Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

Modified top-hat transformation based on contour structuring element to detect infrared small target

Abstract: Top-hat transformation has been widely used for infrared small target detection, but it is sensitive to the clutter and may destroy the details of the image, which make it an ineffective way for small target detection. To detect the infrared small target simply and efficiently, a new Top-hat transformation based on contour structuring element is proposed and then modified according to the property of the target region to enhance and detect the infrared small target in this paper. Experimental results indicated that the proposed method was efficient under the conditions of heavy clutter and dim target intensity.

Multi structuring element top-hat transform to detect linear features

Abstract: Multi structuring element top-hat transform is proposed in this paper to improve the performance of top-hat transform. The desired image feature is treated as a set. And, the set is divided into different subsets. Then, multi structuring elements corresponding to different subsets are constructed. After that, top-hat transform is performed by using the constructed structuring elements, and the results are combined and processed to reconstruct the desired image feature. To verify the efficiency of multi structuring element top-hat transform, the application of linear feature detection is discussed. The analysis and experimental results of multi structuring element top-hat transform show that, because of the efficient performance of multi structuring element top-hat transform, the linear features of different real images in different applications can be efficiently detected. Moreover, multi structuring element top-hat transform can be widely used in different applications.

Multiple linear feature detection based on multiple-structuring-element center-surround top-hat transform.

Abstract: Linear feature detection is an important technique in different applications of image processing. To detect linear features in different types of images, a simple but effective algorithm based on a multiple-structuring-element center-surround top-hat transform is proposed. The center-surround top-hat transform is discussed and analyzed. Based on the properties of this transform for image feature detection, multiple structuring elements are constructed corresponding to the possible linear features at different directions. The whole algorithm is divided into four parts. First, the algorithm uses the center-surround top-hat transform to detect all the possible linear features at different directions through constructing multiple structuring elements. Second, the detected linear feature regions at each direction are processed by a closing operation to remove the possible holes or unconnected regions. Third, the processed results of the detected linear feature regions at all directions are combined to form all the possible detected linear feature regions. Fourth, the combined result is refined by using some simple operations to form the final result. Experimental results on different types of images from different applications verified the effective performance of the proposed algorithm. Moreover, the experimental results indicate that the proposed algorithm could be used in different applications.

Classifying Fleece Fabric Appearance by Extended Morphological Fractal Analysis

Abstract: Image analysis techniques have been widely accepted as objective methods for evalu ating fabric appearance. This paper presents the development of a fairly new fractal analysis method (extended morphological fractal analysis) for characterizing polar fleece fabric appearance after abrasion. The digital gray level image is treated as a three- dimensional surface whose fractal dimension is calculated by performing a series of dilations and erosions on this surface and plotting the area of the resulting set of surfaces against the size of the structuring element. In contrast to a single morphological fractal .parameter, which is scale-invariant, extended fractal analysis is able to characterize fabric textures where the roughness of these textures is not necessarily scale-invariant. This approach can be used to physically describe surface roughness and texture regularity with the parameter MFV (multiscale fractal vector) and to classify the appearance grade with the Bayes classification method. Our experimen...

Morphological operations by locally variable structuring elements and their applications to region extraction in ultrasound images

Abstract: A new adaptive morphological operation using variable structuring elements is defined. The level of the structuring element changes, depending on the local region of the processing image. The operations enhance the boundary and are useful for contour extraction of the ambiguous images such as ultrasound ones. The enhancing effect of involving parameters is studied and experimental results from a gelatine phantom mimicking human tissue are also discussed.