A new class of adaptive alpha-trimmed filters well suited for processing of images corrupted with non-symmetrical p.d.f. speckle and impulsive noise is proposed. It is shown that in certain cases one can provide a bet...
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ISBN:
(纸本)0819427446
A new class of adaptive alpha-trimmed filters well suited for processing of images corrupted with non-symmetrical p.d.f. speckle and impulsive noise is proposed. It is shown that in certain cases one can provide a better speckle reduction and impulse removal by non-symmetrical trimming. Appropriate edge/detail preservation is ensured due to adaptation of the scanning window size or application of detail preserving L-pq-filter. Selection of adaptation parameter is discussed as well. The efficiency and properties of the proposed filters are demonstrated and quantitatively evaluated for test and real data.
Optimal binary filters estimate an ideal random set by means of an observed random set. By parameterizing the ideal and observation random sets, one can examine the robustness of filter design relative to parameter st...
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ISBN:
(纸本)0819427446
Optimal binary filters estimate an ideal random set by means of an observed random set. By parameterizing the ideal and observation random sets, one can examine the robustness of filter design relative to parameter states. This paper addresses the question as to which states possess the most robust optimal filters. Based on the prior distribution of the states, a measure of robustness is defined for each state and the state possessing maximal robustness is determined. The paper focuses on sparse noise, for which an analytic formulation oi robustness is known. It proposes a parametric model From which to approximate robustness by estimating model parameters from image data.
In this paper, a unified method of image computing for spatial and spectral feature extraction is described. It is named as " pixel swapping method". This method facilitates identification of types and featu...
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ISBN:
(纸本)0819427446
In this paper, a unified method of image computing for spatial and spectral feature extraction is described. It is named as " pixel swapping method". This method facilitates identification of types and features of objects such as point-like objects, Line-like objects or region-like objects, and to detect line-start/end points, line intersections and vertices. It is also used to identify spatial association among objects. This method Mn be extended to non-linear imageprocessing, as well as, the conventional numeric imageprocessing. As an example of application of this method, automated road feature extraction from LANDSAT TM data will be demonstrated, employing fuzzy spectral and spatial imageprocessing using the "pixel swapping" method.
The proceedings contains 30 papers from the conference on nonlinear image processing ix. Topics discussed include: optimized approach to maximum entropy;designing robust binary filters;compact representation of W-oper...
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The proceedings contains 30 papers from the conference on nonlinear image processing ix. Topics discussed include: optimized approach to maximum entropy;designing robust binary filters;compact representation of W-operators;color image interpolation using vector rational filters;optimal thresholding for color images and multiscale method for feature-preserving compression.
A subjective analysis is performed on various classical, and order statistic-based color edge detectors. Order statistic edge detectors imply that image pixels within a specific region, are treated statistically such ...
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ISBN:
(纸本)0819427446
A subjective analysis is performed on various classical, and order statistic-based color edge detectors. Order statistic edge detectors imply that image pixels within a specific region, are treated statistically such that outliers can be rejected from the general trends in the data. A different type of subjective rating system is employed here and the rationale behind its use is explained. The importance of subjective edge detection lies in the development of a color edge detector which can accurately simulate what is seen by humans.
We have already proposed the learning type of median and mean hybrid (LMMH) filters which have the desirable properties of both linear fitter and nonlinear filters: The LMMH filters are designed by using LMS algorithm...
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ISBN:
(纸本)0819427446
We have already proposed the learning type of median and mean hybrid (LMMH) filters which have the desirable properties of both linear fitter and nonlinear filters: The LMMH filters are designed by using LMS algorithm: therefore, both the noisy signal and it's original signal are required when learning of those. We call the pair of images (i.e. noisy image and it's original image) the learning signals. Although the original signal of the noisy image is not given in the practical application. In this paper, we propose a novel making method of learning signals for LMMH filters. in this method, we extract the signal information from the noisy signal and synthesize learning signals by using the information. In the simulations, the new learning signals obtained by the proposed method are shown to be effective for LMMH filters' learning.
There is no formation model for natural images, unlike for speech or the specific signals generated by medical or satellite imagery. Autocorrelations and spectral analysis are convenient but limited tools. As Gaussian...
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ISBN:
(纸本)0819427446
There is no formation model for natural images, unlike for speech or the specific signals generated by medical or satellite imagery. Autocorrelations and spectral analysis are convenient but limited tools. As Gaussiannity is nothing more than a rough approximation, higher order, or non-linear, models are required to account for the finer characteristics of real-world images, A joint modeling of neighboring pixels by means of finite mixture distributions is proposed. Each vector of M pixels is considered as being drawn form one of K M-variate distributions. Each component random vector is defined as the unitary transformation of a vector of M independent generalized-Gaussian random variables. This modeling technique permits to tackle a problem of high dimensionality (the estimation of a joint distribution of large order) with a limited number of parameters. The standard Expectation-Maximization (EM) or Stochastic EM algorithms can be used in order to estimate the model parameters from the data. The procedure can be applied to blocks of pixels or sets of subband samples and is tested on a variety of digital images. The applications range from image compression and joint source and channel coding to image restoration and image segmentation.
Unsharp Masking (UM) is well known one of the most classical techniques in image enhancement. Due to the presence of the highpass filter, the UM operators very sensitive to noise. In order to conquer this defect, Ramp...
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ISBN:
(纸本)0819427446
Unsharp Masking (UM) is well known one of the most classical techniques in image enhancement. Due to the presence of the highpass filter, the UM operators very sensitive to noise. In order to conquer this defect, Ramponi has proposed the cubic UM operator which used not only highpass filtering but also edge sensor. By introducing edge sensor, edge enhancement is realized without noise amplification, to a certain extent. It is clear that the combination of edge sensor and highpass filter is effective for sharpening images corrupted by noise. In this paper, we introduce fuzzy rules in the UM operation. Fuzzy rules link the outputs of edge sensor and highpass filter to conclusions about sharpening component of processed image. We show how effectiveness of proposed method by some application results.
The frequency spectra are concise, but complete descriptors of image texture. The peaks in the spectrum, or equivalently, the dominant poles in an autoregressive model represent the granularity and orientation of the ...
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ISBN:
(纸本)0819427446
The frequency spectra are concise, but complete descriptors of image texture. The peaks in the spectrum, or equivalently, the dominant poles in an autoregressive model represent the granularity and orientation of the texture - two of its most salient visual characteristics. In this paper, a Gaussian model is developed for the peaks of the frequency spectra, and simple warping functions are defined on these peaks, to formulate a diverse and powerful set of operators, e.g. shape from texture, planar texture rendering using perspective projection, frontalization, shift and scaling of texture. Our approach to shape from texture converts perspective distortion of texture into a range image by warping the spectrum, and our planar rendering technique warps the spectrum to simulate the perspective effect. Thus, our unified texture model gives rise to real-time algorithms for computer vision, graphics and multi-media, which are direct, and are based on local modeling rather than global optimization.
In the analysis of grayscale images, straight line segments are usually extracted by using the Hough Transform method. Straight line detection using Hough Transform has the disadvantage that detecting peaks in the acc...
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ISBN:
(纸本)0819427446
In the analysis of grayscale images, straight line segments are usually extracted by using the Hough Transform method. Straight line detection using Hough Transform has the disadvantage that detecting peaks in the accumulator array is not always a reliable process. Thus, a significant amount of error may result. In this paper, we propose to extract straight line segments from binary images using binary morphological operations. In addition to the endpoint coordinates, the width of a line segment can also be reliably computed in the process. In the proposed approach, a set of line-shaped fixed-length structuring elements with orientation ranging from 0 to 180 degrees is used to extract line segments of all orientations. The algorithm is flexible for different applications. Line segments of different thickness, length, and orientation can be extracted with high precision. Experiment results we obtained on engineering map drawings demonstrate the good performance of the algorithm.
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