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38
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1996
"... We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and c ..."
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Cited by 778 (21 self)
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We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and combines aspects of snakes/balloons and region growing. Indeed the classic snakes/balloons and region growing algorithms can be directly derived from our approach. We provide theoretical analysis of region competition including accuracy of boundary location, criteria for initial conditions, and the relationship to edge detection using filters. It is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on grey level images, color images and texture images. The novel color model allows us to eliminate intensity gradients and shadows, thereby obtaining segmentation based on the albedos of objects. It also helps detect highlight regions. 1 Division of Appli...
Texture analysis and classification with treestructured wavelet transform
 IEEE Trans. Image Processing
, 1993
"... AbstractOne difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of textures effectively. Recent developments in multiresolution analysis such as the Gabor and wavelet transforms help to overcome this difficulty. In this research, we propose a m ..."
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Cited by 314 (1 self)
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AbstractOne difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of textures effectively. Recent developments in multiresolution analysis such as the Gabor and wavelet transforms help to overcome this difficulty. In this research, we propose a multiresolution approach based on a modified wavelet transform called the treestructured wavelet transform or wavelet packets for texture analysis and classification. The development of this new transform is motivated by the observation that a large class of natural textures can be modeled as quasiperiodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, we are able to zoom into any desired frequency channels for further decomposition. In contrast, the conventional pyramidstructured wavelet transform performs further decomposition only in low frequency channels. We develop a progressive texture classification algorithm which is not only computationally attractive but also has excellent performance. The performance of our new method is compared with that of several other methods using the DCT, DST, DHT, pyramidstructured wavelet transforms, Gabor filters, and Laws filters.
Video Orbits of the Projective Group: A Simple Approach to Featureless Estimation of Parameters
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 1997
"... We present direct featureless methods for estimating the eight parameters of an "exact" projective (homographic) coordinate transformation to register pairs of images, together with the application of seamlessly combining a plurality of images of the same scene, resulting in a single image ..."
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Cited by 102 (9 self)
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We present direct featureless methods for estimating the eight parameters of an "exact" projective (homographic) coordinate transformation to register pairs of images, together with the application of seamlessly combining a plurality of images of the same scene, resulting in a single image (or new image sequence) of greater resolution or spatial extent. The approach is "exact" for two cases of static scenes: 1) images taken from the same location of an arbitrary threedimensional (3D) scene, with a camera that is free to pan, tilt, rotate about its optical axis, and zoom, or 2) images of a flat scene taken from arbitrary locations. The featureless projective approach generalizes interframe camera motion estimation methods that have previously used an affine model (which lacks the degrees of freedom to "exactly" characterize such phenomena as camera pan and tilt) and/or which have relied upon finding points of correspondence between the image frames. The featureless projective approach...
Scalespace Properties of the Multiscale Morphological DilationErosion
 IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1996
"... A multiscale morphological dilationerosion smoothing operation and its associated scalespace expansion for multidimensional signals are proposed. Properties of this smoothing operation are developed and, in particular a scalespace monotonic property for signal extrema is demonstrated. Scalespace ..."
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Cited by 64 (2 self)
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A multiscale morphological dilationerosion smoothing operation and its associated scalespace expansion for multidimensional signals are proposed. Properties of this smoothing operation are developed and, in particular a scalespace monotonic property for signal extrema is demonstrated. Scalespace fingerprints from this approach have advantages over Gaussian scalespace fingerprints in that they: are defined for negative values of the scale parameter; have
A quadtree approach to image segmentation which combines statistical and spatial information. Pattern Recognition
, 1985
"... AbstractA new approach to the problem of image segmentation is presented. By combining a nonparametric classifier, based on a clustering algorithm, with a quadtree representation f the image, the scheme isboth simple to implement and performs well, giving satisfactory esults at signaltonoise r ..."
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Cited by 40 (1 self)
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AbstractA new approach to the problem of image segmentation is presented. By combining a nonparametric classifier, based on a clustering algorithm, with a quadtree representation f the image, the scheme isboth simple to implement and performs well, giving satisfactory esults at signaltonoise ratios well below 1. The results of an analysis of the algorithm are borne out by a comprehensive setof tests on Gaussian images and synthetic textures, which demonstrate itsprincipal features. Segmentation Quadtree Texture Region description Boundary estimation 1.
Localization of Corresponding Points in Fingerprints by Complex Filtering
, 2003
"... For the alignment of two fingerprints certain landmark points are needed. These should be automaticly extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (singular points, SPs) in the fingerprints. We identify an SP by its symmetry properties. SPs are ext ..."
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Cited by 39 (10 self)
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For the alignment of two fingerprints certain landmark points are needed. These should be automaticly extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (singular points, SPs) in the fingerprints. We identify an SP by its symmetry properties. SPs are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filters, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.
Adaptive Multidimensional Filtering
 LINKÖPING UNIVERSITY, SWEDEN
, 1992
"... This thesis contains a presentation and an analysis of adaptive filtering strategies for multidimensional data. The size, shape and orientation of the filter are signal controlled and thus adapted locally to each neighbourhood according to a predefined model. The filter is constructed as a linear we ..."
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Cited by 31 (1 self)
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This thesis contains a presentation and an analysis of adaptive filtering strategies for multidimensional data. The size, shape and orientation of the filter are signal controlled and thus adapted locally to each neighbourhood according to a predefined model. The filter is constructed as a linear weighting of fixed oriented bandpass filters having the same shape but different orientations. The adaptive filtering methods have been tested on both real data and synthesized test data in 2D, e.g. still images, 3D, e.g. image sequences or volumes, with good results. In 4D, e.g. volume sequences, the algorithm is given in its mathematical form. The weighting coefficients are given by the inner products of a tensor representing the local structure of the data and the tensors representing the orientation of the filters. The procedure and filter design in estimating the representation tensor are described. In 2D, the tensor contains information about the local energy, the optimal orientation and a certainty of the orientation. In 3D, the information in the tensor is the energy, the normal to the best fitting local plane and the tangent to the best fitting line, and certainties of these orientations. In the case of time sequences, a quantitative comparison of the proposed method and other (optical flow) algorithms is presented. The estimation of control information is made in different scales. There are two main reasons for this. A single filter has a particular limited pass band which may or may not be tuned to the different sized objects to describe. Second, size or scale is a descriptive feature in its own right. All of this requires the integration of measurements from different scales. The increasing interest in wavelet theory supports the idea that a multiresolution approach is necessary. Hence the resulting adaptive filter will adapt also in size and to different orientations in different scales.
Texture Segmentation Using GaussianMarkov Random Fields and Neural Oscillator Networks
, 2001
"... We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a GaussianMarkov random fields (GMRF) model. Unlike a GMRFbased approach, our method does not employ model parameters as fe ..."
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Cited by 24 (3 self)
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We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a GaussianMarkov random fields (GMRF) model. Unlike a GMRFbased approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The second part is a twodimensional (2D) array of locally excitatory globally inhibitory oscillator networks (LEGION). After being filtered for noise suppression, features are used to determine the local couplings in the network. When LEGION runs, the oscillators corresponding to the same texture tend to synchronize, whereas different texture regions tend to correspond to distinct phases. In simulations, a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks. We provide results on real texture images to demonstrate the performance of our method. Index TermsDynamical systems, Gaussian Markov random fields, LEGION, neural networks, relaxation oscillators, texture segmentation. I.
Organization of Architectures for Cognitive Vision Systems
 In Proceedings of Workshop on Cognitive Vision
, 2003
"... Abstract. The purpose of cognitive systems is to produce a response to appropriate percepts. The response may be a direct physical action which may change the state of the system. It may be delayed in the form of a reconfiguration of internal models in relation to the interpreted context of the syst ..."
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Cited by 14 (4 self)
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Abstract. The purpose of cognitive systems is to produce a response to appropriate percepts. The response may be a direct physical action which may change the state of the system. It may be delayed in the form of a reconfiguration of internal models in relation to the interpreted context of the system. Or it may be to generate in a subsequent step a generalized symbolic representation which will allow its intentions of actions to be communicated to some other system. As important as the percepts, is the dependence upon context. A fundamental property of cognitive vision systems is that they shall be extendable. This requires that systems both acquire and store information about the environment autonomously – on their own terms. The distributed organization foreseen for processing and for memory to allow learning, implies that later acquired information has to be stored in relation to earlier. The semantic character of the information which this requires, implies a storage with respect to similarity, and the availability
Signal Representation and Processing using Operator Groups
 Linköping University, Sweden
, 1995
"... This thesis presents a signal representation in terms of operators. The signal is assumed to be an element of a vector space and subject to transformations of operators. The operators form continuous groups, socalled Lie groups. The representation can be used for signals in general, in particular i ..."
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Cited by 11 (3 self)
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This thesis presents a signal representation in terms of operators. The signal is assumed to be an element of a vector space and subject to transformations of operators. The operators form continuous groups, socalled Lie groups. The representation can be used for signals in general, in particular if spatial relations are undefined, and it does not require a basis of the signal space to be useful. Special attention is given to orthogonal operator groups which are generated by antiHermitian operators by means of the exponential mapping. It is shown that the eigensystem of the group generator is strongly related to properties of the corresponding operator group. For oneparameter orthogonal operator groups, a phase concept is introduced. This phase can for instance be used to distinguish between spatially even and odd signals and, therefore, corresponds to the usual phase for multidimensional signals. Given one operator group that represents the variation of the signal and one operator ...