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Uncontrolled modulation imaging

Uncontrolled modulation imaging

引用

conference on computer vision and pattern recognition (cvpr)

作者： Y.Y. Schechner S.K. Nayar Department Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Department Computer Science Columbia University NY USA

To obtain high dynamic range or hyperspectral images, multiple frames of the same field of view are acquired while the imaging settings are modulated; images are taken at different exposures or through different wavelength bands. A major problem associated with such modulations has been the need for perfect synchronization between image acquisition and modulation control. In the past, this problem has been addressed by using sophisticated servo-control mechanisms. In this work, we show that the process of modulation imaging can be made much simpler by using vision algorithms to automatically relate each acquired frame to its corresponding modulation level. This correspondence is determined solely from the acquired image sequence and does not require measurement or control of the modulation. The image acquisition and the modulation work continuously, in parallel, and independently. We refer to this approach as computational synchronization. It makes the imaging process simple and easy to implement. We have developed a prototype modulation imaging system that uses computational synchronization and used it to acquire high dynamic range and multispectral images.

关键词： Hyperspectral imaging Dynamic range Multispectral imaging Control systems Filters Biomedical imaging Image color analysis Automatic control Image sequences Layout

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Automatic method for correlating horizons across faults in 3D seismic data

Automatic method for correlating horizons across faults in 3...

引用

conference on computer vision and pattern recognition (cvpr)

作者： F. Admasu K. Toennies Computer Vision Group University of Magdeburg Germany

Horizons are visible boundaries between certain sediment layers in seismic data, and a fault is a crack of horizons and it is recognized in seismic data by the discontinuities of horizons layers. Interpretation of seismic data is a time-consuming manual task which is only partially supported by computer methods. In this paper, we present an automatic method for horizon correlation across faults in 3D seismic data. As automating horizons correlations using only seismic data features is not feasible, we reformulated the correlation task as a non-rigid continuous point matching problem. Seismic features on both sides of the fault are gathered and an optimal match is found based on geological fault displacement model. One side of the fault is the floating image while the other side is the reference image. First, very prominent regions on both sides are automatically extracted and a match between them is found. Sparse fault displacements are then computed for these regions and they are used to calculate parameters for the fault displacement model. A multi-resolution simulated annealing optimization scheme is then used for the continuous point matching. The method was applied to real 3D seismic data, and has shown to produce geologically acceptable horizons correlations.

关键词： computer vision Geology Acoustic reflection Image analysis Geologic measurements Seismic measurements Seismic waves Image segmentation Sediments Optimal matching

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Searching the Web with mobile images for location recognition

Searching the Web with mobile images for location recognitio...

引用

conference on computer vision and pattern recognition (cvpr)

作者： T. Yeh K. Tollmar T. Darrell MIT CSAIL Cambridge MA USA

We describe an approach to recognizing location from mobile devices using image-based Web search. We demonstrate the usefulness of common image search metrics applied on images captured with a camera-equipped mobile device to find matching images on the World Wide Web or other general-purpose databases. Searching the entire Web can be computationally overwhelming, so we devise a hybrid image-and-keyword searching technique. First, image-search is performed over images and links to their source Web pages in a database that indexes only a small fraction of the Web. Then, relevant keywords on these Web pages are automatically identified and submitted to an existing text-based search engine (e.g. Google) that indexes a much larger portion of the Web. Finally, the resulting image set is filtered to retain images close to the original query. It is thus possible to efficiently search hundreds of millions of images that are not only textually related but also visually relevant. We demonstrate our approach on an application allowing users to browse Web pages matching the image of a nearby location.

关键词： Image recognition Web pages Image databases Web search Search engines Image retrieval Content based retrieval Web sites Indexes Personal digital assistants

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Probabilistic expression analysis on manifolds

Probabilistic expression analysis on manifolds

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ya Chang Changbo Hu M. Turk University of California Santa Barbara USA

In this paper, we propose a probabilistic video-based facial expression recognition method on manifolds. The concept of the manifold of facial expression is based on the observation that the images of all possible facial deformations of an individual make a smooth manifold embedded in a high dimensional image space. An enhanced Lipschitz embedding is developed to embed the aligned face appearance in a low dimensional space while keeping the main structure of the manifold. In the embedded space, a complete expression sequence becomes a path on the expression manifold, emanating from a center that corresponds to the neutral expression. Each path consists of several clusters. A probabilistic model of transition between the clusters and paths is learned through training videos in the embedded space. The likelihood of one kind of facial expression is modeled as a mixture density with the clusters as mixture centers. The transition between different expressions is represented as the evolution of the posterior probability of the six basic paths. The experimental results demonstrate that the probabilistic approach can recognize expression transitions effectively. We also synthesize image sequences of changing expressions through the manifold model.

关键词： Face recognition Humans Face detection Space exploration Videos Image sequences Image converters Prototypes Psychology Encoding

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Learning a restricted Bayesian network for object detection

Learning a restricted Bayesian network for object detection

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conference on computer vision and pattern recognition (cvpr)

作者： H. Schneiderman Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Many classes of images have the characteristics of sparse structuring of statistical dependency and the presence of conditional independencies among various groups of variables. Such characteristics make it possible to construct a powerful classifier by only representing the stronger direct dependencies among the variables. In particular, a Bayesian network compactly represents such structuring. However, learning the structure of a Bayesian network is known to be NP complete. The high dimensionality of images makes structure learning especially challenging. This paper describes an algorithm that searches for the structure of a Bayesian network based classifier in this large space of possible structures. The algorithm seeks to optimize two cost functions: a localized error in the log-likelihood ratio function to restrict the structure and a global classification error to choose the final structure of the network. The final network structure is restricted such that the search can take advantage of pre-computed estimates and evaluations. We use this method to automatically train detectors of frontal faces, eyes, and the iris of the human eye. In particular, the frontal face detector achieves state-of-the-art performance on the MIT-CMU test set for face detection.

关键词： Bayesian methods Object detection Face detection Mutual information Detectors Humans Telephony computer vision Probability distribution Graphical models

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3D head tracking based on recognition and interpolation using a time-of-flight depth sensor

3D head tracking based on recognition and interpolation usin...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.B. Gokturk C. Tomasi Canesta Inc. USA Duke University USA

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.

关键词： Interpolation Head Robustness Clustering algorithms Testing pattern matching Image generation Image sensors Image segmentation Application software

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Probability models for high dynamic range imaging

Probability models for high dynamic range imaging

引用

conference on computer vision and pattern recognition (cvpr)

作者： C. Pal R. Szeliski M. Uyttendaele N. Jojic Microsoft Research Limited Redmond WA USA

Methods for expanding the dynamic range of digital photographs by combining images taken at different exposures have recently received a lot of attention. Current techniques assume that the photometric transfer function of a given camera is the same (modulo an overall exposure change) for all the input images. Unfortunately, this is rarely the case with today's camera, which may perform complex nonlinear color and intensity transforms on each picture. In this paper, we show how the use of probability models for the imaging system and weak prior models for the response functions enable us to estimate a different function for each image using only pixel intensity values. Our approach also allows us to characterize the uncertainty inherent in each pixel measurement. We can therefore produce statistically optimal estimates for the hidden variables in our model representing scene irradiance. We present results using this method to statistically characterize camera imaging functions and construct high-quality high dynamic range (HDR) images using only image pixel information.

关键词： Dynamic range Cameras Layout Pixel Charge coupled devices computer vision Application software Apertures Photometry Transfer functions

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Detection and removal of rain from videos

Detection and removal of rain from videos

引用

conference on computer vision and pattern recognition (cvpr)

作者： K. Garg S.K. Nayar Department of Computer Science Columbia University New York USA

The visual effects of rain are complex. Rain consists of spatially distributed drops falling at high velocities. Each drop refracts and reflects the environment, producing sharp intensity changes in an image. A group of such falling drops creates a complex time varying signal in images and videos. In addition, due to the finite exposure time of the camera, intensities due to rain are motion blurred and hence depend on the background intensities. Thus, the visual manifestations of rain are a combination of both the dynamics of rain and the photometry of the environment. In this paper, we present the first comprehensive analysis of the visual effects of rain on an imaging system. We develop a correlation model that captures the dynamics of rain and a physics-based motion blur model that explains the photometry of rain. Based on these models, we develop efficient algorithms for detecting and removing rain from videos. The effectiveness of our algorithms is demonstrated using experiments on videos of complex scenes with moving objects and time-varying textures. The techniques described in this paper can be used in a wide range of applications including video surveillance, vision based navigation, video/movie editing and video indexing/retrieval.

关键词： Rain Visual effects Photometry Cameras Image analysis Layout Video surveillance Navigation Motion pictures Indexing

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Joint feature-basis subset selection

Joint feature-basis subset selection

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Avidan Mitsubishi Electric Research Laboratories Inc. Cambridge MA USA

We treat feature selection and basis selection in a unified framework by introducing the masking matrix. If one considers feature selection as finding a binary mask vector that determines which features participate in the learning process, and similarly, basis selection as finding a binary mask vector that determines which basis vectors are needed for the learning process, then the masking matrix is, in particular, the outer product of the feature masking vector and the basis masking vector. This representation allows for a joint estimation of both features and basis. In addition, it allows one to select features that appear in only part of the basis functions. This joint selection of feature/basis subset is not possible when using feature selection and basis selection algorithms independently, thus, the masking matrix help extend feature and basis selection methods while blurring the lines between them. The problem of searching for an optimal masking matrix is NP-hand and we offer a sub-optimal probabilistic method to find it. In particular we demonstrate our ideas on the problem of feature and basis selection for SVM classification and show results for the problem of image classification on faces and vehicles.

关键词： Support vector machines Support vector machine classification Image classification Machine learning Speech recognition Text categorization Runtime Vehicles Text recognition Machine learning algorithms

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Autocalibration & 3D reconstruction with non-central catadioptric cameras

Autocalibration & 3D reconstruction with non-central catadio...

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conference on computer vision and pattern recognition (cvpr)

作者： B. Micusik T. Pajdla Center for Machine Perception Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

We present a technique for modeling non-central catadioptric cameras consisting of perspective cameras and curved mirrors. The real catadioptric cameras have to be treated as non-central cameras, since they do not possess a single viewpoint. We present a method for solving the correspondence problem, auto-calibrating cameras, and computing a 3D metric reconstruction automatically from two uncalibrated non-central catadioptric images. The method is demonstrated on spherical, parabolic, and hyperbolic mirrors. We observed that the reconstruction & auto-calibration with non-central catadioptric cameras is as easy (or as difficult) as with central catadioptric cameras, provided that the correspondence problem can be solved with a suitable approximate central model. It turns out that it is the number of parameters of the camera model that matters rather than the exact centrality of the projection. Our technique allows to autocalibrate catadioptric cameras even with genuinely non-central mirrors such as spheres (simple model, low blur, easy to manufacture) or uniform resolution mirrors (optimized projection).

关键词： Cameras Mirrors Image reconstruction Lenses Solid modeling Cybernetics Virtual manufacturing Shape Image resolution computer society

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