Image Binarization
Function
This Step is used to binarize pixels based on whether their values are above or below a specified threshold.
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When the pixel grayscale values in an image have only two possible values (maximum and minimum), i.e., "black or white," such an image is called a binary image. The process of converting a non-binary image into a binary image through calculation is called image binarization. |
Usage Scenario
This Step is generally used for image processing. It is typically applied to segment pixels in 2D images based on a specified threshold.
Parameter Description
| Parameter | Description |
|---|---|
Binary Method |
Parameter description: This parameter specifies the method for image binarization. Value list:
Default value: Adaptive thresholding |
Invert Binary Image |
Parameter description: Select this option to invert the binary result of the entire image: pixels that would be 255 become 0, and pixels that would be 0 become 255.
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Adaptive Thresholding
The following parameters are available after selecting the Adaptive thresholding method.
| Parameter | Description |
|---|---|
Image Channel Type |
Parameter description: Thresholding is applied based on the selected image channel.
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Threshold Calculation Method |
Parameter description: This parameter specifies a method for calculating the threshold for each pixel in the image. Value list: Mean, Weighted mean
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Thresholding Type |
Parameter description: This parameter determines the rule for binarizing the image. Value list: Binary, Binary inverted
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Kernel Size |
Parameter description: The neighborhood size used during threshold calculation, in pixels (px).
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Constant |
Parameter description: A constant used for threshold calculation. Increasing this value darkens the output image, while decreasing it brightens the image.
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Dual Thresholding
The following parameters are available after selecting the Dual thresholding method.
| Parameter | Description |
|---|---|
Threshold 1, Threshold 2 |
Parameter description:
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Dynamic Thresholding
The following parameters are available after selecting the Dynamic thresholding method.
| Parameter | Description |
|---|---|
Thresholding Type |
Parameter description: This parameter determines the rule for binarizing the image. Value list:
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Filter Type |
Parameter description: This parameter selects the filter to apply to the image. Value list:
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Pixel Value Offset |
Parameter description: Adds a constant value to, or subtracts it from, the filtered pixel values to adjust the binarization result. |
Kernel Size |
Parameter description: This parameter sets the filter window size in pixels (px). Please enter an odd number as there should always be a center pixel in the window. Any even numbers entered will be incremented by one. |
Thresholding
The following parameters are available after selecting the Thresholding method.
| Parameter | Description |
|---|---|
Image Channel Type |
Parameter description: Thresholding is applied based on the selected image channel.
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Threshold (0–255) |
Parameter description: Manually define a fixed global threshold used to segment pixels that meet the threshold condition. |
Thresholding Type |
Parameter description: This parameter determines the rule for binarizing the image. Value list:
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Parameter Tuning Examples
Case 1: Clean Background with High Target-Background Contrast
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Scenario:The target object and background have a significant grayscale difference with stable lighting conditions, such as detecting the position of a black object on a white background.
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Recommended approach: Select the Thresholding method and manually set a fixed threshold for quick and effective separation of the target from the background.
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Tuning approach:
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First observe the grayscale histogram of the image to determine the grayscale value ranges of the target and background.
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Select a value between the two grayscale ranges as the initial threshold (e.g., if the target range is 0–80 and the background range is 150–255, set the initial threshold to 115).
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Adjust the threshold during the process to progressively optimize the segmentation result.
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If the target is incompletely segmented, lower the threshold; if there is too much background noise, raise the threshold.
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Select "Binary" or "Binary inverted" as needed to adjust the foreground and background colors.
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Case 2: Uneven Lighting with Background Gradient
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Scenario: In natural lighting environments or scenes with shadows, brightness varies significantly across different regions of the image. Using a global threshold can easily lead to under-segmentation or over-segmentation.
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Recommended approach: Select the Adaptive thresholding method. The system dynamically calculates the threshold based on the pixel neighborhood, adapting to lighting variations.
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Tuning approach:
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Set the initial "Kernel Size" to 21 pixels and observe the segmentation result.
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If the target contour is blurry or boundary detection is unclear, increase the kernel size (e.g., 31 or 41) to enlarge the neighborhood and make the threshold calculation more stable.
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If target details are lost, decrease the kernel size (e.g., 11 or 15) to make the threshold more sensitive.
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Adjust the "Constant" parameter: increasing the constant darkens the overall image (more foreground), while decreasing the constant brightens it (more background).
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Select the appropriate channel (Grayscale, Hue, Saturation, etc.) based on the image channels to achieve the best segmentation.
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Case 3: Image Contains Noise, Need to Remove Salt-and-Pepper Noise
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Scenario: Images acquired by low-quality cameras or in harsh environments contain random black and white noise points (salt-and-pepper noise). Direct binarization would retain the noise.
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Recommended approach: Select the Dynamic thresholding method combined with Median filtering to effectively remove salt-and-pepper noise.
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Tuning approach:
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Select the "Dynamic thresholding" method.
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Select "Median" as the filter type. Median filtering is particularly suitable for removing salt-and-pepper noise.
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Set the "Kernel Size" to 5 or 7 (odd number) to define the filter window size.
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Select the "Thresholding Type" based on the target characteristics:
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To detect brighter targets, select "Bright region".
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To detect darker targets, select "Dark region".
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To detect pixels within a certain range, select "In range".
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To detect pixels outside a certain range, select "Out of range".
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Adjust the "Pixel Value Offset" (offset) to control the segmentation tolerance: increasing the offset includes more pixels, while decreasing it makes the segmentation stricter.
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Observe the result and progressively fine-tune the kernel size and offset value.
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Case 4: Need to Extract a Specific Brightness Range
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Scenario: The target object’s grayscale values are concentrated in a specific range, and pixels within this range need to be precisely extracted, such as detecting regions of specific brightness on a semiconductor chip surface.
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Recommended approach: Select the Dual thresholding method to precisely control the pixel range by setting two threshold boundaries.
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Tuning approach:
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Observe the grayscale value range of the target region, e.g., target pixel grayscale is 100–180.
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Set Threshold 1 to the lower bound and Threshold 2 to the upper bound of the range (e.g., Threshold 1 = 100, Threshold 2 = 180).
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This extracts pixels between the two thresholds as 255, and all other pixels as 0.
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If reverse extraction is needed (extracting pixels outside the range), adjust Threshold 1 and Threshold 2 so that Threshold 1 > Threshold 2.
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Progressively adjust the boundary values of both thresholds based on the segmentation result to achieve precise grayscale range filtering.
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