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Machine learning for clinical diagnosis from functional magnetic resonance imaging

Machine learning for clinical diagnosis from functional magn...

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conference on computer vision and pattern recognition (cvpr)

作者： Lei Zhang D. Samaras D. Tomasi N. Volkow R. Goldstein Department of Computer Science SUNY at Stony Brook Stony Brook NY USA Department of Medical Image Brockhaven National Laboratory Upton NY USA

Functional magnetic resonance imaging (fMRI) has enabled scientists to look into the active human brain. FMRI provides a sequence of 3D brain images with intensities representing brain activations. Standard techniques for fMRI analysis traditionally focused on finding the area of most significant brain activation for different sensations or activities. In this paper, we explore a new application of machine learning methods to a more challenging problem: classifying subjects into groups based on the observed 3D brain images when the subjects are performing the same task. Here we address the separation of drug-addicted subjects from healthy non-drug-using controls. In this paper, we explore a number of classification approaches. We introduce a novel algorithm that integrates side information into the use of boosting. Our algorithm clearly outperformed well-established classifiers as documented in extensive experimental results. This is the first time that machine learning techniques based on 3D brain images are applied to a clinical diagnosis that currently is only performed through patient self-report. Our tools can therefore provide information not addressed by traditional analysis methods and substantially improve diagnosis.

关键词： Machine learning Clinical diagnosis Magnetic resonance imaging Brain Machine learning algorithms Humans Magnetic analysis Learning systems Boosting Information analysis

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Overtaking Vehicle Detection Using Dynamic and Quasi-Static Background Modeling

Overtaking Vehicle Detection Using Dynamic and Quasi-Static ...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Junxian Wang G. Bebis R. Miller Computer Vision Laboratory University of Nevada Reno NV USA Vehicle Design R&A Department Ford Motor Company Limited Dearborn MI USA

Robust and reliable detection of overtaking vehicles is an important component of any on-board driver assistance system. Optical flow, with the abundant motion information present in image sequences, has been studied extensively for vehicle detection. However, using dense optical ?ow for vehicle detection is sensitive to shocks and vibrations of the mobile camera; image outliers caused by illumination changes; and high computational complexity. To improve vehicle detection performance and reduce computational complexity, we propose an efficient and robust methodology for overtaking vehicle detection based on homogeneous sparse optical flow and eigenspace modeling. Specifically, our method models the background into dynamic and quasi-static regions. Instead of using dense optical flow to model the dynamic parts of the background, we employ homogeneous sparse optical flow, which makes detection more robust to camera shocks and vibrations. Moreover, to make detection robust to illumination changes, we employ a blockbased eigenspace approach to represent quasi-static regions in the background. A region-based hysteresis-thresholding approach, augmented by a localized spatial segmentation procedure, attains a good tradeoff between true detections and false positives. The proposed methodology has been evaluated using challenging traf?c scenes illustrating good performance.

关键词： Vehicle detection Vehicle dynamics Optical sensors Robustness Image motion analysis Electric shock Cameras Lighting Computational complexity Vehicle driving

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Unsupervised Modeling of Signs Embedded in Continuous Sentences

Unsupervised Modeling of Signs Embedded in Continuous Senten...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： S. Nayak S. Sarkar B. Loeding Department of Computer Science & Engineering University of South Florida Tampa FL USA Department of Special Education University of South Florida Lakeland FL USA

The common practice in sign language recognition is to ?rst construct individual sign models, in terms of discrete state transitions, mostly represented using Hidden Markov Models, from manually isolated sign samples and then to use it to recognize signs in continuous sentences. In this paper we (i) propose a continuous state space model, where the states are based on purely image-based features, without the use of special gloves, and (ii) present an unsupervised approach to both extract and learn models for continuous basic units of signs, which we term as signemes, from continuous sentences. Given a set of sentences with a common sign, we can automatically learn the model for part of the sign, or signeme, that is least affected by coarticulation effects. While there are coarticulation effects in speech recognition, these effects are even stronger in sign language. The model itself is in term of traces in a space of Relational Distributions. Each point in this space represents a Relational Distribution, capturing the spatial relationships between low-level features, such as edge points. We perform speed normalization and then incrementally extract the common sign between sentences, or signemes, with a dynamic programming framework at the core to compute warped distance between two subsentences. We test our idea using the publicly available Boston SignStream Dataset by building signeme models of 18 signs. We test the quality of the models by considering how well we can localize the sign in a new sentence. We also present preliminary results for the ability to generalize across signers.

关键词： Handicapped aids Hidden Markov models Speech recognition Automatic speech recognition State-space methods Lakes Testing computer science computer science education Continuing education

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Object class recognition using multiple layer boosting with heterogeneous features

Object class recognition using multiple layer boosting with ...

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conference on computer vision and pattern recognition (cvpr)

作者： Wei Zhang Bing Yu G.J. Zelinsky D. Samaras Department of Computer Science SUNY at Stony Brook Stony Brook NY USA Department of Psychology SUNY at Stony Brook Stony Brook NY USA

We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The first layer selects PCA-SIFT and shape context features and combines the two feature types to form a strong classifier. Although previous approaches have used either feature type to train an AdaBoost model, our approach is the first to combine these complementary sources of information into a single feature pool and to use Adaboost to select those features most important for class recognition. The second layer adds to these local and global descriptions information about the spatial relationships between features. Through comparisons to the training sample, we first find the most prominent local features in Layer I, then capture the spatial relationships between these features in Layer 2. Rather than discarding this spatial information, we therefore use it to improve the strength of our classifier. We compared our method to (R. Fergus et al., 2003, A. Opelt et al., 2004, J. Thureson et al., 2004) and in all cases our approach outperformed these previous methods using a popular benchmark for object class recognition (R. Fergus et al., 2003). ROC equal error rates approached 99%. We also tested our method using a dataset of images that better equates the complexity between object and non-object images, and again found that our approach outperforms previous methods.

关键词： Boosting Object recognition Shape Benchmark testing Target recognition Object detection Face detection computer science Psychology Context modeling

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Synthetic Aperture Focusing using a Shear-Warp Factorization of the Viewing Transform

Synthetic Aperture Focusing using a Shear-Warp Factorization...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： V. Vaish G. Garg E. Talvala E. Antunez B. Wilburn M. Horowitz M. Levoy Department of Computer Science University of Stanford Stanford CA USA Department of Electrical Engineering University of Stanford Stanford CA USA

Synthetic aperture focusing consists of warping and adding together the images in a 4D light field so that objects lying on a specified surface are aligned and thus in focus, while objects lying of this surface are misaligned and hence blurred. This provides the ability to see through partial occluders such as foliage and crowds, making it a potentially powerful tool for surveillance. If the cameras lie on a plane, it has been previously shown that after an initial homography, one can move the focus through a family of planes that are parallel to the camera plane by merely shifting and adding the images. In this paper, we analyze the warps required for tilted focal planes and arbitrary camera configurations. We characterize the warps using a new rank- 1 constraint that lets us focus on any plane, without having to perform a metric calibration of the cameras. We also show that there are camera configurations and families of tilted focal planes for which the warps can be factorized into an initial homography followed by shifts. This shear-warp factorization permits these tilted focal planes to be synthesized as efficiently as frontoparallel planes. Being able to vary the focus by simply shifting and adding images is relatively simple to implement in hardware and facilitates a real-time implementation. We demonstrate this using an array of 30 videoresolution cameras; initial homographies and shifts are performed on per-camera FPGAs, and additions and a final warp are performed on 3 PCs.

关键词： Focusing Cameras Surveillance Layout computer vision computer science Calibration Hardware Field programmable gate arrays Personal communication networks

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Fast and Robust Background Updating for Real-time Traffic Surveillance and Monitoring

Fast and Robust Background Updating for Real-time Traffic Su...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： S.M. Bhandarkar Xingzhi Luo Department of Computer Science University of Georgia Athens GA USA

Background updating is an important aspect of dynamic scene analysis. Three critical problems: sudden camera perturbation, sudden or gradual illumination change and the sleeping person problem, which arise frequently in realworld surveillance and monitoring systems, are addressed in the proposed scheme. The paper presents a multi-color model where multiple color clusters are used to represent the background at each pixel location. In the proposed background updating scheme, the updates to the mean and variance of each color cluster at each pixel location incorporate the most recently observed color values. Each cluster is assigned a weight which measures the time duration and temporal recurrence frequency of the cluster. The sleeping person problem is tackled by virtue of the observation that at a given pixel location, the time durations and recurrence frequencies of the color clusters representing temporarily static objects are smaller compared to those of color clusters representing the true background colors when measured over a sufficiently long temporal history. The camera perturbation problem is solved using a fast camera motion detection algorithm that allows the current background image to be registered with the background model maintained in memory. Sudden illumination changes are handled by using an adaptive histogram template whereas gradual illumination changes are automatically resolved with the adaptive background model. The background updating scheme is shown to be robust even when the scene is very busy and also computationally efficient, making it suitable for realtime traffic surveillance and monitoring systems. Experimental results on real traffic monitoring and surveillance videos are presented.

关键词： Robustness Surveillance Monitoring Traffic control Cameras Lighting Frequency measurement Time measurement Image analysis Weight measurement

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Activity recognition and abnormality detection with the switching hidden semi-Markov model

Activity recognition and abnormality detection with the swit...

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conference on computer vision and pattern recognition (cvpr)

作者： T.V. Duong H.H. Bui D.Q. Phung S. Venkatesh Department of Computing Curtin University of Technology Perth WA Australia Artificial Intelligence Center SRI International Inc. Menlo Park CA USA

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the switching hidden semi-markov model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

关键词： Hidden Markov models Pervasive computing Humans Atomic layer deposition Buildings Artificial intelligence Learning Training data Computational modeling Aging

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Calibration of pan-tilt-zoom (PTZ) cameras and omni-directional cameras

Calibration of pan-tilt-zoom (PTZ) cameras and omni-directio...

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conference on computer vision and pattern recognition (cvpr)

作者： S.R. Thirthala S.N. Sinha M. Pollefeys Department of Computer Science UNC Chapel Hill USA

In the first part we discuss the problem of recovering the calibration of a network of pan-tilt-zoom cameras. The intrinsic parameters of each camera over its full range of zoom settings are estimated through a two step procedure. We first determine the intrinsic parameters at the camera's lowest zoom setting very accurately by capturing an extended panorama. The camera intrinsics are then determined at discrete steps in a monotonically increasing zoom sequence that spans the full zoom range of the cameras. Both steps are fully automatic and do not assume any knowledge of the scene structure. We validate our approach by calibrating two different types of pan tilt zoom cameras placed in an outdoor environment. We also show the high-resolution panoramic mosaics built from the images captured during this process. The second section deals with the calibration of omnidirectional cameras. A broad class of both central and non-central cameras, such as fish-eye and catadioptric cameras, can be reduced to 1D radial cameras under the assumption of known center of radial distortion. We study the multi-view geometry of 1D radial cameras. For cameras in general configuration, we introduce a quadrifocal tensor. From this tensor a metric reconstruction of the 1D cameras as well as the observed features can be obtained. In a second phase this reconstruction can then be used as a calibration object to estimate a non-parametric non-central model for the cameras. We study some degenerate cases, including pure rotation. In the case of a purely rotating camera we obtain a trifocal tensor. This allows us to obtain a metric reconstruction of the plane at infinity. Next, we use the plane at infinity as a calibration device to non-parametrically estimate the radial distortion.

关键词： Cameras Calibration Tensile stress Image reconstruction Geometry H infinity control computer science Layout Phase estimation computer vision

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Discriminant analysis with tensor representation

Discriminant analysis with tensor representation

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conference on computer vision and pattern recognition (cvpr)

作者： Shuicheng Yan Dong Xu Qiang Yang Lei Zhang Xiaoou Tang Hong-Jiang Zhang Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China MOE-Microsoft Key Laboratory of Multimedia Computing and Communication & Department of EEIS University of Science and Technology Hefei Anhui China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China Microsoft Research Asia Beijing China

ISBN: (纸本)0769523722

In this paper, we present a novel approach to solving the supervised dimensionality reduction problem by encoding an image object as a general tensor of 2nd or higher order. First, we propose a discriminant tensor criterion (DTC), whereby multiple interrelated lower-dimensional discriminative subspaces are derived for feature selection. Then, a novel approach called k-mode cluster-based discriminant analysis is presented to iteratively learn these subspaces by unfolding the tensor along different tensor dimensions. We call this algorithm discriminant analysis with tensor representation (DATER), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes; 2) for classification problems involving higher-order tensors, the DATER algorithm can avoid the curse of dimensionality dilemma and overcome the small sample size problem; and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in generalized eigenvalue decomposition. We provide extensive experiments by encoding face images as 2nd or 3rd order tensors to demonstrate that the proposed DATER algorithm based on higher order tensors has the potential to outperform the traditional subspace learning algorithms, especially in the small sample size cases.

关键词： Tensile stress Iterative algorithms Linear discriminant analysis Scattering Asia Image coding Clustering algorithms Algorithm design and analysis Principal component analysis Unsupervised learning

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Gene Selection via a Spectral Approach

Gene Selection via a Spectral Approach

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： L. Wolf A. Shashua S. Mukherjee The McGovern Institute for brain research M.I.T. Cambridge MA USA School of Engineering School of Engineering Hebrew University Jerusalem Israel Institute of Statistics and Decision Sciences Duke University Durham NC USA

Array technologies have made it possible to record simultaneously the expression pattern of thousands of genes. A fundamental problem in the analysis of gene expression data is the identification of highly relevant genes that either discriminate between phenotypic labels or are important with respect to the cellular process studied in the experiment. Examples include: cell cycle or heat shock in yeast experiments, chemical or genetic perturbations of mammalian cell lines, and genes involved in class discovery for human tumors. We focus on the task of unsupervised gene selection. Selecting a small subset of genes is particularly challenging as the data sets involved are typically characterized by a small sample size and a very large feature space. We propose a model independent approach which scores candidate gene selections using spectral properties of the candidate af?nity matrix. The algorithm is simple to implement, yet contains a number of remarkable properties which guarantee consistent sparse selections. We applied our algorithm on five different data sets. The first consists of time course data from four well studied Hematopoietic cell lines (HL-60, Jurkat, NB4, and U937). The other four data sets include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished data set (lymph status). We compared our approach both with other unsupervised methods (SOM,PCA,GS) and with supervised methods (SNR,RMB,RFE). The results show that our approach considerably outperforms all the other unsupervised approaches in our study, is competitive with supervised methods and in some cases even outperforms supervised approaches.

关键词： Gene expression Neoplasms Statistics computer science Electric shock Fungi Chemicals Genetics Humans Sparse matrices

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