OpenRaiser/Intern-Atlas huggingface dataset:
Real-Time Video Phase-Locked Loops
Authors: Jeffrey E. Boyd, Maxwell Sayles
Venue: ICCV
Year: 2001

In the perception of gaits, timing is everything; specifi-cally, the relative timing of the individual motions in a gait, and when events occur periodically, as they do in a gait,then relative timing is equivalent to phase. The importance of phase in gaits appears in the medical, psychology, andcomputer vision literature. The video phase locked loop (vPLL) [3] is a novel sys-tem that perceives gaits, is sensitive to the phases of the component motions of the gait, and is model-free. vPLLsprovide a mechanism to perform two critical tasks in gait perception: frequency entrainment and phase locking [1].A vPLL can lock on oscillations in pixels that arise because of oscillatory motion. In doing so, the vPLL matches its in-ternal oscillators to the oscillations in pixel intensities, thus performing frequency entrainment. Phase locking occursas individual phased-locked loops at each pixel site lock simultaneously. The abundance of data extracted by the vPLLmakes gait recognition possible.
Demonstration of Segmentation with Interactive Graph Cuts
Authors: Yuri Boykov, Marie-Pierre Jolly
Venue: ICCV
Year: 2001

We demonstrate a new technique for general purpose interactive segmentation of N-dimensional images. The method creates two segments: “object” and “background”. The technical details can be found in our paper [1] in this proceedings. Below we concentrate on the actual interface. The user can enter seeds via mouse-operated brush of red (for object) or blue (for background) color. The size of the brush can be changed depending on the size of the object. The user should paint some pixels in the object of interest and some in the background. The seeds provide some clues on what the user intends to segment. As soon as initial seeds are entered, the whole image/volume can be segmented automatically. Basically, the algorithm tries to “predict” how the user would want to paint the rest of the image. Segmentation results are presented by highlighting the object and background segments with red and blue colors. Thus, the object segment appears reddish while the background appears bluish. This gives an intuitive feeling that the algorithm completes the painting started by the user. An optimal segmentation can be very efficiently recomputed when the user adds or removes any seeds. This allows the user to correct any result imperfections quickly via very intuitive interactions. If the algorithm makes a mistake, the user can add a stroke of red paint in the bluish segment (or blue paint in the reddish segment). The new segmentation would very quickly repaint the whole image to comply with additional hints from the user. Our method is not sensitive to exact positioning of seeds. Normally, the results would not change in the seeds are moved within the same object in the image or volume. Our method applies to N-D images (volumes). In case of 3D data the seeds are entered in selected representative slices. The information is automatically propagated between the slices because we compute our optimal segmentation directly in the volume. Thus, the whole volume can be segmented based on seeds in a single slice. 2. Examples
Optimal Method for the Affine F-Matrix and Its Uncertainty Estimation in the Sense of both Noise and Outliers
Authors: Sami S. Brandt, Jukka Heikkonen
Venue: ICCV
Year: 2001

We propose, in maximum likelihood sense, an optimal method for the affine fundamental matrix estimation in the presence of both Gaussian noise and outliers. It is based on weighting the squared residuals by the iteratively completed, residual posterior probabilities to be relevant. The proposed principle is also used for the covariance matrix estimation of the affine F-matrix where the novelty is in the fact that all data is used rather than the (erroneously) relevant classified matching points. The experiments on both synthetic and real data verify the optimality of the method in the sense of both false matches and Gaussian noise in data.
Incorporating Process Knowledge into Object Recognition for Assemblies
Authors: Elke Braun, Jannik Fritsch, Gerhard Sagerer
Venue: ICCV
Year: 2001

In this paper we present an object recognition framework integrating several recognition paradigms and context information from the scene history to recognize elementary parts contained in assemblies. We use a symbolic approach to detect actions based on the object changes in the scene to monitor the construction process. The information about the elements used to construct a new assembly serves as additional source of information for recognition. Process knowledge is exploited also for selecting the best interpretation out of several alternatives for a single scene which result from contradictions and uncertainties during integration of the different cues.
What Value Covariance Information in Estimating Vision Parameters?
Authors: Michael J. Brooks, Wojciech Chojnacki, Darren Gawley, Anton van den Hengel
Venue: ICCV
Year: 2001

Many parameter estimation methods used in computer vision are able to utilise covariance information describing the uncertainty of data measurements. This paper considers the value of this information to the estimation process when applied to measured image point locations. Covariance matrices are first described and a procedure is then outlined whereby covariances may be associated with image features located via a measurement process. An empirical study is made of the conditions under which covariance information enables generation of improved parameter estimates. Also explored is the extent to which the noise should be anisotropic and inhomogeneous if improvements are to be obtained over covariance-free methods. Critical in this is the devising of synthetic experiments under which noise conditions can be precisely controlled. Given that covariance information is, in itself, subject to estimation error, tests are also undertaken to determine the impact of imprecise covariance information upon the quality of parameter estimates. Finally, an experiment is carried out to assess the value of covariances in estimating the fundamental matrix from real images. 1.
Document Restoration Using 3D Shape: A General Deskewing Algorithm for Arbitrarily Warped Documents
Authors: Michael S. Brown, W. Brent Seales
Venue: ICCV
Year: 2001

We present a framework for restoring arbitrarily warped and deformed documents to their original planar shape. The impetus for this work is the need for tools and techniques to help digitally preserve and restore fragile manuscripts. Current digitization is performed under the assumption that the documents are flat, with subsequent image-processing and restoration algorithms either relying on this assumption or attempting to overcome it without shape information. Although most manuscripts were originally flat, many become deformed from damage and deterioration. Physical flattening is not possible without risking further, possibly irreversible, damage. Our framework addresses this restoration problem with two primary contributions. First, we present a working 3D digitization setup that acquires a 3D model with accurate shape-to-texture registration under multiple lighting conditions. Second, we show how the 3D model and a mass-spring particle system can be used together as a framework for digital flattening. We show that this restoration process can correct document deformations and can significantly improve subsequent document analysis.
Multi-View Scene Capture by Surfel Sampling: From Video Streams to Non-Rigid 3D MotionShape & Reflectance
Authors: Rodrigo L. Carceroni, Kiriakos N. Kutulakos
Venue: ICCV
Year: 2001

In this paper we study the problem of recovering the 3D shape, reflectance, and non-rigid motion of a dynamic 3D scene. Because these properties are completely unknown, our approach uses multiple views to build a piecewise continuous geometric and radiometric representation of the scene's trace in space-time. Basic primitive of this representation is the dynamic surfel, which (1) encodes the instantaneous local shape, reflectance, and motion of a small region in the scene, and (2) enables accurate prediction of the region's dynamic appearance under known illumination conditions. We show that complete surfel-based reconstructions can be created by repeatedly applying an algorithm called surfel sampling that combines sampling and parameter estimation to fit a single surfel to a small, bounded region of space-time. Experimental results with the Phong reflectance model and complex real scenes (clothing, skin, shiny objects) illustrate our method's ability to explain pixels and pixel variations in terms of their physical causes-shape, reflectance, motion, illumination, and visibility.
Learning Spectral Calibration Parameters For Color Inspection
Authors: Paulo Carvalho 0001, Amâncio Santos, António Dourado, Bernardete Ribeiro
Venue: ICCV
Year: 2001

Light sensor spectral calibration is an ill-defined problem. For the identification problem one needs a priori knowledge of the characteristics of the sensor which is difficult to get in most situations. A new methodology is presented in this paper that does not rely on any a priori knowledge of the sensor's characteristics. The method uses an extended generalized cross-validation function to measure predictability of the identified sensor's spectral behavior. The prediction error is minimized with a hybrid genetic algorithm. Further an extended image formation model is introduced to model changes in additive and multiplicative errors. The calibration problem is formulated to be independent of these changes by previously identifying and removing them from the images.
Fast Algorithm for Nearest Neighbor Search Based on a Lower Bound Tree
Authors: Yong-Sheng Chen, Yi-Ping Hung, Chiou-Shann Fuh
Venue: ICCV
Year: 2001

This paper presents a novel algorithm for fast nearest neighbor search. At the preprocessing stage, the proposed algorithm constructs a lower bound tree by agglomeratively clustering the sample points in the database. Calculation of the distance between the query and the sample points can be avoided if the lower bound of the distance is already larger than the minimum distance. The search process can thus be accelerated because the computational cost of the lower bound which can be calculated by using the internal node of the lower bound tree, is less than that of the distance. To reduce the number of the lower bounds actually calculated the winner-update search strategy is used for traversing the tree. Moreover, the query and the sample points can be transformed for further efficiency improvement. Our experiments show that the proposed algorithm can greatly speed up the nearest neighbor search process. When applying to the real database used in Nayar's object recognition system, the proposed algorithm is about one thousand times faster than the exhaustive search.
Trust-Region Methods for Real-Time Tracking
Authors: Hwann-Tzong Chen, Tyng-Luh Liu
Venue: ICCV
Year: 2001

Optimization methods based on iterative schemes can be divided into two classes: linesearch methods and trust-region methods. While linesearch techniques are commonly found in various vision applications, not much attention is paid to trust-region methods. Motivated by the fact that linesearch methods can be considered as special cases of trust-region methods, we propose to apply trust-region methods to visual tracking problems. Our approach integrates trust-region methods with the Kullback Leibler distance to track a rigid or non-rigid object in real-time. If not limited by the speed of a camera, the algorithm can achieve frame rate above 60 fps. To justify our method, a variety of experiments/comparisons are carried out for the trust-region tracker and a linesearch-based mean-shift tracker with same initial conditions. The experimental results support our conjecture that a trust-region tracker should perform superiorly to a linesearch one.
Example-Based Facial Sketch Generation with Non-parametric Sampling
Authors: Hong Chen, Ying-Qing Xu, Heung-Yeung Shum, Song Chun Zhu, Nanning Zheng 0001
Venue: ICCV
Year: 2001

In this paper, we present an example-based facial sketch system. Our system automatically generates a sketch from an input image, by learning from example sketches drawn with a particular style by an artist. There are two key elements in our system: a non-parametric sampling method and a flexible sketch model. Given an input image pixel and its neighborhood, the conditional distribution of a sketch point is computed by querying the examples and finding all similar neighborhoods. An "expected sketch image" is then drawn from the distribution to reflect the drawing style. Finally, facial sketches are obtained by incorporating the sketch model. Experimental results demonstrate the effectiveness of our techniques.
Sparse PCA: Extracting Multi-scale Structure from Data
Authors: Chakra Chennubhotla 0001, Allan D. Jepson
Venue: ICCV
Year: 2001

Sparse Principal Component Analysis (S-PCA) is a novel framework for learning a linear, orthonormal basis representation for structure intrinsic to an ensemble of images. S-PCA is based on the discovery that natural images exhibit structure in a low-dimensional subspace in a sparse, scale-dependent form. The S-PCA basis optimizes an objective function which trades off correlations among output coefficients for sparsity in the description of basis vector elements. This objective function is minimized by a simple, robust and highly scalable adaptation algorithm, consisting of successive planar rotations of pairs of basis vectors. The formulation of S-PCA is novel in that multi-scale representations emerge for a variety of ensembles including face images, images from outdoor scenes and a database of optical flow vectors representing a motion class.
People Tracking Using Hybrid Monte Carlo Filtering
Authors: Kiam Choo, David J. Fleet
Venue: ICCV
Year: 2001

Particle filters are used for hidden state estimation with nonlinear dynamical systems. The inference of 3-D human motion is a natural application, given the nonlinear dynamics of the body and the nonlinear relation between states and image observations. However, the application of particle filters has been limited to cases where the number of state variables is relatively small, because the number of samples needed with high dimensional problems can be prohibitive. We describe a filter that uses hybrid Monte Carlo (HMC) to obtain samples in high dimensional spaces. It uses multiple Markov chains that use posterior gradients to rapidly explore the state space, yielding fair samples from the posterior. We find that the HMC filter is several thousand times faster than a conventional particle filter on a 28 D people tracking problem.
The Variable Bandwidth Mean Shift and Data-Driven Scale Selection
Authors: Dorin Comaniciu, Visvanathan Ramesh, Peter Meer
Venue: ICCV
Year: 2001

We present two solutions for the scale selection problem in computer vision. The first one is completely nonparametric and is based on the the adaptive estimation of the normalized density gradient. Employing the sample point estimator, we define the Variable Bandwidth Mean Shift, prove its convergence, and show its superiority over the fixed bandwidth procedure. The second technique has a semiparametric nature and imposes a local structure on the data to extract reliable scale information. The local scale of the underlying density is taken as the bandwidth which maximizes the magnitude of the normalized mean shift vector. Both estimators provide practical tools for autonomous image and quasi real-time video analysis and several examples are shown to illustrate their effectiveness.
Constrained Active Appearance Models
Authors: Timothy F. Cootes, Christopher J. Taylor 0001
Venue: ICCV
Year: 2001

Active Appearance Models (AAMs) have been shown to be useful for interpreting images of deformable objects. Here we place the AAM matching algorithm in a statistical framework, allowing extra constraints to be applied. This enables the models to be combined with other methods of object location. We demonstrate how user interaction can be used to guide the search and give results of experiments showing the effect of constraints on the performance of model matching.
Automatic Segmentation and Indexing in a Database of Bird Images
Authors: Madirakshi Das, R. Manmatha
Venue: ICCV
Year: 2001

The aim of this work is to index images in domain specific databases using colors computed from the object of interest only, instead of the whole image. The main problem in this task is the segmentation of the region of interest from the background. Viewing segmentation as a figure/ground segregation problem leads to a new approach-eliminating the background leaves the figure or object of interest. To find possible object colors, we first find background colors and eliminate them. We then use an edge image at an appropriate scale to eliminate those parts of the image which are not in focus and do not contain significant structures. The edge information is combined with the color-based background elimination to produce object (figure) regions. We test our approach on a database of bird images. We show that in 87% or 450 bird images tested, the segmentation is sufficient to determine the colors of the bird correctly for retrieval purposes. We also show that our approach provides improved retrieval performance.
Markov Face Models
Authors: Sarat C. Dass, Anil K. Jain 0001
Venue: ICCV
Year: 2001

The spatial distribution of gray level intensities in an image can be naturally modeled using Markov random field (MRF) models. We develop and investigate the performance of face detection algorithms derived from MRF considerations. For enhanced detection, the MRF models are defined for every permutation of site indices (pixels) in the image. We find the optimal permutation that provides maximum discriminatory power to identify faces from nonfaces. The methodology presented here is a generalization of the face detection algorithm described previously where a most discriminating Markov chain model was used. The MRF models successfully detect faces in a number of test images.
Motion Estimation from Disparity Images
Authors: David Demirdjian, Trevor Darrell
Venue: ICCV
Year: 2001

A new method for 3D rigid motion estimation from stereo is proposed in this paper. The appealing feature of this method is that it directly uses the disparity images obtained from stereo matching. We assume that the stereo rig has parallel cameras and show, in that case, the geometric and topological properties of the disparity images. Then we introduce a rigid transformation (called d-motion) that maps two disparity images of a rigidly moving object. We show how it is related to the Euclidean rigid motion and a motion estimation algorithm is derived. We show with experiments that our approach is simple and more accurate than standard approaches.
Combining Single View Recognition and Multiple View Stereo for Architectural Scenes
Authors: Anthony R. Dick, Philip H. S. Torr, Simon J. Ruffle, Roberto Cipolla
Venue: ICCV
Year: 2001

This paper describes a structure from motion and recognition paradigm for generating 3D models from 2D sets of images. In particular we consider the domain of architectural photographs. A model based approach is adopted with the architectural model built from a "Lego kit" of parameterised parts. The approach taken is different from traditional stereo or shape from X approaches in that identification of the parameterised components (such as windows, doors, buttresses etc) from one image is combined with parallax information in order to generate the 3D model. This model based approach has two main benefits: first, it allows the inference of shape and texture where the evidence from the images is weak; and second, it recovers not only geometry and texture but also an interpretation of the model, which can be used for automatic enhancement techniques such as the application of reflective textures to windows.
Self-Calibration of a Stereo Rig Using Monocular Epipolar Geometry
Authors: Fadi Dornaika
Venue: ICCV
Year: 2001

This paper addresses the problem of self-calibration from one unknown motion of an uncalibrated stereo rig. Unlike the existing methods for stereo rig self-calibration, which have been focused on applying the autocalibration paradigm using both motion and stereo correspondences, our method does not require the recovery of stereo correspondences. Our method combines purely algebraic constraints with implicit geometric constraints. Assuming that the rotational part of the stereo geometry has two unknown degrees of freedom and the principle point of each camera is known, we show that the computation of the intrinsic and extrinsic parameters of the stereo rig can be recovered from the motion correspondences only, i.e. the monocular fundamental matrices. We provide a stability study for the method in the presence of image noise. Synthetic and real experiments demonstrate the feasibility and robustness of the proposed method.
Real-Time Tracking of Highly Articulated Structures in the Presence of Noisy Measurements
Authors: Tom Drummond, Roberto Cipolla
Venue: ICCV
Year: 2001

This paper presents a novel approach for model-based real-time tracking of highly articulated structures such as humans. This approach is based on an algorithm which efficiently propagates statistics of probability distributions through a kinematic chain to obtain maximum a posteriori estimates of the motion of the entire structure. This algorithm yields the least squares solution in linear time (in the number of components of the model) and can also be applied to non-Gaussian statistics using a simple but powerful trick. The resulting implementation runs in real-time on standard hardware without any pre-processing of the video data and can thus operate on live video. Results from experiments performed using this system are presented and discussed.
A Novel Modeling Algorithm for Shape Recovery of Unknown Topology
Authors: Ye Duan, Hong Qin 0001
Venue: ICCV
Year: 2001

This paper presents a novel modeling algorithm that is capable of simultaneously recovering correct shape geometry as well as its unknown topology from arbitrarily complicated datasets. Our algorithm starts from a simple seed model (of genus zero) that can be arbitrarily initiated by users within any dataset. The deformable behavior of our model is governed by a locally defined objective function associated with each vertex of the model. Through the numerical computation of function optimization, our algorithm can adaptively subdivide the model geometry, automatically detect self-collision of the model, properly modify its topology (because of the occurrence of self-collision), continuously evolve the model towards the object boundary, and reduce fitting error and improve fitting quality via global subdivision. Commonly used mesh optimization techniques are employed throughout the geometric deformation and topological variation in order to ensure the model both locally smooth and globally well conditioned. We have applied our algorithm to various real/synthetic range data as well as volumetric image data in order to empirically verify and validate its usefulness. Based on our experiments, the new modeling algorithm proves to be very powerful and extremely valuable for shape recovery in computer vision, reverse engineering in computer graphics, and iso-surface extraction in visualization.