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Elongatedness
In image processing, elongatedness for a region is the ratio between the length and width of the minimum bounding rectangle of the region. It is considered a feature of the region. It can be evaluated as the ratio between the area of the region to its thickness squared: elongatedness = \frac = \frac. where the maximum thickness, d, of a holeless region is given by the number of times the region can be eroded Erosion is the action of surface processes (such as water flow or wind) that removes soil, rock, or dissolved material from one location on the Earth's crust, and then transports it to another location where it is deposited. Erosion is disti ... before disappearing. References {{Reflist Image processing ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Elongatedness
In image processing, elongatedness for a region is the ratio between the length and width of the minimum bounding rectangle of the region. It is considered a feature of the region. It can be evaluated as the ratio between the area of the region to its thickness squared: elongatedness = \frac = \frac. where the maximum thickness, d, of a holeless region is given by the number of times the region can be eroded Erosion is the action of surface processes (such as Surface runoff, water flow or wind) that removes soil, Rock (geology), rock, or dissolved material from one location on the Earth's crust#Crust, Earth's crust, and then sediment transport, tra ... before disappearing. References {{Reflist Image processing ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Minimum Bounding Rectangle
In computational geometry, the minimum bounding rectangle (MBR), also known as bounding box (BBOX) or envelope, is an expression of the maximum extents of a two-dimensional object (e.g. point, line, polygon) or set of objects within its coordinate system; in other words , , , . The MBR is a 2-dimensional case of the minimum bounding box. MBRs are frequently used as an indication of the general position of a geographic feature or dataset, for either display, first-approximation spatial query, or spatial indexing purposes. The degree to which an "overlapping rectangles" query based on MBRs will be satisfactory (in other words, produce a low number of "false positive" hits) will depend on the extent to which individual spatial objects occupy (fill) their associated MBR. If the MBR is full or nearly so (for example, a mapsheet aligned with axes of latitude and longitude will normally entirely fill its associated MBR in the same coordinate space), then the "overlapping rectangles" ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Image Processing
An image is a visual representation of something. It can be two-dimensional, three-dimensional, or somehow otherwise feed into the visual system to convey information. An image can be an artifact, such as a photograph or other two-dimensional picture, that resembles a subject. In the context of signal processing, an image is a distributed amplitude of color(s). In optics, the term “image” may refer specifically to a 2D image. An image does not have to use the entire visual system to be a visual representation. A popular example of this is of a greyscale image, which uses the visual system's sensitivity to brightness across all wavelengths, without taking into account different colors. A black and white visual representation of something is still an image, even though it does not make full use of the visual system's capabilities. Images are typically still, but in some cases can be moving or animated. Characteristics Images may be two or three-dimensional, such as a pho ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature (computer Vision)
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions. More broadly a ''feature'' is any piece of information which is relevant for solving the computational task related to a certain application. This is the same sense as feature in machine learning and pattern recognition generally, though image processing has a very sophisticated collection of features. The feature concept is very general and the choice of features in a particular computer vision system may be highly dependent on the specific problem at h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Erosion (morphology)
Erosion (usually represented by ⊖) is one of two fundamental operations (the other being dilation) in morphological image processing from which all other morphological operations are based. It was originally defined for binary images, later being extended to grayscale images, and subsequently to complete lattices. The erosion operation usually uses a structuring element for probing and reducing the shapes contained in the input image. Binary erosion In binary morphology, an image is viewed as a subset of a Euclidean space \mathbb^d or the integer grid \mathbb^d, for some dimension ''d''. The basic idea in binary morphology is to probe an image with a simple, pre-defined shape, drawing conclusions on how this shape fits or misses the shapes in the image. This simple "probe" is called structuring element, and is itself a binary image (i.e., a subset of the space or grid). Let ''E'' be a Euclidean space or an integer grid, and ''A'' a binary image in ''E''. The erosion of the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
