OpenRaiser/Intern-Atlas huggingface dataset:
OpenVL: Towards A Novel Software Architecture for Computer Vision
Authors: Changsong Shen, Sidney S. Fels, James J. Little
Venue: CVPR
Year: 2007

This paper presents our progress on OpenVL -a novel software architecture to address efficiency through facilitating hardware acceleration, reusability and scalability for computer vision. A logical image understanding pipeline is introduced to allow parallel processing. As well, we discuss our middleware -VLUT that enables applications to operate transparently over a heterogeneous collection of hardware implementations. OpenVL works as a state machine, with an event-driven mechanism to provide users with application-level interaction. Various explicit or implicit synchronization and communication methods are supported among distributed processes in the logical pipelines. The intent of OpenVL is to allow users to quickly and easily recover useful information from multiple scenes across various software environments and hardware platforms. We implement two different human tracking systems to validate the critical underlying concepts of OpenVL.
Mapping Natural Image Patches by Explicit and Implicit Manifolds
Authors: Kent Shi, Song Chun Zhu
Venue: CVPR
Year: 2007

Image patches are fundamental elements for object modeling and recognition. However, there has not been a panoramic study of the structures of the whole ensemble of natural image patches in the literature. In this article, we study the structures of this ensemble by mapping natural image patches into two types of subspaces which we call "explicit manifolds " and "implicit manifolds " respectively. On explicit manifolds, one finds those simple and regular image primitives, such as edges, bars, corners and junctions. On implicit manifolds, one finds those complex and stochastic image patches, such as textures and clutters. On different types of manifolds, different perceptual metrics are used. We propose a method for learning a probabilistic distribution on the space of patches by pursuing both types of manifolds using a common information theoretical criterion. The connection between the two types of manifolds is realized by image scaling, which changes the entropy of the image patches. The explicit manifolds live in low entropy regimes while the implicit manifolds live in high entropy regimes. We study the transition between the two types of manifolds over scale and show that the complexity of the manifolds peaks in a middle entropy regime.
Real-time Visual Tracking under Arbitrary Illumination Changes
Authors: Geraldo F. Silveira, Ezio Malis
Venue: CVPR
Year: 2007

In this paper, we investigate how to improve the robustness of visual tracking methods with respect to generic lighting changes. We propose a new approach to the direct image alignment of either Lambertian or non-Lambertian objects under shadows, inter-reflections, glints as well as ambient, diffuse and specular reflections which may vary in power, type, number and space. The method is based on a proposed model of illumination changes together with an appropriate geometric model of image motion. The parameters related to these models are obtained through an efficient second-order optimization technique which minimizes directly the intensity discrepancies. Comparison results with existing direct methods show significant improvements in the tracking performance. Extensive experiments confirm the robustness and reliability of our method.
Are Digraphs Good for Free-Text Keystroke Dynamics?
Authors: Terence Sim, Rajkumar Janakiraman
Venue: CVPR
Year: 2007

Research in keystroke dynamics has largely focused on the typing patterns found in fixed text (e.g. userid and passwords). In this regard, digraphs and trigraphs have proven to be discriminative features. However, there is increasing interest in free-text keystroke dynamics, in which the user to be authenticated is free to type whatever he/she wants, rather than a pre-determined text. The natural question that arises is whether digraphs and trigraphs are just as discriminative for free text as they are for fixed text. We attempt to answer this question in this paper. We show that digraphs and trigraphs, if computed without regard to what word was typed, are no longer discriminative. Instead, word-specific digraphs/trigraphs are required. We also show that the typing dynamics for some words depend on whether they are part of a larger word. Our study is the first to investigate these issues, and we hope our work will help guide researchers looking for good features for free-text keystroke dynamics.
A Probabilistic Model for Object Recognition, Segmentation, and Non-Rigid Correspondence
Authors: Ian Simon, Steven M. Seitz
Venue: CVPR
Year: 2007

We describe a method for fully automatic object recognition and segmentation using a set of reference images to specify the appearance of each object. Our method uses a generative model of image formation that takes into account occlusions, simple lighting changes, and object deformations. We take advantage of local features to identify, locate, and extract multiple objects in the presence of large viewpoint changes, nonrigid motions with large numbers of degrees of freedom, occlusions, and clutter. We simultaneously compute an object-level segmentation and a dense correspondence between the pixels of the appropriate reference images and the image to be segmented.
Simultaneous Covariance Driven Correspondence (CDC) and Transformation Estimation in the Expectation Maximization Framework
Authors: Michal Sofka, Gehua Yang, Charles V. Stewart
Venue: CVPR
Year: 2007

This paper proposes a new registration algorithm, Co-variance Driven Correspondences (CDC), that depends fundamentally on the estimation of uncertainty in point correspondences. This uncertainty is derived from the covariance matrices of the individual point locations and from the covariance matrix of the estimated transformation parameters. Based on this uncertainty, CDC uses a robust objective function and an EM-like algorithm to simultaneously estimate the transformation parameters, their covariance matrix, and the likely correspondences. Unlike the Robust Point Matching (RPM) algorithm, CDC requires neither an annealing schedule nor an explicit outlier process. Experiments on synthetic and real images using a polynomial transformation models in 2D and in 3D show that CDC has a broader domain of convergence than the well-known Iterative Closest Point (ICP) algorithm and is more robust to missing or extraneous structures in the data than RPM.
One-class Machine Learning for Brain Activation Detection
Authors: Xiaomu Song, George Iordanescu, Alice M. Wyrwicz
Venue: CVPR
Year: 2007

Machine learning methods, such as support vector machine (SVM), have been applied to fMRI data analysis, where most studies focus on supervised detection and classification of cognitive states. In this work, we study the general fMRI activation detection using SVM in an unsupervised way instead of the classification of cognitive states. Specifically, activation detection is formulated as an outlier (activated voxels) detection problem of the one-class support vector machine (OCSVM). An OCSVM implementation, v-SVM, is used where parameter v controls the outlier ratio, and is usually unknown. We propose a detection method that is not sensitive to v randomly set within a range known a priori. In cases that this range is also unknown, we consider v estimation using geometry and texture features. Results from both synthetic and experimental data demonstrate the effectiveness of the proposed methods.
Speckle Tracking in 3D Echocardiography with Motion Coherence
Authors: Xubo B. Song, Andriy Myronenko, David J. Sahn
Venue: CVPR
Year: 2007

Tracking of speckles in echocardiography enables the study of myocardium deformation, and thus can provide insights about heart structure and function. Most of the current methods are based on 2D speckle tracking, which suffers from errors due to through-plane decorrelation. Speckle tracking in 3D overcomes such limitation. However, 3D speckle tracking is a challenging problem due to relatively low spatial and temporal resolution of 3D echocardiography. To ensure accurate and robust tracking, high level spatial and temporal constraints need to be incorporated. In this paper, we introduce a novel method for speckle tracking in 3D echocardiography. Instead of tracking each speckle independently, we enforce a motion coherence constraint, in conjunction with a dynamic model for the speckles. This method is validated on in vivo porcine hearts, and is proved to be accurate and robust.
Closed-Loop Tracking and Change Detection in Multi-Activity Sequences
Authors: Bi Song, Namrata Vaswani, Amit K. Roy-Chowdhury
Venue: CVPR
Year: 2007

We present a novel framework for tracking of a long sequence of human activities, including the time instances of change from one activity to the next, using a closed-loop, non-linear dynamical feedback system. A composite feature vector describing the shape, color and motion of the objects, and a non-linear, piecewise stationary, stochastic dynamical model describing its spatio-temporal evolution, are used for tracking. The tracking error or expected log likelihood, which serves as a feedback signal, is used to automatically detect changes and switch between activities happening one after another in a long video sequence. Whenever a change is detected, the tracker is re initialized automatically by comparing the input image with learned models of the activities. Unlike some other approaches that can track a sequence of activities, we do not need to know the transition probabilities between the activities, which can be difficult to estimate in many application scenarios. We demonstrate the effectiveness of the method on multiple indoor and outdoor real-life videos and analyze its performance.
Bottom-up Recognition and Parsing of the Human Body
Authors: Praveen Srinivasan, Jianbo Shi
Venue: CVPR
Year: 2007

Recognizing humans, estimating their pose and segmenting their body parts are key to high-level image understanding. Because humans are highly articulated, the range of deformations they undergo makes this task extremely challenging. Previous methods have focused largely on heuristics or pairwise part models in approaching this problem. We propose a bottom-up parsing of increasingly more complete partial body masks guided by a parse tree. At each level of the parsing process, we evaluate the partial body masks directly via shape matching with exemplars, without regard to how the parses are formed. The body is evaluated as a whole, not the sum of its constituent parses, unlike previous approaches. Multiple image segmentations are included at each of the levels of the parsing, to augment existing parses or to introduce ones. Our method yields both a pose estimate as well as a segmentation of the human. We demonstrate competitive results on this challenging task with relatively few training examples on a dataset of baseball players with wide pose variation. Our method is comparatively simple and could be easily extended to other objects.
Riemannian Analysis of Probability Density Functions with Applications in Vision
Authors: Anuj Srivastava, Ian Jermyn, Shantanu H. Joshi
Venue: CVPR
Year: 2007

Applications in computer vision involve statistically analyzing an important class of constrained, non-negative functions, including probability density functions (in texture analysis), dynamic time-warping functions (in activity analysis), and re-parametrization or non-rigid registration functions (in shape analysis of curves). For this one needs to impose a Riemannian structure on the spaces formed by these functions. We propose a "spherical" version of the Fisher-Rao metric that provides closed-form expressions for geodesies and distances, and allows fast computation of sample statistics. To demonstrate this approach, we present an application in planar shape classification.
An Efficient Minimal Solution for Infinitesimal Camera Motion
Authors: Henrik Stewénius, Chris Engels, David Nistér
Venue: CVPR
Year: 2007

Given five motion vectors observed in a calibrated camera, what is the rotational and translational velocity of the camera? This problem is the infinitesimal motion analogue to the five-point relative orientation problem, which has previously been solved through the derivation of a tenth-degree polynomial and extraction of its roots. Here, we present the first efficient solution to the infinitesimal version of the problem. The solution is faster than its finite counterpart. In our experiments, we investigate over which range of motions and scene distances the infinitesimal approximation is valid and show that the infinitesimal approximation works well in applications such as camera tracking.
Discontinuity Preserving Filtering over Analytic Manifolds
Authors: Raghav Subbarao, Peter Meer
Venue: CVPR
Year: 2007

Discontinuity preserving filtering of images is an important low-level vision task. With the development of new imaging techniques like diffusion tensor imaging (DTI), where the data does not lie in a vector space, previous methods like the original mean shift are not applicable. In this paper, we use the nonlinear mean shift algorithm to develop filtering methods for data lying on analytic manifolds. We work out the computational details of using mean shift on Sym <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , the manifold of n times n symmetric positive definite matrices. We apply our algorithm to chromatic noise filtering, which requires mean shift over the Grassmann manifold G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3,1</sub> , and obtain better results then standard mean shift filtering. We also use our method for DTI filtering, which requires smoothing over Sym <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> .
A Direct and Efficient Method for Piecewise-Planar Surface Reconstruction from Stereo Images
Authors: Shigeki Sugimoto, Masatoshi Okutomi
Venue: CVPR
Year: 2007

In this paper, we propose a direct method for 3D surface reconstruction from stereo images. We reconstruct a 3D surface by estimating all depths of the vertices of a mesh composed of piecewise triangular patches on the reference (template) image. The analyses described in this paper subsume that the deformation of the mesh between the stereo images is specified by homographies, each of which represents the deformation of a single patch. The homography deforms each patch which has 3 d.o.f under epipolar constraints. We first formulate a fast "direct" method for estimating the three parameters of a 3D plane by incorporating inverse compositional expression into the sum of squared differences (SSD) function of two stereo images. This method is about eight times faster than the conventional method. Then we extend the direct method to the estimation of the vertex depths in the mesh for reconstructing piecewise-planar surfaces. The validity of the proposed method is demonstrated through results of experiments using synthetic and real images.
Imaging the Finger Force Direction
Authors: Yu Sun 0004, John M. Hollerbach, Stephen A. Mascaro
Venue: CVPR
Year: 2007

This paper presents a method of imaging the coloration pattern in the fingernail and surrounding skin to infer fingertip force direction during planar contact. Nail images from 7 subjects were registered to reference images with RANSAC and then warped to an atlas with elastic registration. Recognition of fingertip force direction, based on linear discriminant analysis, shows that there are common color pattern features in the fingernail and surrounding skin for different subjects. Based on the common features, the overall recognition accuracy is 92%.
Flash Cut: Foreground Extraction with Flash and No-flash Image Pairs
Authors: Jian Sun 0009, Jian Sun 0001, Sing Bing Kang, Zongben Xu, Xiaoou Tang, Heung-Yeung Shum
Venue: CVPR
Year: 2007

In this paper, we propose a novel approach for foreground layer extraction using flash/no-flash image pairs, which we call flash cut. Flash cut is based on the simple observation that only the foreground is significantly brightened by the flash and the background appearance change is very small, if the background is distant. Changes due to flash, motion, and color information are fused in an MRF framework to produce high quality segmentation results. Flash cut handles some amount of camera shake, and foreground motion, which makes it practical for anyone with a flash-equipped camera to use. We validate our approach on a variety of indoor and outdoor examples.
A Multi-Resolution Dynamic Model for Face Aging Simulation
Authors: Jin-Li Suo, Feng Min, Song Chun Zhu, Shiguang Shan, Xilin Chen 0001
Venue: CVPR
Year: 2007

In this paper we present a dynamic model for simulating face aging process. We adopt a high resolution grammatical face model[1] and augment it with age and hair features. This model represents all face images by a multi-layer And-Or graph and integrates three most prominent aspects related to aging changes: global appearance changes in hair style and shape, deformations and aging effects of facial components, and wrinkles appearance at various facial zones. Then face aging is modeled as a dynamic Markov process on this graph representation which is learned from a large dataset. Given an input image, we firstly compute the graph representation, and then sample the graph structures over various age groups according to the learned dynamic model. Finally we generate new face images with the sampled graphs. Our approach has three novel aspects: (1) the aging model is learned from a dataset of 50,000 adult faces at different ages; (2) we explicitly model the uncertainty in face aging and can sample multiple plausible aged faces for an input image; and (3) we conduct a simple human experiment to validate the simulated aging process.
Secure Biometric Templates from Fingerprint-Face Features
Authors: Yagiz Sutcu, Qiming Li, Nasir D. Memon
Venue: CVPR
Year: 2007

Since biometric data cannot be easily replaced or revoked, it is important that biometric templates used in biometric applications should be constructed and stored in a secure way, such that attackers would not be able to forge biometric data easily even when the templates are compromised. This is a challenging goal since biometric data are "noisy" by nature, and the matching algorithms are often complex, which make it difficult to apply traditional cryptographic techniques, especially when multiple modalities are considered. In this paper, we consider a "fusion " of a minutiae-based fingerprint authentication scheme and an SVD-based face authentication scheme, and show that by employing a recently proposed cryptographic primitive called "secure sketch ", and a known geometric transformation on minutiae, we can make it easier to combine different modalities, and at the same time make it computationally infeasible to forge an "original" combination of fingerprint and face image that passes the authentication. We evaluate the effectiveness of our scheme using real fingerprints and face images from publicly available sources.
Unsupervised Clustering using Multi-Resolution Perceptual Grouping
Authors: Tanveer Fathima Syeda-Mahmood, Fei Wang 0002
Venue: CVPR
Year: 2007

Clustering is a common operation for data partitioning in many practical applications. Often, such data distributions exhibit higher level structures which are important for problem characterization, but are not explicitly discovered by existing clustering algorithms. In this paper, we introduce multi-resolution perceptual grouping as an approach to unsupervised clustering. Specifically, we use the perceptual grouping constraints of proximity, density, contiguity and orientation similarity. We apply these constraints in a multi-resolution fashion, to group sample points in high dimensional spaces into salient clusters. We present an extensive evaluation of the clustering algorithm against state-of-the-art supervised and unsupervised clustering methods on large datasets.
Local Structure Detection with Orientation-invariant Radial Configuration
Authors: Lech Szumilas, Rene Donner, Georg Langs, Allan Hanbury
Venue: CVPR
Year: 2007

Local image descriptors have proved themselves as useful tools for many computer vision tasks such as matching points between multiple images of a scene and object recognition. Current descriptors, such as SIFT, are designed to match image features with unique local neighborhoods. However, the interest point detectors used with SIFT often fail to select perceptible local structures in the image, and the SIFT descriptor does not directly encode the local neighborhood shape. In this paper we propose a symmetry based interest point detector and radial local structure descriptor which consistently captures the majority of basic local image structures and provides a geometrical description of the structure boundaries. This approach concentrates on the extraction of shape properties in image patches, which are an intuitive way to represent local appearance for matching and classification. We explore the specificity and sensitivity of this local descriptor in the context of classification of natural patterns. The implications of the performance comparison with standard approaches like SIFT are discussed.
Robust Estimation of Texture Flow via Dense Feature Sampling
Authors: Yu-Wing Tai, Michael S. Brown, Chi-Keung Tang
Venue: CVPR
Year: 2007

Texture flow estimation is a valuable step in a variety of vision related tasks, including texture analysis, image segmentation, shape-from-texture and texture remapping. This paper describes a novel and effective technique to estimate texture flow in an image given a small example patch. The key idea consists of extracting a dense set of features from the example patch where discrete orientations are encapsulated into the feature vector such that rotation can be simulated as a linear shift of the vector. This dense feature space is then compressed by PCA and clustered using EM to produce a set of small set of principal features. Obtaining these principal features at varying image scales, we can compute the per-pixel scale and orientation likelihoods for the distorted texture. The final texture flow estimation is formulated as the MAP solution of a labeling Markov network which is solved using belief propagation. Experimental results on both synthetic and real images demonstrate good results even for highly distorted examples.
Recognizing Night Walkers Based on One Pseudoshape Representation of Gait
Authors: Daoliang Tan, Kaiqi Huang, Shiqi Yu 0001, Tieniu Tan
Venue: CVPR
Year: 2007

Gait is a promising biometric cue which can facilitate the recognition of human beings, particularly when other biometrics are unavailable. Existing work for gait recognition, however, lays more emphasis on the problem of daytime walker recognition and overlooks the significance of walker recognition at night. This paper deals with the problem of recognizing nighttime walkers. We take advantage of infrared gait patterns to accomplish this task: 1) Walker detection is improved using intensity compensation-based background subtraction; 2) pseudoshape-based features are proposed to describe gait patterns; 3) the dimension of gait features is reduced through the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques; 4) temporal cues are exploited in the form of the relevant component analysis (RCA) learning; 5) the nearest neighbor classifier is used to recognize unknown gait. Experimental results justify the effectiveness of our method and show that our method has an encouraging potential for the application in surveillance systems.