CN102156984B - Method for determining optimal mark image by adaptive threshold segmentation - Google Patents
Method for determining optimal mark image by adaptive threshold segmentation Download PDFInfo
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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 optimal mark 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 brightness of a pixel has represented the mean flow rate response of some atural objects, even need to 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 the raising of decades of times.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 a unavoidable 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.The key link that image segmentation is the high spatial resolution remote sense image surface in object classification and the identifying also is one of difficult point of field of remote sensing image processing.
In order to cut apart all earth 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; Equations of The Second Kind is to merge by the zone to realize image segmentation, comprises level merging from bottom to top, top-down region split and merge etc.; The 3rd class is to realize image segmentation according to the watershed transform of image gradient, and has developed on this basis the multiple improvement algorithms such as merging after cutting apart front mark and cutting apart.
The watershed transform method has fast operation, can generate the advantages such as zone closed and that be communicated with, thereby is widely used in Remote Sensing Image Segmentation.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 impact 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 from the bottom in 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 so that follow-up watershed segmentation only for these marks the catchment basin carry out.Conventional mark generating algorithm can tentatively solve the over-segmentation problem, but still has the problems such as optimum marking-threshold is difficult to determine.
Summary of the invention
Goal of the invention: for the problem and shortage of above-mentioned existing existence, 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 optimal mark image.
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 optimal mark image, comprises following steps:
(1) average E and the variances sigma of calculating gradient image f to be marked (x, y);
(2) centered by the average E in described step (1), take a σ as step-length, 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 coefficient range, is integer;
(3) with the various combination H of average E in the described step (2) and variances sigma
λGradient image f (x, y) is carried out Threshold segmentation, obtain corresponding bianry image sequence f ' (x, y);
(4) successively each the bianry image area among statistic procedure (3) the described bianry image sequence f ' (x, y) 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.
Gradient image f to be marked (x, y) in the described step (1) can be used to the edge feature image that carries out watershed transform itself, also can be external label information, such 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 as required greater or lesser numerical value; The scope that coefficient lambda swings is [10,10], also can adopt as required greater or lesser scope.
Threshold segmentation simple calculations in the described step (3), the bianry image sequence f ' (x, y) of acquisition can adopt the three-dimensional array storage.
Number of regions statistics in the described step (4), must deduct the too small zone of area, can realize by a Minimum Area area parameters of prior setting minsize, minsize can value be 50 pixels, also can adopt as required greater or lesser value.
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 adaptively according to the principle of maximum area the optimal mark image that the watershed divide changes, 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 process flow diagram of the present invention;
Fig. 2 is the 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 for explanation the present invention and is not used in and limits the scope of the invention, after having read the present invention, those skilled in the art all fall within the application's claims limited range 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, automatically obtain optimal mark image.Its main process is: average and the variance of at first calculating gradient image to be marked, then making up average and variance 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 namely 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 is f (x, y), calculate its average E:
In the formula, N is the pixel number of image.Calculate its variances sigma:
Centered by average, take 0.1 times of variance as step-length, set 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 coefficient.
With H
λFor threshold value gradient image to be marked is carried out simple threshold values and cut apart, obtain bianry image sequence f ' (x, y), value is 1 expressive notation zone, and 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 processed automatically, the sensor information Intelligent Recognition.An example of the present invention realizes at the PC platform, and 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 the major surface features 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 major surface features is all cut apart exactly.
Claims (4)
1. one kind is utilized adaptive threshold to cut apart the method for determining optimal mark image, it is characterized in that comprising following steps:
(1) average E and the variances sigma of calculating gradient image f to be marked (x, y):
(2) centered by the average E in described step (1), take a σ as step-length, 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 coefficient range, is integer;
(3) with the various combination H of average E in the described step (2) and variances sigma
λAs threshold value gradient image f (x, y) is carried out Threshold segmentation, obtain corresponding bianry image sequence f'(x, y);
(4) the successively described bianry image sequence of statistic procedure (3) f'(x, y) in each bianry image area 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. described a kind of adaptive threshold that utilizes is cut apart the method for determining optimal mark image according to claim 1, it is characterized in that: the gradient image f to be marked (x, y) in the described step (1) is be used to the edge feature image that carries out watershed transform or external label information.
3. described a kind of adaptive threshold that utilizes is cut apart the method for determining optimal mark image according to 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. described a kind of adaptive threshold that utilizes is cut apart the method for determining optimal mark image according to claim 1, it is characterized in that: bianry image sequence f'(x, y in the described step (3)) be stored in the three-dimensional array.
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| CN102622598B (en) * | 2012-01-13 | 2014-07-09 | 西安电子科技大学 | SAR (Synthesized Aperture Radar) image target detection method based on zone markers and grey statistics |
| 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 |
| 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 |
| CN107991532B (en) * | 2017-11-21 | 2020-07-24 | 厦门理工学院 | Harmonic threshold value merging method based on multiple operation modes |
| CN107808385B (en) * | 2017-11-22 | 2021-05-25 | 新疆大学 | Color image watershed segmentation method based on power law distribution |
| 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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| CN100385461C (en) * | 2006-06-01 | 2008-04-30 | 电子科技大学 | Detection method for moving target in infrared image sequence under complex background |
| CN100512374C (en) * | 2007-12-05 | 2009-07-08 | 北京航空航天大学 | A method for image edge detection based on threshold sectioning |
| CN101556693B (en) * | 2009-03-30 | 2011-10-19 | 西安电子科技大学 | Division method for extracted watershed SAR image with threshold method and marking |
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