the problems of modelling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing. We borrow tools from system identification to capture the "essence" of dynamic textures; we do so by learning (i.e. identifying) models that are optimal in the sense of maximum likelihood or minimum prediction error variance. For the special case of second-order stationary processes we identify the model in closed form. Once learned, a model has predictive power and can be used for extrapolating synthetic sequences to infinite length with negligible computational cost. We present experimental evidence that, within our framework, even low dimensional models can capture very complex visual phenomena.
Stochastic Road Shape Estimation
Authors: Ben Southall, Camillo J. Taylor
Venue: ICCV
Year: 2001

We describe a new system for estimating road shape ahead of a vehicle for the purpose of driver assistance. The method utilises a single on board colour camera, together with inertial and velocity information, to estimate both the position of the host car with respect to the lane it is following and also the width and curvature of the lane ahead at distances of up to 80 metres. The system's image processing extracts a variety of different styles of lane markings from road imagery, and is able to compensate for a range of lighting conditions. Road shape and car position are estimated using a particle filter. The system, which runs at 10.5 frames per second, has been applied with some success to several hours' worth of data captured from highways under varying imaging conditions.
Accurate Optical Flow in Noisy Image Sequences
Authors: Hagen Spies, Hanno Scharr
Venue: ICCV
Year: 2001

Optical flow estimation in noisy image sequences requires a special denoising strategy. Towards this end we introduce a new tensor-driven anisotropic diffusion scheme which is designed to enhance optical-flow-like spatio-temporal structures. This is achieved by selecting diffusivities in a special manner depending on the eigenvalues of the well known structure tensor. We illustrate how the proposed choice differs from edge- and coherence-enhancing anisotropic diffusion. Furthermore we extend a recently discovered discretization scheme for anisotropic diffusion to 3D data. An automatic stop criterion to terminate the diffusion after a suitable time is given. The performance of the introduced method is examined quantitatively using image sequences with a substantial amount of noise added.
Automatic Registration of 2-D with 3-D Imagery in Urban Environments
Authors: Ioannis Stamos, Peter K. Allen
Venue: ICCV
Year: 2001

We are building a system that can automatically acquire 3D range scans and 2D images to build geometrically correct, texture mapped 3D models of urban environments. This paper deals with the problem of automatically registering the 3D range scans with images acquired at other times and with unknown camera calibration and location. The method involves the utilization of parallelism and orthogonality constraints that naturally exist in urban environments. We present results for building a texture mapped 3-D model of an urban building.
Propagation of Innovative Information in Non-Linear Least-Squares Structure from Motion
Authors: Drew Steedly, Irfan A. Essa
Venue: ICCV
Year: 2001

We present a new technique that improves upon existing structure from motion (SFM) methods. We propose a SFM algorithm that is both recursive and optimal. Our method incorporates innovative information from new frames into an existing solution without optimizing every camera pose and scene structure parameter. To do this, we incrementally optimize larger subsets of parameters until the error is minimized. These additional parameters are included in the optimization by tracing connections between points and frames. In many cases, the complexity of adding a frame is much smaller than full bundle adjustment of all the parameters. Our algorithm is best described us incremental bundle adjustment as it allows new information to be added to art existing non-linear least-squares solution.
Topology Free Hidden Markov Models: Application to Background Modeling
Authors: Bjoern Stenger, Visvanathan Ramesh, Nikos Paragios, Frans Coetzee, Joachim M. Buhmann
Venue: ICCV
Year: 2001

Hidden Markov models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a stationarity assumption on the model parameters). But, real-world applications are often non-stationary in nature. This leads to the need for a dynamic mechanism to learn and update the model topology as well as its parameters. This paper presents a new framework for HMM topology and parameter estimation in an online, dynamic fashion. The topology and parameter estimation is posed as a model selection problem with an MDL prior. Online modifications to the topology are made possible by incorporating a state splitting criterion. To demonstrate the potential of the algorithm, the background modeling problem is considered. Theoretical validation and real experiments are presented.
Segmentation and Range Sensing Using a Moving-Aperture Lens
Authors: Anbumani Subramanian, Lakshmi R. Iyer, A. Lynn Abbott, Amy E. Bell
Venue: ICCV
Year: 2001

This paper is concerned with the use of a novel motorized lens to perform segmentation of image sequences. The lens has the effect of introducing small, repeating movements of the camera center, so that objects appear to translate in the image by an amount that depends on distance from the plane of focus. For a stationary scene, optical flow magnitudes and direction are therefore directly related to three-dimensional object distance from the observer. We describe a segmentation-procedure that exploits these controlled observer movements, and we present experimental results that demonstrate the successful extraction of objects at different depths. Potential applications of this approach include video compression, compositing, and passive range sensing.
Invariant Mixture Recognition in Hyperspectral Images
Authors: Pei-hsiu Suen, Glenn Healey
Venue: ICCV
Year: 2001

We present an algorithm for identifying linear mixtures of a specified set of materials in 0.4-2.5 /spl mu/m airborne imaging spectrometer data. The algorithm is invariant to the illumination and atmospheric conditions and the relative amounts of the specified materials within a pixel. Only the spectral reflectance functions for the specified materials are required by the algorithm. Invariance over illumination and atmosphere conditions is achieved by incorporating a physical model for scene variability in the constrained optimization formulation. The algorithm also computes estimates of the amounts of the specified materials in identified mixtures. We demonstrate the effectiveness of the algorithm using real and synthetic HYDICE imagery acquired over a range of conditions and altitudes.
The Impact of Viewing Geometry on Vision Through the Atmosphere
Authors: Pei-hsiu Suen, Glenn Healey, David Slater
Venue: ICCV
Year: 2001

An increase in the off-nadir viewing angle for an airborne visible/near-infrared through short-wave infrared (VNIR/SWIR) imaging spectrometer leads to a decrease in upward atmospheric transmittance and an increase in line-of-sight scattered path radiance. These effects combine to reduce the spectral contrast between different materials in the sensed signal. We analyze the impact of viewing angle on material discriminability for 237 materials over a wide range of conditions. Material discriminability is quantified using a statistical algorithm that employs a subspace model to represent the set of spectra for a material as conditions vary. We show that reliable material discrimination is possible over a range of conditions even for large off-nadir viewing angles. We illustrate the performance of material identification over different viewing angles using simulated forest hyperspectral images.
Self-Calibrating Camera Projector Systems for Interactive Displays and Presentations
Authors: Rahul Sukthankar, Tat-Jen Cham, Gita Sukthankar, James M. Rehg, David Hsu, Thomas Leung
Venue: ICCV
Year: 2001

The authors demonstrate a self-calibrating system that employs uncalibrated cameras and microportable projectors to create novel interactive displays and presentations. Three benefits of ther system are detailed.
Guiding Random Particles by Deterministic Search
Authors: Josephine Sullivan, Jens Rittscher
Venue: ICCV
Year: 2001

Among the algorithms developed towards the goal of robust and efficient tracking, two approaches which stand out due to their success are those based on particle filtering and variational approaches. The Bayesian approach led to the development of the particle filter, which performs a random search guided by a stochastic motion model. On the other hand, localising an object can be based on minimising a cost function. This minimum can be found using variational methods. The search paradigms differ in these two methods. One is stochastic and model-driven while the other is deterministic and data-driven. This paper presents a new algorithm to incorporate the strengths of both approaches into one consistent framework. To allow this fusion a smooth, wide likelihood function is constructed, based on a sum-of-squares distance measure and an appropriate sampling scheme is introduced. Based on low-level information this scheme automatically mixes the two methods of search and adapts the computational demands of the algorithm to the difficulty of the problem at hand. The ability to effectively track complex motions without the need for finely tuned motion models is demonstrated.
Surface Matching by 3D Point's Fingerprint
Authors: Yiyong Sun, Mongi A. Abidi
Venue: ICCV
Year: 2001

This paper proposes a new efficient surface representation method for the application of surface matching. We generate a feature carrier for the surface point, which is a set of 2D contours that are the projection of geodesic circles onto the tangent plane. The carrier is named point's fingerprint because its pattern is similar to human fingerprint and discriminating for each point. Each point's fingerprint carries the information of the normal variation along geodesic circles. Corresponding points on surfaces from different views are found by comparing fingerprints of the points. This representation scheme includes more local geometry information than some previous works that only use one contour as the feature carrier. It is not histogram based so that it is able to carry more features to improve comparison accuracy. To speed up the matching, we use a novel candidate point selection method based on the shape irregularity of the projected local geodesic circle. The point's fingerprint is successfully used to register both synthetic and real 2 1/2 data.
Caustics of Catadioptric Cameras
Authors: Rahul Swaminathan, Michael D. Grossberg, Shree K. Nayar
Venue: ICCV
Year: 2001

Conventional vision systems and algorithms assume the camera to have a single viewpoint. However, sensors need not always maintain a single viewpoint. For instance, an incorrectly aligned system could cause non-single viewpoints. Also, systems could be designed to specifically deviate from a single viewpoint to trade-off image characteristics such as resolution and field of view. In these cases, the locus of viewpoints forms what is called a caustic. In this paper, we present an in-depth analysis of caustics of catadioptric cameras with conic reflectors. Properties of caustics with respect to field of view and resolution are presented. Finally, we present ways to calibrate conic catadioptric systems and estimate their caustics from known camera motion.
Tele-Graffiti: A Pen and Paper-Based Remote Sketching System
Authors: Naoya Takao, Jianbo Shi, Simon Baker, Iain A. Matthews, Bart C. Nabbe
Venue: ICCV
Year: 2001

Tele-Graffiti is a system allowing two.or more users to communicate remotely via hand-drawn sketches. What one person writes at one site is captured using a video camera, transmitted to the other site(s), and displayed there using an LCD projector. The advantage of our system over other intelligent desktops and white-boards is that the users are free to move the pieces of paper on which they are writing. In Tele-Graffiti, paper detection and tracking is based on real-time paper boundary detection.
Selecting Objects With Freehand Sketches
Authors: Kar-Han Tan, Narendra Ahuja
Venue: ICCV
Year: 2001

We present in this paper the design of an interactive tool for selecting objects using simple freehand sketches. The objective is to extract object boundaries precisely while requiring little skill and time from the user. The tool proposed achieves this objective by integrating user input and image computation in a two-phase algorithm. In the first phase, the input sketch is used along with a coarse global segmentation of the image to derive an initial selection and a triangulation of the region around the boundary. The triangles are used to formulate subproblems of local finer-grained segmentation and selection. Each of the subproblems is processed independently in the second phase, where a linear approximation of the local boundary as well as a local, finer-grained segmentation are computed. The approximate boundary is then used with the local segmentation to compute a final selection, represented with an alpha channel to fully capture diffused object boundaries. Experimental results show that the tool allows very simple sketches to be used to select objects with complex boundaries. Therefore, the tool has immediate applications in graphics systems for image editing, manipulation, synthesis, retrieval and processing.
A Global Matching Framework for Stereo Computation
Authors: Hai Tao, Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: ICCV
Year: 2001

This paper presents a new global matching framework for stereo computation. In this framework, the second view is first predicted from the reference view using the depth information. A global match measure is then defined as the similarity function between the predicted image and the actual image. Stereo computation is converted into a search problem where the goal is to find the depth map that maximizes the global match measure. The major advantage of this framework is that the global visibility constraint is inherently enforced in the computation. This paper explores several key components of this framework including (1) three color segmentation based depth representations, (2) an incremental warping algorithm that dramatically reduces the computational complexity, and (3) scene constraints such as the smoothness constraint and the color similarity constraint. Experimental results using different types of depth representations are presented. The quality of the computed depth maps is demonstrated through image-based rendering from new viewpoints.