OpenRaiser/Intern-Atlas huggingface dataset:
Directed assistance for ink-bleed reduction in old documents
Authors: Zheng Lu 0002, Zheng Wu, Michael S. Brown
Venue: CVPR
Year: 2009

Ink-bleed interference is a serious problem that affects the legibility of old documents. Ink-bleed can be reduced using pixel classification based on user-supplied markup that labels examples of ink-bleed, foreground-ink, and background. The main challenge is ensuring that the user's markup sufficiently captures the characteristics of the document. This is particularly troublesome for old documents that can exhibit significant change within the same page. In this paper, we address this markup problem using a “directed assistance” approach in which the user provides a small amount of initial markup. The image is then classified and regions with low classification confidence are grouped and displayed to the user for another round of markup. The key idea is to direct the user to where markup is needed. In addition, local markup can be weighted in the classification algorithm to produce better results.
An implicit Markov random field model for the multi-scale oriented representations of natural images
Authors: Siwei Lyu
Venue: CVPR
Year: 2009

In this paper, we describe a new Markov random field (MRF) model for natural images in multiscale oriented representations. The MRF in this model is specified with the singleton conditional densities (the density of one subband coefficient given its Markovian neighbors), while the clique potentials and joint density of this model are implicitly defined. The singleton conditional densities are chosen to have maximum entropy and consistent with observed statistical properties of natural images. We then describe parameter learning for this model, and a sparse prior to choose optimal model structure. Using this model as image prior, we develop an iterative image denoising method, and a solution to restoring images with missing blocks of subband coefficients.
Object detection using a max-margin Hough transform
Authors: Subhransu Maji, Jitendra Malik
Venue: CVPR
Year: 2009

We present a discriminative Hough transform based object detector where each local part casts a weighted vote for the possible locations of the object center. We show that the weights can be learned in a max-margin framework which directly optimizes the classification performance. The discriminative training takes into account both the codebook appearance and the spatial distribution of its position with respect to the object center to derive its importance. On various datasets we show that the discriminative training improves the Hough detector. Combined with a verification step using a SVM based classifier, our approach achieves a detection rate of 91.9% at 0.3 false positives per image on the ETHZ shape dataset, a significant improvement over the state of the art, while running the verification step on at least an order of magnitude fewer windows than in a sliding window approach.
Imbalanced RankBoost for efficiently ranking large-scale image/video collections
Authors: Michele Merler, Rong Yan, John R. Smith
Venue: CVPR
Year: 2009

Ranking large scale image and video collections usually expects higher accuracy on top ranked data, while tolerates lower accuracy on bottom ranked ones. In view of this, we propose a rank learning algorithm, called Imbalanced RankBoost, which merges RankBoost and iterative thresholding into a unified loss optimization framework. The proposed approach provides a more efficient ranking process by iteratively identifying a cutoff threshold in each boosting iteration, and automatically truncating ranking feature computation for the data ranked below. Experiments on the TRECVID 2007 high-level feature benchmark show that the proposed approach outperforms RankBoost in terms of both ranking effectiveness and efficiency. It achieves an up to 21% improvement in terms of mean average precision, or equivalently, a 6-fold speedup in the ranking process.
Memory-based Particle Filter for face pose tracking robust under complex dynamics
Authors: Dan Mikami, Kazuhiro Otsuka, Junji Yamato
Venue: CVPR
Year: 2009

A novel particle filter, the memory-based particle filter (M-PF), is proposed that can visually track moving objects that have complex dynamics. We aim to realize robustness against abrupt object movements and quick recovery from tracking failure caused by factors such as occlusions. To that end, we eliminate the Markov assumption from the previous particle filtering framework and predict the prior distribution of the target state from the long-term dynamics. More concretely, M-PF stores the past history of the estimated target states, and employs a random sampling from the history to generate prior distribution; it represents a novel PF formulation.Our method can handle nonlinear, time-variant, and non-Markov dynamics, which is not possible within existing PF frameworks. Accurate prior prediction based on proper dynamics model is especially effective for recovering lost tracks, because it can provide possible target states, which can drastically change since the track was lost. We target the face pose of seated humans in this paper. Quantitative evaluations with magnetic sensors confirm improved accuracy in face pose estimation and successful recovery from tracking loss. The proposed M-PF suggests a new paradigm for modeling systems with complex dynamics and so offers a various visual tracking applications.
Contextual classification with functional Max-Margin Markov Networks
Authors: Daniel Munoz, James A. Bagnell, Nicolas Vandapel, Martial Hebert
Venue: CVPR
Year: 2009

We address the problem of label assignment in computer vision: given a novel 3D or 2D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To this end, the Markov Random Field framework has proven to be a model of choice as it uses contextual information to yield improved classification results over locally independent classifiers. In this work we adapt a functional gradient approach for learning high-dimensional parameters of random fields in order to perform discrete, multi-label classification. With this approach we can learn robust models involving high-order interactions better than the previously used learning method. We validate the approach in the context of point cloud classification and improve the state of the art. In addition, we successfully demonstrate the generality of the approach on the challenging vision problem of recovering 3-D geometric surfaces from images.
Fast multiple shape correspondence by pre-organizing shape instances
Authors: Brent C. Munsell, Andrew Temlyakov, Song Wang 0002
Venue: CVPR
Year: 2009

Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general, shape-correspondence methods can be grouped into one of two categories: global methods and pair-wise methods. In this paper, we develop a new method that attempts to address the limitations of both the global and pair-wise methods. In particular, we reorganize the input population into a tree structure that incorporates global information about the population of shape instances, where each node in the tree represents a shape instance and each edge connects two very similar shape instances. Using this organized tree, neighboring shape instances can be corresponded efficiently and accurately by a pair-wise method. In the experiments, we evaluate the proposed method and compare its performance to five available shape correspondence methods and show the proposed method achieves the accuracy of a global method with speed of a pair-wise method.
Image registration by minimization of residual complexity
Authors: Andriy Myronenko, Xubo B. Song
Venue: CVPR
Year: 2009

Accurate definition of similarity measure is a key component in image registration. Most commonly used intensity-based similarity measures rely on the assumptions of independence and stationarity of the intensities from pixel to pixel. Such measures cannot capture the complex interactions among the pixel intensities, and often result in less satisfactory registration performances, especially in the presence of nonstationary intensity distortions. We propose a novel similarity measure that accounts for intensity non-stationarities and complex spatially-varying intensity distortions. We derive the similarity measure by analytically solving for the intensity correction field and its adaptive regularization. The final measure can be interpreted as one that favors a registration with minimum compression complexity of the residual image between the two registered images. This measure produces accurate registration results on both artificial and real-world problems that we have tested, whereas many other state-of-the-art similarity measures have failed to do so.
Global active contour-based image segmentation via probability alignment
Authors: Andriy Myronenko, Xubo B. Song
Venue: CVPR
Year: 2009

Active contours is a popular technique for image segmentation. However, active contour tend to converge to the closest local minimum of its energy function and often requires a close boundary initialization. We introduce a new approach that overcomes the close boundary initialization problem by reformulating the external energy term. We treat the active contour as a mean curve of the probability density function p(x). It moves to minimize the Kullback-Leibler (KL) divergence between p(x) and the probability density function derived from the image. KL divergence forces p(x) to “cover all image areas” and the uncovered areas are heavily penalized, which allows the active contour to go over the edges. Also we use deterministic annealing on the width of p(x) to implement a coarse-to-fine search strategy. In the limit, when the width of p(x) goes to zero, the KL divergence function converges to the conventional external energy term (which can be seen a special case) of active contours. Our method produces robust segmentation results from arbitrary initialization positions.
A compressive sensing approach for expression-invariant face recognition
Authors: Pradeep Nagesh, Baoxin Li
Venue: CVPR
Year: 2009

We propose a novel technique based on compressive sensing for expression-invariant face recognition. We view the different images of the same subject as an ensemble of intercorrelated signals and assume that changes due to variation in expressions are sparse with respect to the whole image. We exploit this sparsity using distributed compressive sensing theory, which enables us to grossly represent the training images of a given subject by only two feature images: one that captures the holistic (common) features of the face, and the other that captures the different expressions in all training samples. We show that a new test image of a subject can be fairly well approximated using only the two feature images from the same subject. Hence we can drastically reduce the storage space and operational dimensionality by keeping only these two feature images or their random measurements. Based on this, we design an efficient expression-invariant classifier. Furthermore, we show that substantially low dimensional versions of the training features, such as (i) ones extracted from critically-downsampled training images, or (ii) low-dimensional random projection of original feature images, still have sufficient information for good classification. Extensive experiments with publically-available databases show that, on average, our approach performs better than the state-of-the-art despite using only such super-compact feature representation.
VideoTrek: A vision system for a tag-along robot
Authors: Oleg Naroditsky, Zhiwei Zhu, Aveek Das, Supun Samarasekera, Taragay Oskiper, Rakesh Kumar 0001
Venue: CVPR
Year: 2009

We present a system that combines multiple visual navigation techniques to achieve GPS-denied, non-line-of-sight SLAM capability for heterogeneous platforms. Our approach builds on several layers of vision algorithms, including sparse frame-to-frame structure from motion (visual odometry), a Kalman filter for fusion with inertial measurement unit (IMU) data and a distributed visual landmark matching capability with geometric consistency verification. We apply these techniques to implement a tag-along robot, where a human operator leads the way and a robot autonomously follows. We show results for a real-time implementation of such a system with real field constraints on CPU power and network resources.
Automated extraction of signs from continuous sign language sentences using Iterated Conditional Modes
Authors: Sunita Nayak, Sudeep Sarkar, Barbara L. Loeding
Venue: CVPR
Year: 2009

Recognition of signs in sentences requires a training set constructed out of signs found in continuous sentences. Currently, this is done manually, which is a tedious process. In this work, we consider a framework where the modeler just provides multiple video sequences of sign language sentences, constructed to contain the vocabulary of interest. We learn the models of the recurring signs, automatically. Specifically, we automatically extract the parts of the signs that are present in most occurrences of the sign in context. These parts of the signs that is stable with respect to adjacent signs, are referred to as signemes. Each video is first transformed into a multidimensional time series representation, capturing the motion and shape aspects of the sign. We then extract signemes from multiple sentences, concurrently, using Iterated Conditional Modes (ICM). We show results by learning multiple instances of 10 different signs from a set of 136 sign language sentences. We classify the extracted signemes as correct, partially correct or incorrect depending on whether both the start and end locations are correct, only one of them is correct or both are incorrect, respectively. Out of the 136 extracted video signemes, 98 were correct, 20 were partially correct and 18 were incorrect. To demonstrate the generality of the unsupervised modeling idea, we also show the ability to automatically extract common spoken words in audio. We consider the English glosses (spoken) corresponding to the sign language sentences and extract the audio counterparts of the signs. Of the 136 such instances, we recovered 127 correct, 8 partially correct, and 1 incorrect representation of the words.
Recognizing human group activities with localized causalities
Authors: Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim
Venue: CVPR
Year: 2009

The aim of this paper is to address the problem of recognizing human group activities in surveillance videos. This task has great potentials in practice, however was rarely studied due to the lack of benchmark database and the difficulties caused by large intra-class variations. Our contributions are two-fold. Firstly, we propose to encode the group-activities with three types of localized causalities, namely self-causality, pair-causality, and group-causality, which characterize the local interaction/reasoning relations within, between, and among motion trajectories of different humans respectively. Each type of causality is expressed as a specific digital filter, whose frequency responses then constitute the feature representation space. Finally, each video clip of certain group activity is encoded as a bag of localized causalities/filters. We also collect a human group-activity video database, which involves six popular group activity categories with about 80 video clips for each in average, captured in five different sessions with varying numbers of participants. Extensive experiments on this database based on our proposed features and different classifiers show the promising results on this challenging task.
Contextualizing histogram
Authors: Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim
Venue: CVPR
Year: 2009

In this paper, we investigate how to incorporate spatial and/or temporal contextual information into classical histogram features with the aim of boosting visual classification performance. Firstly, we show that the stationary distribution derived from the normalized histogram-bin co-occurrence matrix characterizes the row sums of the original histogram-bin co-occurrence matrix. This underlying rationale of the histogram-bin co-occurrence features then motivates us to propose the concept of general contextualizing histogram process, which encodes the spatial and/or temporal contexts as local homogeneity distributions and produces the so called contextualized histograms by convoluting these local homogeneity distributions with the histogram-bin index images/videos. Finally, the third and even higher order contextualized histograms are instantiated for encoding more complicated and informative spatial and/or temporal contextual information into histograms. We evaluate these proposed methods on face recognition and group activity classification problems, and the results demonstrate that the contextualized histograms significantly boost the visual classification performance.
Facial deblur inference to improve recognition of blurred fFaces
Authors: Masashi Nishiyama, Hidenori Takeshima, Jamie Shotton, Tatsuo Kozakaya, Osamu Yamaguchi
Venue: CVPR
Year: 2009

This paper proposes a novel method for deblurring facial images to recognize faces degraded by blur. The main problem is how to infer a point spread function (PSF) representing the process of blur. Inferring a PSF from a single facial image is an ill-posed problem. To make this problem more tractable, our method uses learned prior information derived from a training set of blurred facial images of several individuals. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another and form a cluster. During training, we compute a statistical model of each PSF cluster in this feature space. For PSF inference we compare a query image of unknown blur with each model and select the closest one. Using the PSF corresponding to that model, the query image is deblurred, ready for recognition. Experiments on a standard face database artificially degraded by focus or motion blur show that our method substantially improves the recognition performance compared with state-of-the-art methods. We also demonstrate improved performance on real blurred images.
On bias correction for geometric parameter estimation in computer vision
Authors: Takayuki Okatani, Koichiro Deguchi
Venue: CVPR
Year: 2009

Maximum likelihood (ML) estimation is widely used in many computer vision problems involving the estimation of geometric parameters, from conic fitting to bundle adjustment for structure and motion. This paper presents a detailed discussion on the bias of ML estimates derived for these problems. Statistical theory states that although ML estimates attain maximum accuracy in the limit as the sample size goes to infinity, they can have non-negligible bias with small sample sizes. In the case of computer vision problems, the ML optimality holds when regarding variance in observation errors as the sample size. A natural question is how large the bias will be for a given strength of observation errors. To answer this for a general class of problems, we analyze the mechanism of how the bias of ML estimates emerges, and show that the differential geometric properties of geometric constraints used in the problems determines the magnitude of bias. Based on this result, we present a numerical method of computing bias-corrected estimates.
Efficiently training a better visual detector with sparse eigenvectors
Authors: Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhang 0002
Venue: CVPR
Year: 2009

Face detection plays an important role in many vision applications. Since Viola and Jones [1] proposed the first real-time AdaBoost based object detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we have adopted Greedy Sparse Linear Discriminant Analysis (GSLDA) [2] for its computational efficiency; and slightly better detection performance is achieved compared with [1]. Moreover, we propose a new technique, termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA), to efficiently train object detectors. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distributions, e.g., face detection, demonstrates that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. This finding provides a significant opportunity to argue that Adaboost and similar approaches are not the only methods that can achieve high classification results for high dimensional data such as object detection.
3D morphable face models revisited
Authors: Ankur Patel, William A. P. Smith
Venue: CVPR
Year: 2009

In this paper we revisit the process of constructing a high resolution 3D morphable model of face shape variation. We demonstrate how the statistical tools of thin-plate splines and Procrustes analysis can be used to construct a morphable model that is both more efficient and generalises to novel face surfaces more accurately than previous models. We also reformulate the probabilistic prior that the model provides on the distribution of parameter vector lengths. This distribution is determined solely by the number of model dimensions and can be used as a regularisation constraint in fitting the model to data without the need to empirically choose a parameter controlling the trade off between plausibility and quality of fit. As an example application of this improved model, we show how it may be fitted to a sparse set of 2D feature points (approximately 100). This provides a rapid means to estimate high resolution 3D face shape for a face in any pose given only a single face image. We present experimental results using ground truth data and hence provide absolute reconstruction errors. On average, the per vertex error of the reconstructed faces is less than 3.6 mm.
Wavelet energy map: A robust support for multi-modal registration of medical images
Authors: Olivier Pauly, Nicolas Padoy, Holger Poppert, Lorena Esposito, Nassir Navab
Venue: CVPR
Year: 2009

Multi-modal registration is the task of aligning images from an object acquired with different imaging systems, sensors or parameters. The current gold standard for medical images is the maximization of mutual information by computing the joint intensity distribution. However intensities are highly sensitive to various kinds of noise and denoising is a very challenging task often involving a priori knowledge and parameter tuning. We propose to perform registration on a novel robust information support: the wavelet energy map, giving a measure of local energy for each pixel. This spatial feature is derived from local spectral components computed with a redundant wavelet transform. The multi-frequential aspect of our method is particularly adapted to robust registration of images showing ambiguities such as tissues, complex textures and multiple interfaces. We show the benefits of the wavelet energy map approach in comparison to the classical framework in 2D and 3D rigid registration experiments on synthetic and real data.