Structuring element

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

Morphological Scale-Space based Vessel Segmentation of Retinal Image

Abstract: This paper introduces a novel procedure to segment retinal vessels using new technique namely Morphological Angular Scale-Space (MASS). Line structuring element is rotated about the seed point to determine the curvature of the vessels thereby ensuring that the components remains connected along vessels segmented. Scale-Space is created by varying the length of the structuring element which gradually reduces non-vessel like elements from the processing image. Information from the lower scale image been given as a feedback to build the image of higher scales thereby extracting and retaining vessels effectively from lower scales moving to higher scale-space. The method, attains lowest mean square error value at a certain scale, which is observed to be better than most of the other morphological techniques of vessel segmentation. A publicly available DRIVE database is used for analysis of the MASS technique as well as comparing with other methods.

Adaptive structuring elements based on salience information

Abstract: Adaptive structuring elements modify their shape and size according to the image content and may outperform fixed structuring elements. Without any restrictions, they suffer from a high computational complexity, which is often higher than linear with respect to the number of pixels in the image. This paper introduces adaptive structuring elements that have predefined shape, but where the size is adjusted to the local image structures. The size of adaptive structuring elements is determined by the salience map that corresponds to the salience of the edges in the image, which can be computed in linear time. We illustrate the difference between the new adaptive structuring elements and morphological amoebas. As an example of its usefulness, we show how the new adaptive morphological operations can isolate the text in historical documents.

Efficient neural network implementation of morphological operations

Abstract: This paper introduces a neural network implementation of gray scale operators. In this structure, synaptic weights are represented by a gray scale structuring element and trained by a learning algorithm based on an optimal criterion called the overall equality index. The proposed algorithm leads to a computationally simple implementation, with numerical examples to illustrate its performance.

An efficient approach to automatic shape recognition

Abstract: Automated shape recognition in two dimensions is analyzed, and an efficient approach is provided to reduce the redundancy of the matching process. Shape recognition is achieved through locating the objects to be recognized within the field of view. Two theorems are presented. The first theorem is applied to the shape-recognition problem and indicates a clear advantage in reducing the redundancy involved in the matching process when morphological operations are used. Experiments show that a large amount of computing time is saved as a consequence of this theorem. This theorem also offers advantages when performing automated shape recognition on images acquired in a noisy environment. The second theorem suggests that edges of shapes can be used as a structuring element, and all possible outcomes due to noisy environments need to be exhausted. >

Supervised Building Extraction Using Morphological Profiles with Adaptive Structures

Abstract: Morphological profiles are one of the highly effective tools for image classification when structural information is critical Morphological profiles, however create high dimensional feature space and increase the complexity of classifier In this paper we have quantitatively evaluated the fact that the classification accuracy of each profile is dependent on the size and shape of the structuring element We propose a classification scheme which uses morphological profiles with adaptive structuring element We relate the shape and size of structuring element which is used for producing morphological profiles with the discrete wavelet transform of the image The size and shape of structuring element adapts to the frequency content of the pixel's neighborhood With this adaptive structuring element we can produce a single profile which is quite effective for classification The results show that with proposed scheme significant improvement was obtained in the classification accuracy with reduced dimensionality of feature space