A new class of morphological filters is proposed for image enhancement. The filter, known as the generalized morphological filter (GMF), uses multiple structuring elements and combines linear and morphological operati...
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A new class of morphological filters is proposed for image enhancement. The filter, known as the generalized morphological filter (GMF), uses multiple structuring elements and combines linear and morphological operations. The GMF can be designed to suppress various types of noise yet preserve geometrical structure in an image. A study of several aspects of the performance of the filter is presented. The study includes geometrical feature preservation, noise suppression, structuring element selection, and the root signal structure. For the sake of comparison, averaging and median filters are also used in the experiments and corresponding figures of merit of the performance of the filter. The empirical study shows that the generalized morphological filter possesses effective noise suppression with reduced geometrical feature blurring.
Edge-based image segmentation is a two-stage process;edge enhancement followed by edge linking. Modern approaches for edge enhancement use either the gradient of the Gaussian operator (VG) or the Laplacian of the Gaus...
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Two commonly used optical correlation techniques, matched spatial filtering and joint-Fourier transform correlation, are briefly reviewed. A recently proposed real-time joint-Fourier transform correlation is then disc...
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In this paper we present some enlightening results to show why and how stability assessment for Linear Time-Varying (LTV) systems based solely on the location of the "frozen-time eigenvalues (FTE)" fails to ...
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In this paper we apply computer visualization techniques such as computer (3D) graphics, animation, and photorealistic color rendering to the simulation of dynamical processes and control systems in limited-resource e...
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The authors develop an empirical measure for the selection of the Gaussian filter that is commonly used for edge enhancement. The measure is based totally on the image at hand. Edge enhancement by a Gaussian filter ha...
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The authors develop an empirical measure for the selection of the Gaussian filter that is commonly used for edge enhancement. The measure is based totally on the image at hand. Edge enhancement by a Gaussian filter has two distinct advantages: (1) the filter is fully described by a single parameter, the standard deviation sigma ; (2) the two-dimensional filter is separable and can be easily implemented. The filter's spatial support is a function of sigma . This support is normally in the range of +or-3.5 sigma . An empirical measure is described for the selection of the Gaussian filter's spatial support using the power spectrum density of the input image. Classic Fourier analysis is used to obtain a measure for the spatial support of the Gaussian filter given a particular image. Experimental results suggest that this measure can be used as an aid in deciding the Gaussian filter's spatial support needed to enhance the edges.< >
A modular, unsupervised neural network architecture is described. It can be used for data clustering and classification. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system which l...
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A modular, unsupervised neural network architecture is described. It can be used for data clustering and classification. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns online in a stable and efficient manner. The system consists of a fuzzy k-means learning rule embedded within a control structure similar to that found in the adaptive resonance theory (ART-1) network. AFLC adaptively clusters analog inputs into classes without prior knowledge of the entire data set or of the number of clusters present in the data. The classification of an input takes place in a two-stage process; a simple competitive stage and a euclidean metric comparison stage. The AFLC algorithm and its operating characteristics are described. The algorithm is compared to an adaptive Bayesian classifier for some real data.< >
The authors outline an edge-prediction-adjustment strategy for the detection of diffused edges using an image pyramid structure. An initial edge map is obtained from an appropriate high-level image of the pyramid usin...
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The authors outline an edge-prediction-adjustment strategy for the detection of diffused edges using an image pyramid structure. An initial edge map is obtained from an appropriate high-level image of the pyramid using a conventional edge detection method. Next, using a simple linear interpolation, the predicted edge map for the adjacent low-level image is determined. Based on the information of the predicted edges, edge adjustment is carried out using a sequential search in a small neighborhood of each of the predicted edges with the aid of a dynamic programming based method. Simulation results on both synthetic and medical images indicate that the performance of the proposed approach is much better than that of the commonly used differential of a Gaussian-based approach.< >
In this paper we present some enlightening results to show why and how stability assessment for Linear Time-Varying (LTV) systems based solely on the location of the "frozen-time eigenvalues (FTE)" fails to ...
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In this paper we present some enlightening results to show why and how stability assessment for Linear Time-Varying (LTV) systems based solely on the location of the "frozen-time eigenvalues (FTE)" fails to be sufficient or necessary, using two classes of parametrized periodic LTV systems derived from two examples given by Markus-Yamabe [6] and Wu [11]. Exact domain of stability in the parameter space obtained using analytical or numerical solutions of the Floquet characteristic Exponents are presented, and compared to that predicted by FTEs. The results are useful in the study of robustness and stabilization of Linear Time Invariant (LTI) systems, as will be shown in this paper that an unstable LTI system maybe stabilized or destabilized by periodic structural perturbations (pumping) without any control input.
This paper describes a method for reducing the information contained in an image sequence, while retaining the information necessary for the interpretation of the sequence by a human observer. The method consists of f...
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This paper describes a method for reducing the information contained in an image sequence, while retaining the information necessary for the interpretation of the sequence by a human observer. The method consists of first locating the redundant information, reducing the degree of redundancy, and coding the result. The sequence is treated as a single 3-D data volume, the voxels of which are grouped into several regions, obtained by a 3-D split and merge algorithm. To find these regions, we first obtain an initial region space by splitting the image sequence until the gray-level variation over each region can be approximated by a 3-D polynomial, to a specified accuracy. This results in a set of parallelepipedic regions of various sizes. To represent the gray-level variation over these regions, the coefficients of the approximating polynomial are used as features. The most similar regions are then merged, using a region adjacency graph. The information is coded by representing the borders of the regions using a pyramidal structure in the x, y, t space. The coefficients of the approximating polynomials are coded in a straightforward manner. For 256 x 256 pixel, 25 frames/s image sequences, compressions allowing transmission rates near 64 kbit/s are obtained.
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