CN112017206A - Directional sliding self-adaptive threshold value binarization method based on line structure light image - Google Patents
Directional sliding self-adaptive threshold value binarization method based on line structure light image Download PDFInfo
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- CN112017206A CN112017206A CN202010895929.0A CN202010895929A CN112017206A CN 112017206 A CN112017206 A CN 112017206A CN 202010895929 A CN202010895929 A CN 202010895929A CN 112017206 A CN112017206 A CN 112017206A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
Abstract
A directed sliding adaptive threshold value binarization method based on a line structure light image comprises the following steps of 1, firstly, taking a pixel point set where line structure light is located as a target, and setting an initial sliding window by taking a pixel point to be solved as a center; step 2, defining the average value of all pixel gray values in the sliding window as a threshold value T; step 3, setting a boundary factor tau for moving the sliding window to enable the sliding window to only perform directional sliding near the linear structured light; and 4, carrying out binarization on the image through a threshold value T. The invention makes the sliding window only do directional sliding near the line structure light by setting the boundary factor tau of the sliding window moving. The sliding range of the sliding window is reduced, the whole image is prevented from being traversed, the traversing efficiency is improved, and the effect of rapid processing is achieved.
Description
Technical Field
The invention belongs to the technical field of image binarization, and particularly relates to a directed sliding self-adaptive threshold value binarization method based on a line structure light image.
Background
Image binarization is an important problem in image processing, and is widely applied to the fields of image segmentation, image enhancement, image identification and the like. The method changes an original image into an image target and a background binary image which are only expressed by two gray values by using the difference of gray characteristics of an object to be extracted and the background of the object in the image. Most of binarization methods are threshold-based methods, that is, a proper threshold is found to divide the pixel gray level of the original image into two types, i.e., a threshold larger than the threshold and a threshold smaller than the threshold, so as to obtain a binarization result. At present, threshold-based binarization methods can be classified into a global threshold method and a local threshold method.
The local threshold method can set different thresholds according to the brightness of different regions of an image, and the threshold at the time is a local threshold corresponding to the brightness distribution of each small region on the image, so that different regions on the same image have different thresholds, and a good effect is achieved when the image with uneven brightness of the line structure is dealt with, but a rapid processing method is still lacked in the threshold binarization applied to the line structure light image.
Disclosure of Invention
Aiming at the problems of a threshold value binarization method for a linear structured light image in the prior art, the invention provides a directed sliding self-adaptive threshold value binarization method based on the linear structured light image, which is suitable for identifying the image of linear structured light through image binarization.
A directed sliding adaptive threshold value binarization method based on a line structure light image comprises the following steps of 1, firstly, taking a pixel point set where line structure light is located as a target, and setting an initial sliding window by taking a pixel point to be solved as a center; step 2, defining the average value of all pixel gray values in the sliding window as a threshold value T; step 3, setting a boundary factor tau for moving the sliding window to enable the sliding window to only perform directional sliding near the linear structured light; and 4, carrying out binarization on the image through a threshold value T.
Further, the initial sliding window in step 1 is a sliding window with a size of 3 × 3, which is set with the pixel point to be solved as the center.
Further, the calculation formula of the threshold value T is:
for a point that cannot be fully covered by a sliding window, the normal threshold is the mean of the closest sliding window coverage to that point.
Further, the boundary factor τ includes an upper boundary factor
Lower boundary factor
And right boundary factor
Boundary factors in three directions, upper boundary factor
And a lower boundary factor
Respectively play a role in limiting the sliding of the sliding window in the upper and lower directions, and the right boundary factor
Determines the direction in which the sliding window slides.
Further, an upper bound factor
The maximum value of three upper neighborhoods of the pixel point to be solved, and the lower boundary factor
The maximum value of three neighborhoods at the lower side of the pixel point to be solved, and the right border factor
The maximum value of the three neighborhoods at the right side of the pixel point to be solved is obtained.
Further, the initial pixel point to be solved is the pixel point with the maximum gray scale value in the second row of the image.
The invention makes the sliding window only do directional sliding near the line structure light by setting the boundary factor tau of the sliding window moving. The sliding range of the sliding window is reduced, the whole image is prevented from being traversed, the traversing efficiency is improved, and the effect of rapid processing is achieved.
Drawings
Fig. 1 is a schematic view of a sliding window of size 3 × 3.
Detailed Description
The following further describes embodiments of the present invention in conjunction with the summary of the invention and the accompanying drawings.
The invention provides a directed sliding self-adaptive threshold value binarization method based on a line structure light image, which is suitable for identifying the line structure light image through image binarization.
A directed sliding adaptive threshold value binarization method based on a line structure light image comprises the following steps of 1, firstly, taking a pixel point set where line structure light is located as a target, and setting an initial sliding window by taking a pixel point to be solved as a center; step 2, defining the average value of all pixel gray values in the sliding window as a threshold value T; step 3, setting a boundary factor tau for moving the sliding window to enable the sliding window to only perform directional sliding near the linear structured light; and 4, carrying out binarization on the image through a threshold value T.
The initial sliding window in step 1 is a sliding window with a size of 3 × 3, which is set with the pixel point to be solved as the center.
Determining a threshold value T: as shown in the schematic drawing of 3 × 3 sliding window size of FIG. 1 for finding points
Setting an initial sliding window with the size of 3 multiplied by 3 for the center, defining the current threshold value T as the average value of all pixel gray values of the current sliding window, and calculating the T according to the formula (1). The normal threshold is distance for points that cannot be completely covered by the sliding window (e.g., points at the edges and corners in FIG. 1)The mean of the closest sliding window coverage for that point.
In the formula (2), the initial candidate point is defined
The pixel point with the maximum gray value in the second row of the image is obtained.
Determining the boundary factor τ: by setting the boundary factor tau of the sliding window movement, the sliding window only performs directional sliding near the line structure light. The boundary factor tau includes an upper boundary factor
Lower boundary factor
And right boundary factor
In the three directions, the light source is arranged in the three directions,
and
respectively play a role in limiting the sliding in the upper and lower directions,
determines the direction of the sliding. Defining an upper boundary factor
To be a point to be solved
Maximum of three neighborhoods at the upper side, lower boundary factor
To be a point to be solved
Maximum of three neighborhoods at the bottom, right boundary factor
To be a point to be solved
The calculation process of the maximum value of the three neighborhoods on the right side is shown as formula (3).
The sliding range of the sliding window is effectively reduced by setting the boundary factor, and the image binarization efficiency is further improved.
The invention makes the sliding window only do directional sliding near the line structure light by setting the boundary factor tau of the sliding window moving. The sliding range of the sliding window is reduced, the whole image is prevented from being traversed, the traversing efficiency is improved, and the effect of rapid processing is achieved.
Claims (6)
1. A directed sliding adaptive threshold value binarization method based on a line structure light image is characterized by comprising the following steps of 1, firstly, taking a pixel point set where line structure light is located as a target, and setting an initial sliding window by taking a pixel point to be solved as a center; step 2, defining the average value of all pixel gray values in the sliding window as a threshold value T; step 3, setting a boundary factor tau for moving the sliding window to enable the sliding window to only perform directional sliding near the linear structured light; and 4, carrying out binarization on the image through a threshold value T.
2. The method according to claim 1, wherein the initial sliding window in step 1 is a sliding window of 3 × 3 size centered on the pixel to be solved.
3. The method according to claim 2, wherein the calculation formula of the threshold value T is as follows:
for a point that cannot be fully covered by a sliding window, the normal threshold is the mean of the closest sliding window coverage to that point.
4. The method according to any one of claims 2-3, wherein the boundary factor τ comprises an upper boundary factor
Lower boundary factor
And right boundary factor
Boundary factors in three directions, upper boundary factor
And a lower boundary factor
Respectively play a role in limiting the sliding of the sliding window in the upper and lower directions, and the right boundary factor
Determines the direction in which the sliding window slides.
5. The method according to claim 4, wherein the upper boundary factor is an upper boundary factor
The maximum value of three upper neighborhoods of the pixel point to be solved, and the lower boundary factor
The maximum value of three neighborhoods at the lower side of the pixel point to be solved, and the right border factor
The maximum value of the three neighborhoods at the right side of the pixel point to be solved is obtained.
6. The method according to any one of claims 1, 3 and 5, wherein the initial pixel to be solved is the pixel with the largest gray scale value in the second row of the image.
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Application publication date: 20201201 |