CN110033458A - It is a kind of based on pixel gradient distribution image threshold determine method - Google Patents
It is a kind of based on pixel gradient distribution image threshold determine method Download PDFInfo
- Publication number
- CN110033458A CN110033458A CN201910183935.0A CN201910183935A CN110033458A CN 110033458 A CN110033458 A CN 110033458A CN 201910183935 A CN201910183935 A CN 201910183935A CN 110033458 A CN110033458 A CN 110033458A
- Authority
- CN
- China
- Prior art keywords
- gradient
- pixel
- image
- threshold
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000011218 segmentation Effects 0.000 claims abstract description 23
- 238000007619 statistical method Methods 0.000 claims abstract description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 4
- 238000004422 calculation algorithm Methods 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002902 bimodal effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Facsimile Image Signal Circuits (AREA)
Abstract
本发明公开了一种基于像素梯度分布的图像阈值确定方法。对于灰度分布直方图呈单峰分布的图像,计算图像中每一像素点的梯度,并对所获得的梯度值进行统计分析,绘制图像的像素梯度分布直方图,并基于像素梯度分布直方图的形态确定最佳的分割阈值。本发明能够准确快速地确定灰度分布直方图呈单峰分布的图像的分割阈值,提高了图像的分割效果。
The invention discloses an image threshold determination method based on pixel gradient distribution. For an image with a unimodal distribution of grayscale distribution histogram, calculate the gradient of each pixel in the image, perform statistical analysis on the obtained gradient value, draw the pixel gradient distribution histogram of the image, and based on the pixel gradient distribution histogram The morphology determines the optimal segmentation threshold. The invention can accurately and quickly determine the segmentation threshold of the image whose gray scale distribution histogram is in a unimodal distribution, thereby improving the segmentation effect of the image.
Description
技术领域technical field
本发明属于数字图像信息提取领域,特别涉及了一种基于像素梯度分布的图像阈值确定方法。The invention belongs to the field of digital image information extraction, and particularly relates to an image threshold determination method based on pixel gradient distribution.
背景技术Background technique
得益于迅速发展的数字成像技术(CT、SEM、FIB/SEM),样本内部的微观孔隙结构得以直观呈现。对于目标和背景反差明显的图像,即其灰度分布直方图呈现明显的双峰或多峰分布,现有算法通过选取两波峰之间的波谷点作为最佳阈值,从而对图像中目标和背景进行准确有效地分割。除此之外,仍有一部分图像由于目标区域相对于背景区域而言面积较小或目标与背景之间的灰度过渡较平缓,即其灰度分布直方图呈现明显的单峰分布。实践表明,现有算法对灰度分布直方图呈单峰分布图像的分割存在较大的误差,分割结果的真实性和可靠性较低。Thanks to the rapidly developing digital imaging technology (CT, SEM, FIB/SEM), the microscopic pore structure inside the sample can be visualized. For the image with obvious contrast between the target and the background, that is, its gray distribution histogram shows obvious bimodal or multimodal distribution, the existing algorithm selects the trough point between the two peaks as the optimal threshold, so as to determine the target and background in the image. Segmentation is performed accurately and efficiently. In addition, there are still some images because the target area is smaller than the background area or the grayscale transition between the target and the background is relatively smooth, that is, the grayscale distribution histogram shows an obvious unimodal distribution. Practice has shown that the existing algorithms have large errors in the segmentation of images with a unimodal distribution of gray distribution histograms, and the authenticity and reliability of the segmentation results are low.
发明内容SUMMARY OF THE INVENTION
为了解决上述背景技术提出的技术问题,本发明提出了一种基于像素梯度分布的图像阈值确定方法,准确快速地确定灰度分布直方图呈单峰分布的图像的分割阈值,提高图像的分割效果。In order to solve the technical problems raised by the above background technology, the present invention proposes an image threshold determination method based on pixel gradient distribution, which can accurately and quickly determine the segmentation threshold of an image whose grayscale distribution histogram exhibits a unimodal distribution, and improve the image segmentation effect. .
为了实现上述技术目的,本发明的技术方案为:In order to realize the above-mentioned technical purpose, the technical scheme of the present invention is:
一种基于像素梯度分布的图像阈值确定方法,对于灰度分布直方图呈单峰分布的图像,计算图像中每一像素点的梯度,并对所获得的梯度值进行统计分析,绘制图像的像素梯度分布直方图,并基于像素梯度分布直方图的形态确定最佳的分割阈值。An image threshold determination method based on pixel gradient distribution. For an image whose grayscale distribution histogram is unimodal, the gradient of each pixel in the image is calculated, and the obtained gradient value is statistically analyzed, and the pixels of the image are drawn. Gradient distribution histogram, and determine the optimal segmentation threshold based on the shape of the pixel gradient distribution histogram.
基于上述技术方案的优选方案,包括以下步骤:The preferred solution based on the above-mentioned technical scheme, comprises the following steps:
(1)选择梯度算子模板;(1) Select the gradient operator template;
(2)将步骤(1)选择的梯度算子模板的矩阵在需要进行梯度计算的图像上移动,计算图像上每一像素点的梯度值;(2) the matrix of the gradient operator template selected in step (1) is moved on the image that needs to carry out gradient calculation, and the gradient value of each pixel point on the image is calculated;
(3)对不同灰度值的像素点的梯度值进行叠加统计,绘制梯度分布直方图;(3) The gradient values of the pixels with different gray values are superimposed and counted, and the gradient distribution histogram is drawn;
(4)图像的梯度分布直方图中的波峰区域对应于图像中目标和背景边界区域中像素点,而波谷区域则对应于同一区域类别中的像素点;分别找出与波峰和波谷相对应的像素点的灰度值,这两个灰度值具有最大的可能性分别位于边界和背景,据此确定分割阈值。(4) The peak area in the histogram of the gradient distribution of the image corresponds to the pixel points in the target and background boundary areas in the image, while the trough area corresponds to the pixel points in the same area category; find out the corresponding peaks and troughs respectively. The gray value of the pixel point, the two gray values have the greatest possibility to be located at the boundary and the background, respectively, and the segmentation threshold is determined accordingly.
基于上述技术方案的优选方案,在步骤(1)中,所述梯度算子模板选择3×3的Sobel算子。Based on the preferred solution of the above technical solution, in step (1), the gradient operator template selects a 3×3 Sobel operator.
基于上述技术方案的优选方案,在步骤(3)中,对每一灰度值下的梯度值进行平均处理,得到其平均梯度值,根据平均梯度值绘制梯度分布直方图。Based on the preferred solution of the above technical solution, in step (3), the gradient value under each gray value is averaged to obtain its average gradient value, and a gradient distribution histogram is drawn according to the average gradient value.
基于上述技术方案的优选方案,在步骤(4)中,找出与波峰和波谷相对应的像素点的灰度值,取两者的均值作为最佳分割阈值。Based on the preferred solution of the above technical solution, in step (4), find out the gray value of the pixel point corresponding to the peak and the trough, and take the average value of the two as the optimal segmentation threshold.
采用上述技术方案带来的有益效果:The beneficial effects brought by the above technical solutions:
相比于现有的数字图像阈值确定方法,本发明通过将像素邻域的梯度特性考虑进来,可以更加准确有效地对灰度直方图呈单峰分布的图像进行有效的分割,弥补了现有技术误差大、精度低的缺点。同时,本发明涉及到的算法简便,计算量小,计算速率快。Compared with the existing digital image threshold determination method, the present invention can more accurately and effectively segment the image whose grayscale histogram presents a unimodal distribution by taking into account the gradient characteristics of the pixel neighborhood, which makes up for the existing method. The disadvantages of large technical error and low precision. At the same time, the algorithm involved in the present invention is simple, the calculation amount is small, and the calculation speed is fast.
附图说明Description of drawings
图1是本发明的方法流程图;Fig. 1 is the method flow chart of the present invention;
图2是实施例中提供的测试图;Fig. 2 is the test chart provided in the embodiment;
图3是图2对应的灰度直方图;Fig. 3 is a grayscale histogram corresponding to Fig. 2;
图4是利用Otsu算法处理图2的分割结果图;Fig. 4 utilizes Otsu algorithm to process the segmentation result diagram of Fig. 2;
图5是利用MaxEntropy算法处理图2的分割结果图;Fig. 5 utilizes MaxEntropy algorithm to process the segmentation result diagram of Fig. 2;
图6是利用Valley-Emphasis算法处理图2的分割结果图;Fig. 6 utilizes Valley-Emphasis algorithm to process the segmentation result diagram of Fig. 2;
图7是利用本发明得到的梯度图;Fig. 7 is the gradient map that utilizes the present invention to obtain;
图8是利用本发明得到的灰度分布直方图;Fig. 8 is the gray distribution histogram that utilizes the present invention to obtain;
图9是利用本发明得到的阈值图;Fig. 9 is the threshold value diagram that utilizes the present invention to obtain;
图10是利用本发明得到的分割结果图。FIG. 10 is a graph of segmentation results obtained by the present invention.
具体实施方式Detailed ways
以下将结合附图,对本发明的技术方案进行详细说明。The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings.
对于灰度分布直方图呈单峰分布的图像,引入像素邻域的某种性质来进行阈值的确定。本发明选择像素点的梯度作为研究对象,研究表明,像素梯度大小反映了不同区域间差异性的大小。通过对原始图像中的每一像素点进行梯度计算,并对所获得的梯度值进行统计分析,绘制图像的像素梯度分布直方图,基于像素梯度分布直方图的形态确定最佳的分割阈值。如图1所示,具体步骤如下:For the image whose gray distribution histogram shows a unimodal distribution, a certain property of the pixel neighborhood is introduced to determine the threshold. The present invention selects the gradient of the pixel point as the research object, and research shows that the size of the pixel gradient reflects the size of the difference between different regions. By calculating the gradient of each pixel in the original image and performing statistical analysis on the obtained gradient value, the pixel gradient distribution histogram of the image is drawn, and the optimal segmentation threshold is determined based on the shape of the pixel gradient distribution histogram. As shown in Figure 1, the specific steps are as follows:
步骤1:梯度算子模板的选择Step 1: Selection of gradient operator template
Sobel算子通过对不同位置处的像素点赋以不同的权重值,从而使得到的像素点梯度值更为准确。因此,在本实施例中,选用3×3的Sobel模板来进行后续的像素点梯度值计算。The Sobel operator assigns different weights to the pixels at different positions, so that the obtained gradient values of the pixels are more accurate. Therefore, in this embodiment, a 3×3 Sobel template is selected to perform subsequent pixel point gradient value calculation.
步骤2:像素梯度值的计算Step 2: Calculation of pixel gradient values
将Sobel算子模板的小矩阵在需要进行梯度计算的图像上移动,在每一像素点位置处进行卷积操作计算该像素点的梯度值。The small matrix of the Sobel operator template is moved on the image that needs to be gradient calculated, and the convolution operation is performed at each pixel position to calculate the gradient value of the pixel.
步骤3:梯度分布直方图的绘制Step 3: Drawing the gradient distribution histogram
对不同灰度值的像素点的梯度值进行叠加统计,考虑到不同区域间的边界像素点的数量较少,即使对其梯度值进行叠加计算,直方图中对边界点的突出效果十分有限。因此,本发明中对每一灰度值下的梯度值进行平均处理,得到其平均梯度值,从而消除像素点数量的影响。The gradient values of pixels with different gray values are superimposed and counted. Considering that the number of boundary pixels between different regions is small, even if the gradient values are superimposed and calculated, the prominent effect of the boundary points in the histogram is very limited. Therefore, in the present invention, the gradient value under each gray value is averaged to obtain the average gradient value, thereby eliminating the influence of the number of pixels.
步骤4:最佳分割阈值的确定Step 4: Determination of the optimal segmentation threshold
图像梯度分布直方图中的波峰区域对应于图像中目标和背景边界区域中像素点,而波谷区域则对应于同一区域类别中的像素点。分别找出与波峰和波谷相对应的像素点的灰度值,两灰度值具有最高的可能性分别位于边界和背景,在本实施例中,取两者的均值作为确定的最佳阈值。The peak area in the image gradient distribution histogram corresponds to the pixels in the boundary area of the target and background in the image, while the trough area corresponds to the pixels in the same area category. The grayscale values of the pixels corresponding to the peaks and troughs are respectively found, and the two grayscale values have the highest probability of being located at the boundary and the background, respectively. In this embodiment, the average value of the two is taken as the determined optimal threshold.
在本实施例中,将本发明方法与现有的三种阈值分割算法——Otsu、MaxEntropy、Valley-Emphasis进行比较,从而验证本发明的有效性。In this embodiment, the method of the present invention is compared with three existing threshold segmentation algorithms--Otsu, MaxEntropy, Valley-Emphasis, so as to verify the effectiveness of the present invention.
选择图2所示的4张图片(a)、(b)、(c)、(d)作为测试图片,其对应的灰度直方图如图3所示(横坐标为灰度值,纵坐标为频率)。4张测试图片依次被Otsu、MaxEntropy、Valley-Emphasis算法处理后的分割图如图4-6所示。可以看出,Otsu和MaxEntropy确定的阈值过大,导致大量像素点被误分,而Valley-Emphasis算法则不够稳定,对于某些图像的分割效果可以接受,另外一些图像则存在与Otsu和MaxEntropy算法相同的问题,也即说明Valley-Emphasis算法的鲁棒性较差。Select the 4 pictures (a), (b), (c), (d) shown in Figure 2 as test pictures, and the corresponding grayscale histograms are shown in Figure 3 (the abscissa is the gray value, and the ordinate is the gray value). is the frequency). Figure 4-6 shows the segmentation images of the four test images processed by the Otsu, MaxEntropy, and Valley-Emphasis algorithms in turn. It can be seen that the threshold determined by Otsu and MaxEntropy is too large, resulting in a large number of pixel points being misclassified, while the Valley-Emphasis algorithm is not stable enough, and the segmentation effect of some images is acceptable, and other images exist with Otsu and MaxEntropy algorithm. The same problem means that the robustness of the Valley-Emphasis algorithm is poor.
然后利用本发明对同样的测试图像进行分割,过程如下所示。Then use the present invention to segment the same test image, and the process is as follows.
首先,获得图像的梯度图,较亮像素点代表高梯度,较暗像素点代表低梯度,如图7所示。First, the gradient map of the image is obtained. Brighter pixels represent high gradients, and darker pixels represent low gradients, as shown in Figure 7.
其次,获得图像的梯度分布直方图,如图8所示(横坐标为灰度值,纵坐标为平均梯度)。Next, obtain the gradient distribution histogram of the image, as shown in Figure 8 (the abscissa is the gray value, and the ordinate is the average gradient).
再次,确定最高峰和最低谷位置,得到最优阈值,如图9所示。Again, determine the position of the highest peak and lowest valley to obtain the optimal threshold, as shown in Figure 9.
最后,利用确定的阈值对图像进行分割,结果如图10所示。Finally, the image is segmented using the determined threshold, and the result is shown in Figure 10.
可以明显看出,本发明的分割性能明显优于其他常用阈值算法的分割结果,证明本发明是准确且有效的。It can be clearly seen that the segmentation performance of the present invention is obviously better than the segmentation results of other commonly used threshold algorithms, which proves that the present invention is accurate and effective.
实施例仅为说明本发明的技术思想,不能以此限定本发明的保护范围,凡是按照本发明提出的技术思想,在技术方案基础上所做的任何改动,均落入本发明保护范围之内。The embodiment is only to illustrate the technical idea of the present invention, and cannot limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention all fall within the protection scope of the present invention. .
Claims (5)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910183935.0A CN110033458A (en) | 2019-03-12 | 2019-03-12 | It is a kind of based on pixel gradient distribution image threshold determine method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910183935.0A CN110033458A (en) | 2019-03-12 | 2019-03-12 | It is a kind of based on pixel gradient distribution image threshold determine method |
Publications (1)
Publication Number | Publication Date |
---|---|
CN110033458A true CN110033458A (en) | 2019-07-19 |
Family
ID=67235134
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910183935.0A Pending CN110033458A (en) | 2019-03-12 | 2019-03-12 | It is a kind of based on pixel gradient distribution image threshold determine method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110033458A (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111028258A (en) * | 2019-11-14 | 2020-04-17 | 中国科学院力学研究所 | An Adaptive Threshold Extraction Method for Large-scale Grayscale Images |
CN111489371A (en) * | 2020-04-22 | 2020-08-04 | 西南科技大学 | Image segmentation method for scene with histogram approximate to unimodal distribution |
CN112200800A (en) * | 2020-10-30 | 2021-01-08 | 福州大学 | Electrowetting display defect detection method based on gray level histogram gradient weighted target variance |
CN112396061A (en) * | 2020-11-25 | 2021-02-23 | 福州大学 | Otsu target detection method based on target gray scale tendency weighting |
WO2021087700A1 (en) * | 2019-11-04 | 2021-05-14 | 深圳市汇顶科技股份有限公司 | Image processing apparatus, processor chip and electronic device |
CN113592750A (en) * | 2021-07-30 | 2021-11-02 | 成都市晶林科技有限公司 | Infrared enhancement method based on gradient histogram |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101950116A (en) * | 2010-09-14 | 2011-01-19 | 浙江工业大学 | Video automatic focusing method applied to multi-main-body scene |
CN102982534A (en) * | 2012-11-01 | 2013-03-20 | 北京理工大学 | Canny edge detection dual threshold acquiring method based on chord line tangent method |
CN102999916A (en) * | 2012-12-12 | 2013-03-27 | 清华大学深圳研究生院 | Edge extraction method of color image |
CN108596932A (en) * | 2018-04-18 | 2018-09-28 | 哈尔滨理工大学 | A kind of overlapping cervical cell image partition method |
-
2019
- 2019-03-12 CN CN201910183935.0A patent/CN110033458A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101950116A (en) * | 2010-09-14 | 2011-01-19 | 浙江工业大学 | Video automatic focusing method applied to multi-main-body scene |
CN102982534A (en) * | 2012-11-01 | 2013-03-20 | 北京理工大学 | Canny edge detection dual threshold acquiring method based on chord line tangent method |
CN102999916A (en) * | 2012-12-12 | 2013-03-27 | 清华大学深圳研究生院 | Edge extraction method of color image |
CN108596932A (en) * | 2018-04-18 | 2018-09-28 | 哈尔滨理工大学 | A kind of overlapping cervical cell image partition method |
Non-Patent Citations (6)
Title |
---|
A.M.GROENEWALD ET AL.: "related approaches to gradient-based thresholding", 《PATTERN RECOGNITION LETTERS》 * |
FATEME MOSTAJER KHEIRKHAH ET AL.: "modified histogram-based segmentation and adaptive distance tracking of sperm cells image sequences", 《COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE》 * |
刘俊等: "一种基于梯度的直方图阈值图像分割改进方法", 《计算机与数字工程》 * |
岳贤军: "基于梯度直方图的产品表面缺陷图像自适应阈值分割方法研究", 《南通大学学报(自然科学版)》 * |
董慧颖等: "《典型目标识别与图像除雾技术》", 31 July 2016 * |
贾超等: "构件内部裂缝缺陷的有限元三维重建", 《燕山大学学报》 * |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2021087700A1 (en) * | 2019-11-04 | 2021-05-14 | 深圳市汇顶科技股份有限公司 | Image processing apparatus, processor chip and electronic device |
CN111028258A (en) * | 2019-11-14 | 2020-04-17 | 中国科学院力学研究所 | An Adaptive Threshold Extraction Method for Large-scale Grayscale Images |
CN111028258B (en) * | 2019-11-14 | 2023-05-16 | 中国科学院力学研究所 | An Adaptive Threshold Extraction Method for Large-Scale Grayscale Images |
CN111489371A (en) * | 2020-04-22 | 2020-08-04 | 西南科技大学 | Image segmentation method for scene with histogram approximate to unimodal distribution |
CN112200800A (en) * | 2020-10-30 | 2021-01-08 | 福州大学 | Electrowetting display defect detection method based on gray level histogram gradient weighted target variance |
CN112200800B (en) * | 2020-10-30 | 2022-10-28 | 福州大学 | A Defect Detection Method for Electrowetting Displays Based on Grayscale Histogram |
CN112396061A (en) * | 2020-11-25 | 2021-02-23 | 福州大学 | Otsu target detection method based on target gray scale tendency weighting |
CN113592750A (en) * | 2021-07-30 | 2021-11-02 | 成都市晶林科技有限公司 | Infrared enhancement method based on gradient histogram |
CN113592750B (en) * | 2021-07-30 | 2023-10-20 | 成都市晶林科技有限公司 | Infrared enhancement method based on gradient histogram |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN110033458A (en) | It is a kind of based on pixel gradient distribution image threshold determine method | |
CN110378313B (en) | Cell cluster identification method and device and electronic equipment | |
CN113538433A (en) | Mechanical casting defect detection method and system based on artificial intelligence | |
CN116664559B (en) | Machine vision-based memory bank damage rapid detection method | |
CN113109368B (en) | Glass crack detection method, device, equipment and medium | |
US8983199B2 (en) | Apparatus and method for generating image feature data | |
CN103543394A (en) | A method for extracting quantitative parameters of high-voltage electrical equipment discharge ultraviolet imaging | |
CN110288618B (en) | Multi-target segmentation method for uneven-illumination image | |
CN114170165A (en) | Chip surface defect detection method and device | |
CN117635615B (en) | Defect detection method and system for realizing punching die based on deep learning | |
CN117094975A (en) | Method and device for detecting surface defects of steel and electronic equipment | |
CN111325728A (en) | Product defect detection method, device, equipment and storage medium | |
CN110263778A (en) | A kind of meter register method and device based on image recognition | |
CN116596899A (en) | Method, device, terminal and medium for identifying circulating tumor cells based on fluorescence image | |
CN110060246B (en) | Image processing method, device and storage medium | |
CN115439462A (en) | Wafer defect detection method | |
CN115719326A (en) | PCB defect detection method and device | |
CN111861984B (en) | Method and device for determining lung region, computer equipment and storage medium | |
CN117274216B (en) | Ultrasonic carotid plaque detection method and system based on level set segmentation | |
CN108764343A (en) | A kind of localization method of tracking target frame in track algorithm | |
CN117911419A (en) | Method and device for detecting steel rotation angle enhancement of medium plate, medium and equipment | |
CN105761237A (en) | Mean shift-based chip X-ray image layer segmentation | |
CN115588109B (en) | Image template matching method, device, equipment and application | |
Nagase et al. | Automatic calculation and visualization of nuclear density in whole slide images of hepatic histological sections | |
CN112651936B (en) | Method and system for image segmentation of steel plate surface defects based on image local entropy |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190719 |
|
RJ01 | Rejection of invention patent application after publication |