CN103440661A - Micrometer-wood-fiber diameter detection algorithm based on HSV space and area selection - Google Patents
Micrometer-wood-fiber diameter detection algorithm based on HSV space and area selection Download PDFInfo
- Publication number
- CN103440661A CN103440661A CN2013103980454A CN201310398045A CN103440661A CN 103440661 A CN103440661 A CN 103440661A CN 2013103980454 A CN2013103980454 A CN 2013103980454A CN 201310398045 A CN201310398045 A CN 201310398045A CN 103440661 A CN103440661 A CN 103440661A
- Authority
- CN
- China
- Prior art keywords
- diameter
- wood fiber
- micro
- algorithm
- image
- 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.)
- Granted
Links
Images
Landscapes
- Image Analysis (AREA)
Abstract
基于HSV空间与区域选择的微米木纤维直径检测算法包括以下步骤：拍摄微米木纤维显微放大图像，将RGB图像转换到HSV颜色空间，基于S分量进行二值化，基于用户需求，选择去除分支、毛刺等不规则形态特征，提取木纤维的主体区域，利用形态学单尺度边缘检测算子获得木纤维边缘，对边缘提取后的图像采用基于最大内切圆算法的改进的分段纤维测量法获取木纤维直径。本发明的方法不仅充分利用图像的色彩信息，而且基于最大内切圆算法的直径检测，不仅摒弃传统构建直线方程的思想，而且克服了木纤维图像的旋转、平移，尽量减少纤维毛刺对直径检测的影响，对指导仿真研究和实现微米木纤维直径智能化检测具有重要的意义。
The micron wood fiber diameter detection algorithm based on HSV space and area selection includes the following steps: take a microscopic enlarged image of micron wood fiber, convert the RGB image to the HSV color space, perform binarization based on the S component, and select to remove branches based on user needs Irregular morphological features such as burrs, burrs, etc., extract the main area of wood fibers, use morphological single-scale edge detection operators to obtain wood fiber edges, and use the improved segmented fiber measurement method based on the maximum inscribed circle algorithm for the edge extracted image Gets the wood fiber diameter. The method of the invention not only makes full use of the color information of the image, but also detects the diameter based on the maximum inscribed circle algorithm, not only abandons the traditional idea of constructing a straight line equation, but also overcomes the rotation and translation of the wood fiber image, and minimizes the impact of fiber burrs on diameter detection It is of great significance to guide the simulation research and realize the intelligent detection of micron wood fiber diameter.
Description
Affiliated technical field
The present invention relates to a kind of Micro-wood Fiber examine of diameter algorithm based on micro-image, relate in particular to a kind of based on hsv color space and regioselective improved Micro-wood Fiber examine of diameter algorithm.
Background technology
The fibre diameter detection method has pneumatic tester method, optical microscope sciagraphy, optical microscope autoscan method, laser scanning method, scanning electron microscopy etc. at present, but because of instrument expensive, range of application is limited, can not be generalized in production well, thereby the difficulty that the robotization of xylogen diameter detects is increased.RGB color model commonly used is owing to there being very strong correlativity between R, G, B three-component, and the hsv color space more meets the feature of human eye to Color perception, and its three components are separate, stronger robustness is arranged.The people such as Wang Xiali are in document 1 " a kind of vehicle license extracting method based on the hsv color space " (computer engineering, 2004,30 (17)) in, utilize the hsv color space directly to be processed the coloured image that contains license plate of input, design a kind of car plate method of inspection, realize the car plate location.Mathematical morphology has perfect Fundamentals of Mathematics, obtained good effect in the rim detection field, as Li Jie waits in document 2 " research of the edge detection algorithm based on mathematical morphology " (computer science, 2012,39 (6A)) in by a kind of improved morphological operator, effectively detect edge, and kept edge-smoothing.Fiber areal survey method can be utilized the feature of each segmentation well, Zhang Peng flies to wait the people in document 3 " a kind of method based on the pretreated quick test fibre diameter of image " (Shanghai Textile science and technology, 2011, (39) 12) in, the wool fiber length direction, every about 9 pixels, fiber is carried out to the diameter measurement based on fitting a straight line from fiber one end, this algorithm has shortened the examine of diameter time, improved measurement efficiency, the advantage of performance digital image processing techniques.The application of maximum inscribed circle is more, the people such as Wu Wei are at document 4 " selection and the Position Research of area-of-interest in palm vein identification " (photoelectron laser, 2013, (24) 1) in, the ROI localization method of proposition based on thenar area, near wrist side maximum inscribed circle, to determine two invariant feature points on palm, utilize these two invariant feature points to determine ROI, ROI image to thenar area and centre of the palm district carries out the recognition performance contrast experiment, result shows, this method make multiple recognition performance index all be improved significantly.The above is based on hsv color space and regioselective Micro-wood Fiber examine of diameter algorithm theoretical foundation is provided.
Summary of the invention
The purpose of this invention is to provide a kind of based on HSV space and regioselective improved Micro-wood Fiber examine of diameter algorithm, this algorithm adopts the S component realize target based on the hsv color space to extract, based on the user's request concept, calculate the diameter of each segmentation and obtain mean value by improve traditional areal survey method based on the maximum inscribed circle algorithm, realizing the Micro-wood Fiber examine of diameter.Algorithm is not subject to the noise effect of background, and examine of diameter is effective, and applicability is stronger.
In order to achieve the above object, the present invention adopts following technical scheme:
Based on HSV space and regioselective Micro-wood Fiber examine of diameter algorithm, comprise the following steps:
(1) read in the micro-enlarged drawing of Micro-wood Fiber, the RGB image conversion that obtains, to the hsv color space, is extracted to the S component and carries out Threshold segmentation, carry out the operations such as mathematical morphology and filtering and noise reduction, obtain the binaryzation target of removing background;
(2) if the Micro-wood Fiber form is irregular, first carry out the user area selection, extract the xylogen body region, and carry out morphology single scale rim detection;
(3) the improved segmentation fibre diameter that the image after the edge detection carries out based on the maximum inscribed circle algorithm is measured, and the net result using the mean value of each sectional area diameter as the Micro-wood Fiber diameter, and algorithm finishes.
The accompanying drawing explanation
Fig. 1 is based on HSV space and regioselective Micro-wood Fiber examine of diameter algorithm flow chart;
Fig. 2 is the micro-enlarged image of Micro-wood Fiber of experiment use;
Fig. 3 is xylogen image hsv color space three-component figure, and wherein figure (a) divides spirogram for S divides spirogram, figure (c) for V for H divides spirogram, figure (b);
Fig. 4 is based on the S component xylogen image is carried out to binaryzation extraction result;
Fig. 5 carries out the extraction of xylogen body region to binary image based on user's request;
Fig. 6 carries out the result of rim detection to the image after extracted region;
Fig. 7 carries out the result of one of them areal survey based on maximum inscribed circle to xylogen;
Embodiment:
Fig. 1 is based on HSV space and regioselective Micro-wood Fiber examine of diameter algorithm flow chart; Of the present inventionly based on HSV space and regioselective Micro-wood Fiber examine of diameter algorithm, comprise the following steps:
(1) by the RGB image conversion that collects to the hsv color space;
(2) extract the S component and carry out the extraction of binaryzation target;
(3) carry out the operations such as mathematical morphology and filtering and noise reduction, obtain the binaryzation target of removing background;
(4) extract the Micro-wood Fiber body region based on user's request;
(5) adopt morphology single scale operator to detect the Micro-wood Fiber edge;
(6) image after the edge extraction adopts the improved areal survey method detection fibers diameter based on the maximum inscribed circle algorithm;
(7) to each segmentation diameter calculating mean value, as the net result of Micro-wood Fiber diameter.
Fig. 2 is the micro-enlarged image of Micro-wood Fiber collected;
Fig. 3 is xylogen image hsv color space three-component figure, and wherein figure (a) divides spirogram for S divides spirogram, figure (c) for V for H divides spirogram, figure (b).Because the S component has highlighted complete xylogen particle preferably, therefore adopt the S component to remove the background extracting target;
Fig. 4 is for to carry out based on S spatial extraction target the micro-enlarged image of xylogen by above-mentioned analysis, the figure as a result that the operations such as the line number of going forward side by side morphology and filtering and noise reduction obtain.From this interpretation, the method can successfully be isolated preferably xylogen from background;
Fig. 5 be to gained binary segmentation result image based on the user's request concept, remove the figure as a result of the irregular morphological feature of xylogen (such as bifurcated, burr etc.).Can find out from experimental result, based on user's request, not only obtain the xylogen body region of comparatively complete rule, be convenient to ensuing diameter measurement, and introduce user's participation, make algorithm more there is interactivity;
Fig. 6 is that the image after above-mentioned target is extracted carries out morphology single scale operator edge detection, and the operator of employing is:
Wherein, f (x, y) is pixel, and b (i, j) is structural elements.Algorithm picks disc structure unit, radius is 3;
Fig. 7 carries out the result of one of them areal survey based on maximum inscribed circle to xylogen.
Claims (4)
1. based on HSV space and regioselective Micro-wood Fiber examine of diameter algorithm, comprise the following steps:
(1) read in the micro-enlarged drawing of Micro-wood Fiber, the RGB image conversion that obtains, to the hsv color space, is extracted to the S component and carries out Threshold segmentation, carry out the operations such as mathematical morphology and filtering and noise reduction, obtain the binaryzation target of removing background;
(2) if the Micro-wood Fiber form is irregular, first carry out the user area selection, extract the xylogen body region, and carry out morphology single scale rim detection;
(3) the improved segmentation fibre diameter that the image after the edge detection carries out based on the maximum inscribed circle algorithm is measured, and the net result using the mean value of each sectional area diameter as the Micro-wood Fiber diameter, and algorithm finishes.
2. according to claim 1ly based on HSV space and regioselective Micro-wood Fiber examine of diameter algorithm, it is characterized in that, the RGB image of described step (1) is according to formula
transform to the hsv color space, choose the S component that the target-to-background contrast is larger and carry out binary conversion treatment.
3. according to claim 1ly based on HSV space and regioselective Micro-wood Fiber examine of diameter algorithm, it is characterized in that, the user-selected area of described step (2) is removed the xylogen burr, and morphology single scale edge detection operator is:
wherein, f (x, y) is pixel, and b (i, j) is structural elements.Algorithm adopts disc structure unit, and radius is 3.
4. according to claim 1 based on HSV space and regioselective Micro-wood Fiber examine of diameter algorithm, it is characterized in that, the improved segmentation fibre diameter mensuration based on maximum inscribed circle of described step (3), according to user's request by the fiber segmentation, and sectional area is equivalent to polygon, obtain the diameter of this minute section fibre by the diameter that calculates this polygon maximum inscribed circle, each segmentation diameter is averaged as the diameter of micrometer fibers.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310398045.4A CN103440661B (en) | 2013-09-05 | 2013-09-05 | Based on HSV space and regioselective Micro-wood Fiber diameter detection algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310398045.4A CN103440661B (en) | 2013-09-05 | 2013-09-05 | Based on HSV space and regioselective Micro-wood Fiber diameter detection algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103440661A true CN103440661A (en) | 2013-12-11 |
| CN103440661B CN103440661B (en) | 2016-08-17 |
Family
ID=49694354
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310398045.4A Expired - Fee Related CN103440661B (en) | 2013-09-05 | 2013-09-05 | Based on HSV space and regioselective Micro-wood Fiber diameter detection algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103440661B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110853000A (en) * | 2019-10-30 | 2020-02-28 | 北京中交国通智能交通系统技术有限公司 | Detection method of track |
| CN115597507A (en) * | 2022-10-27 | 2023-01-13 | 中钢集团郑州金属制品研究院股份有限公司（Cn） | Diameter detection method of synthetic fiber |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102496144A (en) * | 2011-11-15 | 2012-06-13 | 哈尔滨工程大学 | NSCT (nonsubsampled contourlet transform) sonar image enhancement method based on HSV (hue, saturation and value) color space |
| CN102496157A (en) * | 2011-11-22 | 2012-06-13 | 上海电力学院 | Image detection method based on Gaussian multi-scale transform and color complexity |
| CN102930547A (en) * | 2012-11-13 | 2013-02-13 | 中国农业大学 | Cotton foreign fiber image online segmentation method and system on the condition of wind power delivery |
| CN103268615A (en) * | 2013-06-05 | 2013-08-28 | 东北林业大学 | Wood powder edge detection algorithm based on target extraction and improved morphological operator |
-
2013
- 2013-09-05 CN CN201310398045.4A patent/CN103440661B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102496144A (en) * | 2011-11-15 | 2012-06-13 | 哈尔滨工程大学 | NSCT (nonsubsampled contourlet transform) sonar image enhancement method based on HSV (hue, saturation and value) color space |
| CN102496157A (en) * | 2011-11-22 | 2012-06-13 | 上海电力学院 | Image detection method based on Gaussian multi-scale transform and color complexity |
| CN102930547A (en) * | 2012-11-13 | 2013-02-13 | 中国农业大学 | Cotton foreign fiber image online segmentation method and system on the condition of wind power delivery |
| CN103268615A (en) * | 2013-06-05 | 2013-08-28 | 东北林业大学 | Wood powder edge detection algorithm based on target extraction and improved morphological operator |
Non-Patent Citations (3)
| Title |
|---|
| 任洪娥等: "基于改进形态学算子的木粉边缘检测算法", 《计算机工程与应用》 * |
| 伊海燕: "基于图像分析的羊绒纤维特征参数提取技术的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑(月刊)》 * |
| 齐英杰等: "微米木纤维图像的模拟再现理论和应用研究", 《林业科技》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110853000A (en) * | 2019-10-30 | 2020-02-28 | 北京中交国通智能交通系统技术有限公司 | Detection method of track |
| CN110853000B (en) * | 2019-10-30 | 2023-08-11 | 北京中交国通智能交通系统技术有限公司 | Rut detection method |
| CN115597507A (en) * | 2022-10-27 | 2023-01-13 | 中钢集团郑州金属制品研究院股份有限公司（Cn） | Diameter detection method of synthetic fiber |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103440661B (en) | 2016-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111256594B (en) | Method for measuring physical characteristics of surface state of aircraft skin | |
| Rathi et al. | Brain tumor detection and classification using deep learning classifier on MRI images | |
| JP2014524070A5 (en) | ||
| ES2328440B1 (en) | METHOD FOR IDENTIFYING GUIGNARDIA CITRICARPA. | |
| CN113096121B (en) | Pavement crack detection method and system based on cross fracture mechanics and image processing | |
| CN103955674B (en) | Palm print image acquisition device and palm print image positioning and segmenting method | |
| CN104268515A (en) | Sperm morphology anomaly detection method | |
| CN101806583A (en) | Microscopic image-based fiber fineness measurement method | |
| WO2014066231A1 (en) | Cell identification method and device, and urine analyzer | |
| CN101510295A (en) | Design method for machine vision system based on PCIe and Vision Assistan | |
| CN103440661A (en) | Micrometer-wood-fiber diameter detection algorithm based on HSV space and area selection | |
| Arachchige et al. | An image processing algorithm for accurate extraction of the centerline from human metaphase chromosomes | |
| CN104156930B (en) | Image fusion method and apparatus based on dual-scale space | |
| Huang et al. | A three-stage detection algorithm for automatic crack-width identification of fine concrete cracks | |
| CN103971369A (en) | Optic disc positioning method for retina image | |
| CN111397536A (en) | Three-dimensional profile measurement method of large-size reinforced concrete target penetrating bullet hole | |
| Shirazi et al. | Accurate microscopic red blood cell image enhancement and segmentation | |
| Marcolin et al. | Three-dimensional face analysis via new geometrical descriptors | |
| CN103091324A (en) | Method for testing fiber orientation distribution of non-woven material by using multifocal-plane image fusion | |
| Soni et al. | A Robust Vein Pattern-based Recognition System. | |
| Zhou et al. | 3d dendrite reconstruction and spine identification | |
| CN102708363A (en) | Self-adaptive fingerprint identification system | |
| Hildebrandt et al. | Statistical latent fingerprint residue recognition in contact-less scans to support fingerprint segmentation | |
| Fang et al. | A new fingerprint thinning algorithm | |
| Lv et al. | Maximizing structural similarity in multimodal biomedical microscopic images for effective registration |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160817 Termination date: 20170905 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |