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10
Fast parametric elastic image registration
 IEEE Transactions on Image Processing
, 2003
"... Abstract—We present an algorithm for fast elastic multidimensional intensitybased image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard realworld problems, it is capable of accepting expert hints in the form of so ..."
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Cited by 102 (8 self)
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Abstract—We present an algorithm for fast elastic multidimensional intensitybased image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard realworld problems, it is capable of accepting expert hints in the form of soft landmark constraints. Much fewer landmarks are needed and the results are far superior compared to pure landmark registration. Particular attention has been paid to the factors influencing the speed of this algorithm. The Bspline deformation model is shown to be computationally more efficient than other alternatives. The algorithm has been successfully used for several twodimensional (2D) and threedimensional (3D) registration tasks in the medical domain, involving MRI, SPECT, CT, and ultrasound image modalities. We also present experiments in a controlled environment, permitting an exact evaluation of the registration accuracy. Test deformations are generated automatically using a random hierarchical fractional waveletbased generator. Index Terms—Elastic registration, image registration, landmarks, splines. I.
Probabilistic recognition of activity using local appearance
 in CVPR
, 1999
"... This paper addresses the problem of probabilistic recognition of activities from local spatiotemporal appearance. Joint statistics of spacetime filters are employed to define histograms which characterize the activities to be recognized. These histograms provide the joint probability density funct ..."
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Cited by 58 (4 self)
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This paper addresses the problem of probabilistic recognition of activities from local spatiotemporal appearance. Joint statistics of spacetime filters are employed to define histograms which characterize the activities to be recognized. These histograms provide the joint probability density functions required for recognition using Bayes rule. The result is a technique for recognition of activities which is robust to partial occlusions as well as changes in illumination. In this paper the framework and background for this approach is first described. Then the family of spatiotemporal receptive fields used for characterizing activities is presented. This is followed by a review of probabilistic recognition of patterns from joint statistics of receptive field responses. The approach is validated with the results of experiments in the discrimination of persons walking in different directions, and the recognition of a simple set of hand gestures in an augmented reality scenario. 1
An Efficient Neuromorphic Analog Network For Motion Estimation
, 1999
"... Optical flow estimation is a critical mechanism for autonomous mobile robots as it provides a range of useful information. As realtime processing is mandatory in this case, an efficient solution is the use of specific VLSI analog circuits. This paper presents a simple and regular architecture based ..."
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Cited by 14 (3 self)
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Optical flow estimation is a critical mechanism for autonomous mobile robots as it provides a range of useful information. As realtime processing is mandatory in this case, an efficient solution is the use of specific VLSI analog circuits. This paper presents a simple and regular architecture based on analog circuits which implements the entire processing line from photoreceptor to accurate and reliable optical flow estimation. The algorithm we propose, is an energybased method using a novel wideband velocitytuned filter whichproves to be an efficient alternativetothe well known Gabor filters. Our approach shows that a high level of accuracy can be obtained from a small number of loosely tuned filters. It exhibits similar or improved performance to that of other existing algorithms, but with a much lower complexity.
Elastic Image Registration using Parametric Deformation Models
, 2001
"... The main topic of this thesis is elastic image registration for biomedical applications. We start with an overview and classification of existing registration techniques. We revisit the landmark interpolation which appears in the landmarkbased registration techniques and add some generalizations. ..."
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Cited by 10 (1 self)
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The main topic of this thesis is elastic image registration for biomedical applications. We start with an overview and classification of existing registration techniques. We revisit the landmark interpolation which appears in the landmarkbased registration techniques and add some generalizations. We develop a general elastic image registration algorithm. It uses a grid of uniform Bsplines to describe the deformation. It also uses Bsplines for image interpolation. Multiresolution in both image and deformation model spaces yields robustness and speed. First we describe a version of this algorithm targeted at finding unidirectional deformation in EPI magnetic resonance images. Then we present the enhanced and generalized version of this algorithm which is significantly faster and capable of treating multidimensional deformations. We apply this algorithm to the registration of SPECT data and to the motion estimation in ultrasound image sequences. A semiautomatic version of the registration algorithm is capable of accepting expert hints in the form of soft landmark constraints. Much fewer landmarks are needed and the results are far superior compared to pure landmark registration. In the second part of this thesis, we deal with the problem of generalized sampling and variational reconstruction. We explain how to reconstruct an object starting from several measurements using arbitrary linear operators. This comprises the case of traditional as well as generalized sampling. Among all possible reconstructions, we choose the one minimizing an a priori given quadratic variational criterion. We give an overview of the method and present several examples of applications. We also provide the mathematical details of the theory and discuss the choice of the variational criterio...
Robust Motion Estimation Using Spatial Gabor Filters
 In: X European Conf. Signal Process
, 2000
"... This paper presents a new algorithm for motion estimation. It combines Gabor lter decomposition and robust least squares estimation in a multiresolution framework. Spatial Gabor lter bank provides a multichannel decomposition of frame sequence. Then, applying the brightness constancy constraint on e ..."
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Cited by 6 (3 self)
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This paper presents a new algorithm for motion estimation. It combines Gabor lter decomposition and robust least squares estimation in a multiresolution framework. Spatial Gabor lter bank provides a multichannel decomposition of frame sequence. Then, applying the brightness constancy constraint on each channel between two consecutive frames, we obtain an overdetermined system of velocity equations at each pixel. In order to be robust to outliers, this overdetermined system is solved using a robust least squares technique. We have used a multiresolution framework in order to manage large and small displacements. Performances of our algorithm are tested on synthetic and real sequences, and are compared with other techniques. 1
Probabilistic Sensor for the Perception of Activities
 Proc. European Conf. Computer Vision
, 2000
"... This paper presents a new technique for the perception of activities using statistical description of spatiotemporal properties. With this approach, the probability of an activityin a spatiotemporal image sequence is computed by applying Bayes rule to the joint statistics of the responses of motio ..."
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Cited by 4 (0 self)
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This paper presents a new technique for the perception of activities using statistical description of spatiotemporal properties. With this approach, the probability of an activityin a spatiotemporal image sequence is computed by applying Bayes rule to the joint statistics of the responses of motion energy receptive fields. A set of motion energy receptive fields are designed in order to sample the power spectrum of a moving texture. Their structure relates to the spatiotemporal energy models of Adelson and Bergen where measures of local visual motion information are extracted comparing the outputs of triad of Gabor energy filters. Then the probability density function required for Bayes rule is estimated for each class of activity by computing multidimensionalhistograms from the outputs from the set of receptive fields. The perception of activities is achieved according to Bayes rule. The result at a given time is the map of the conditional probabilities that each pixel belongs to an activity of the training set. The approach is validated with experiments in the perception of activities of walking persons in a visual surveillance scenario. Results are robust to changes in illumination conditions, to occlusions and to changes in texture. 1.
Analogue Architectures for Vision: Cellular Neural Networks and Neuromorphic Circuits
, 1999
"... Vision machines based on actual computational methods require the development of simple lowlevel feature detectors. The lowlevel feature detectors measure local image properties as scale, orientation, and velocity. Analog VLSI devices that mimic some functionality of biological systems appear to b ..."
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Cited by 1 (0 self)
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Vision machines based on actual computational methods require the development of simple lowlevel feature detectors. The lowlevel feature detectors measure local image properties as scale, orientation, and velocity. Analog VLSI devices that mimic some functionality of biological systems appear to be robust, lowpower consuming, and fast enough to solve vision problems in real time.
Probabilistic Recognition of Activity
 In IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins
, 1999
"... This paper addresses the problem of probabilistic recognition of activities from local spatiotemporal appearance. Joint statistics of spacetime filters are employed to define histograms which characterize the activities to be recognized. These histograms provide the joint probability density fu ..."
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This paper addresses the problem of probabilistic recognition of activities from local spatiotemporal appearance. Joint statistics of spacetime filters are employed to define histograms which characterize the activities to be recognized. These histograms provide the joint probability density functions required for recognition using Bayes rule. The result is a technique for recognition of activities which is robust to partial occlusions as well as changes in illumination.
Biomedical Image Registration by Elastic Warping
, 1999
"... Introduction Registration algorithms can be used for a wide variety of image processing tasks. Examples include motion analysis, stereotactic normalization, intersubject as well as intrasubject studies, intermodality matching, and distortion compensation. Given two images f 1 , f 2 representing ..."
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Introduction Registration algorithms can be used for a wide variety of image processing tasks. Examples include motion analysis, stereotactic normalization, intersubject as well as intrasubject studies, intermodality matching, and distortion compensation. Given two images f 1 , f 2 representing the same object, we want to identify geometric correspondences between homologous features in both images. Specifically, we want to find a correspondence function (also called deformation function or deformation field) g(x 1 ) =x 2 , where x 1 , x 2 are matching coordinates in images f 1 , f 2 . Here, we intend to concentrate on a nonlinear registration (also called elastic matching), characterised by the nonlinearity of the function<
Probabilistic Recognition of Activity using Local Appearance
 In IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins
, 1999
"... This paper addresses the problem of probabilistic recognition of activities from local spatiotemporal appearance. Joint statistics of spacetime filters are employed to define histograms which characterize the activities to be recognized. These histograms provide the joint probability density fu ..."
Abstract
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This paper addresses the problem of probabilistic recognition of activities from local spatiotemporal appearance. Joint statistics of spacetime filters are employed to define histograms which characterize the activities to be recognized. These histograms provide the joint probability density functions required for recognition using Bayes rule. The result is a technique for recognition of activities which is robust to partial occlusions as well as changes in illumination. In this paper the framework and background for this approach is first described. Then the family of spatiotemporal receptive fields used for characterizing activities is presented. This is followed by a review of probabilistic recognition of patterns from joint statistics of receptive field responses. The approach is validated with the results of experiments in the discrimination of persons walking in different directions, and the recognition of a simple set of hand gestures in an augmented reality scena...