OpenRaiser/Intern-Atlas huggingface dataset:
A Model-Based Vehicle Segmentation Method for Tracking
Authors: Xuefeng Song, Ramakant Nevatia
Venue: ICCV
Year: 2005

Our goal is to detect and track moving vehicles on a road observed from cameras placed on poles or buildings. Inter-vehicle occlusion is significant under these conditions and traditional blob tracking methods is unable to separate the vehicles in the merged blobs. We use vehicle shape models, in addition to camera calibration and ground plane knowledge, to detect, track and classify moving vehicles in presence of occlusion. We use a 2-stage approach. In the first stage, hypothesis for vehicle types, positions and orientations are formed by a coarse search, which is then refined by a data driven Markov chain Monte Carlo (DDMCMC) process. We show results and evaluations on some real urban traffic video sequence using three types of vehicle models
Manifold Clustering
Authors: Richard Souvenir, Robert Pless
Venue: ICCV
Year: 2005

Manifold learning has become a vital tool in data driven methods for interpretation of video, motion capture, and handwritten character data when they lie on a low dimensional, nonlinear manifold. This work extends manifold learning to classify and parameterize unlabeled data which lie on multiple, intersecting manifolds. This approach significantly increases the domain to which manifold learning methods can be applied, allowing parameterization of example manifolds such as figure eights and intersecting paths which are quite common in natural data sets. This approach introduces several technical contributions which may be of broader interest, including node-weighted multidimensional scaling and a fast algorithm for weighted low-rank approximation for rank-one weight matrices. We show examples for intersecting manifolds of mixed topology and dimension and demonstrations on human motion capture data.
Convex Grouping Combining Boundary and Region Information
Authors: Joachim S. Stahl, Song Wang 0002
Venue: ICCV
Year: 2005

Convexity is an important geometric property of many natural and man-made structures. Prior research has shown that it is imperative to many perceptual-organization and image-understanding tasks. This paper presents a new grouping method for detecting convex structures from noisy images in a globally optimal fashion. Particularly, this method combines both region and boundary information: the detected structural boundary is closed and well aligned with detected edges while the enclosed region has good intensity homogeneity. We introduce a ratio-form cost function for measuring the structural desirability, which avoids a possible bias to detect small structures. A new fragment-pruning algorithm is developed to achieve the structural convexity. The proposed method can also be extended to detect open boundaries, which correspond to the structures that are partially cropped by the image perimeter and incorporate a human-computer interaction for detecting a convex boundary around a specified point. We test the proposed method on a set of real images and compare it with the Jacobs'convex-grouping method.
Spherical Matching for Temporal Correspondence of Non-Rigid Surfaces
Authors: Jonathan Starck, Adrian Hilton 0001
Venue: ICCV
Year: 2005

This paper introduces spherical matching to estimate dense temporal correspondence of non-rigid surfaces with genus-zero topology. The spherical domain gives a consistent 1D parameterization of non-rigid surfaces for matching. Non-rigid 3D surface correspondence is formulated as the recovery of a bijective mapping between two surfaces in the 2D domain. Formulating matching as a 2D bijection guarantees a continuous one-to-one surface correspondence without overfolding. This overcomes limitations of direct estimation of non-rigid surface correspondence in the 3D domain. A multiple resolution coarse-to-fine algorithm is introduced to robustly estimate the dense correspondence which minimizes the disparity in shape and appearance between two surfaces. Spherical matching is applied to derive the temporal correspondence between non-rigid surfaces reconstructed at successive frames from multiple view video sequences of people. Dense surface correspondence is recovered across complete motion sequences for both textured and uniform regions, without the requirement for a prior model of human shape or kinematics structure for tracking
Efficiently Registering Video into Panoramic Mosaics
Authors: Drew Steedly, Chris Pal, Richard Szeliski
Venue: ICCV
Year: 2005

We present an automatic and efficient method to register and stitch thousands of video frames into a large panoramic mosaic. Our method preserves the robustness and accuracy of image stitchers that match all pairs of images while utilizing the ordering information provided by video. We reduce the cost of searching for matches between video frames by adaptively identifying key frames based on the amount of image-to-image overlap. Key frames are matched to all other key frames, but intermediate video frames are only matched to temporally neighboring key frames and intermediate frames. Image orientations can be estimated from this sparse set of matches in time quadratic to cubic in the number of key frames but only linear in the number of intermediate frames. Additionally, the matches between pairs of images are compressed by replacing measurements within small windows in the image with a single representative measurement. We show that this approach substantially reduces the time required to estimate the image orientations with minimal loss of accuracy. Finally, we demonstrate both the efficiency and quality of our results by registering several long video sequences
More-Than-Topology-Preserving Flows for Active Contours and Polygons
Authors: Ganesh Sundaramoorthi, Anthony J. Yezzi
Venue: ICCV
Year: 2005

Active contour and active polygon models have been used widely for image segmentation. In some applications, the topology of the object(s) to be detected from an image is known a priori, despite an unknown complex geometry, and it is important that the active contour or polygon maintain the desired topology. In this work, we construct a novel geometric flow that can be added to image based evolutions of active contours and polygons so that the topology of the initial contour or polygon is preserved. Indeed, the proposed geometric flow ensures more than just correct topology; it ensures that the active contour or polygon is, in some sense, kept far away from a topology change. Smoothness properties similar to curvature flow are also guaranteed by the proposed geometric flow. The proposed topology preserving geometric flow is the gradient flow arising from an energy that is based on electrostatic principles. The evolution of a single point on the contour depends on all other points of the contour, which is different from traditional curve evolutions in computer vision literature
Common Pattern Discovery Using Earth Mover's Distance and Local Flow Maximization
Authors: Hung-Khoon Tan, Chong-Wah Ngo
Venue: ICCV
Year: 2005

In this paper, we present a novel segmentation-insensitive approach for mining common patterns from 2 images. We develop an algorithm using the earth movers distance (EMD) framework, unary and adaptive neighborhood color similarity. We then propose a novel local flow maximization approach to provide the best estimation of location and scale of the common pattern. This is achieved by performing an iterative optimization in search of the most stable flows' centroid. Common pattern discovery is difficult owing to the huge search space and problem domain. We intend to solve this problem by reducing the search space through identifying the location and a reduced spatial space for common pattern discovery. Experimental results justify the effectiveness and the potential of the approach
Non-Orthogonal Binary Subspace and Its Applications in Computer Vision
Authors: Hai Tao, Ryan Crabb, Feng Tang
Venue: ICCV
Year: 2005

This paper presents a novel approach that represents an image or a set of images using a non-orthogonal binary subspace (NBS) spanned by box-like base vectors. These base vectors possess the property that the inner product operation with them can be computed very efficiently. We investigate the optimized orthogonal matching pursuit method for finding the best NBS base vectors. It is demonstrated in this paper how the NBS based expansion can be applied to speed up several common computer vision algorithms, including normalized cross correlation (NCC), sum of squared difference (SSD) matching, appearance subspace projection and subspace-based object recognition. Promising experimental results on facial and natural images are demonstrated in this paper.
Modelling Shapes with Uncertainties: Higher Order Polynomials, Variable Bandwidth Kernels and Non Parametric Density Estimation
Authors: Maxime Taron, Nikos Paragios, Marie-Pierre Jolly
Venue: ICCV
Year: 2005

In this paper, we introduce a new technique for shape modelling in the space of implicit polynomials. Registration consists of recovering an optimal one-to-one transformation of a higher order polynomial along with uncertainties measures that are determined according to the covariance matrix of the correspondences at the zero isosurface. In the modelling phase, these measures are used to weight the importance of the training samples phase according to a variable bandwidth non-parametric density estimation process. The selection of the most appropriate kernels to represent the training set is done through the maximum likelihood criterion. Excellent results for patterns of digits, related with the registration and the modelling aspects of our approach demonstrate the potentials of our method
Shape Classifer Based on Generalized Probabilistic Descent Method with Hidden Markov Descriptor
Authors: Ninad Thakoor, Jean Gao
Venue: ICCV
Year: 2005

The goal of this paper is to present a weighted likelihood discriminant for minimum error shape classification. Different from traditional maximum likelihood (ML) methods, in which classification is based on probabilities from independent individual class models as is the case for general hidden Markov model (HMM) methods, proposed method utilizes information from all classes to minimize classification error. The proposed approach uses a HMM for shape curvature as its 2D shape descriptor. In this contribution, we introduce a weighted likelihood discriminant function and present a minimum error classification strategy based on generalized probabilistic descent (GPD) method. We believe our sound theory based implementation reduces classification error by combining HMM with GPD theory. We show comparative results obtained with our approach and classic ML classification along with Fourier descriptor and Zernike moments based classification for fighter planes and vehicle shapes.
Shape from Symmetry
Authors: Sebastian Thrun, Ben Wegbreit
Venue: ICCV
Year: 2005

We describe a technique for reconstructing probable occluded surfaces from 3D range images. The technique exploits the fact that many objects possess shape symmetries that can be recognized even from partial 3D views. Our approach identifies probable symmetries and uses them to attend the partial 3D shape model into the occluded space. To accommodate objects consisting of multiple parts, we describe a technique for segmenting objects into parts characterized by different symmetries. Results are provided for a real-world database of 3D range images of common objects, acquired through an active stereo rig
HMM Based Falling Person Detection Using Both Audio and Video
Authors: B. Ugur Töreyin, Yigithan Dedeoglu, A. Enis Çetin
Venue: ICCV
Year: 2005

Automatic detection of a falling person in video is an important problem with applications in security and safety areas including supportive home environments and CCTV surveillance systems. Human motion in video is modeled using Hidden Markov Models (HMM) in this paper. In addition, the audio track of the video is also used to distinguish a person simply sitting on a floor from a person stumbling and falling. Most video recording systems have the capability of recording audio as well and the impact sound of a falling person is also available as an additional clue. Audio channel data based decision is also reached using HMMs and fused with results of HMMs modeling the video data to reach a final decision.
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
Authors: Zhuowen Tu
Venue: ICCV
Year: 2005

In this paper, a new learning framework - probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the probabilistic boosting-tree automatically constructs a tree in which each node combines a number of weak classifiers (evidence, knowledge,) into a strong classifier (a conditional posterior probability). It approaches the target posterior distribution by data augmentation (tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier, which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. Also, clustering is naturally embedded in the learning phase and each sub-tree represents a cluster of certain level. The proposed framework is very general and it has interesting connections to a number of existing methods such as the A* algorithm, decision tree algorithms, generative models, and cascade approaches. In this paper, we show the applications of PBT for classification, detection, and object recognition. We have also applied the framework in segmentation
Simultaneous Multiple 3D Motion Estimation via Mode Finding on Lie Groups
Authors: Oncel Tuzel, Raghav Subbarao, Peter Meer
Venue: ICCV
Year: 2005

We propose a new method to estimate multiple rigid motions from noisy 3D point correspondences in the presence of outliers. The method does not require prior specification of number of motion groups and estimates all the motion parameters simultaneously. We start with generating samples from the rigid motion distribution. The motion parameters are then estimated via mode finding operations on the sampled distribution. Since rigid motions do not lie on a vector space, classical statistical methods can not be used for mode finding. We develop a mean shift algorithm which estimates modes of the sampled distribution using the Lie group structure of the rigid motions. We also show that proposed mean shift algorithm is general and can be applied to any distribution having a matrix Lie group structure. Experimental results on synthetic and real image data demonstrate the superior performance of the algorithm.
KALMANSAC: Robust Filtering by Consensus
Authors: Andrea Vedaldi, Hailin Jin, Paolo Favaro, Stefano Soatto
Venue: ICCV
Year: 2005

We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximum-likelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a sampling-based algorithm that has constant complexity. We derive our algorithm from the equations of the optimal filter, which makes our approximation explicit. Our work is motivated by real-time tracking and the estimation of structure from motion (SFM). We test our algorithm for on-line outlier rejection both for tracking and for SFM. We show that our approach can tolerate a large proportion of outliers, whereas previous causal robust statistical inference methods failed with less than half as many. Our work can be thought of as the extension of random sample consensus algorithms to dynamic data, or as the implementation of pseudo-Bayesian filtering algorithms in a sampling framework.
Features for Recognition: Viewpoint Invariance for Non-Planar Scenes
Authors: Andrea Vedaldi, Stefano Soatto
Venue: ICCV
Year: 2005

Most current local feature detectors/descriptors implicitly assume that the scene is (locally) planar, an assumption that is violated at surface discontinuities. We show that this restriction is, at least in theory, unnecessary, as one can construct local features that are viewpoint-invariant for generic non-planar scenes. However, we show that any such feature necessarily sacrifices shape information, in the sense of being non shape-discriminative. Finally, we show that if viewpoint is factored out as part of the matching process, rather than explicitly in the representation, then shape is discriminative indeed. We illustrate our theoretical results empirically by showing that, even for simple scenes, current affine descriptors fail where even a naive 3-D viewpoint invariant succeeds in matching
Learning Models for Predicting Recognition Performance
Authors: Rong Wang 0007, Bir Bhanu
Venue: ICCV
Year: 2005

This paper addresses one of the fundamental problems encountered in performance prediction for object recognition. In particular we address the problems related to estimation of small gallery size that can give good error estimates and their confidences on large probe sets and populations. We use a generalized two-dimensional prediction model that integrates a hypergeometric probability distribution model with a binomial model explicitly and considers the distortion problem in large populations. We incorporate learning in the prediction process in order to find the optimal small gallery size and to improve its performance. The Chernoff and Chebychev inequalities are used as a guide to obtain the small gallery size. During the prediction we use the expectation-maximum (EM) algorithm to learn the match score and the non-match score distributions (the number of components, their weights, means and covariances) that are represented as Gaussian mixtures. By learning we find the optimal size of small gallery and at the same time provide the upper bound and the lower bound for the prediction on large populations. Results are shown using real-world databases
Mutual Information-Based 3D Surface Matching with Applications to Face Recognition and Brain Mapping
Authors: Yalin Wang 0001, Ming-Chang Chiang, Paul M. Thompson
Venue: ICCV
Year: 2005

Face recognition and many medical imaging applications require the computation of dense correspondence vector fields that match one surface with another. In brain imaging, surface-based registration is useful for tracking brain change, and for creating statistical shape models of anatomy. Based on surface correspondences, metrics can also be designed to measure differences in facial geometry and expressions. To avoid the need for a large set of manually-defined landmarks to constrain these surface correspondences, we developed an algorithm to automate the matching of surface features. It extends the mutual information method to automatically match general 3D surfaces (including surfaces with a branching topology). We use diffeomorphic flows to optimally align the Riemann surface structures of two surfaces. First, we use holomorphic I-forms to induce consistent conformal grids on both surfaces. High genus surfaces are mapped to a set of rectangles in the Euclidean plane and closed genus-zero surfaces are mapped to the sphere. Next, we compute stable geometric features (mean curvature and conformal factor) and pull them back as scalar fields onto the 2D parameter domains. Mutual information is used as a cost functional to drive a fluid flow in the parameter domain that optimally aligns these surface features. A diffeomorphic surface-to-surface mapping is then recovered that matches surfaces in 3D. Lastly, we present a spectral method that ensures that the grids induced on the target surface remain conformal when pulled through the correspondence field. Using the chain rule, we express the gradient of the mutual information between surfaces in the conformal basis of the source surface. This finite-dimensional linear space generates all conformal reparameterizations of the surface. Illustrative experiments apply the method to face recognition and to the registration of brain structures, such as the hippocampus in 3D MRI scans, a key step in understanding brain shape alterations in Alzheimer's disease and schizophrenia.
Retrieval with Knowledge-driven Kernel Design: An Approach to Improving SVM-Based CBIR with Relevance Feedback
Authors: Lei Wang 0001, Yan Gao, Kap Luk Chan, Ping Xue 0001, Wei-Yun Yau
Venue: ICCV
Year: 2005

The performance of SVM-based image retrieval is often constrained by the scarcity of training samples. The total number of image samples labeled by users in a retrieval session is very limited, and this small number of labeled samples cannot effectively represent the true distributions of positive and negative image classes, especially for the negative image class. This paper proposes a novel approach to deal with this problem. Instead of treating it as a problem, the mere existence of the small number of labeled images and their desired distribution in the kernel space is considered as prior knowledge from image retrieval to aid the design of the kernel used by SVMs. This is achieved by maximizing a criterion, such as one based on scatter matrices, through gradient-based search methods, incurring very little computational overhead to real-time retrieval process. Experimental results on two benchmark image databases demonstrate the improved retrieval performance by the dynamically designed kernel and hence the effectiveness of the proposed approach for SVM based image retrieval
Surface Parameterization Using Riemann Surface Structure
Authors: Yalin Wang 0001, Xianfeng Gu, Kiralee M. Hayashi, Tony F. Chan, Paul M. Thompson, Shing-Tung Yau
Venue: ICCV
Year: 2005

We propose a general method that parameterizes general surfaces with complex (possible branching) topology using Riemann surface structure. Rather than evolve the surface geometry to a plane or sphere, we instead use the fact that all orientable surfaces are Riemann surfaces and admit conformal structures, which induce special curvilinear coordinate systems on the surfaces. We can then automatically partition the surface using a critical graph that connects zero points in the global conformal structure on the surface. The trajectories of iso-parametric curves canonically partition a surface into patches. Each of these patches is either a topological disk or a cylinder and can be conformally mapped to a parallelogram by integrating a holomorphic I-form defined on the surface. The resulting surface subdivision and the parameterizations of the components are intrinsic and stable. For surfaces with similar topology and geometry, we show that the parameterization results are consistent and the subdivided surfaces can be matched to each other using constrained harmonic maps. The surface similarity can be measured by direct computation of distance between each pair of corresponding points on two surfaces. To illustrate the technique, we computed conformal structures for anatomical surfaces in MRI scans of the brain and human face surfaces. We found that the resulting parameterizations were consistent across subjects, even for branching structures such as the ventricles, which are otherwise difficult to parameterize. Our method provides a surface-based framework for statistical comparison of surfaces and for generating grids on surfaces for PDE-based signal processing.