China
Africa
Explore chapters and articles related to this topic
Fundamentals of image analysis and interpretation
Published in Michael O’Byrne, Bidisha Ghosh, Franck Schoefs, Vikram Pakrashi, Image-Based Damage Assessment for Underwater Inspections, 2019
Bidisha Ghosh, Michael O’Byrne, Franck Schoefs, Vikram Pakrashi
Morphology deals with filtering based on geometric properties. The most fundamental morphological operators are dilation and erosion. Dilation makes an object larger by adding pixels to its boundaries, while erosion has the opposite effect by removing pixels from object boundaries. This is demonstrated in Figure 4.12, where the goal is to isolate the contiguous region corresponding to the worn-away bridge surface. Performing a simple thresholding operation provides a good starting point; however, it may be noticed that there are a lot of spurious white pixels remaining in Figure 4.12(b). These spurious pixels are dotted throughout the image in small clusters. Most of these spurious pixels can be removed by performing an erosion operation, as shown in Figure 4.12(c). Erosion is a process by which a structuring element (can be any shape with a specified size, e.g., a circle of radius 15 pixels) works its way throughout the image and removes pixels from the boundaries of objects (the number of pixels removed depends on the structuring element size). The erosion operation has the effect of removing objects that cannot completely contain the structuring element, for instance, if a circular object of radius 14 pixels was present in the image, it would be completely removed by the structuring element of radius 15 pixels.
Mathematical Morphology
Published in Vipin Tyagi, Understanding Digital Image Processing, 2018
In morphological image processing, the operations are applied to an input image using a structuring element and an output image of the same size is generated. In a morphological operation, the pixel values of the output image are based on a comparison of a corresponding pixel in the input image with its neighbors. Two basic morphological image operations are dilation and erosion. The process of dilation adds pixels to the boundaries of objects in an image, while erosion is opposite to dilation and removes pixels on object boundaries. The number of pixels to be added or to be removed from the objects in an image depends on the size and shape of the structuring element which is used to process the image. The other morphological operations are obtained by combining these two operations.
Feature Extraction with Statistics and Decision Science Algorithms
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
As indicated, dilation expands the image by adding pixels in the structuring element, that is, a union between g and B. On the contrary, erosion is used to perform an intersection between them. This kind of analysis (based on binary images) is often called binary morphology, which can also be extended to grayscale images by considering them as a topographic relief. However, in grayscale morphology, the pointwise minimum and maximum operators will be used instead of the intersection and union, respectively (Tuia et al., 2009). More specifically, dilation adds pixels to the boundaries of objects in an image (i.e., grows boundary regions), while erosion is used to remove pixels on object boundaries (i.e., shrinks boundary regions). According to this principle, the number of pixels added or removed from the objects depends totally on the size and shape of the given structuring element.
Morphologic for knowledge dynamics: revision, fusion and abduction
Published in Journal of Applied Non-Classical Logics, 2023
Isabelle Bloch, Jérôme Lang, Ramón Pino Pérez, Carlos Uzcátegui
The structuring element can also be seen as a binary relation between points (Bloch et al., 2007), i.e. iff where R denotes a relation on . Dilation and erosion are then expressed as follows: These formulas apply for any binary relation R. If and only if R is reflexive (i.e. for all x), then δ is extensive () and ε is anti-extensive (). These properties hold in the case illustrated in Figure 1. The objects in the original image are then expanded by dilation, to an extent that depends on the shape and the size of the structuring element, and reduced by erosion. Similar interpretations hold for any relation R, and these properties will also be important in the remainder of this paper.
Land use and land cover change detection by using principal component analysis and morphological operations in remote sensing applications
Published in International Journal of Computers and Applications, 2021
Erosion and dilation are basic functions of morphology and are related to each other. Dilation is based on set operations, whereas convolution is based on arithmetic operations. Dilation expands an image, whereas erosion shrinks an image. The opening of a set generally smooth’s the contours of an image, breaks narrow isthmuses, and eliminates thin protrusions. The closing of a set also tends to smooth sections of contours; however, it fuses narrow breaks and long thin-gulfs, eliminates small holes, and fills gaps in contours.
Generation of patterned indentations for additive manufacturing technologies
Published in IISE Transactions, 2019
Ulas Yaman, Melik Dolen, Christoph Hoffmann
In image processing, morphological operations (such as dilation and erosion) are frequently employed to enlarge or shrink certain patterns in binary images. For that purpose, a mask is continuously applied throughout the contour of the selected pattern. Depending on the formation of the mask and the subsequent logical operations performed on the image, the desired outcome is obtained (Serra, 1982).