CN106558062A - One dimensional object complexity map of gray level image is as partitioning algorithm and segmentation step - Google Patents
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Abstract
The present invention relates to an a kind of dimensional object complexity map of gray level image is as partitioning algorithm and segmentation step, this complexity image segmentation algorithm includes：Determine optimal segmenting threshold, to split objective area in image and background area；When the gray scale difference for solving the target and background in image to be split well of the invention fails to understand aobvious, conventional method segmentation loses can the information of image, the problem than more serious segmentation errors easily occur, and with segmentation efficiency high, the advantage of fast operation.
Description
Technical field
The present invention relates to an a kind of dimensional object complexity map of gray level image is as partitioning algorithm and segmentation step.
Background technology
Image segmentation is the premise of image understanding and computer vision, and the basic fundamental of image processing and analysis it
One.The Threshold segmentation of image has been applied to many fields, for example, in infrared technique application, Infrared Imaging Track Systems
The segmentation of middle target, the segmentation of Infrared Non-destructive Testing mid-infrared thermal image；In remote sensing application, mesh in diameter radar image
Target segmentation etc.；In medical application, the segmentation of magnetic resonance image (MRI), the segmentation of Blood Corpuscle Image；In agriculture project application,
The segmentation of fruit image and background during fruit quality Non-Destructive Testing；In commercial production application, machine vision applies to produce
Quality detection etc..Extensive application of the image Segmentation Technology in these fields, fully shows that image segmentation is played very important
Effect.
At present, threshold segmentation algorithm species is relatively more, and because which has, calculating is simple, operation efficiency is higher, speed is fast
Advantage and commonly used, the key of Threshold segmentation is the problem by which kind of principle selected threshold.For this problem
Chinese scholars have carried out substantial amounts of research, and propose various Research on threshold selection.Wherein, the classical algorithm of comparison mainly has
One-dimensional maximum variance between clusters (Otsu algorithms), one-dimension information maximum entropy algorithm and Minimum Cross-Entropy Algorithm, these classic algorithm
With its segmentation effect preferably, the scope of application is relatively wide, simple effectively cause the common concern of people.But, these classical ways
When the gray scale difference of target and background in the picture is not clear aobvious, adopting said method segmentation loses can the information of image, occur
Than more serious segmentation errors.
It is less than satisfactory in order to solve the problems, such as one-dimensional classical way segmentation effect when signal noise ratio (snr) of image is relatively low, some
Scholar increases average gray level rectangular histogram on the basis of original gray level histogram and constitutes two dimensional gray level-neighborhood averaging gray scale
The histogrammic thought of level carrys out selected threshold, make segmentation effect be improved significantly, but due to the increase of solution space dimension, cause
Calculate complicated, operation time extends.
The content of the invention
It is an object of the invention to provide an a kind of dimensional object complexity map of gray level image is as partitioning algorithm and segmentation step,
It is unobvious with the change of background gray scale in image object to be split to solve, make the problem of the segmentation errors of image information loss.
In order to solve above-mentioned technical problem, the invention provides a kind of image complexity image segmentation algorithm, including：It is determined that
Optimal segmenting threshold, to split objective area in image and background area.
Further, determine that the method for optimal segmenting threshold includes：
Step Sa, respectively defines background image complexity by gray value by the pixel in image complicated with target image
Degree；
Step Sb, calculates optimal segmenting threshold.
Further, background image complexity and mesh are defined respectively by gray value by the pixel in image in step Sa
The method of logo image complexity includes：
Image pixel number is set as N, the scope of gray value is [0, L-1], L maximum gray scales；Gray value is in image
Pixel is divided into background image complexity C by gray value0With target image complexity C1；Wherein
Background image complexity C0Pixel composition by gray value between [0, T], background image complexity C0Total pixel number
For N0, target image complexity C1Pixel composition by gray value between [T+1, L-1], target image complexity C1Total pixel
Number is N1；
Background image complexity C0With target image complexity C1Correspond to respectively：
Wherein h0I () is background image complexity C0In corresponding pixel i of same gray level number, h1I () is target figure
As complexity C1In corresponding pixel i of same gray level number；And
The method that optimal segmenting threshold is calculated in step Sb includes：
The absolute value C (T) of the background of image and the difference of target image complexity is calculated, i.e.,
Gray value value successively in the range of [0, L-1], so that corresponding intensity slicing value g when absolute value C (T) is minimum
The optimal segmenting threshold is defined as, i.e.,
Another aspect, present invention also offers a kind of image complexity segmentation step, comprises the steps：
Step S1, counts rectangular histogram h (i) of gray level image, to obtain its background image complexity C0Pixel count N0, and
Background image complexity C0In corresponding pixel i of same gray level number h0(i)；Target image complexity C1Pixel count be
N1, and target image complexity C1In corresponding pixel i of same gray level number h1(i)；
Step S2, calculates background image complexity C0, target image complexity C1；
Step S3, it is absolute with the difference of target image complexity according to the background that the scope of gray value calculates image successively
Value C (T) value；
Step S4, repeat step S2, step S3, until corresponding intensity slicing value g when the absolute value C (T) for calculating is minimum
It is defined as splitting the optimal segmenting threshold of objective area in image and background area.
Further, background image complexity C is calculated in step S20, target image complexity C1Method include：
Image pixel number is set as N, the scope of gray value is [0, L-1], L maximum gray scales；Gray value is in image
Pixel is divided into background image complexity C by gray value0With target image complexity C1；Wherein
Background image complexity C0Pixel composition by gray value between [0, T], background image complexity C0Total pixel number
For N0, target image complexity C1Pixel composition by gray value between [T+1, L-1], target image complexity C1Total pixel
Number is N1；
Background image complexity C0With target image complexity C1Correspond to respectively：
Further, the background of image and the difference of target image complexity are calculated in step S3 according to the scope of gray value successively
The absolute value C (T) of value；And
Determine in step S4 that the method for intensity slicing value g includes：
Absolute value C (T) is calculated, i.e.,
Gray value value successively in the range of [0, L-1], so that corresponding intensity slicing value g when absolute value C (T) is minimum
The optimal segmenting threshold is defined as, i.e.,
The invention has the beneficial effects as follows, the present invention solves the gray area of the target and background in image to be split well
When unobvious, conventional method segmentation loses can the information of image, the problem than more serious segmentation errors easily occur, and have
There are segmentation efficiency high, the advantage of fast operation.
Description of the drawings
The present invention is further described with reference to the accompanying drawings and examples.
Fig. 1 is the flow chart of the method for the determination optimal segmenting threshold of the present invention；
Fig. 2 (a) is standard lena image, and Fig. 2 (b) is low-light (level) infrared image；
Fig. 3 (a) is standard lena image Jing Otsu algorithm image segmentation results, and Fig. 3 (b) is low-light (level) infrared image Jing
Otsu algorithm image segmentation results；
Fig. 4 (a) is standard lena image Jing information maximization entropy algorithm segmentation results, and Fig. 4 (b) is low-light (level) infrared image Jing
Information maximization entropy algorithm segmentation result；
Fig. 5 (a) is standard lena image Jing Minimum Cross-Entropy Algorithm segmentation results, and Fig. 5 (b) is low-light (level) infrared image Jing
Minimum Cross-Entropy Algorithm segmentation result；
Fig. 6 (a) is standard lena image Jing this image complexity partitioning algorithm segmentation results, and Fig. 6 (b) is infrared for low-light (level)
Image Jing this image complexity partitioning algorithm segmentation results.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further detailed explanation.These accompanying drawings are simplified schematic diagram, only with
The basic structure of the illustration explanation present invention, therefore which only shows the composition relevant with the present invention.
Embodiment 1
The invention provides a kind of image complexity image segmentation algorithm, including：Determine optimal segmenting threshold, with segmentation figure
Target area and background area as in.
As shown in figure 1, determining that the method for optimal segmenting threshold includes：
Step Sa, respectively defines background image complexity by gray value by the pixel in image complicated with target image
Degree；
Step Sb, calculates optimal segmenting threshold.
Specifically, background image complexity and mesh are defined respectively by gray value by the pixel in image in step Sa
The method of logo image complexity includes：
Image pixel number is set as N, the scope of gray value is [0, L-1], L maximum gray scales；Gray value is in image
Pixel is divided into background image complexity C by gray value0With target image complexity C1；Wherein
Background image complexity C0Pixel composition by gray value between [0, T], background image complexity C0Total pixel number
For N0, target image complexity C1Pixel composition by gray value between [T+1, L-1], target image complexity C1Total pixel
Number is N1；
Background image complexity C0With target image complexity C1Correspond to respectively：
Wherein h0I () is background image complexity C0In corresponding pixel i of same gray level number, h1I () is target figure
As complexity C1In corresponding pixel i of same gray level number；And
The method that optimal segmenting threshold is calculated in step Sb includes：
The absolute value C (T) of the background of image and the difference of target image complexity is calculated, i.e.,
Gray value value successively in the range of [0, L-1], so that corresponding intensity slicing value g when absolute value C (T) is minimum
The optimal segmenting threshold is defined as, i.e.,
Embodiment 2
On the basis of embodiment 1, the present embodiment 2 additionally provides a kind of image complexity segmentation step.
This image complexity segmentation step comprises the steps：
Step S1, counts rectangular histogram h (i) of gray level image, to obtain its background image complexity C0Pixel count N0, and
Background image complexity C0In corresponding pixel i of same gray level number h0(i)；Target image complexity C1Pixel count be
N1, and target image complexity C1In corresponding pixel i of same gray level number h1(i)；
Step S2, calculates background image complexity C0, target image complexity C1；
Step S3, it is absolute with the difference of target image complexity according to the background that the scope of gray value calculates image successively
Value C (T)；
Step S4, repeat step S2, step S3, until corresponding intensity slicing value g when the absolute value C (T) for calculating is minimum
It is defined as splitting the optimal segmenting threshold of objective area in image and background area.
Specifically, background image complexity C is calculated in step S20, target image complexity C1Method include：
Image pixel number is set as N, the scope of gray value is [0, L-1], L maximum gray scales；Gray value is in image
Pixel is divided into background image complexity C by gray value0With target image complexity C1；Wherein
Background image complexity C0Pixel composition by gray value between [0, T], background image complexity C0Total pixel number
For N0, target image complexity C1Pixel composition by gray value between [T+1, L-1], target image complexity C1Total pixel
Number is N1；
Background image complexity C0With target image complexity C1Correspond to respectively：
Specifically, the background of image and the difference of target image complexity are calculated in step S3 according to the scope of gray value successively
The absolute value C (T) of value；And
Determine in step S4 that the method for intensity slicing value g includes：
The absolute value C (T) is calculated, i.e.,
Gray value value successively in the range of [0, L-1], so that corresponding intensity slicing value g when absolute value C (T) is minimum
The optimal segmenting threshold is defined as, i.e.,
In above-described embodiment 1 and embodiment 2, the value of L is 256.
The step of below by way of specific simulation example to embodiment 1 and embodiment 2, is described in detail.
As shown in Fig. 2 experimental image chooses two width images, the standard lena ash of 512 × 512 normal illumination of a width resolution
Degree image, 269 × 350 low-light (level) infrared hybrid optical system of another width resolution, experimental tool matlab, experimental image such as Fig. 1.
For the arithmetic speed of check algorithm, treatment effeciency, intersect with classical otsu algorithms, information maximization entropy algorithm and minimum
Entropy algorithm is compared.Program execution environments：Windows system Intel Pentinum CPU G860 dominant frequency 3.0GHZ, table 1,
Table 2 is respectively each algorithm segmentation standard lena image and low-light (level) infrared image result data, algorithm segmentation effect such as Fig. 3,4,
5th, shown in 6.
1 standard lena method comparison result of table
2 low-light (level) infrared image method comparison result of table
From in terms of table 1,2 Riming time of algorithm of table, Otsu algorithms are essentially identical with this algorithm operation time, both less than maximum entropy
Algorithm and Minimum Cross-Entropy Algorithm.Experiment shows, the Otsu algorithms speed of service and the basic phase of this algorithm speed of service in 4 kinds of algorithms
When Minimum Cross-Entropy Algorithm is basically identical with maximum entropy algorithm run time, and the two operation time is most long, shows minimum cross entropy
Algorithm is most slow with maximum entropy algorithm arithmetic speed.
From in terms of the image segmentation of Fig. 3,4,5,6, the image that Otsu algorithms are split with Minimum Cross-Entropy Algorithm has information to lose
Lose more serious phenomenon, in such as Fig. 3, the lena facial informations in 5 under normal illumination nose and lip do not have substantially, in low-light (level)
In infrared image, the phenomenon performance is particularly evident, and the right fawn does not have substantially.Information maximization entropy algorithm and this image complexity
Algorithm is to nose and lip outline information ratio in facial information in the bianry image of the lena carrying out image threshold segmentation under normal illumination
The effect that Otsu algorithms are split with Minimum Cross-Entropy Algorithm will be got well.Information maximization entropy algorithm compared with this algorithm, split by this algorithm
Lena bianry images nose it is better than what information maximization entropy algorithm was split with lip outline information；This advantage is red in low-light (level)
What is showed in the bianry image of outer segmentation becomes apparent from, and the fawn profile on the right is all split well.
In sum, in the algorithm speed of service, this algorithm=Otsu algorithms<Maximum entropy algorithm=minimum cross entropy is calculated
Method, i.e. this algorithm operational efficiency are substantially better than maximum entropy algorithm and Minimum Cross-Entropy Algorithm, and the algorithm speed of service is fast.In algorithm point
Cut this algorithm in effect>Maximum entropy algorithm>Otsu algorithms>Minimum Cross-Entropy Algorithm, i.e. this algorithm are optimum, and solve well
When the gray scale difference of the target and background in image to be split is not clear aobvious, conventional method segmentation loses can the information of image, go out
Now than more serious segmentation errors.
At present all to there is image object to be split in image segmentation unobvious with background grey scale change for several classic algorithm,
The segmentation errors problem for losing image information, is to solve this problem, and this image complexity image segmentation algorithm is complicated from image
The angle of the object complexity of degree, it is proposed that a kind of target chooses optimal with the difference minima of the image object complexity of background
The method of threshold value, compares through several classic algorithm split-run tests, shows that this algorithm speed of service is fast, and effectively can solve
Segmentation effect less than satisfactory problem when signal noise ratio (snr) of image is relatively low.
With the above-mentioned desirable embodiment according to the present invention as enlightenment, by above-mentioned description, relevant staff is complete
Various change and modification can be carried out in the range of without departing from this invention technological thought entirely.The technology of this invention
Property scope is not limited to the content in description, it is necessary to its technical scope is determined according to right.
Claims (6)
1. a kind of image complexity image segmentation algorithm, it is characterised in that include：
Determine optimal segmenting threshold, to split objective area in image and background area.
2. image segmentation algorithm according to claim 1, it is characterised in that
Determine that the method for optimal segmenting threshold includes：
Step Sa, respectively defines background image complexity and target image complexity by gray value by the pixel in image；
Step Sb, calculates optimal segmenting threshold.
3. image segmentation algorithm according to claim 2, it is characterised in that
Background image complexity is defined respectively by gray value by the pixel in image in step Sa complicated with target image
The method of degree includes：
Image pixel number is set as N, the scope of gray value is [0, L-1], L maximum gray scales；Gray value is the pixel in image
It is divided into background image complexity C by gray value0With target image complexity C1；Wherein
Background image complexity C0Pixel composition by gray value between [0, T], background image complexity C0Total pixel number is
N0, target image complexity C1Pixel composition by gray value between [T+1, L-1], target image complexity C1Total pixel number
For N1；
Background image complexity C0With target image complexity C1Correspond to respectively：
Wherein h0I () is background image complexity C0In corresponding pixel i of same gray level number, h1I () is multiple for target image
Miscellaneous degree C1In corresponding pixel i of same gray level number；And
The method that optimal segmenting threshold is calculated in step Sb includes：
The absolute value C (T) of the background of image and the difference of target image complexity is calculated, i.e.,
Gray value value successively in the range of [0, L-1], so that the corresponding intensity slicing value g definition when absolute value C (T) is minimum
For the optimal segmenting threshold, i.e.,
4. a kind of image complexity segmentation step, comprises the steps：
Step S1, counts rectangular histogram h (i) of gray level image, to obtain its background image complexity C0Pixel count N0, and background
Image complexity C0In corresponding pixel i of same gray level number h0(i)；Target image complexity C1Pixel count be N1, and
Target image complexity C1In corresponding pixel i of same gray level number h1(i)；
Step S2, calculates background image complexity C0, target image complexity C1；
Step S3, calculates the absolute value C of the background of image and the difference of target image complexity successively according to the scope of gray value
(T) value；
Step S4, repeat step S2, step S3, until the absolute value C (T) for calculating corresponding intensity slicing value g definition when minimum
To split the optimal segmenting threshold of objective area in image and background area.
5. image complexity segmentation step according to claim 4, it is characterised in that
Background image complexity C is calculated in step S20, target image complexity C1Method include：
Image pixel number is set as N, the scope of gray value is [0, L-1], L maximum gray scales；Gray value is the pixel in image
It is divided into background image complexity C by gray value0With target image complexity C1；Wherein
Background image complexity C0Pixel composition by gray value between [0, T], background image complexity C0Total pixel number is
N0, target image complexity C1Pixel composition by gray value between [T+1, L-1], target image complexity C1Total pixel number
For N1；
Background image complexity C0With target image complexity C1Correspond to respectively：
。
6. image complexity segmentation step according to claim 5, it is characterised in that
Calculate the absolute value C of the background of image and the difference of target image complexity in step S3 according to the scope of gray value successively
(T) value；And
Determine in step S4 that the method for intensity slicing value g includes：
Absolute value C (T) is calculated, i.e.,
Gray value value successively in the range of [0, L-1], so that the corresponding intensity slicing value g definition when absolute value C (T) is minimum
For the optimal segmenting threshold, i.e.,
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