Noise Analysis of a New Singularity Index

Muralidhar, Gautam S.; Bovik, Alan C.; Markey, Mia K.

doi:10.1109/tsp.2013.2283460

Cited by 6 publications

(2 citation statements)

References 23 publications

(67 reference statements)

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“…The Multiscale Singularity Index [10], [11] is a useful method for detecting singular curvilinear structures over multiple scales. The algorithm is useful for locating channels in satellite images.…”

Section: Modified Multiscale Singularity Indexmentioning

confidence: 99%

Automatic Channel Network Extraction From Remotely Sensed Images by Singularity Analysis

Isikdogan

Bovik

Passalacqua

2015

IEEE Geosci. Remote Sensing Lett.

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Abstract-Quantitative analysis of channel networks plays an important role in river studies. To provide a quantitative representation of channel networks, we propose a new method that extracts channels from remotely sensed images and estimates their widths. Our fully automated method is based on a recently proposed Multiscale Singularity Index that responds strongly to curvilinear structures but weakly to edges. The algorithm produces a channel map, using a single image where water and non-water pixels have contrast, such as a Landsat nearinfrared band image or a water index defined on multiple bands. The proposed method provides a robust alternative to the procedures that are used in remote sensing of fluvial geomorphology and makes classification and analysis of channel networks easier. The source code of the algorithm is available at:

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Section: Modified Multiscale Singularity Indexmentioning

confidence: 99%

Automatic Channel Network Extraction From Remotely Sensed Images by Singularity Analysis

Isikdogan

Bovik

Passalacqua

2015

IEEE Geosci. Remote Sensing Lett.

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“…However, a detailed theoretical analyses described in [29] reveals that the singularity index has better robustness to noise and yields a much smaller side lobe response to edges than does the second derivative operator. The results of these analyses [29] motivate the use of the new singularity index in the current application in which the disparity space is partitioned into piece-wise smooth regions, edges, and curvilinear structures. For the approach to succeed, the operator response to edges and curvilinear structures needs to be mutually exclusive, and the new singularity index has this property.…”

Section: A a New Singularity Indexmentioning

confidence: 99%

Disparity Estimation on Stereo Mammograms

Muralidhar

Bovik

Markey

2015

IEEE Trans. on Image Process.

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We consider the problem of depth estimation on digital stereo mammograms. Being able to elucidate 3D information from stereo mammograms is an important precursor to conducting 3D digital analysis of data from this promising new modality. The problem is generally much harder than the classic stereo matching problem on visible light images of the natural world, since nearly all of the 3D structural information of interest exists as complex network of multilayered, heavily occluded curvilinear structures. Toward addressing this difficult problem, we formulate a new stereo model that minimizes a global energy functional to densely estimate disparity on stereo mammogram images, by introducing a new singularity index as a constraint to obtain better estimates of disparity along critical curvilinear structures. Curvilinear structures, such as vasculature and spicules, are particularly salient structures in the breast, and being able to accurately position them in 3D is a valuable goal. Experiments on synthetic images with known ground truth and on real stereo mammograms highlight the advantages of the proposed stereo model over the canonical stereo model.

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