Journal ArticleDOI
Finding Curvilinear Features In Speckled Images
R. Samadani,John F. Vesecky +1 more
TLDR
Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data.Abstract:
A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice.read more
Citations
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Journal ArticleDOI
Detection of linear features in SAR images: application to road network extraction
TL;DR: The authors propose a two-step algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images.
Journal ArticleDOI
Fuzzy fusion techniques for linear features detection in multitemporal SAR images
TL;DR: This paper is concerned with the automatic detection of linear features in SAR satellite data, with application to road network extraction, and uses fuzzy operators to test and compare different fusion strategies involving different fusion operators.
Journal ArticleDOI
FoSA: F* Seed-growing Approach for crack-line detection from pavement images
TL;DR: FSA - F* Seed-growing approach for automatic crack-line detection, which exploits a seed-growing strategy to remove the requirement that the start and end points should be set in advance and narrows the global searching space to the interested local space to improve its efficiency.
Journal ArticleDOI
A first step toward automatic interpretation of SAR images using evidential fusion of several structure detectors
TL;DR: A method aiming to characterize the spatial organization of the main cartographic elements of a synthetic aperture radar (SAR) image and thus giving an almost automatic interpretation of the scene is proposed.
Journal ArticleDOI
Computer Vision Techniques for Automatic Structural Assessment of Underground Pipes
TL;DR: In this paper, a system for the automatic assessment of the structural condition of underground pipes is presented, which consists of image preprocessing, a sequence of morphological operations to accurately extract pipe joints and laterals (where smaller pipe is connected to main bigger pipe), and statistical filters for detecting surface cracks in the pipeline network.
References
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Journal ArticleDOI
A statistical and geometrical edge detector for SAR images
Ridha Touzi,A. Lopes,P. Bousquet +2 more
TL;DR: The decision threshold can be theoretically determined for a given probability of false alarm as a function of the number of looks of the image under study and the size of the processing neighborhood.
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On the optimal detection of curves in noisy pictures
TL;DR: A technique for recognizing systems of lines is presented, in which the heuristic of the problem is not embedded in the recognition algorithm but is expressed in a figure of merit, which allows for greater flexibility and adequacy in the particular problem.
Journal ArticleDOI
On detecting edges in speckle imagery
TL;DR: Methods for detecting sustained intensity changes in images corrupted by speckle are analyzed and a ratio-of-averages edge detector is used in conjunction with the LoG, which is found to be much more effective than either of the individual edge detectors.
Journal ArticleDOI
Maximum Likelihood Estimation of Markov-Process Blob Boundaries in Noisy Images
TL;DR: The likelihood maximization approach provides a unified view for seemingly different approaches to boundary estimation, such as sequential boundary finding and region growing, and bounds on the accuracy of boundary estimation are readily derived with this formulation.
Journal ArticleDOI
Aspects of dynamic programming in signal and image processing
L. Scharf,H. Elliott +1 more
TL;DR: A call for renewed attention to the potential of dynamic programming for solving knotty, nonlinear filtering problems in signal and image processing, and outline successes the authors have recently enjoyed in nonlinear frequency tracking and random boundary estimation in noisy black and white images.