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Estimation of blood flow speed and vessel location from thermal video

Estimation of blood flow speed and vessel location from ther...

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conference on computer vision and pattern recognition (cvpr)

作者： M. Garbey A. Merla I. Pavlidis Computer Science Department University of Houston Houston TX USA

In this paper we present a novel method for estimation of blood flow speed and vessel location from thermal video. The method is based on a bioheat transfer model that reflects the thermo-physiological processes in a skin region proximal to a major vessel. The model assumes the form of a partial differential equation (PDE) with boundary conditions. Initially, we test the soundness of our model by performing direct numerical simulation. Then, we solve the inverse problem both in steady and dynamic states on data provided by a thermal imaging system. Our method opens exciting possibilities in biometrics and biomedicine. Among others, it promises to revolutionize polygraph examinations by eliminating wiring and improving accuracy. It also establishes the feasibility of continuous 2D physiological monitoring of human patients in a contact-free manner.

关键词： Blood flow Skin Partial differential equations Boundary conditions Acoustic testing Performance evaluation Numerical simulation Inverse problems Biomedical imaging Biometrics

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Are two rotational flows sufficient to calibrate a smooth non-parametric sensor?

Are two rotational flows sufficient to calibrate a smooth no...

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conference on computer vision and pattern recognition (cvpr)

作者： E. Grossmann Eun-Joo Lee P. Hislop D. Nister H. Stewenius Department IRO Universite de Montréal Canada Computer Science Department University of Kentucky USA Department of Mathematics University of Kentucky USA Center for Visualization University of Kentucky USA

We present an attempt to determine whether the shape of a generic central-projection camera, such as the eye of an insect or a log-polar camera, can be determined from two motion flows resulting from purely rotational motions with non-collinear axes. Our first contribution is to write the smooth non-parametric calibration problem as a differential equation. It is unclear at present whether this problem has unique solution, up to an orthogonal transformation. Our second contribution is a discretized version of this smooth problem, for which we give a calibration algorithm - a third contribution. Using this algorithm, we explore numerically the properties of the discrete self-calibration problem, giving some insight on the nature of the problem. We show examples of successful self-calibration, but cannot give a definite affirmative answer to the question in the title.

关键词： Cameras Calibration Optical sensors Visualization computer vision Sensor phenomena and characterization computer science Mathematics Shape Insects

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Fully Automatic Registration of 3D Point Clouds

Fully Automatic Registration of 3D Point Clouds

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conference on computer vision and pattern recognition (cvpr)

作者： A. Makadia A. Patterson K. Daniilidis Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

We propose a novel technique for the registration of 3D point clouds which makes very few assumptions: we avoid any manual rough alignment or the use of landmarks, displacement can be arbitrarily large, and the two point sets can have very little overlap. Crude alignment is achieved by estimation of the 3D-rotation from two Extended Gaussian Images even when the data sets inducing them have partial overlap. The technique is based on the correlation of the two EGIs in the Fourier domain and makes use of the spherical and rotational harmonic transforms. For pairs with low overlap which fail a critical verification step, the rotational alignment can be obtained by the alignment of constellation images generated from the EGIs. Rotationally aligned sets are matched by correlation using the Fourier transform of volumetric functions. A fine alignment is acquired in the final step by running Iterative Closest Points with just few iterations.

关键词： Fourier transforms Layout Iterative algorithms Iterative closest point algorithm computer vision Three-dimensional displays Information science Image generation

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Efficient Optimal Kernel Placement for Reliable Visual Tracking

Efficient Optimal Kernel Placement for Reliable Visual Track...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhimin Fan Ming Yang Ying Wu Gang Hua Ting Yu Department of Electrical & Computer Engineering Northwestern University USA

This paper describes a novel approach to optimal kernel placement in kernel-based tracking. If kernels are placed at arbitrary places, kernel-based methods are likely to be trapped in ill-conditioned locations, which prevents the reliable recovery of the motion parameters and jeopardizes the tracking performance. The theoretical analysis presented in this paper indicates that the optimal kernel placement can be evaluated based on a closed-form criterion, and achieved efficiently by a novel gradient-based algorithm. Based on that, new methods for temporal-stable multiple kernel placement and scale-invariant kernel placement are proposed. These new theoretical results and new algorithms greatly advance the study of kernel-based tracking in both theory and practice. Extensive real-time experimental results demonstrate the improved tracking reliability.

关键词： Kernel Target tracking Motion estimation Shape measurement Observability Reliability engineering Algorithm design and analysis Convolution Optimization methods Particle tracking

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Face recognition using 2.5D Shape Information

Face Recognition using 2.5D Shape Information

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conference on computer vision and pattern recognition (cvpr)

作者： W.A.P. Smith E.R. Hancock Department of Computer Science University of York UK

In this paper we investigate whether the 2.5D shape information delivered by a novel shape-from-shading algorithm can be used for illumination insensitive face recognition. We present a robust and efficient facial shape-fromshading algorithm which uses principal geodesic analysis to model the variation in surface orientation across a face. We show how this algorithm can be used to recover accurate facial shape and albedo from real world images. Our second contribution is to use the recovered 2.5D shape information in a variety of recognition methods. We present a novel recognition strategy in which similarity is measured in the space of the principal geodesic parameters. We also use the recovered shape information to generate illumination normalised prototype images on which recognition can be performed. Finally we show that, from a single input image, we are able to generate the basis images employed by a number of well known illumination-insensitive recognition algorithms. We also demonstrate that the principal geodesics provide an efficient parameterisation of the space of harmonic basis images.

关键词： Face recognition Shape Lighting Image recognition computer science Robustness Algorithm design and analysis Level measurement Prototypes Image generation

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Feature correspondence by interleaving shape and texture computations

Feature correspondence by interleaving shape and texture com...

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conference on computer vision and pattern recognition (cvpr)

作者： D. Beymer Artificial Intelligence Laboratory and Center for Biological and Computational Learning Massachusetts Institute of Technology Cambridge MA USA

The correspondence problem in computer vision is basically a matching task between two or more sets of features. We introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. The representation consists of two image measurements made at the feature points: shape and texture. Feature geometry, or shape, is represented using the (x,y) locations of features relative to the some standard reference shape. Image grey levels, or texture, are represented by mapping image grey levels onto the standard reference shape. Computing this representation is essentially a correspondence task and in this paper we explore on automatic technique for "vectorizing" face images. Our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. In addition to describing the vectorizer, an application to the problem of facial feature detection is presented.

关键词： Interleaved codes Biology computing Image texture analysis Artificial intelligence Learning Shape measurement Geometry Face detection Face recognition Image analysis

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Incremental learning of object detectors using a visual shape alphabet

Incremental learning of object detectors using a visual shap...

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conference on computer vision and pattern recognition (cvpr)

作者： A. Opelt A. Pinz A. Zisserman Institute of Electrical Measurement and Sign. Proc. Graz University of Technology Austria Department of Engineering Science University of Oxford UK

We address the problem of multiclass object detection. Our aims are to enable models for new categories to benefit from the detectors built previously for other categories, and for the complexity of the multiclass system to grow sublinearly with the number of categories. To this end we introduce a visual alphabet representation which can be learnt incrementally, and explicitly shares boundary fragments (contours) and spatial configurations (relation to centroid) across object categories. We develop a learning algorithm with the following novel contributions: (i) AdaBoost is adapted to learn jointly, based on shape features; (ii) a new learning schedule enables incremental additions of new categories; and (iii) the algorithm learns to detect objects (instead of categorizing images). Furthermore, we show that category similarities can be predicted from the alphabet. We obtain excellent experimental results on a variety of complex categories over several visual aspects. We show that the sharing of shape features not only reduces the number of features required per category, but also often improves recognition performance, as compared to individual detectors which are trained on a per-class basis.

关键词： Object detection Detectors Shape Scheduling algorithm Electric variables measurement Learning systems Assembly Training data computer society computer vision

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Adapting an object detector by considering the worst case: A conservative approach

Adapting an object detector by considering the worst case: A...

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作者： Chen, Guang Han, Tony X. Lao, Shihong Electrical and Computer Engineering Dept. University of Missouri Columbia MO United States Core Technology Center Omron Corporation Kyoto Japan

ISBN: (纸本)9781457703942

The performance of an offline-trained classifier can be improved on-site by adapting the classifier towards newly acquired data. However, the adaptation rate is a tuning parameter affecting the performance gain substantially. Poor selection of the adaptation rate may worsen the performance of the original classifier. To solve this problem, we propose a conservative model adaptation method by considering the worst case during the adaptation process. We first construct a random cover of the set of the adaptation data from its partition. For each element in the cover (i.e. a portion of the whole adaptation data set), we define the cross-entropy error function in the form of logistic regression. The element in the cover with the maximum cross-entropy error corresponds to the worst case in the adaptation. Therefore we can convert the conservative model adaptation into the classic min-max optimization problem: finding the adaptation parameters that minimize the maximum of the cross-entropy errors of the cover. Taking the object detection as a testbed, we implement an adapted object detector based on binary classification. Under different adaptation scenarios and different datasets including PASCAL, ImageNet, INRIA, and TUD-Pedestrian, the proposed adaption method achieves significant performance gain and is compared favorably with the state-of-the-art adaptation method with the fine tuned adaptation rate. Without the need of tuning the adaptation rates, the proposed conservative model adaptation method can be extended to other adaptive classification tasks. © 2011 ieee.

关键词： Errors

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The optimal implementation of morphological operations on neighborhood connected array processors

The optimal implementation of morphological operations on ne...

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conference on computer vision and pattern recognition (cvpr)

作者： Xu Jianning Dept. of Math. & Comput. Sci. Classboro State Coll. NJ USA

Neighborhood-connected array processors can implement Minkowski addition and Minkowski subtraction operations with structuring elements larger than the neighborhood size by breaking the operation into a sequence of successive neighborhood operations. The author provides a complete solution to the problem of decomposing convex polygons into subsets of the 3*3 neighborhood set. With the help of the Freeman chain code notation, it is proved that all convex polygons have neighborhood decompositions. Based on this result, an efficient algorithm is developed which can find an optimal decomposition for any convex polygon. The decomposition produced by the algorithm can be used to determine the most efficient implementation of a morphological operation with convex polygonal structuring elements as a sequence of successive neighborhood operations. Therefore, the algorithm is applicable to neighborhood-connected array processors, or any machine structure that can quickly perform 3*3 neighborhood operations.< >

关键词： Morphological operations Shape Image sequence analysis Mathematics computer science Educational institutions Morphology Image analysis Logic Arithmetic

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Lie groups, space-variant Fourier analysis and the exponential chirp transform

Lie groups, space-variant Fourier analysis and the exponenti...

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conference on computer vision and pattern recognition (cvpr)

作者： G. Bonmassar E.L. Schwartz Department Biomedical Engineering Boston University Boston MA USA Department Cognitive and Neural Systems Boston University Boston MA USA

The use of visual representations in which retinal neurons receptive fields are not constant over the visual field is universal in the visual systems of higher vertebrates, and is coming to play an important role in active vision applications. The breaking of translation symmetry that is unavoidably associated with non-uniform sampling presents a major algorithmic complication for image processing. In this paper we use a Lie group approach to derive a kernel which provides a quasi-shift (i.e. approximate shift) invariant template matching capability, under normal convolution in the distorted (range) coordinates of the non-uniform mapping. We work out the special case of the log-polar mapping, which is of great interest in vision; in this case, we call the associated linear integral transform the "exponential chirp transform" (ECT). The method is, however, general for other forms of mapping, or warp, function.

关键词： Chirp Fourier transforms Pixel Kernel Image processing Image sampling Electrical capacitance tomography Machine vision Convolution Biomedical engineering

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