An Adaptive Threshold Segmentation Method in Dynamic Environment
An adaptive threshold and dynamic environment technology, applied in image analysis, image enhancement, instruments, etc., can solve problems such as good segmentation effect, chaotic segmentation, large background difference, etc., and achieve the effect of wide application range and elimination of noise influence
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2019-05-28
- Estimated Expiration
- Not applicable · inactive patent
Smart Images

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Abstract
Description
technical field
[0001] The invention relates to the fields of image processing and pattern recognition, in particular to an adaptive threshold segmentation method in a dynamic environment. Background technique
[0002] How to extract the object we are interested in from an image has always been an enduring topic in image processing. The earliest research and the most widely used image segmentation technology is the image segmentation technology, which is A classic problem in the field of image processing and analysis is also one of the difficulties in this field. Image segmentation is actually a division problem. According to specific division criteria, the pixels in the image are filtered and divided. The result of the division is usually to distinguish the background from the extract, or to highlight the extract, or to eliminate noise. Through division, the Images are grouped into meaningful regions, and we can extract our objects of interest. In the field of image segme...
Examples
Embodiment 1
[0048] Embodiment 1: as figure 2 As shown in the algorithm flow chart, it includes the following specific processes:
[0049] Step 1: First collect the first frame of RGB image from the camera as a priori image.
[0050] Step 2: Convert the extracted RGB image to HSV and YCbCr image, the conversion process is as follows:
[0051]RGB is converted to YCbCr, the conversion formula is:
[0052]
[0053] Step 3: Find the center point. In order to ignore the influence of light intensity, the cb and cr channels are used for segmentation during the whole process. The whole process is as follows: In the first frame of the image, the approximate range of the initial obstacles is clustered or manually Select the obstacle part (select the rectangular part containing the obstacle), if the width of the selected rectangle is w, the height is h, the center point is p(y,u,v), y is the y channel, u is the cb channel, and v is cr channel, src i,j (y,u,v) are pixels converted to ycbcr col...