Feature extraction in speckled imagery using dynamic B‐spline deformable contours under the    model

Gambini, Juliana; Mejail, Marta; Jacobo-Berlles, Julio; Frery, Alejandro C.

doi:10.1080/01431160600702616

Cited by 31 publications

(51 citation statements)

References 7 publications

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“…The smaller this measure, the harder the task of finding a boundary between the region and its background. As presented by Gambini et al (2006), the separability indexes in real images range from 1.18 to 4.19. In that paper, the authors simulated SAR imagery with a variety of shapes and parameters; using (2) in the expression presented in (10) one can compute the theoretical separability between background and foreground.…”

Section: Resultsmentioning

confidence: 99%

“…Each situation is an image with halves where samples from the G 0 A (α, 1, 1) distribution are observed: (α 1 , α 2 ) = (−1.1, −3), (α 1 , α 2 ) = (−1.1, −10) and (α 1 , α 2 ) = (−3, −10), respectively. These values reflect the noisiest possible image (one look) and roughness corresponding to different types of areas, namely extremely heterogeneous, heterogeneous and homogeneous, as reported by Gambini et al (2006).…”

Section: Methodology For Error Evaluationmentioning

confidence: 90%

“…This initial region detection is computed over nonoverlapping blocks of the image using as additional input the number of blocks and the type of region to be detected (homogeneous, heterogeneous or extremely heterogeneous); the details are presented in Gambini et al (2006). This step is crucial, and prior information about the typical size of objects to be detected should be provided.…”

Section: Boundary Detectionmentioning

confidence: 99%

“…An evaluation of this method using a family of different synthetic star-shaped regions was carried out in Gambini et al (2006). It exhibits a very good performance from the point of view of the mean square error.…”

Section: Maximum Likelihoodmentioning

confidence: 99%

“…Under the G model, regions with different degrees of roughness can be characterized by the parameters. Therefore, this information can be used to find boundaries between regions with different textures (see, for instance, Gambini et al 2006).…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of edge detection methods with local information in speckled imagery

et al. 2007

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We compare the accuracy of five approaches for contour detection in speckled imagery. Some of these methods take advantage of the statistical properties of speckled data, and all of them employ active contours using B-spline curves. Images obtained with coherent illumination are affected by a noise called speckle, which is inherent to the imaging process. These data have been statistically modeled by a multiplicative model using the G0 distribution, under which regions with different degrees of roughness can be characterized by the value of a parameter. We use this information to find boundaries between regions with different textures. We propose and compare five strategies for boundary detection: three based on the data (maximum discontinuity on raw data, fractal dimension and maximum likelihood) and two based on estimates of the roughness parameter (maximum discontinuity and anisotropic smoothed roughness estimates). In order to compare these strategies, a Monte Carlo experience was performed to assess the accuracy of fitting a curve to a region. The probability of finding the correct edge with less than a specified error is estimated and used to compare the techniques. The two best procedures are then compared in terms of their computational cost and, finally, we show that the maximum likelihood approach on the raw data using the G0 law is the best technique.

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Section: Resultsmentioning

confidence: 99%

Section: Methodology For Error Evaluationmentioning

confidence: 90%

Section: Boundary Detectionmentioning

confidence: 99%

Section: Maximum Likelihoodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy of edge detection methods with local information in speckled imagery

et al. 2007

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Statistical Information Theory in SAR and PolSAR Image Analysis

Frery,

Nascimento

2024

Information‐Theoretic Radar Signal Processing

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Improved likelihood inference for the roughness parameter of the GA0 distribution

2007

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This paper presents adjusted profile likelihoods for α, the roughness parameter of the G 0 A (α, γ, L) distribution. This distribution has been widely used in the modeling, processing and analysis of data corrupted by speckle noise, e.g., synthetic aperture radar images. Specifically, we consider the following modified profile likelihoods: (i) the one proposed by Cox and Reid, and (ii) approximations to adjusted profile likelihood proposed by Barndorff-Nielsen, namely the approximations proposed by Severini and one based on results by Fraser, Reid and Wu. We focus on point estimation and on signalized likelihood ratio tests, the parameter of interest being the roughness parameter that indexes the distribution. As far as point estimation is concerned, the numerical evidence presented in the paper favors the Cox and Reid adjustment, and in what concerns signalized likelihood ratio tests, the results favor the approximation to Barndorff-Nielsen's adjustment based on the results by Fraser, Reid and Wu. An application to real synthetic aperture radar imagery is presented and discussed.

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Feature extraction in speckled imagery using dynamic B‐spline deformable contours under the model

Cited by 31 publications

References 7 publications

Accuracy of edge detection methods with local information in speckled imagery

Accuracy of edge detection methods with local information in speckled imagery

Statistical Information Theory in SAR and PolSAR Image Analysis

Improved likelihood inference for the roughness parameter of the GA0 distribution

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