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4 Image Theory of RFID System Physical Anti-Collision
heights represent the data distribution. The grayscale histogram belongs to a grayscale
function. The number of pixels in the grayscale set describes the frequency of a certain
grayscale (that is, the number of 0–255 pixels in the image).
The grayscale histogram describes the proportion of various grayscales in the
image (for an image with a pixel depth of 8 bits, a total of 256 values ranging from
0 to 255) in the entire image.
(2) Double-peak method selection threshold
The principle of this method is that the background and the foreground together
constitute an image, or the two groups of colors constitute an image, and the gray
levels of the pixels of the two groups appear as peaks, and the valley corresponds
to the threshold. After the threshold is determined by this method, the image can
be segmented. This method is relatively simple, and the threshold can be roughly
determined through observation, but this method cannot reflect the image details
well, so there are certain limitations.
4.3.3 Edge Detection Algorithm
The image contains many features, and the edge is the most basic one among the
features. The image can be effectively identified through the edge. The edge belongs
to a key parameter that interprets the image, identifies the target, or describes the
image. It is located between the area, the target, and the background. Edges are the
basis of image and texture features. The edge detection method can be understood
as a calculation for points, by which image processing can be understood as signal
processing. There is a lot of information in the image, and the edge belongs to a tight
description of the image. The edge information is extremely critical to the image, so
in the field of preprocessing, edge detection is of great significance.
(1) Edge
The image contains many features, and the edge is just one of them. The edges can
outline the image, allowing people to understand the image in a direct way. When
there are significant changes in the image, the pixel set of these changes is the edge,
which is related to the object, but it is not consistent. If the color of the background
and the boundary of the object are the same, it is difficult to identify the edge. The
discontinuity of grayscale can be described by the edge. In essence, the grayscale
abrupt boundary is the edge, which can explain the formation of one area and the
end of another area.
In the fields of machine vision, pattern recognition, image segmentation, and
boundary detection, edges have important significance, and they belong to the basis
of contour detection and boundary detection. There are a lot of edges between
primitives, objects, backgrounds, and objects, so edges are the key features when
4 Image Theory of RFID System Physical Anti-Collision
heights represent the data distribution. The grayscale histogram belongs to a grayscale
function. The number of pixels in the grayscale set describes the frequency of a certain
grayscale (that is, the number of 0–255 pixels in the image).
The grayscale histogram describes the proportion of various grayscales in the
image (for an image with a pixel depth of 8 bits, a total of 256 values ranging from
0 to 255) in the entire image.
(2) Double-peak method selection threshold
The principle of this method is that the background and the foreground together
constitute an image, or the two groups of colors constitute an image, and the gray
levels of the pixels of the two groups appear as peaks, and the valley corresponds
to the threshold. After the threshold is determined by this method, the image can
be segmented. This method is relatively simple, and the threshold can be roughly
determined through observation, but this method cannot reflect the image details
well, so there are certain limitations.
4.3.3 Edge Detection Algorithm
The image contains many features, and the edge is the most basic one among the
features. The image can be effectively identified through the edge. The edge belongs
to a key parameter that interprets the image, identifies the target, or describes the
image. It is located between the area, the target, and the background. Edges are the
basis of image and texture features. The edge detection method can be understood
as a calculation for points, by which image processing can be understood as signal
processing. There is a lot of information in the image, and the edge belongs to a tight
description of the image. The edge information is extremely critical to the image, so
in the field of preprocessing, edge detection is of great significance.
(1) Edge
The image contains many features, and the edge is just one of them. The edges can
outline the image, allowing people to understand the image in a direct way. When
there are significant changes in the image, the pixel set of these changes is the edge,
which is related to the object, but it is not consistent. If the color of the background
and the boundary of the object are the same, it is difficult to identify the edge. The
discontinuity of grayscale can be described by the edge. In essence, the grayscale
abrupt boundary is the edge, which can explain the formation of one area and the
end of another area.
In the fields of machine vision, pattern recognition, image segmentation, and
boundary detection, edges have important significance, and they belong to the basis
of contour detection and boundary detection. There are a lot of edges between
primitives, objects, backgrounds, and objects, so edges are the key features when