CN102156984A - Method for determining optimal mark image by adaptive threshold segmentation - Google Patents
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Abstract
The invention discloses a method for determining an optimal mark image by adaptive threshold segmentation, which comprises the following steps of: calculating a mean value E and a variance Sigma of a gradient image f (x, y) to be marked; establishing different combinations H Lambda of the mean value E and the variance Sigma by using the mean value E as the centre and using a certain multiple of the variance Sigma as a step length; carrying out threshold segmentation on the gradient image by the different combinations H Lambda to obtain a corresponding binary image sequence f' (x, y); counting the number C Lambda of closed communicated regions each of which areas are greater than a minimum region area parameter in the binary image sequence f' (x, y); and selecting a binary image which has the maximum number C Lambda as a mark image. In the method, the optimal mark image of watershed change can be generated adaptively according to a principle of maximum region number by simply setting the minimum region area parameter; an algorithm is simple, fast and convenient; the minimum region area parameter is insensitive to an image segmentation result; and the minimum region area parameter is not required to be changed ordinarily after being set.
Description
Technical field
The present invention relates to a kind of image processing method, particularly a kind of adaptive threshold that utilizes is cut apart the method for determining optimum marking image.
Background technology
Along with the development of sensor technology, the spatial resolution of satellite remote sensing images is progressively improving.Image resolution ratio before several years is at ten meter levels, and the mean flow rate response of some atural objects has been represented in the brightness of a pixel, even need carry out pixel and decompose.And present up-to-date commercial satellite can reach the spatial resolution of 0.5m, and the level of detail of image obtains tens of times raising.Pixel correspondence the part of atural object target on high-definition picture, and the heterogeneity of ground object target manifests day by day, so image segmentation becomes an inevitable problem.Image segmentation is based on the process that homogeney or heterogeneous criterion are divided into piece image some significant subregions, this process is converted into cut zone to input picture, to further extraction target signature, carry out target measurement and classification and other high-rise processing all are very important.Image segmentation is the key link of high spatial resolution remote sense image surface in object class and identifying, also is one of the difficult point in remote sensing image processing field.
In order to cut apart all the atural object objects in the remote sensing images, mainly contain three classes and realize approach: the first kind is to connect and region labeling realization image segmentation by rim detection, edge; Second class is to merge by the zone to realize image segmentation, comprises level merging from bottom to top, top-down regional split and merging etc.; The 3rd class is to realize image segmentation according to the watershed transform of image gradient, and has developed the multiple improvement algorithms such as merging after cutting apart preceding mark and cutting apart on this basis.
The watershed transform method has fast operation, can generate advantages such as zone closed and that be communicated with, thereby is widely used in remote sensing images are cut apart.But the shortcoming of watershed algorithm also clearly, and it is apparent in view that is exactly the over-segmentation phenomenon.This mainly is because the influence of picture noise, make the gradient of image have the lower zonule of many gray-scale values, these local minimum area correspondences the bottom, catchment basin of watershed algorithm, and the process of water burst at first is the bottom from the basin, so a large amount of irrelevant local minimum area in small, broken bits is to cause the basic reason of over-segmentation.One of ways of addressing this issue is exactly those significant local minimum area of mark, make follow-up watershed segmentation only at these marks the catchment basin carry out.Conventional mark generating algorithm can tentatively solve the over-segmentation problem, but still has problems such as optimum marking-threshold is difficult to determine.
Summary of the invention
Goal of the invention:, the purpose of this invention is to provide a kind of adaptive threshold that utilizes that is easy to definite optimum marking-threshold and cut apart the method for determining optimum marking image at the problem and shortage of above-mentioned existing existence.
Technical scheme: for achieving the above object, the technical solution used in the present invention is that a kind of adaptive threshold that utilizes is cut apart the method for determining optimum marking image, comprises following steps:
(1) calculates gradient image f to be marked (x, average E y) and variances sigma;
(2) being the center with the average E in the described step (1), is step-length with a σ, and a is the positive number of predefined, sets up the various combination H of average E and variances sigma
λ, H
λ=E+ λ a σ, in the formula, λ is the coefficient scope, is integer;
(3) with the various combination H of average E in the described step (2) and variances sigma
λTo gradient image f (x y) carries out Threshold Segmentation, obtain corresponding bianry image sequence f ' (x, y);
(4) successively the described bianry image sequence of statistic procedure (3) f ' (x, y) each the bianry image area in is greater than the number C of the sealing connected region of Minimum Area area
λ
(5) select number of regions C in the described step (4)
λThe bianry image when maximum image that serves as a mark.
(x y), can be the edge feature image itself that is used to carry out watershed transform to gradient image f to be marked in the described step (1), also can be external label information, as textural characteristics, semantic feature image.
The various combination H of average E and variances sigma in the described step (2)
λIn the swing of average E, step-length can adopt 0.1 times of variance, also can adopt greater or lesser numerical value as required; The scope of coefficient lambda swing is [10,10], also can adopt greater or lesser scope as required.
Threshold Segmentation in the described step (3) is calculated simple and fast, and (x y) can adopt the three-dimensional array storage to the bianry image sequence f ' of acquisition.
Number of regions statistics in the described step (4), must deduct the too small zone of area, can realize that minsize can value be 50 pixels, also can adopt greater or lesser value as required by a Minimum Area area parameters of prior setting minsize.
The marking image of determining in the described step (5) also must be eliminated the too small zone of area to reduce insignificant issue, still can realize by Minimum Area area parameters minsize.
Beneficial effect: the present invention only need set a Minimum Area area parameters simply, can generate the optimum marking image that the watershed divide changes according to the principle of maximum area adaptively, the algorithm simple and fast, and the Minimum Area area parameters generally need not to change after setting to image segmentation result and insensitive.
Description of drawings
Fig. 1 is a process flow diagram of the present invention;
Fig. 2 is a result schematic diagram of the present invention of utilizing computer programming language to realize;
Fig. 3 utilizes mark shown in Figure 2 to carry out the result schematic diagram that watershed transform is cut apart.
Embodiment
Below in conjunction with the drawings and specific embodiments, further illustrate the present invention, should understand these embodiment only is used to the present invention is described and is not used in and limit the scope of the invention, after having read the present invention, those skilled in the art all fall within the application's claims institute restricted portion to the modification of the various equivalent form of values of the present invention.
Basic ideas of the present invention are: design a kind of adaptive mobile threshold segmentation method, gradient image is cut apart, obtain optimum marking image automatically.Its main process is: average and the variance of at first calculating gradient image to be marked, making up average and variance then carries out simple threshold values to gradient image and cuts apart, obtain some bianry images, and the number of the sealing connected region in the statistics bianry image, the bianry image that number of regions is maximum promptly is used as marking image.
As shown in Figure 1, setting Minimum Area area parameters minsize is 50 pixels.
If gradient image to be marked be f (x, y), calculate its average E:
In the formula, N is the pixel number of image.Calculate its variances sigma:
With the average is the center, is step-length with 0.1 times of variance, sets up the various combination H of average and variance
λ:
H
λ＝E+λ·0.1σ，λ＝-10，-9，-8，...，10
In the formula, λ is a coefficient.
With H
λFor threshold value gradient image to be marked is carried out simple threshold values and cut apart, acquisition bianry image sequence f ' (x, y), value is 1 expressive notation zone, value is 0 expression background:
Area is greater than the number C of the sealing connected region of Minimum Area area parameters minsize in the statistics bianry image sequence
λ, select the number of regions C in the described step (4)
λThe bianry image when maximum image that serves as a mark.
The present invention can be applicable to that remote sensing images are handled automatically, the sensor information Intelligent Recognition.An example of the present invention realizes that on the PC platform through experimental verification, this adaptive threshold dividing method can obtain comparatively ideal marking image, and the accuracy that watershed transform is cut apart after the mark is higher.As shown in drawings, Fig. 2 is the result of the present invention who utilizes computer programming language to realize, the result shows main atural object such as house, vegetation, road all by mark exactly, and Fig. 3 utilizes this mark to carry out the result that watershed transform is cut apart, and the result shows that main atural object is all cut apart exactly.
Claims (4)
1. one kind is utilized adaptive threshold to cut apart the method for determining optimum marking image, it is characterized in that comprising following steps:
(1) calculates gradient image f to be marked (x, average E y) and variances sigma;
(2) being the center with the average E in the described step (1), is step-length with a σ, and a is the positive number of predefined, sets up the various combination H of average E and variances sigma
λ, H
λ=E+ λ a σ, in the formula, λ is the coefficient scope, is integer;
(3) with the various combination H of average E in the described step (2) and variances sigma
λTo gradient image f (x y) carries out Threshold Segmentation, obtain corresponding bianry image sequence f ' (x, y);
(4) successively the described bianry image sequence of statistic procedure (3) f ' (x, y) each the bianry image area in is greater than the number C of the sealing connected region of Minimum Area area
λ
(5) select number of regions C in the described step (4)
λThe bianry image when maximum image that serves as a mark.
2. cut apart the method for determining optimum marking image according to the described a kind of adaptive threshold that utilizes of claim 1, it is characterized in that: (x y) is edge feature image or the external label information that is used to carry out watershed transform to the gradient image f to be marked in the described step (1).
3. cut apart the method for determining optimum marking image according to the described a kind of adaptive threshold that utilizes of claim 1, it is characterized in that: the various combination H of average E and variances sigma in the described step (2)
λSwing at average E.
4. cut apart the method for determining optimum marking image according to the described a kind of adaptive threshold that utilizes of claim 1, it is characterized in that: (x y) is stored in the three-dimensional array bianry image sequence f ' in the described step (3).
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| CN102622598A (en) * | 2012-01-13 | 2012-08-01 | 西安电子科技大学 | SAR (Synthesized Aperture Radar) image target detection method based on zone markers and grey statistics |
| CN102799183A (en) * | 2012-08-21 | 2012-11-28 | 上海港吉电气有限公司 | Mobile machinery vision anti-collision protection system for bulk yard and anti-collision method |
| CN103778624A (en) * | 2013-12-20 | 2014-05-07 | 中原工学院 | Fabric defect detection method based on optical threshold segmentation |
| CN104899853A (en) * | 2014-03-04 | 2015-09-09 | 腾讯科技（深圳）有限公司 | Image region dividing method and device |
| CN104050670B (en) * | 2014-06-24 | 2016-08-17 | 广州中医药大学 | In conjunction with simple mutual and the complex background leaf image dividing method in labelling watershed |
| CN107808385A (en) * | 2017-11-22 | 2018-03-16 | 新疆大学 | Coloured image watershed segmentation methods based on power-law distribution |
| CN107991532A (en) * | 2017-11-21 | 2018-05-04 | 厦门理工学院 | A kind of harmonic data thresholds merging method based under a variety of methods of operation |
| CN109191456A (en) * | 2018-09-19 | 2019-01-11 | 电子科技大学 | Lung CT image processing method and system based on two-dimentional S-transformation |
| CN109377507A (en) * | 2018-09-19 | 2019-02-22 | 河海大学 | A method of the high-spectrum remote sensing segmentation based on curve of spectrum spectral distance |
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| CN102622598A (en) * | 2012-01-13 | 2012-08-01 | 西安电子科技大学 | SAR (Synthesized Aperture Radar) image target detection method based on zone markers and grey statistics |
| CN102799183A (en) * | 2012-08-21 | 2012-11-28 | 上海港吉电气有限公司 | Mobile machinery vision anti-collision protection system for bulk yard and anti-collision method |
| CN102799183B (en) * | 2012-08-21 | 2015-03-25 | 上海港吉电气有限公司 | Mobile machinery vision anti-collision protection system for bulk yard and anti-collision method |
| CN103778624A (en) * | 2013-12-20 | 2014-05-07 | 中原工学院 | Fabric defect detection method based on optical threshold segmentation |
| CN104899853B (en) * | 2014-03-04 | 2019-12-06 | 腾讯科技（深圳）有限公司 | Image area dividing method and device |
| CN104899853A (en) * | 2014-03-04 | 2015-09-09 | 腾讯科技（深圳）有限公司 | Image region dividing method and device |
| CN104050670B (en) * | 2014-06-24 | 2016-08-17 | 广州中医药大学 | In conjunction with simple mutual and the complex background leaf image dividing method in labelling watershed |
| CN107991532A (en) * | 2017-11-21 | 2018-05-04 | 厦门理工学院 | A kind of harmonic data thresholds merging method based under a variety of methods of operation |
| CN107808385A (en) * | 2017-11-22 | 2018-03-16 | 新疆大学 | Coloured image watershed segmentation methods based on power-law distribution |
| CN107808385B (en) * | 2017-11-22 | 2021-05-25 | 新疆大学 | Color image watershed segmentation method based on power law distribution |
| CN109377507A (en) * | 2018-09-19 | 2019-02-22 | 河海大学 | A method of the high-spectrum remote sensing segmentation based on curve of spectrum spectral distance |
| CN109191456A (en) * | 2018-09-19 | 2019-01-11 | 电子科技大学 | Lung CT image processing method and system based on two-dimentional S-transformation |
| CN109377507B (en) * | 2018-09-19 | 2022-04-08 | 河海大学 | Hyperspectral remote sensing image segmentation method based on spectral curve spectral distance |
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