Spatially Variant Morphological Restoration and Skeleton Representation

Bouaynaya, Nidhal; Charif-Chefchaouni, M.; Schonfeld, Dan

doi:10.1109/tip.2006.877475

Cited by 33 publications

(9 citation statements)

References 50 publications

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“…The performance of the filter is measured by the peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR). Let f o denotes the original image of size M × N and f r the restored image (Bouaynaya et al, 2006). The PSNR is defined by 2 2 10 1 1 10 * log 255 / (…”

Section: Resultsmentioning

confidence: 99%

Spatial-Variant Morphological Filters With Nonlocal-Patch-Distance-Based Amoeba Kernel for Image Denoising

Yang

2015

Image Anal Stereol

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Filters of the Spatial-Variant amoeba morphology can preserve edges better, but with too much noise being left. For better denoising, this paper presents a new method to generate structuring elements for SpatiallyVariant amoeba morphology. The amoeba kernel in the proposed strategy is divided into two parts: one is the patch distance based amoeba center, and another is the geodesic distance based amoeba boundary, by which the nonlocal patch distance and local geodesic distance are both taken into consideration. Compared to traditional amoeba kernel, the new one has more stable center and its shape can be less influenced by noise in pilot image. What's more important is that the nonlocal processing approach can induce a couple of adjoint dilation and erosion, and combinations of them can construct adaptive opening, closing, alternating sequential filters, etc. By designing the new amoeba kernel, a family of morphological filters therefore is derived. Finally, this paper presents a series of results on both synthetic and real images along with comparisons with current state-of-the-art techniques, including novel applications to medical image processing and noisy SAR image restoration.

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Section: Resultsmentioning

confidence: 99%

Spatial-Variant Morphological Filters With Nonlocal-Patch-Distance-Based Amoeba Kernel for Image Denoising

Yang

2015

Image Anal Stereol

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“…So, we comparef with the well known and widely used mathematical morphology based algorithm to show the efficiency off . Morphological alternating sequential filters (ASFs) have been recognized as one class of important and efficient non-linear filters [16][17][18]. ASFs are iterative operations of opening and closing using structuring elements with the same shape and increasing sizes.…”

Section: One Example For Impulsive Noise Suppressionmentioning

confidence: 99%

Analysis of new top-hat transformation and the application for infrared dim small target detection

Bai

Zhou

2010

Pattern Recognition

477

195

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“…Statistical analysis of the morphological opening is described in [16] whereas evolution of the filtering is explained by Brockett et al in [17]. Spatially variant morphology restoration and skeleton representation is studied by Bouaynaya et al in [18]. A detailed description of the spatially variant mathematical morphology for binary and grey images can be found in [19] and [20].…”

Section: Related Researchmentioning

confidence: 99%

Multiple Luminaire Identification in Airborne Images of Airport's Approach Lighting Using Mathematical Morphology With Variable Length Structuring Element

Chowdhury

Rafferty

Ferguson

2012

IEEE J. Sel. Top. Signal Process.

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Abstract-In night aviation, to land an aircraft, a pilot needs to be able to identify an airport. The approach lighting system (ALS) at an airport is used to provide identification and guidance to pilots. ALS consists of more than 100 luminaires which are installed in a defined pattern following strict guidelines by the International Civil Aviation Organization (ICAO). ICAO also has strict regulations for maintaining the performance level of the luminaires. However, to date there is no automated technique by which to monitor the performance of the lighting. We suggest using images of the lighting pattern captured using a camera placed inside an aircraft. Based on the information contained within these images, the performance of the luminaires has to be evaluated which requires identification of over 100 luminaires within the pattern of ALS image. This research proposes analysis of the pattern using morphology filters which use a variable length structuring element (VLSE). The dimension of the VLSE changes continuously within an image and varies for different images. A novel technique for automatic determination of the VLSE is proposed and it allows successful identification of the luminaires from the image data as verified through the use of simulated and real data.Index Terms-Approach lighting system, block wise image analysis, composite structure construction, inter luminaire distance approximation, pattern analysis, tracking of luminaires.

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Spatially Variant Morphological Restoration and Skeleton Representation

Cited by 33 publications

References 50 publications

Spatial-Variant Morphological Filters With Nonlocal-Patch-Distance-Based Amoeba Kernel for Image Denoising

Spatial-Variant Morphological Filters With Nonlocal-Patch-Distance-Based Amoeba Kernel for Image Denoising

Analysis of new top-hat transformation and the application for infrared dim small target detection

Multiple Luminaire Identification in Airborne Images of Airport's Approach Lighting Using Mathematical Morphology With Variable Length Structuring Element

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