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Diffeomorphic sulcal shape analysis for cortical surface registration

Diffeomorphic sulcal shape analysis for cortical surface reg...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shantanu H. Joshi Ryan P. Cabeen Anand A. Joshi Roger P. Woods Katherine L. Narr Arthur W. Toga Laboratory of Neuro Imaging University of California Los Angeles Los Angeles CA USA

We present an intrinsic framework for constructing sulcal shape atlases on the human cortex. We propose the analysis of sulcal and gyral patterns by representing them by continuous open curves in R 3 . The space of such curves, also termed as the shape manifold is equipped with a Riemannian L 2 metric on the tangent space, and shows desirable properties while matching shapes of sulci. On account of the spherical nature of the shape space, geodesics between shapes can be computed analytically. Additionally, we also present an optimization approach that computes geodesics in the quotient space of shapes modulo rigid rotations and reparameterizations. We also integrate the elastic shape model into a surface registration framework for a population of 176 subjects, and show a considerable improvement in the constructed surface atlases.

关键词： Shape Humans Geophysics computing Brain modeling Active contours Image analysis Laboratories Neuroimaging pattern analysis Cerebral cortex

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Collect-cut: Segmentation with top-down cues discovered in multi-object images

Collect-cut: Segmentation with top-down cues discovered in m...

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conference on computer vision and pattern recognition (CVPR)

作者： Yong Jae Lee Kristen Grauman University of Texas Austin USA

We present a method to segment a collection of unlabeled images while exploiting automatically discovered appearance patterns shared between them. Given an unlabeled pool of multi-object images, we first detect any visual clusters present among their sub-regions, where inter-region similarity is measured according to both appearance and contextual layout. Then, using each initial segment as a seed, we solve a graph cuts problem to refine its boundary - enforcing preferences to include nearby regions that agree with an ensemble of representative regions discovered for that cluster, and exclude those regions that resemble familiar objects. Through extensive experiments, we show that the segmentations computed jointly on the collection agree more closely with true object boundaries, when compared to either a bottom-up baseline or a graph cuts foreground segmentation that can only access cues from a single image.

关键词： Image segmentation Design engineering Optical design Optical fiber networks Physical layer Optical receivers Optical transmitters Bit error rate Optical crosstalk computer architecture

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Compact projection: Simple and efficient near neighbor search with practical memory requirements

Compact projection: Simple and efficient near neighbor searc...

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conference on computer vision and pattern recognition (CVPR)

作者： Kerui Min Linjun Yang John Wright Lei Wu Xian-Sheng Hua Yi Ma School of Computer Science Fudan University China Microsoft Research Asia China MOE-MS Keynote Lab of MCC University of Science and Technology China

Image similarity search is a fundamental problem in computer vision. Efficient similarity search across large image databases depends critically on the availability of compact image representations and good data structures for indexing them. Numerous approaches to the problem of generating and indexing image codes have been presented in the literature, but existing schemes generally lack explicit estimates of the number of bits needed to effectively index a given large image database. We present a very simple algorithm for generating compact binary representations of imagery data, based on random projections. Our analysis gives the first explicit bound on the number of bits needed to effectively solve the indexing problem. When applied to real image search tasks, these theoretical improvements translate into practical performance gains: experimental results show that the new method, while using significantly less memory, is several times faster than existing alternatives.

关键词： Image databases Indexing Nearest neighbor searches Image retrieval Information retrieval computer science computer vision Image representation Asia Data structures

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Neuron geometry extraction by perceptual grouping in ssTEM images

Neuron geometry extraction by perceptual grouping in ssTEM i...

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conference on computer vision and pattern recognition (CVPR)

作者： Verena Kaynig Thomas Fuchs Joachim M. Buhmann Electron Microscopy ETH Zurich Zurich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland

In the field of neuroanatomy, automatic segmentation of electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting. The probability output of a random forest classifier is used in a regular cost function, which enforces gap completion via perceptual grouping constraints. The global solution is efficiently found by graph cut optimization. We demonstrate substantial qualitative and quantitative improvement over state-of the art segmentations on two considerably different stacks of ssTEM images as well as in segmentations of streets in satellite imagery. We demonstrate that the superior performance of our method yields fully automatic 3D reconstructions of dendrites from ssTEM data.

关键词： Neurons Geometry Image segmentation Biomembranes Image reconstruction Transmission electron microscopy Electron microscopy Animals Protocols Cost function

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Removal of 3D facial expressions: A learning-based approach

Removal of 3D facial expressions: A learning-based approach

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conference on computer vision and pattern recognition (CVPR)

作者： Gang Pan Song Han Zhaohui Wu Yuting Zhang Department of Computer Science University of Zhejiang China

This paper focuses on the task of recovering the neutral 3D face of a person when given his/her 3D face model with facial expression. We propose a learning-based expression removal framework to tackle this task. Our basic idea is to model expression residue from samples, and then use the inferred expression residue from the input expressional face model to recover the neutral one. A two-step non-rigid alignment method is introduced to make all the face models topologically share a common structure. Then we construct two spaces, normal space and expression residue space, for modeling expression. Therefore, the expression removal problem can be formalized as the inference of expression residue from normal spaces. The neutral face model can be generated in a Poisson-based framework by the inferred expression residue. The experimental results on BU-3DFED database demonstrate the effectiveness of our approach.

关键词： Face recognition Muscles Deformable models Interpolation computer science Databases Degradation Prototypes Shape Mesh generation

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Online multi-class LPBoost

Online multi-class LPBoost

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conference on computer vision and pattern recognition (CVPR)

作者： Amir Saffari Martin Godec Thomas Pock Christian Leistner Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria

Online boosting is one of the most successful online learning algorithms in computer vision. While many challenging online learning problems are inherently multi-class, online boosting and its variants are only able to solve binary tasks. In this paper, we present Online Multi-Class LPBoost (OMCLP) which is directly applicable to multi-class problems. From a theoretical point of view, our algorithm tries to maximize the multi-class soft-margin of the samples. In order to solve the LP problem in online settings, we perform an efficient variant of online convex programming, which is based on primal-dual gradient descent-ascent update strategies. We conduct an extensive set of experiments over machine learning benchmark datasets, as well as, on Caltech 101 category recognition dataset. We show that our method is able to outperform other online multi-class methods. We also apply our method to tracking where, we present an intuitive way to convert the binary tracking by detection problem to a multi-class problem where background patterns which are similar to the target class, become virtual classes. Applying our novel model, we outperform or achieve the state-of-the-art results on benchmark tracking videos.

关键词： Boosting Target tracking Machine learning computer vision Working environment noise Learning systems Videos Game theory Application software Robustness

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Automatic point-based facial trait judgments evaluation

Automatic point-based facial trait judgments evaluation

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conference on computer vision and pattern recognition (CVPR)

作者： Mario Rojas Q. David Masip Alexander Todorov Jordi Vitrià Comput. Vision Center UAB Spain Universitat Oberta de Catalunya Barcelona Spain Computer Vision Center Edifici O Spain Department of Psychology Princeton University Princeton New Jersey USA Department de Matematica Aplicada i Analisi Universitat de Barcelona Spain

Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies suggest that two orthogonal dimensions, valence and dominance, can describe the basis of the human judgments from faces. In this paper, we used a corpus of behavioral data of judgments on different trait dimensions to automatically learn a trait predictor from facial pixel images. We study whether trait evaluations performed by humans can be learned using machine learning classifiers, and used later in automatic evaluations of new facial images. The experiments performed using local point-based descriptors show promising results in the evaluation of the main traits.

关键词： Humans Face Performance evaluation Pixel Databases Principal component analysis Psychology Machine learning Protocols computer vision

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Scalable active matching

Scalable active matching

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conference on computer vision and pattern recognition (CVPR)

作者： Ankur Handa Margarita Chli Hauke Strasdat Andrew J. Davison Department of Computing Imperial College London UK

In matching tasks in computer vision, and particularly in real-time tracking from video, there are generally strong priors available on absolute and relative correspondence locations thanks to motion and scene models. While these priors are often partially used post-hoc to resolve matching consensus in algorithms like RANSAC, it was recently shown that fully integrating them in an `Active Matching' (AM) approach permits efficient guided image processing with rigorous decisions guided by Information Theory. AM's weakness was that the overhead induced by intermediate Bayesian updates required meant poor scaling to cases where many correspondences were sought. In this paper we show that relaxation of the rigid probabilistic model of AM, where every feature measurement directly affects the prediction of every other, permits dramatically more scalable operation without affecting accuracy. We take a general graph-theoretic view of the structure of prior information in matching to sparsify and approximate the interconnections. We demonstrate the performance of two variations, CLAM and SubAM, in the context of sequential camera tracking. These algorithms are highly competitive with other techniques at matching hundreds of features per frame while retaining great intuitive appeal and the full probabilistic capability to digest prior information.

关键词： Image reconstruction Layout Cameras Calibration Clouds Geometry Stereo image processing Surface reconstruction Stereo vision computer vision

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Reconstruction of display and eyes from a single image

Reconstruction of display and eyes from a single image

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conference on computer vision and pattern recognition (CVPR)

作者： Dirk Schnieders Xingdou Fu Kwan-Yee K. Wong Department of Computer Science University of Hong Kong Hong Kong China

This paper introduces a novel method for reconstructing human eyes and visual display from reflections on the cornea. This problem is difficult because the camera is not directly facing the display, but instead captures the eyes of a person in front of the display. Reconstruction of eyes and display is useful for point-of-gaze estimation, which can be approximated from the 3D positions of the iris and display. It is shown that iris boundaries (limbus) and display reflections in a single intrinsically calibrated image provide enough information for such an estimation. The proposed method assumes a simplified geometric eyeball model with certain anatomical constants which are used to reconstruct the eye. A noise performance analysis shows the sensitivity of the proposed method to imperfect data. Experiments on various subjects show that it is possible to determine the approximate area of gaze on a display from a single image.

关键词： Image reconstruction Eyes Three dimensional displays Iris Humans Cornea Cameras Acoustic reflection Solid modeling Performance analysis

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Refinement of digital elevation models from shadowing cues

Refinement of digital elevation models from shadowing cues

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conference on computer vision and pattern recognition (CVPR)

作者： James Hogan William A. P. Smith Department of Computer Science University of York UK

In this paper we derive formal constraints relating terrain elevation and observed cast shadows. We show how an optimisation framework can be used to refine surface estimates using shadowing constraints from one or more images. The method is particularly applicable to the digital elevation models produced by the Shuttle Radar Topography Mission (SRTM), which have an abundance of voids in mountainous areas where elevation data is missing. Cast shadow maps are detected automatically from multi-spectral satellite imagery using a simple heuristic which is reliable over varying types of surface cover. We show that the combination of our shadow segmentation and terrain correction methods can restore the structure of mountain ridges in interpolated SRTM voids using five satellite images, decreasing the RMS error by over 25%.

关键词： Digital elevation models Shadow mapping Surface topography Satellites Constraint optimization Radar imaging Spaceborne radar Radar detection Image segmentation Image restoration

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