OpenRaiser/Intern-Atlas huggingface dataset:
A performance evaluation of local descriptors
Authors: Krystian Mikolajczyk, Cordelia Schmid
Venue: CVPR
Year: 2003

In this paper we compare the performance of interest point descriptors. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their performance depends on the interest point detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the point detector. Our evaluation uses as criterion detection rate with respect to false positive rate and is carried out for different image transformations. We compare SIFT descriptors (Lowe, 1999), steerable filters (Freeman and Adelson, 1991), differential invariants (Koenderink ad van Doorn, 1987), complex filters (Schaffalitzky and Zisserman, 2002), moment invariants (Van Gool et al., 1996) and cross-correlation for different types of interest points. In this evaluation, we observe that the ranking of the descriptors does not depend on the point detector and that SIFT descriptors perform best. Steerable filters come second ; they can be considered a good choice given the low dimensionality.
Practical Non-parametric Density Estimation on a Transformation Group for Vision
Authors: Erik G. Miller, Christophe Chefd&apos, Hotel
Venue: CVPR
Year: 2003

It is now common practice in machine vision to define the variability in an object's appearance in a factored manner, as a combination of shape and texture transformations. In this context, we present a simple and practical method for estimating non-parametric probability densities over a group of linear shape deformations. Samples drawn from such a distribution do not lie in a Euclidean space, and standard kernel density estimates may perform poorly. While variable kernel estimators may mitigate this problem to some extent, the geometry of the underlying configuration space ultimately demands a kernel, which accommodates its group structure. In this perspective, we propose a suitable invariant estimator on the linear group of non-singular matrices with positive determinant. We illustrate this approach by modeling image transformations in digit recognition problems, and present results showing the superiority of our estimator to comparable Euclidean estimators in this domain.
Local Appearance-Based Models using High-Order Statistics of Image Features
Authors: Baback Moghaddam, David Guillamet, Jordi Vitrià
Venue: CVPR
Year: 2003

We propose a novel local appearance modeling method for object detection and recognition in cluttered scenes. The approach is based on the joint distribution of local feature vectors at multiple salient points and factorization with the independent component analysis (ICA). The resulting densities are simple multiplicative distributions modeled through adaptive Gaussian mixture models. This leads to computationally tractable joint probability densities, which can model high-order dependencies. Furthermore, different models are compared based on appearance, color and geometry information. Also, the combination of all of them results in a hybrid model, which obtains the best results using the COIL-100 object database. Our technique has been tested under different natural and cluttered scenes with different degrees of occlusions with promising results. Finally, a large statistical test with the MNIST digit database is used to demonstrate the improved performance obtained by explicit modeling of high-order dependencies.
Adaptive View-Based Appearance Models
Authors: Louis-Philippe Morency, Ali Rahimi, Trevor Darrell
Venue: CVPR
Year: 2003

We present a method for online rigid object tracking using an adaptive view-based appearance model. When the object's pose trajectory crosses itself, our tracker has bounded drift and can track objects undergoing large motion for long periods of time. Our tracker registers each incoming frame against the views of the appearance model using a two-frame registration algorithm. Using a linear Gaussian filter, we simultaneously estimate the pose of the object and adjust the view-based model as pose-changes are recovered from the registration algorithm. The adaptive view-based model is populated online with views of the object as it undergoes different orientations in pose space, allowing us to capture non-Lambertian effects. We tested our approach on a real-time rigid object tracking task using stereo cameras and observed an RMS error within the accuracy limit of an attached inertial sensor.
Polydioptric Camera Design and 3D Motion Estimation
Authors: Jan Neumann, Cornelia Fermüller, Yiannis Aloimonos
Venue: CVPR
Year: 2003

Most cameras used in computer vision applications are still based on the pinhole principle inspired by our own eyes. It has been found though that this is not necessarily the optimal image formation principle for processing visual information using a machine. We describe how to find the optimal camera for 3D motion estimation by analyzing the structure of the space formed by the light rays passing through a volume of space. Every camera corresponds to a sampling pattern in light ray space, thus the question of camera design can be rephrased as finding the optimal sampling pattern with regard to a given task. This framework suggests that large field-of-view multi-perspective (polydioptric) cameras are the optimal image sensors for 3D motion estimation. We conclude by proposing design principles for polydioptric cameras and describe an algorithm for such a camera that estimates its 3D motion in a scene independent and robust manner.
Recognising and Monitoring High-Level Behaviours in Complex Spatial Environments
Authors: Nam Thanh Nguyen, Hung Hai Bui, Svetha Venkatesh, Geoff A. W. West
Venue: CVPR
Year: 2003

The recognition of activities from sensory data is important in advanced surveillance systems to enable prediction of high-level goals and intentions of the target under surveillance. The problem is complicated by sensory noise and complex activity spanning large spatial and temporal extents. The paper presents a system for recognizing high-level human activities from multi-camera video data in complex spatial environments. The Abstract Hidden Markov mEmory Model (AHMEM) is used to deal with noise and scalability. The AHMEM is an extension of the Abstract Hidden Markov Model (AHMM) that allows us to represent a richer class of both state-dependent and context-free behaviors. The model also supports integration with low-level sensory models and efficient probabilistic inference. We present experimental results showing the ability of the system to perform real-time monitoring and recognition of complex behaviors of people from observing their trajectories within a real, complex indoor environment.
On Region Merging: The Statistical Soundness of Fast Sorting, with Applications
Authors: Frank Nielsen, Richard Nock
Venue: CVPR
Year: 2003

This work explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose error is, as we formally show, close to the best possible. This approach can be approximated in a very fast segmentation algorithm for processing images described using most common numerical feature spaces. Simple modifications of the algorithm allow us to cope with occlusions and/or hard noise levels. Experiments on grey-level and color images, obtained with a short C-code, display the quality of the segmentations obtained.
Low-Dimensional Representations of Shaded Surfaces under Varying Illumination
Authors: Peter Nillius, Jan-Olof Eklundh
Venue: CVPR
Year: 2003

The aim of this paper is to find the best representation for the appearance of surfaces with Lambertian reflectance under varying illumination. Previous work using principal component analysis (PCA) found the best sub-space to represent all images of an object under a varying point light source. We extend this to images from any illumination distribution. Specifically we calculate the bases for all configurations of a point plus ambient light source and two point light sources, as well as from a database of captured real world illumination. We also reformulate the optimization criterion used in PCA. The resulting basis, we believe has higher representability and is better for analyzing images of shaded objects. The different bases are compared on a database of images to test the representability.
An Efficient Solution to the Five-Point Relative Pose Problem
Authors: David Nistér
Venue: CVPR
Year: 2003

An efficient algorithmic solution to the classical five-point relative pose problem is presented. The problem is to find the possible solutions for relative camera motion between two calibrated views given five corresponding points. The algorithm consists of computing the coefficients of a tenth degree polynomial and subsequently finding its roots. It is the first algorithm well suited for numerical implementation that also corresponds to the inherent complexity of the problem. The algorithm is used in a robust hypothesis-and-test framework to estimate structure and motion in real-time.
Object Recognition Based on Photometric Alignment Using RANSAC
Authors: Takahiro Okabe, Yoichi Sato 0001
Venue: CVPR
Year: 2003

For object recognition under varying illumination conditions, we propose a method based on photometric alignment. The photometric alignment is known as a technique that models both diffuse reflection components and attached shadows under a distant point light source by using three basis images. However, in order to reliably reproduce these components in a test image, we have to take into account outliers such as specular reflection components and shadows in the test image. Accordingly, our proposed method utilizes Random Sample Consensus (RANSAC), which has been used successfully for estimating basis images. In the present study, we have conducted experiments using the Yale Face Database B and confirmed that a combination of the photometric alignment and RANSAC provides a simple but effective method for object recognition under varying illumination conditions.
Toward a Statistically Optimal Method for Estimating Geometric Relations from Noisy Data: Cases of Linear Relations
Authors: Takayuki Okatani, Koichiro Deguchi
Venue: CVPR
Year: 2003

In many problems of computer vision we have to estimate parameters in the presence of nuisance parameters increasing with the amount of data. It is known that, unlike in the cases without nuisance parameters, maximum likelihood estimation (MLE) is not optimal in the presence of nuisance parameters. By optimal we mean that the resulting estimate is unbiased and its variance attains the theoretical lower bound in an asymptotic sense. Thus, naive application of MLE to computer vision has a potential problem. This applies to a wide range of problems from conic fitting to bundle adjustment. For this nuisance parameter problem, studies have been conducted in statistics for a long time, whereas they have hardly been studied in the computer vision community. We cast light on the methods developed in statistics for obtaining optimal estimates and explore the possibility of applying them to computer vision problems. In this paper we focus on the cases where data and nuisance parameters are linearly connected. As examples, optical flow estimation and affine structure and motion problems are considered. Through experiments, we show that the estimation accuracy is improved in several cases.
Hybrid Evolutionary Ridge Regression Approach for High-Accurate Corner Extraction
Authors: Gustavo Olague, Benjamín Hernández, Enrique Dunn
Venue: CVPR
Year: 2003

Corner measurement is of main concern within the following tasks: camera calibration, image matching, object tracking, recognition and reconstruction. This paper presents a hybrid evolutionary ridge regression approach for the problem of corner modeling. We search model parameters characterizing L-corner models by means of fitting the model to the image data. As the model fitting relies on an initial parameter estimation, we use a global approach to find the global minimum. Experimental results applied to an L-corner using several levels of noise show the advantages and disadvantages of our evolutionary algorithm compared to down-hill simplex and simulated annealing.
Perception-based 3D Triangle Mesh Segmentation Using Fast Marching Watersheds
Authors: David L. Page, Andreas F. Koschan, Mongi A. Abidi
Venue: CVPR
Year: 2003

In this paper, we describe an algorithm called fast marching watersheds that segments a triangle mesh into visual parts. This computer vision algorithm leverages a human vision theory known as the minima rule. Our implementation computes the principal curvatures and principal directions at each vertex of a mesh, and then our hill-climbing watershed algorithm identifies regions bounded by contours of negative curvature minima. These regions fit the definition of visual parts according to the minima rule. We present evaluation analysis and experimental results for the proposed algorithm.
View Invariants for Human Action Recognition
Authors: Vasu Parameswaran, Rama Chellappa
Venue: CVPR
Year: 2003

This paper presents two approaches for the representation and recognition of human action in video, aiming for view-point invariance. The paper first presents new results using a 2D approach presented earlier. Inherent limitations of the 2D approach are discussed and a new 3D approach that builds on recent work on 3D model-based invariants, is presented. Each action is represented as a unique curve in a 3D invariance space, surrounded by an acceptance volume ('action volume'). Given a video sequence, 2D quantities from the image are calculated and matched against candidate action volumes in a probabilistic framework. The theory is presented followed by results on arbitrary projections of motion-capture data which demonstrate a high degree of tolerance to viewpoint change.
Graph-Theoretic Approach to Clustering and Segmentation
Authors: Massimiliano Pavan, Marcello Pelillo
Venue: CVPR
Year: 2003

We develop a framework for the image segmentation problem based on a new graph-theoretic formulation of clustering. The approach is motivated by the analogies between the intuitive concept of a cluster and that of a dominant set of vertices, a notion that generalizes that of a maximal complete subgraph to edge-weighted graphs. We also establish a correspondence between dominant sets and the extrema of a quadratic form over the standard simplex, thereby allowing us the use of continuous optimization techniques such as replicator dynamics from evolutionary game theory. Such systems are attractive as they can be coded in a few lines of any high-level programming language, can easily be implemented in a parallel network of locally interacting units, and offer the advantage of biological plausibility. We present experimental results on real-world images which show the effectiveness of the proposed approach.
Evaluation of Local Models of Dynamic Backgrounds
Authors: Robert Pless, John Larson, Scott Siebers, Ben Westover
Venue: CVPR
Year: 2003

Background subtraction is the first step of many video surveillance applications. What is considered background varies by application, and may include regular, systematic, or complex motions. The paper explores the use of several different local spatio-temporal models of a background, defined at each pixel in the image. We present experiments with real image data and conclude that appropriate local representations are sufficient to make background models of complicated real world motions. Empirical studies illustrate, for example, that an optical flow-based model is able to detect emergency vehicles whose motion is different from those typically observed in traffic scenes. We conclude that "different models are appropriate for different scenes", but give criteria by which one can choose which model will be best.
Estimating Surface Characteristics using Physical Reflectance Models
Authors: Hossein Ragheb, Edwin R. Hancock
Venue: CVPR
Year: 2003

We make use of the Beckmann-Kirchhoff and Davies scattering models to estimate surface properties for both dielectric and metallic surfaces based on reflectance measurements. In the case of metallic surfaces, we consider two refinements of the Davies theory, which apply under different restrictions concerning the reflectance geometry. The first of these is due to Bennett and Porteus (1961) and applies for normal incidence and reflectance. The second is due to Torrance (1964) and applies when the incidence radiation is off normal. We then suggest three classes of materials for which the appropriate approximations may be used to estimate the surface roughness, the correlation length and the surface slope. Finally, we use the surface slope estimates to fit the Beckmann-Kirchhoff model to reflectance data. In contrast to previous methods which work at long wavelengths and use special purpose instrumentation, our methods can be performed using visible light and a digital camera.
Background Subtraction based on Cooccurrence of Image Variations
Authors: Makito Seki, Toshikazu Wada, Hideto Fujiwara, Kazuhiko Sumi
Venue: CVPR
Year: 2003

This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the ranges by analyzing input images, the detection sensitivity can be improved. For this narrowing, we employ the property that image variations at neighboring image blocks have strong correlation, also known as "cooccurrence". This approach is essentially different from chronological background image updating or morphological postprocessing. Experimental results for real images demonstrate the effectiveness of our method.
Kinematic Jump Processes For Monocular 3D Human Tracking
Authors: Cristian Sminchisescu, Bill Triggs
Venue: CVPR
Year: 2003

A major difficulty for 3D (three-dimensional) human body tracking from monocular image sequences is the near nonobservability of kinematic degrees of freedom that generate motion in depth. For known link (body segment) lengths, the strict nonobservabilities reduce to twofold 'forwards/backwards flipping' ambiguities for each link. These imply 2/sup # links/ formal inverse kinematics solutions for the full model, and hence linked groups of O(2/sup # links/) local minima in the model-image matching cost function. Choosing the wrong minimum leads to rapid mistracking, so for reliable tracking, rapid methods of investigating alternative minima within a group are needed. Previous approaches to this have used generic search methods that do not exploit the specific problem structure. Here, we complement these by using simple kinematic reasoning to enumerate the tree of possible forwards/backwards flips, thus greatly speeding the search within each linked group of minima. Our methods can be used either deterministically, or within stochastic 'jump-diffusion' style search processes. We give experimental results on some challenging monocular human tracking sequences, showing how the new kinematic-flipping based sampling method improves and complements existing ones.
Face Authentication using the Trace Transform
Authors: Sanun Srisuk, Maria Petrou, Werasak Kurutach, Alexander Kadyrov
Venue: CVPR
Year: 2003

We introduce a face representation, the shape trace transform (STT), for recognizing faces in an authentication system. The STT offers an alternative representation for faces that has a very high discriminatory power. We estimate the dissimilarity between two shapes by a new measure. We propose the Hausdorff context. Reinforcement learning is used to search the optimal parameters of the algorithm, for which the within class variance of the STT is minimized. This research demonstrates that the proposed method provides a new way for face representation. Our system is verified with experiments on the XM2VTS database.
Automated Feature-Based Range Registration of Urban Scenes of Large Scale
Authors: Ioannis Stamos, Marius Leordeanu
Venue: CVPR
Year: 2003

We are building a system that can automatically acquire 3D range scans and 2D images to build geometrically and photometrically correct 3D models of urban environments. A major bottleneck in the process is the automated registration of a large number of geometrically complex 3D range scans in a common frame of reference. In this paper we provide a method for the accurate and efficient registration of a large number of complex range scans. The method utilizes range segmentation and feature extraction algorithms. Our algorithm automatically computes pairwise registrations between individual scans, builds a topological graph, and places the scans in the same frame of reference. We present results for building large scale 3D models of historic sites and urban structures.
Automated multi-camera planar tracking correspondence modeling
Authors: Chris Stauffer, Kinh Tieu
Venue: CVPR
Year: 2003

This paper introduces a method for robustly estimating a planar tracking correspondence model (TCM) for a large camera network directly from tracking data and for employing said model to reliably track objects through multiple cameras. By exploiting the unique characteristics of tracking data, our method can reliably estimate a planar TCM in large environments covered by many cameras. It is robust to scenes with multiple simultaneously moving objects and limited visual overlap between the cameras. Our method introduces the capability of automatic calibration of large camera networks in which the topology of camera overlap is unknown and in which all cameras do not necessarily overlap. Quantitative results are shown for a five camera network in which the topology is not specified.
Nonparametric Belief Propagation
Authors: Erik B. Sudderth, Alexander T. Ihler, William T. Freeman, Alan S. Willsky
Venue: CVPR
Year: 2003

In many applications of graphical models arising in computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. There exist inference algorithms for discrete approximations to these continuous distributions, but for the high-dimensional variables typically of interest, discrete inference becomes infeasible. Stochastic methods such as particle filters provide an appealing alternative. However, existing techniques fail to exploit the rich structure of the graphical models describing many vision problems. Drawing on ideas from regularized particle filters and belief propagation (BP), this paper develops a nonparametric belief propagation (NBP) algorithm applicable to general graphs. Each NBP iteration uses an efficient sampling procedure to update kernel-based approximations to the true, continuous likelihoods. The algorithm can accommodate an extremely broad class of potential functions, including nonparametric representations. Thus, NBP extends particle filtering methods to the more general vision problems that graphical models can describe. We apply the NBP algorithm to infer component interrelationships in a parts-based face model, allowing location and reconstruction of occluded features.