OpenRaiser/Intern-Atlas huggingface dataset:
Estimation of Blood Flow Speed and Vessel Location from Thermal Video
Authors: Marc Garbey, Arcangelo Merla, Ioannis T. Pavlidis
Venue: CVPR
Year: 2004

In this paper we present a novel method for estimation of blood flow speed and vessel location from thermal video. The method is based on a bioheat transfer model that reflects the thermo-physiological processes in a skin region proximal to a major vessel. The model assumes the form of a partial differential equation (PDE) with boundary conditions. Initially, we test the soundness of our model by performing direct numerical simulation. Then, we solve the inverse problem both in steady and dynamic states on data provided by a thermal imaging system. Our method opens exciting possibilities in biometrics and biomedicine. Among others, it promises to revolutionize polygraph examinations by eliminating wiring and improving accuracy. It also establishes the feasibility of continuous 2D physiological monitoring of human patients in a contact-free manner.
How Features of the Human Face Affect Recognition: A Statistical Comparison of Three Face Recognition Algorithms
Authors: Geof H. Givens, J. Ross Beveridge, Bruce A. Draper, Patrick Grother, P. Jonathon Phillips
Venue: CVPR
Year: 2004

Recognition difficulty is statistically linked to 11 subject covariate factors such as age and gender for three face recognition algorithms: principle components analysis, an interpersonal image difference classifier, and an elastic bunch graph matching algorithm. The covariates assess race, gender, age, glasses use, facial hair, bangs, mouth state, complexion, state of eyes, makeup use, and facial expression. We use two statistical models. First, an ANOVA relates covariates to normalized similarity scores. Second, logistic regression relates subject covariates to probability of rank one recognition. These models have strong explanatory power as measured by R/sup 2/ and deviance reduction, while providing complementary and corroborative results. Some factors, like changes to the eye status, affect all algorithms similarly. Other factors, such as race, affect different algorithms differently. Tabular and graphical summaries of results provide a wealth of empirical evidence. Plausible explanations of many results can be motivated from knowledge of the algorithms. Other results are surprising and suggest a need for further study.
Fast Wide Baseline Matching for Visual Navigation
Authors: Toon Goedemé, Tinne Tuytelaars, Luc Van Gool
Venue: CVPR
Year: 2004

A new and fast way to find local image correspondences for wide baseline image matching is described. The targeted application is visual navigation, e.g. of a semi-automatic wheelchair. Such applications pose some additional requirements, like the need to work with natural landmarks rather than artificial markers, and the need to recognize locations fast. The restricted motion of the camera can be exploited to simplify the feature extraction. These features should support their identification from different, but nevertheless restricted viewing directions, and under variable illumination conditions. The paper proposes a specialization of so-called affine invariant regions for these particular conditions, which in this case simplifies to column segments. Their applicability is wider than robot navigation, and includes localization for wearable computing and scene recognition for automatic movie indexing.
3D Head Tracking Based on Recognition and Interpolation Using a Time-of-Flight Depth Sensor
Authors: Salih Burak Göktürk, Carlo Tomasi
Venue: CVPR
Year: 2004

This paper describes a head-tracking algorithm that is based on recognition and correlation-based weighted interpolation. The input is a sequence of 3D depth images generated by a novel time-of-flight depth sensor. These are processed to segment the background and foreground, and the latter is used as the input to the head tracking algorithm, which is composed of three major modules: First, a depth signature is created out of the depth images. Next, the signature is compared against signatures that are collected in a training set of depth images. Finally, a correlation metric is calculated between most possible signature hits. The head location is calculated by interpolating among stored depth values, using the correlation metrics as the weights. This combination of depth sensing and recognition-based head tracking provides more than 90 percent success. Even if the track is temporarily lost, it is easily recovered when a good match is obtained from the training set. The use of depth images and recognition-based head tracking achieves robust real-time tracking results under extreme conditions such as 180-degree rotation, temporary occlusions, and complex backgrounds.
Space-Time Isosurface Evolution for Temporally Coherent 3D Reconstruction
Authors: Bastian Goldlücke, Marcus A. Magnor
Venue: CVPR
Year: 2004

We model the dynamic geometry of a time-varying scene as a 3D isosurface in space-time. The intersection of the isosurface with planes of constant time yields the geometry at a single time instant. An optimal fit of our model to multiple video sequences is defined as the minimum of an energy functional. This functional is given by an integral over the entire hypersurface, which is designed to optimize photo-consistency. A PDE-based evolution derived from the Euler-Lagrange equation maximizes consistency with all of the given video data simultaneously. The result is a 3D model of the scene which varies smoothly over time. The geometry reconstructed by this scheme is significantly better than results obtained by space-carving approaches that do not enforce temporal coherence.
Computing Depth under Ambient Illumination Using Multi-Shuttered Light
Authors: Héctor H. González-Baños, James Davis 0001
Venue: CVPR
Year: 2004

Range imaging has become a critical component of many computer vision applications. The quality of the depth data is of critical importance, but so is the need for speed. Shuttered light-pulse (SLP) imaging uses active illumination hardware to provide high quality depth maps at video frame rates. Unfortunately, current analytical models for deriving depth from SLP imagers are specific to the number of shutters and have a number of deficiencies. As a result, depth estimation often suffers from bias due to object reflectivity, incorrect shutter settings, or strong ambient illumination such as that encountered outdoors. These limitations make SLP imaging unsuitable for many applications requiring stable depth readings. This paper introduces a method that is general to any number of shutters. Using three shutters, the new method produces invariant estimates under changes in ambient illumination, producing high quality depth maps in a wider range of situations.
Direct Super-Resolution and Registration Using Raw CFA Images
Authors: Tomomasa Gotoh, Masatoshi Okutomi
Venue: CVPR
Year: 2004

Super-resolution has been applied mainly to grayscale images, but producing a high-resolution color image using a single CCD sensor has not been investigated in detail until recently. This work aims at producing a high-resolution color image directly from raw "color mosaic" images obtained by a single CCD equipped with a color filter array. This method is based on a generalized formulation of super-resolution that simultaneously performs both resolution enhancement and demosaicing. The other key factor of our topic is a precise sub-pixel registration of multiple raw images. We examined direct registration of raw images based on an imaging model, which shows precise estimation of motion among severely aliased raw images. The proposed method is verified through experiments using synthetic and real images.
Faster Graph-Theoretic Image Processing via Small-World and Quadtree Topologies
Authors: Leo J. Grady, Eric L. Schwartz
Venue: CVPR
Year: 2004

Numerical methods associated with graph-theoretic image processing algorithms often reduce to the solution of a large linear system. We show here that choosing a topology that yields a small graph diameter can greatly speed up the numerical solution. As a proof of concept, we examine two image graphs that preserve local connectivity of the nodes (pixels) while drastically reducing the graph diameter. The first is based on a "small-world" modification of a standard 4-connected lattice. The second is based on a quadtree graph. Using a recently described graph- theoretic image processing algorithm we show that large speed-up is achieved with a minimal perturbation of the solution when these graph topologies are utilized. We suggest that a variety of similar algorithms may also benefit from this approach.
Efficient Tracking with the Bounded Hough Transform
Authors: Michael A. Greenspan, Limin Shang, Piotr Jasiobedzki
Venue: CVPR
Year: 2004

The Bounded Hough Transform is introduced to track objects in a sequence of sparse range images. The method is based upon a variation of the General Hough Transform that exploits the coherence across image frames that results from the relationship between known bounds on the object's velocity and the sensor frame rate. It is extremely efficient, running in O(N) for N range data points, and effectively trades off localization precision for runtime efficiency. The method has been implemented and tested on a variety of objects, including freeform surfaces, using both simulated and real data from Lidar and stereovision sensors. The motion bounds allow the inter-frame transformation space to be reduced to a reasonable, and indeed small size, containing only 729 possible states. In a variation, the rotational subspace is projected onto the translational subspace, which further reduces the transformation space to only 54 states. Experimental results confirm that the technique works well with very sparse data, possibly comprising only tens of points per frame, and that it is also robust to measurement error and outliers.
Models of Large Population Recognition Performance
Authors: Patrick Grother, P. Jonathon Phillips
Venue: CVPR
Year: 2004

We present new binomial models of open- and closed-set identification recognition performance, giving formulae for identification and false match rates as functions of database size, match rank and operating threshold. We compare these with previously published models and with results from face recognition trials on populations of size 4 10/sup 4/. We note verification to be a special case of open-set identification and relate area under the receiver operating characteristic to closed-set identification. We find the binomial model approximates performance at low false match rates but underestimates identification rates on closed sets. We implicate the binomial iid assumption, but show conditioning and score transformation methods that ameliorate this.
Multibody Factorization with Uncertainty and Missing Data Using the EM Algorithm
Authors: Amit Gruber, Yair Weiss
Venue: CVPR
Year: 2004

Multibody factorization algorithms give an elegant and simple solution to the problem of structure from motion even for scenes containing multiple independent motions. Despite this elegance, it is still quite difficult to apply these algorithms to arbitrary scenes. First, their performance deteriorates rapidly with increasing noise. Second, they cannot be applied unless all the points can be tracked in all the frames (as will rarely happen in real scenes). Third, they cannot incorporate prior knowledge on the structure or the motion of the objects. In this paper we present a multibody factorization algorithm that can handle arbitrary noise covariance for each feature as well as missing data. We show how to formulate the problem as one of factor analysis and derive an expectation-maximization based maximum-likelihood algorithm. One of the advantages of our formulation is that we can easily incorporate prior knowledge, including the assumption of temporal coherence. We show that this assumption greatly enhances the robustness of our algorithm and present results on challenging sequences.
Facial Event Classification with Task Oriented Dynamic Bayesian Network
Authors: Haisong Gu, Qiang Ji
Venue: CVPR
Year: 2004

Facial events include all activities of face and facial features in spatial or temporal space, such as facial expressions, face gesture, gaze and furrow happening, etc. Developing an automated system for facial event classification is always a challenging task due to the richness, ambiguity and dynamic nature of facial expressions. This paper presents an efficient approach to real-world facial event classification. By integrating dynamic Bayesian network (DBN) with a general-purpose facial behavior description language, a task-oriented stochastic and temporal framework is constructed to systematically represent and classify facial events of interest. Based on the task oriented DBN, we can spatially and temporally incorporate results from previous times and prior knowledge of the application domain. With the top-down inference, the system can make active selection among multiple visual channels to identify the most effective sensory channels to use. With the bottom-up inference from observed evidences, the current facial event can be classified with a desired confident level via the belief propagation. We applied the task-oriented DBN framework to monitoring driver vigilance. Experimental results demonstrate the feasibility and efficiency of our approach.
A Discriminative Feature Space for Detecting and Recognizing Faces
Authors: Abdenour Hadid, Matti Pietikäinen, Timo Ahonen
Venue: CVPR
Year: 2004

We introduce a novel discriminative feature space which is efficient not only for face detection but also for recognition. The face representation is based on local binary patterns (LBP) and consists of encoding both local and global facial characteristics into a compact feature histogram. The proposed representation is invariant with respect to monotonic gray scale transformations and can be derived in a single scan through the image. Considering the derived feature space, a second-degree polynomial kernel SVM classifier was trained to detect frontal faces in gray scale images. Experimental results using several complex images show that the proposed approach performs favorably compared to the state-of-the-art methods. Additionally, experiments with detecting and recognizing low-resolution faces from video sequences were carried out, demonstrating that the same facial representation can be efficiently used for both detection and recognition.
Multiple Kernel Tracking with SSD
Authors: Gregory D. Hager, Maneesh Dewan, Charles V. Stewart
Venue: CVPR
Year: 2004

Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance properties afforded by traditional density-based measures of image similarity, while connecting these techniques to continuous optimization algorithms. This paper demonstrates a connection between kernel-based algorithms and more traditional template tracking methods. here is a well known equivalence between the kernel-based objective function and an SSD-like measure on kernel-modulated histograms. It is shown that under suitable conditions, the SSD-like measure can be optimized using Newton-style iterations. This method of optimization is more efficient (requires fewer steps to converge) than mean shift and makes fewer assumptions on the form of the underlying kernel structure. In addition, the methods naturally extend to objective functions optimizing more elaborate parametric motion models based on multiple spatially distributed kernels. We demonstrate multi-kernel methods on a variety of examples ranging from tracking of unstructured objects in image sequences to stereo tracking of structured objects to compute full 3D spatial location.
Statistical Feature Fusion for Gait-Based Human Recognition
Authors: Ju Han, Bir Bhanu
Venue: CVPR
Year: 2004

This paper presents a novel approach for human recognition by combining statistical gait features from real and synthetic templates. Real templates are directly computed from training silhouette sequences, while synthetic templates are generated from training sequences by simulating silhouette distortion. A statistical feature extraction approach is used for learning effective features from real and synthetic templates. Features learned from real templates characterize human walking properties provided in training sequences, and features learned from synthetic templates predict gait properties under other conditions. A feature fusion strategy is therefore applied at the decision level to improve recognition performance. We apply the proposed approach to USF HumanID Database. Experimental results demonstrate that the proposed fusion approach not only achieves better performance than individual approaches, but also provides large improvement in performance with respect to the baseline algorithm.
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Authors: Bohyung Han, Dorin Comaniciu, Ying Zhu 0006, Larry S. Davis
Venue: CVPR
Year: 2004

Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The particle filter (condensation) algorithm provides a general framework for estimating the probability density functions (pdf) of general non-linear and non-Gaussian systems. However, since this algorithm is based on a Monte Carlo approach, where the density is represented by a set of random samples, the number of samples is problematic, especially for high dimensional problems. In this paper, we propose an alternative to the classical particle filter in which the underlying pdf is represented with a semi-parametric method based on a mode finding algorithm using mean-shift. A mode propagation technique is designed for this new representation for tracking applications. A quasi-random sampling method in the measurement stage is used to improve performance, and sequential density approximation for the measurements distribution is performed for efficient computation. We apply our algorithm to a high dimensional color-based tracking problem, and demonstrate its performance by showing competitive results with other trackers.
An Algorithm for Multiple Object Trajectory Tracking
Authors: Mei Han, Wei Xu 0007, Hai Tao, Yihong Gong
Venue: CVPR
Year: 2004

Most tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework called Hidden Markov Model, where the distribution of the object state at current time instance is estimated based on current and previous observations. However this approach is prone to errors caused by temporal distractions such as occlusion, background clutter and multi-object confusion. In this paper we propose a multiple object tracking algorithm that seeks the optimal state sequence which maximizes the joint state-observation probability. We name this algorithm trajectory tracking since it estimates the state sequence or "trajectory" instead of the current state. The algorithm is capable of tracking multiple objects whose number is unknown and varies during tracking. We introduce an observation model which is composed of the original image, the foreground mask given by background subtraction and the object detection map generated by an object detector The image provides the object appearance information. The foreground mask enables the likelihood computation to consider the multi-object configuration in its entirety. The detection map consists of pixel-wise object detection scores, which drives the tracking algorithm to perform joint inference on both the number of objects and their configurations efficiently.
Eye Typing off the Shelf
Authors: Dan Witzner Hansen, Arthur E. C. Pece
Venue: CVPR
Year: 2004

The goal of this work is using off-the-shelf components for gaze-based interaction, with focus on eye typing. Avoiding the use of dedicated hardware such as IR light emitters makes eye tracking significantly more difficult and requires robust methods capable of handling large changes in image quality. We employ an active-contour method to obtain robust iris tracking. The main strength of the method is that the contour model avoids explicit feature detection: contours are simply assumed to remove statistical dependencies on opposite sides of the contour. The contour model is utilized in an approach combining particle filtering with the EM algorithm. The method is robust against light changes and camera defocusing. For the purpose of determining where the user is looking calibrations is usually needed. The number of calibration points used in different methods varies from a few to several thousands, depending on the prior knowledge used on the setup and equipment We examine basic properties of gaze determination when the geometry of the camera, screen and user is unknown. In particular we present a lower bound on the number of calibration points needed for gaze determination on planar objects, and we examine degenerate configurations. Based on this lower bound we apply a simple calibration procedure, to facilitate button selections for fast on-screen typing.
Fast, Integrated Person Tracking and Activity Recognition with Plan-View Templates from a Single Stereo Camera
Authors: Michael Harville, Dalong Li
Venue: CVPR
Year: 2004

Plan-view projection of real-time depth imagery can improve the statistics of its intrinsic 3D data, and allows for cleaner separation of occluding and closely-interacting people. We build a probabilistic, real-time multi-person tracking system upon a plan-view image substrate that well preserves both shape and size information of foreground objects. The tracking's robustness derives in part from its "plan-view template" person models, which capture detailed properties of people's body configurations. We demonstrate that these same person models, obtained with a single compact stereo camera unit, may also be used for fast recognition of body pose and activity. Principal components analysis is used to extract plan-view "eigenposes", onto which person models, extracted during tracking, are projected to produce a compact representation of human body configuration. We then formulate pose recognition as a classification problem, and use support vector machines (SVMs) to quickly distinguish between, for example, different directions people are facing, and different body poses such as standing, sitting, bending over, crouching, and reaching. The SVM outputs are transformed to probabilities and integrated across time in a probabilistic framework for real-time activity recognition.