OpenRaiser/Intern-Atlas huggingface dataset:
Bayesian Super-Resolution of Text in Video with a Text-Specific Bimodal Prior
Authors: Katherine Donaldson, Gregory K. Myers
Venue: CVPR
Year: 2005

To increase the range of sizes of video scene text recognizable by optical character recognition (OCR), we developed a Bayesian super-resolution algorithm that uses a text-specific bimodal prior. We evaluated the effectiveness of the bimodal prior, compared with and in conjunction with a piecewise smoothness prior, visually and by measuring the accuracy of the OCR results on the variously super-resolved images. The bimodal prior improved the readability of 4- to 7-pixel-high scene text significantly better than bicubic interpolation, and increased the accuracy of OCR results better than the piecewise smoothness prior.
Modeling Dynamic Scenes with Active Appearance
Authors: Gianfranco Doretto
Venue: CVPR
Year: 2005

In this work, we propose a model for video scenes that contains temporal variability in shape and appearance. We propose a conditionally linear model akin to a dynamic extension of active appearance models. We formulate the problem variationally, and propose a framework where a model complexity cost dictates the "modeling responsibility" of each of the factors: appearance, shape and motion. We render the learning problem well-posed by reverting to a physical and a dynamic prior, and use the finite element method to compute a numerical solution. We illustrate our model to learn and simulate the shape, appearance, and motion of scenes that exhibit some form of temporal regularity, intended in a statistical sense.
Geo-Consistency for Wide Multi-Camera Stereo
Authors: Marc-Antoine Drouin, Martin Trudeau, Sébastien Roy 0001
Venue: CVPR
Year: 2005

This paper presents a new model to overcome the occlusion problems coming from wide baseline multiple camera stereo. Rather than explicitly modeling occlusions in the matching cost function, it detects occlusions in the depth map obtained from regular efficient stereo matching algorithms. Occlusions are detected as inconsistencies of the depth map by computing the visibility of the map as it is reprojected into each camera. Our approach has the particularity of not discriminating between occluders and occludees. The matching cost function is modified according to the detected occlusions by removing the offending cameras from the computation of the matching cost. The algorithm gradually modifies the matching cost function according to the history of inconsistencies in the depth map, until convergence. While two graph-theoretic stereo algorithms are used in our experiments, our framework is general enough to be applied to many others. The validity of our framework is demonstrated using real imagery with different baselines.
Driver State Monitor from DELPHI
Authors: N. Edenborough, Riad I. Hammoud, A. Harbach, A. Ingold, Branislav Kisacanin, Phillip Malawey, T. Newman, G. Scharenbroch, S. Skiver, Matthew R. H. Smith, Andrew Wilhelm, Gerald J. Witt, E. Yoder, Harry Zhang
Venue: CVPR
Year: 2005

We present an automotive-grade, real-time, vision-based driver state monitor. Upon detecting and tracking the driver's facial features, the system analyzes eye-closures and head pose to infer his/her fatigue or distraction. This information is used to warn the driver and to modulate the actions of other safety systems. The purpose of this monitor is to increase road safety by preventing drivers from falling asleep or from being overly distracted, and to improve the effectiveness of other safety systems.
Efficient Multiclass Object Detection by a Hierarchy of Classifiers
Authors: Xiaodong Fan
Venue: CVPR
Year: 2005

We consider detecting object instances from multiple classes on grayscale images. Traditional approaches learn a classifier for each class separately and apply each of them in an exhaustive search over positions and scales. We achieve an efficient detection by organizing the search coarse-to-fine based on a hierarchical partitioning of the entire hypothesis space, the set of all possible object instances, so that groups of hypotheses can be pruned simultaneously without evaluating each one individually. In this paper, we develop an algorithm to jointly learn the hierarchy along with a classifier at each node by exploring the common parts shared among a group of object instances at all levels in the hierarchy. We also show how the confusions of the initial coarse-to-fine search can be resolved by comparing pairs of conflicting detections using cheap binary classifiers. The whole process is illustrated by detecting and recognizing handwritten digits.
Learning the Semantics of Images by Using Unlabeled Samples
Authors: Jianping Fan 0001, Hangzai Luo, Yuli Gao
Venue: CVPR
Year: 2005

In this paper, we have proposed a novel framework to achieve more effective classifier training by using unlabeled samples. By integrating concept hierarchy for semantic image concept organization, a hierarchical mixture model is proposed to enable multi-level image concept modeling and hierarchical classifier training. To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy, we have proposed a novel adaptive EM algorithm to achieve more effective classifier training with higher prediction accuracy. To effectively learn the classifiers for the higher-level semantic image concepts, we have also proposed a novel technique for classifier combining by using hierarchical mixture model. The experimental results on two large-scale image databases are also provided.
Multiple Collaborative Kernel Tracking
Authors: Zhimin Fan 0002, Ying Wu 0001, Ming Yang 0007
Venue: CVPR
Year: 2005

This paper presents a novel multiple collaborative kernel approach to visual tracking. This approach treats kernel-based tracking in a more general setting, i.e., a relaxation and constraints formulation, in which a complex motion is represented by a set of inter-correlated simpler motions. With this formulation, we present a rigorous analysis on a critical issue of kernel observability and obtain a criterion, based on which we propose a new method using collaborative kernels that has the theoretical guarantee of enhanced observability. This new method has been shown to be computationally efficient in both theory and practice, which can be readily applied to complex motions such as articulated motions.
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
Authors: Robert Fergus, Pietro Perona, Andrew Zisserman
Venue: CVPR
Year: 2005

We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semi-supervised manner: the model is learnt from example images containing category instances, without requiring segmentation from background clutter. The model is a sparse representation of the object, and consists of a star topology configuration of parts modeling the output of a variety of feature detectors. The optimal choice of feature types (whose repertoire includes interest points, curves and regions) is made automatically. In recognition, the model may be applied efficiently in an exhaustive manner, bypassing the need for feature detectors, to give the globally optimal match within a query image. The approach is demonstrated on a wide variety of categories, and delivers both successful classification and localization of the object within the image.
Colour Constancy Using the Chromagenic Constraint
Authors: Graham D. Finlayson, Steven D. Hordley, Peter M. Morovic
Venue: CVPR
Year: 2005

In this paper we propose that two images are captured of every scene: a normal image and an image captured where a coloured filter is placed in front of the camera. This additional information is then used in solving for colour constancy. The novelty of our approach is not that we add a colour filter (this is an old idea) but in how we use the additional information. In contradistinction to previous work we propose that the dimensionality of the 6 measurements per image pixel remains at 3 (not 6): we do not add a filter to increase the number of degrees of freedom but rather as a way of estimating the illuminant. We say that a filter is chromagenic if the relationship between filtered and unfiltered RGBs varies with and depends strongly on illumination. The canonical chromagenic algorithm works by testing the applicability of pre-computed relations in situ in an image. We extend the chromagenic approach to incorporate knowledge of the gamut of colours we expect to see under a given light and so in effect we make a hybrid gamut mapping + chromagenic algorithm. Experiments validate our approach with chromagenic gamut mapping shown to deliver significantly better constancy than all other algorithms tested.
Illumination-Invariant Tracking via Graph Cuts
Authors: Daniel Freedman, Matthew W. Turek
Venue: CVPR
Year: 2005

Illumination changes are a ubiquitous problem in computer vision. They present a challenge in many applications, including tracking: for example, an object may move in and out of a shadow. We present a new tracking algorithm which is insensitive to illumination changes, while at the same time using all of the available photometric information. The algorithm is based on computing an illumination-invariant optical flow field; the computation is made robust by using a graph cuts formulation. Experimentally, the new technique is shown to quite reliable in both synthetic and real sequences, dealing with a variety of illumination changes that cause problems for density based trackers.
An On-Line Learning Mechanism for Unsupervised Classification and Topology Representation
Authors: Shen Furao, Osamu Hasegawa
Venue: CVPR
Year: 2005

An on-line learning mechanism is proposed for unsupervised data. Using a similarity threshold and local error based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of online non-stationary data distribution. The definition of a utility parameter -"error-radius" - enables this system to learn the number of nodes needed to solve a task. The usage of a new technique for removing nodes in low probability density regions can separate the clusters with low-density overlaps and dynamically eliminate noise in the input data. Experiment results show that this system can report a reasonable number of clusters and represent the topological structure of unsupervised on-line data with no prior conditions such as a suitable number of nodes or a good initial codebook.
Feature-Level Fusion in Personal Identification
Authors: Yongsheng Gao 0001, Michael Maggs
Venue: CVPR
Year: 2005

The existing studies of multi-modal and multi-view personal identification focused on combining the outputs of multiple classifiers at the decision level. In this study, we investigated the fusion at the feature level to combine multiple views and modals in personal identification. A new similarity measure is proposed, which integrates multiple 2D view features representing a visual identity of a 3D object seen from different viewpoints and from different sensors. The robustness to non-rigid distortions is achieved by the proximity correspondence manner in the similarity computation. The feasibility and capability of the proposed technique for personal identification were evaluated on multiple view human faces and palmprints. This research demonstrates that the feature-level fusion provides a new way to combine multiple modals and views for personal identification.
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
Authors: Bogdan Georgescu, Xiang Sean Zhou, Dorin Comaniciu, Alok Gupta
Venue: CVPR
Year: 2005

The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such strategies require the a priori knowledge to be explicitly defined in the optimization criterion, e.g., "high-gradient border", "smoothness"', or "similar intensity or texture". This approach is limited by the validity of underlying assumptions and cannot capture complex structure appearance. This paper introduces database-guided segmentation as a new data-driven paradigm that directly exploits expert annotation of interest structures in large medical databases. Segmentation is formulated as a two-step learning problem. The first step is structure detection where we learn how to discriminate between the object of interest and background. The resulting classifier based on a boosted cascade of simple features also provides a global rigid transformation of the structure. The second step is shape inference where we use a sample-based representation of the joint distribution of appearance and shape annotations. To learn the association between the complex appearance and shape we propose a feature selection mechanism and the corresponding metric. We show that the selected features are better than using directly the appearance and illustrate the performance of the proposed method on a large set of ultrasound heart images.
Tangent-Corrected Embedding
Authors: Ali Ghodsi 0001, Jiayuan Huang, Finnegan Southey, Dale Schuurmans
Venue: CVPR
Year: 2005

Images and other high-dimensional data can frequently be characterized by a low dimensional manifold (e.g. one that corresponds to the degrees of freedom of the camera). Recently, nonlinear manifold learning techniques have been used to map images to points in a lower dimension space, capturing some of the dynamics of the camera or the subjects. In general, these methods do not take advantage of any prior understanding of the dynamics we might have, relying instead on local Euclidean distances that can be misleading in image space. In practice, we frequently have some prior knowledge regarding the transformations that relate images (e.g. rotation, translation, etc). We present a method for augmenting existing embedding techniques with additional information derived from known transformations, either in the form of tangent spaces that locally characterize the manifold or distances derived from reconstruction errors. The extra information is incorporated directly into the cost function of the embedding technique. The techniques we augment are largely attractive because there is a closed form solution for their cost optimization. Our approach likewise produces a closed form solution for the augmented cost function. Experiments demonstrate the effectiveness of the approach on a variety of image data. 1
Higher Order Whitening of Natural Images
Authors: Joshua Gluckman
Venue: CVPR
Year: 2005

Natural images are approximately scale invariant resulting in long range statistical regularities that typically obey a power law. For example, images have considerable regularity in their second order spatial correlations as measured by the power spectrum. Processing images to remove these expected correlations is known as whitening an image. Because the expected value of the power spectrum has a regular form (a power law) linear processing such as convolution can be used to whiten an image. After whitening an image, higher order regularities that cannot be removed with linear processing still exist in the form of correlations in the magnitude. In this paper it is shown that these correlations also obey a power law and a non-linear method is used to remove them, a process referred to as higher order whitening. The method is invertible demonstrating that while redundancy is removed no information is lost. Experiments are given showing that after higher order whitening the coefficients can be severely quantized yet a good reconstruction is possible despite the nonlinearities.
Near Real-Time Reliable Stereo Matching Using Programmable Graphics Hardware
Authors: Minglun Gong, Yee-Hong Yang
Venue: CVPR
Year: 2005

A near-real-time stereo matching technique is presented in this paper, which is based on the reliability-based dynamic programming algorithm we proposed earlier. The new algorithm can generate semi-dense disparity maps using only two dynamic programming passes, while our previous approach requires 20-30 passes. We also implement the algorithm on programmable graphics hardware, which further improves the processing speed. The experiments on the four Middlebury stereo datasets show that the new algorithm can produce dense (>85% of the pixels) and reliable (error rate <0.3%) matches in near real-time (0.05-0.1 sec). If needed, it can also be used to generate dense disparity maps. Based on the evaluation conducted by the Middlebury Stereo Vision Research Website, the new algorithm is ranked between the variable window and the graph cuts approaches and currently is the most accurate dynamic programming based technique. When more than one reference images are available, the accuracy can be further improved with little extra computation time.
Guided Sampling via Weak Motion Models and Outlier Sample Generation for Epipolar Geometry Estimation
Authors: Liran Goshen, Ilan Shimshoni
Venue: CVPR
Year: 2005

The problem of automatic robust estimation of the epipolar geometry in cases where the correspondences are contaminated with a high percentage of outliers is addressed. This situation often occurs when the images have undergone a significant deformation, either due to large rotation or wide baseline of the cameras. An accelerated algorithm for the identification of the false matches between the views is presented. The algorithm generates a set of weak motion models (WMMs). Each WMM roughly approximates the motion of correspondences from one image to the other. The algorithm represents the distribution of the median of the geometric distances of a correspondence to the WMMs as a mixture model of outlier correspondences and inlier correspondences. The algorithm generates an outlier correspondence sample from the data. This sample is used to estimate the outlier rate and to estimate the outlier pdf. Using these two pdfs the probability that each correspondence is an inlier is estimated. These probabilities enable to guide the sampling. In the RANSAC process this guided sampling accelerates the search process. The resulting algorithm when tested on real images achieves a speedup of between one or two orders of magnitude.
Analytically Solving Radial Distortion Parameters
Authors: Simone Graf, Tobias Hanning
Venue: CVPR
Year: 2005

Most camera calibration algorithms consider parameters that define the lens distortion mapping. The usual way to determine these parameters is to minimize a nonlinear optimization problem in the final step of a calibration algorithm. This neglects the fact that given the projection matrix and the calibration data the parameters of a radial distortion can be determined analytically. In this article we show that the problem can be solved in a closed form. We present an algorithm in which this is done and compare it to classic approaches.
Efficient Image Matching with Distributions of Local Invariant Features
Authors: Kristen Grauman, Trevor Darrell
Venue: CVPR
Year: 2005

Sets of local features that are invariant to common image transformations are an effective representation to use when comparing images; current methods typically judge feature sets' similarity via a voting scheme (which ignores co-occurrence statistics) or by comparing histograms over a set of prototypes (which must be found by clustering). We present a method for efficiently comparing images based on their discrete distributions (bags) of distinctive local invariant features, without clustering descriptors. Similarity between images is measured with an approximation of the Earth Mover's Distance (EMD), which quickly computes minimal-cost correspondences between two bags of features. Each image's feature distribution is mapped into a normed space with a low-distortion embedding of EMD. Examples most similar to a novel query image are retrieved in time sublinear in the number of examples via approximate nearest neighbor search in the embedded space. We evaluate our method with scene, object, and texture recognition tasks.
Linear Combination Representation for Outlier Detection in Motion Tracking
Authors: Guodong Guo, Charles R. Dyer, Zhengyou Zhang
Venue: CVPR
Year: 2005

In this paper we show that Ullman and Basri's linear combination (LC) representation, which was originally proposed for alignment-based object recognition, can be used for outlier detection in motion tracking with an affine camera. For this task LC can be realized either on image frames or feature trajectories, and therefore two methods are developed which we call linear combination of frames and linear combination of trajectories. For robust estimation of the linear combination coefficients, the support vector regression (SVR) algorithm is used and compared with the RANSAC method. SVR based on quadratic programming optimization can efficiently deal with more than 50 percent outliers and delivers more consistent results than RANSAC in our experiments. The linear combination representation can use SVR in a straightforward manner while previous factorization-based or subspace separation methods cannot. Experimental results are presented using real video sequences to demonstrate the effectiveness of our LC+SVR approaches, including a quantitative comparison of SVR and RANSAC.
Vehicle Fingerprinting for Reacquisition and Tracking in Videos
Authors: Yanlin Guo, Steven C. Hsu, Ying Shan, Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: CVPR
Year: 2005

Visual recognition of objects through multiple observations is an important component of object tracking. We address the problem of vehicle matching when multiple observations of a vehicle are separated in time such that frames of observations are not contiguous, thus prohibiting the use of standard frame-to-frame data association. We employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. The challenges of change in pose, aspect and appearances across two disparate observations are handled by combining feature-based quasi-rigid alignment with flexible matching between two or more sequences. The current work uses the domain of vehicle tracking from aerial platforms where typically both the imaging platform and the vehicles are moving and the number of pixels on the object are limited to fairly low resolutions. Extensive evaluation with respect to ground truth is reported in the paper.
Restoration and Recognition in a Loop
Authors: Mithun Das Gupta, Shyamsundar Rajaram, Nemanja Petrovic, Thomas S. Huang
Venue: CVPR
Year: 2005

In this paper we present a novel learning based method for restoring and recognizing images of digits that have been blurred using an unknown kernel. The novelty of our work is an iterative loop that alternates between recognition and restoration stages. In the restoration stage we model the image as an undirected graphical model over the image patches with the compatibility functions represented as non-parametric kernel densities. Compatibility functions are initially learned using uniform random samples from the training data. We solve the inference problem by an extended version of the non-parametric belief propagation algorithm in which we introduce the notion of partial messages. We close the loop by using the confidence scores of the recognition to non-uniformly sample from the training set in order to retrain the compatibility functions. We show experimental results on synthetic and license plate images.