Topic
Structuring element
About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.
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TL;DR: In this article, an unsupervised CT scan image segmentation approach is described, in which the original pixel space is restored and the obtained image is divided into some non-overlapping smaller blocks and the mean intensity value for each block is computed that is used as the local threshold value for the binarization purpose.
16 citations
01 Jan 2012
TL;DR: In this chapter, a simple as well as a novel method has been applied for the colloidal cyst detection and the structuring element is considered in such a manner that a better result is obtained as compared to traditional and basic morphological methods.
Abstract: Image processing has a great impact in the field of medical science. The engineering application spreads over various applications and equally it shows the effective performance. In current research, the medical diagnosis as well as the medical data analysis is most challenging job, as it is very complex task. The complexity is tried to reduced by the help of image processing in this approach. Colloidal Cyst, located in the third ventricle of the human brain is considered in this work for the purpose of detection at the time of diagnosis. Image Processing especially useful for detection, recognition and classification etc. In this chapter, a simple as well as a novel method has been applied for the colloidal cyst detection. The novelty is the structuring element is considered in such a manner that a better result is obtained as compared to traditional and basic morphological methods. The structuring elements used as gradient operator and also has been considered in their complementary forms which produces better results than the initial structuring elements.
16 citations
01 Jan 2003
TL;DR: Fuzzy mathematical morphology can be used to represent and compute in a uniform setting several types of relative position information, such as distance, adjacency and directional relative position, which has found wide applications in image processing and pattern recognition.
Abstract: Publisher Summary The chapter focuses on the fuzzy mathematical morphology that can be used to compute approximate spatial relations between objects in a robot map. Mathematical morphology is originally based on set theory. Introduced in 1964 by Matheron to study porous media, mathematical morphology that has rapidly evolved into a general theory of shape and its transformations, and it has found wide applications in image processing and pattern recognition. The four basic operations of mathematical morphology are dilation, erosion, opening, and closing. The key step is to represent the space in the robot's environment by an occupancy grid, and to treat this grid as a grey-scale image. This approach allows applying techniques from the field of image processing for extracting spatial information from grid. The chapter also discusses the use of this approach in the context to one particular type of robot maps, called topology-based maps that are built from occupancy grids. Fuzzy mathematical morphology can also be used to represent and compute in a uniform setting several types of relative position information, such as distance, adjacency and directional relative position.
16 citations
TL;DR: In this article, an artificial neural network (ANN) is utilized for the selection of structuring element, where ANN is trained by two pre-assigned normalized numbers related to the warp and weft counts of the test fabric.
Abstract: Basic morphological operations such as the erosion, dilation, opening, and closing often fail to detect various types of defects that may be present in woven fabric, mainly because of the heuristic selection of structuring element needed for these operations. In this paper, an artificial neural network (ANN) is utilized for the selection of structuring element, where ANN is trained by two pre‐assigned normalized numbers related to the warp and weft counts of the test fabric. The test gray fabric image is pre‐processed to remove noise and the interlaced grating structure of weft and warp and then converted to a binary image by thresholding. An intensity threshold value of the processed fabric image and the dimension of a sliding window needed for correlation operation are obtained from the trained ANN. Defects are detected after morphological reconstruction of the processed binary fabric image, where an ANN trained structuring element is used. The technique is tested on 317 samples for eight different type...
16 citations
04 Jul 1995
TL;DR: In the present paper a redefinition of Werman and Peleg's fuzzy morphology operations is given and employs the more general indicator framework, given by Sinha and Dougherty (1992).
Abstract: One can analyse the structure of a binary image by looking at patterns of a certain shape at different places on the image. This idea of describing the image by looking at similar patterns at various locations is quantified in mathematical morphology by the concept of a structuring element. Binary images can be regarded as subsets of Euclidean or digital space. Fuzzy sets have proven to be useful to model grey-tone images. As shown by Werman and Peleg (1985), morphology techniques used for analysis of binary images can be applied to grey-tone images using fuzzy logic. In the present paper a redefinition of Werman and Peleg's fuzzy morphology operations is given. This redefinition employs the more general indicator framework, given by Sinha and Dougherty (1992).
16 citations