CN108957572A - A kind of terahertz imaging method, device, equipment and readable storage medium storing program for executing - Google Patents
A kind of terahertz imaging method, device, equipment and readable storage medium storing program for executing Download PDFInfo
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V8/00—Prospecting or detecting by optical means
- G01V8/005—Prospecting or detecting by optical means operating with millimetre waves, e.g. measuring the black losey radiation
Abstract
The invention discloses a kind of terahertz imaging methods, method includes the following steps: carrying out stochastical sampling imaging using back-projection algorithm, obtain stochastical sampling image；The maximum point of gray value is determined in stochastical sampling image, and constructs choice box for maximum point；Choice box is handled according to preset rules, obtains target selection frame；Precise imaging is carried out to the corresponding target area of target selection frame, obtains precise image；It is superimposed precise image and stochastical sampling image, obtains terahertz image.High-resolution imaging can be carried out to dangerous material region, guarantee the accuracy of identification of dangerous material, and the region because reducing Precise imaging due to accelerates image taking speed, reduces the waste of computer resource.When being applied in real-time safety check, safety check speed can be accelerated, promote safety check quality.The invention also discloses a kind of terahertz imaging device, equipment and readable storage medium storing program for executing, have corresponding technical effect.
Description
Technical field
The present invention relates to Terahertz Technology field, more particularly to a kind of terahertz imaging method, device, equipment and readable
Storage medium.
Background technique
THz wave has the characteristics that penetration power is strong, harmless, therefore THz wave is widely used in examining safely
In looking into.For example, channel-type Terahertz human body safety check instrument can carry out safety check imaging to a wide range of interior crowd constantly passed through, greatly
Fast safety check speed.
Conventional synthesis aperture radar imaging algorithm-rear orientation projection used in channel-type Terahertz human body safety check instrument
Algorithm model is accurate, without signal form, bandwidth and the limitation for accumulating angle, adapts to non-uniform holes and there are larger random
The case where kinematic error.But the algorithm needs to traverse all pixels point of imaging region one by one, when imaging region increases
Operand will greatly increase, and operation time becomes very long, cannot achieve real time imagery.And if using stochastical sampling method
It carries out imaging or directly using other imaging algorithms, although image taking speed can be accelerated, will lead to imaging resolution reduction, no
Conducive to the identification of dangerous material.
In conclusion the problems such as how effectively promoting terahertz imaging, is that current those skilled in the art are badly in need of solving
The technical issues of.
Summary of the invention
The object of the present invention is to provide a kind of terahertz imaging method, device, equipment and readable storage medium storing program for executing, to accelerate too
Hertz image taking speed.
In order to solve the above technical problems, the invention provides the following technical scheme:
A kind of terahertz imaging method, comprising:
Stochastical sampling imaging is carried out using back-projection algorithm, obtains stochastical sampling image；
The maximum point of gray value is determined in the stochastical sampling image, and is maximum point building selection
Frame；
The choice box is handled according to preset rules, obtains target selection frame；
Precise imaging is carried out to the corresponding target area of the target selection frame, obtains precise image；
It is superimposed the precise image and the stochastical sampling image, obtains terahertz image.
Preferably, described that the choice box is handled according to preset rules, obtain target selection frame, comprising:
Expand the choice box according to default choice box growing strategy；
Choice box after will be enlarged by merges processing；
The choice box that area is greater than preset area threshold value is determined as target selection frame.
Preferably, the choice box that area is greater than preset area threshold value is determined as target selection frame, comprising:
Area is greater than the choice box of preset area threshold value to each boundary extension predetermined coefficient times, and by the choosing after extension
It selects frame and is determined as target selection frame.
It is preferably, described to expand the choice box according to default choice box growing strategy, comprising:
The pixel that gray value in the stochastical sampling image is greater than default gray threshold is determined as pixel to be selected；
Expand the choice box, forms choice box undetermined；
When the pixel number to be selected covered in the diff area between the choice box undetermined and the choice box
Or the choice box is replaced with the choice box undetermined when being greater than accordingly default judgment threshold by percentage.
Preferably, described to expand the choice box, form choice box undetermined, comprising:
The choice box is expanded to surrounding, forms choice box undetermined.
Preferably, described to expand the choice box, form choice box undetermined, comprising:
Orientation expands the choice box, forms choice box undetermined.
Preferably, it is described will be enlarged by after choice box merge processing, comprising:
Calculate the shortest distance between the every two choice box after expanding；
The choice box that the shortest distance is zero is merged.
A kind of terahertz imaging device, comprising:
Stochastical sampling module obtains stochastical sampling image for carrying out stochastical sampling imaging using back-projection algorithm；
Frame construction module is selected, for determining the maximum point of gray value in the stochastical sampling image, and is institute
State maximum point building choice box；
Target selection frame obtains module, for handling according to preset rules the choice box, obtains target selection
Frame；
Precise imaging module obtains fine for carrying out Precise imaging to the corresponding target area of the target selection frame
Image；
Terahertz image obtains module, for being superimposed the precise image and the stochastical sampling image, obtains Terahertz
Image.
A kind of terahertz imaging equipment, comprising:
Memory, for storing computer program；
Processor, the step of above-mentioned terahertz imaging method is realized when for executing the computer program.
A kind of readable storage medium storing program for executing is stored with computer program, the computer program quilt on the readable storage medium storing program for executing
The step of processor realizes above-mentioned terahertz imaging method when executing.
Using method provided by the embodiment of the present invention, carry out stochastical sampling imaging using back-projection algorithm, obtain with
Machine sampled images；The maximum point of gray value is determined in stochastical sampling image, and constructs choice box for maximum point；According to
Preset rules handle choice box, obtain target selection frame；To the corresponding target area of target selection frame carry out finely at
Picture obtains precise image；It is superimposed precise image and stochastical sampling image, obtains terahertz image.
When carrying out terahertz imaging, stochastical sampling imaging is carried out first with back-projection algorithm, can obtain and adopt at random
Sampled images.Wherein, the Terahertz that the gray value of each of stochastical sampling image pixel has respectively represented corresponding position returns
Wave signal strength.The Terahertz echo signal intensity of usual dangerous material is relatively high.That is, dangerous material whereabouts can be
Occurs the maximum point of gray value in stochastical sampling image.Then, choice box is established for the maximum point determined, and to choosing
Frame is selected to be handled, it is final to obtain target selection frame.The corresponding target area of target selection frame is subjected to Precise imaging, can be obtained
Precise image, precise image and Terahertz random image are overlapped, can get terahertz image, i.e., completion Terahertz at
Picture.In this way, can either guarantee the accuracy of identification of dangerous material to the high-resolution imaging in key area (dangerous material region), and because
Reduce the region of Precise imaging and accelerate image taking speed, reduces the waste of computer resource.It, can when being applied in real-time safety check
Accelerate safety check speed, promotes safety check quality.
Correspondingly, the embodiment of the invention also provides terahertz imaging corresponding with above-mentioned terahertz imaging method dresses
It sets, equipment and readable storage medium storing program for executing, has above-mentioned technique effect, details are not described herein.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is a kind of implementation flow chart of terahertz imaging method in the embodiment of the present invention；
Fig. 2 is the schematic diagram of maximum point in the embodiment of the present invention；
Fig. 3 is the implementation flow chart of another terahertz imaging method in the embodiment of the present invention；
Fig. 4 is the implementation flow chart of another terahertz imaging method in the embodiment of the present invention；
Fig. 5 is the implementation flow chart of another terahertz imaging method in the embodiment of the present invention；
Fig. 6 is the implementation flow chart of another terahertz imaging method in the embodiment of the present invention；
Fig. 7 is that choice box merges schematic diagram in the embodiment of the present invention；
Fig. 8 is the implementation flow chart of another terahertz imaging method in the embodiment of the present invention；
Fig. 9 is the terahertz image of perfectly fine imaging；
Figure 10 is the terahertz image obtained using terahertz imaging method provided in an embodiment of the present invention；
Figure 11 is a kind of structural schematic diagram of terahertz imaging device in the embodiment of the present invention；
Figure 12 is a kind of structural schematic diagram of terahertz imaging equipment in the embodiment of the present invention.
Specific embodiment
Core of the invention is to provide a kind of terahertz imaging method.In view of traditional back-projection algorithm is needed to hardware
The all areas scanned are imaged, and imaging resolution is very high, but due to needing to accurately calculate all pixels point, are consumed
Shi Taichang is unfavorable for real-time detection.And channel-type Terahertz human body safety check instrument hardware imaging region is larger, but is actually using
When significant component of imaging region will be not present people or the object for needing to carry out safety check, imaging meaning pole is carried out to such region
It is small, hardware is but expended significantly calculates cost.It is therefore proposed a kind of terahertz imaging method, first carries out stochastical sampling imaging, then
Based on stochastical sampling imaging analyze and count, determine the target area for needing Precise imaging, then only to target area into
Row Precise imaging, in this way, can accomplish fast imaging under the premise of ensureing the resolution ratio of key area.It is more advantageous in real time
Safety check.
Correspondingly, the embodiment of the invention also provides a kind of terahertz imaging device, equipment and readable storage medium storing program for executing, have
Above-mentioned technical effect, details are not described herein.
In order to enable those skilled in the art to better understand the solution of the present invention, with reference to the accompanying drawings and detailed description
The present invention is described in further detail.Obviously, described embodiments are only a part of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Under every other embodiment obtained, shall fall within the protection scope of the present invention.
Embodiment one:
Referring to FIG. 1, Fig. 1 be the embodiment of the present invention in a kind of flow chart of terahertz imaging method, this method include with
Lower step:
S101, stochastical sampling imaging is carried out using back-projection algorithm, obtains stochastical sampling image.
In the present embodiment, a kind of channel-type terahertz imaging system can be used to obtain terahertz signal, which uses 4
Hair 8 receives array, and the hardware in conjunction with circuit orbit, and the extensive area of 2m*2m can be imaged simultaneously.
Stochastical sampling imaging is carried out using back-projection algorithm.Specifically, sample boxes can be set as the rectangle of a*b size
Entire image is divided into n such rectangle frames by frame, and wherein n is the positive integer greater than 1.K (0 < is chosen in each sample boxes
K < a*b) a point is imaged, such as k=2.Then the signal strength maximum value for calculating each sample boxes, as the sample boxes
Signal strength indication.Then, two dimensional image Wavelet Denoising Method can be carried out, to result (the i.e. terahertz of each pixel after denoising
Hereby echo signal intensity) it is counted, obtain the maximum value and minimum value of the gray value of SAR image.If after Wavelet Denoising Method
Image grayscale Value Data be f (x, y), x, y are respectively horizontal, ordinate, then gray value maximum value is max (f (x, y)), minimum
Value is min (f (x, y)).
In the process, current imaging results also be can record and whether each pixel carried out the state of imaging.It needs
Illustrate it is that the precision of sampling is related with the pixel number of sample boxes size, selection, and then influences to find target area below
Process.Specifically, sampling is finer, image taking speed is accelerated more, but target detection success rate is lower.Therefore, k value can be with
Predefine or be determined and adjust according to actual needs, for example, when need image taking speed faster when, settable biggish k
Value, when needing to detect success rate, then settable lesser k value.
After obtaining stochastical sampling image, step S102 operation can be performed.
S102, the maximum point that gray value is determined in stochastical sampling image, and choice box is constructed for maximum point.
It should be noted that function is in some minimum section, there are independent variable value x, and exist than its it is big with it is smaller
Independent variable, functional value corresponding to these independents variable is respectively less than the corresponding functional value of x.So this functional value is known as maximum.
Even there is f (x)≤f (x0) to all x in some neighborhood of point x0, then claims f that there is a maximum, maximum f in x0
(x0)." very big " is the concept of a locality.
Based on the probability of above-mentioned maximum, the maximum point of the gray value in stochastical sampling image is searched.Because should be with
Machine sampled images are X-Y scheme, therefore, when bigger than the gray value of the pixel of surrounding there are the gray value of a pixel, then
Think that the point is maximum point.Specifically, then the point is maximum when occurring being respectively less than this such as adjacent 8 point of Fig. 2
Point.Specifically, referring to FIG. 2, maximum point is indicated with solid black roundlet, when the gray value for a point occur is than known gray scale
When the gray scale of adjacent 8 points (being indicated in figure with white roundlet) of value is big, this point is maximum point.It should be noted that pole
The quantity of big value point is related to the stochastical sampling image currently obtained, therefore, the maximum point of different stochastical sampling images
Number possibility is identical may also be different.After determining maximum point, in stochastical sampling image with choice box by each
Maximum point is framed.That is, there are how many a maximum points, with regard to how many choice box.Specifically, the choice box can
Think box.If the upper and lower, left and right boundary of choice box use respectively a, b, c, d represent, then choice box can be denoted as box (a,
B, c, d), area S=(b-a+1) * (d-c+1).The side length of the choice box is unit length 1.
S103, choice box is handled according to preset rules, obtains target selection frame.
In the present embodiment, can preset the rule of the processing of choice box, the rule may include to choice box into
The processing mode of combination between the one or more of them of processing modes such as row selection, growth expansion, merging.
After maximum point is framed with choice box, it can use the pre-set rule handle to choice box
Then, choice box is handled, it is final to obtain target selection frame.That is, further being obtained by handling choice box
Obtain target selection frame.The number of target selection frame that is obtained after processing choice box, size can according to the actual situation depending on.Example
Such as, if determining 20 maximum points in step s 102, then the number of choice box is also 20, and target selection frame then base
It is obtained after this 20 choice boxs are handled, the number of target selection frame still can be 20, or be lower than 20；Target
The size of choice box can be identical as the size of choice box, can also be bigger than choice box.
S104, Precise imaging is carried out to the corresponding target area of target selection frame, obtains precise image.
After obtaining target selection frame, Precise imaging is carried out to the corresponding target area of target selection frame, can get fine
Image.Specifically, all pixels point in the corresponding target area of target selection frame can be traversed one by one, carry out finely at
Picture finally obtains precise image.Because in step s101 to have carried out random imaging, i.e., there is the picture for having become picture in target area
Vegetarian refreshments can traverse one by one in practical applications only for pixel unimaged in target area, carry out Precise imaging.
S105, superposition precise image and stochastical sampling image, obtain terahertz image.
After obtaining Precise imaging, Precise imaging can be overlapped in stochastical sampling image, finally obtain imaging region
Interior complete terahertz image.Resolution ratio in the terahertz image relative to the corresponding position in target area is higher, and for
The corresponding position in nontarget area is then lower.
Using method provided by the embodiment of the present invention, carry out stochastical sampling imaging using back-projection algorithm, obtain with
Machine sampled images；The maximum point of gray value is determined in stochastical sampling image, and constructs choice box for maximum point；According to
Preset rules handle choice box, obtain target selection frame；To the corresponding target area of target selection frame carry out finely at
Picture obtains precise image；It is superimposed precise image and stochastical sampling image, obtains terahertz image.When carrying out terahertz imaging,
Stochastical sampling imaging is carried out first with back-projection algorithm, stochastical sampling image can be obtained.Wherein, in stochastical sampling image
The gray value of each pixel has respectively represented the Terahertz echo signal intensity of corresponding position.The Terahertz of usual dangerous material
Echo signal intensity is relatively high.That is, the pole of gray value can occur in stochastical sampling image in dangerous material whereabouts
Big value point.Then, choice box is established for the maximum point determined, and choice box is handled, it is final to obtain target choosing
Select frame.The corresponding target area of target selection frame is subjected to Precise imaging, precise image can be obtained, by precise image and terahertz
Hereby random image is overlapped, and can get terahertz image, i.e. completion terahertz imaging.In this way, can be (dangerous to key area
Product region) high-resolution imaging is carried out, guarantee the accuracy of identification of dangerous material, and the region because reducing Precise imaging due to accelerates into
As speed, the waste of computer resource is reduced.When being applied in real-time safety check, safety check speed can be accelerated, promote safety check quality.
It should be noted that based on the above embodiment one, the embodiment of the invention also provides be correspondingly improved scheme.Rear
Involved in continuous embodiment with can mutually be referred between same steps or corresponding steps in above-described embodiment one, corresponding beneficial effect
Can also be cross-referenced, it is no longer repeated one by one in improvement embodiment below.
Embodiment two:
Referring to FIG. 3, Fig. 3 is the flow chart of another terahertz imaging method in the embodiment of the present invention, this method includes
Following steps:
S201, stochastical sampling imaging is carried out using back-projection algorithm, obtains stochastical sampling image.
S202, the maximum point that gray value is determined in stochastical sampling image, and choice box is constructed for maximum point.
S203, expand choice box according to default choice box growing strategy.
In the present embodiment, choice box growing strategy can be preset.For example, when reaching the pixel around choice box
Gray value when being greater than default gray threshold, will be enlarged by the choice box, to will be greater than the pixel covering of default gray threshold.
After constructing choice box, expanded according to the growing strategy of default choice box.Because in stochastical sampling image
The gray value of pixel is not quite identical, so, the expansion program of each choice box, and shape after expanding may also can not
Unanimously.
S204, will be enlarged by after choice box merge processing.
Cause meets default choice box growing strategy, so that it may expand the choice box, it is understood that there may be two or more
Choice box mutually covers between choice box after expansion, intersects, each other nonseptate situation.In order to avoid it is subsequent at
The pixel for repeating covering to two or more choice boxs as during carries out repeating imaging, it is possible to after will be enlarged by
Mutually covering, intersects, nonseptate choice box merges each other.When merging, another, which can be used directly, to cover
The minimum choice box for two choice boxs that lid currently mutually covers is replaced, that is to say, that a choice box can be used and replace
Change original two and more than two choice boxs.That is, the choice box after merging is than the choice box before merging
Quantity it is less.Merge it is primary after, the choice box after merging if it exists meets with other choice boxs again when merging condition, then again
It is secondary to merge operation, until merging condition is not satisfied between all choice boxs.Certainly, for being unsatisfactory for merging item
The choice box of part then can be without processing.
S205, the choice box that area is greater than preset area threshold value is determined as target selection frame.
In the present embodiment, one can be preset for screening a threshold value of choice box, which is specially one
A area threshold.
It is influenced by noise jamming or other accidentalia, may have part choice box and not expanded or given birth to always
It is long, be also unsatisfactory for merging and require so that the area of the choice box is smaller, herein it is considered that the choice box it is corresponding greatly
Value point may and much be generated due to the factor of dangerous product, but be generated because of other accidentalia,
The choice box can be given up.That is, only leaving the choice box that area is greater than preset area threshold value, and give up face
Product is less than the choice box of preset area threshold value, in this way, the carry out Precise imaging to meaningless region can be reduced, reduces computer money
The waste in source can accelerate image taking speed.
S206, Precise imaging is carried out to the corresponding target area of target selection frame, obtains precise image.
S207, superposition precise image and stochastical sampling image, obtain terahertz image.
Embodiment three,
Referring to FIG. 4, Fig. 4 is the flow chart of another terahertz imaging method in the embodiment of the present invention, this method includes
Following steps:
S301, stochastical sampling imaging is carried out using back-projection algorithm, obtains stochastical sampling image.
S302, the maximum point that gray value is determined in stochastical sampling image, and choice box is constructed for maximum point.
S303, expand choice box according to default choice box growing strategy.
S304, will be enlarged by after choice box merge processing.
S305, the choice box by area greater than preset area threshold value will extend to each boundary extension predetermined coefficient times
Choice box afterwards is determined as target selection frame.
Specifically, can first remove the choice box that all areas are less than preset area threshold value.Image quality in order to balance is kept away
Exempt from lost part fringe region, (λ is constant, can be adjusted according to the actual situation to all existing each λ times of boundary extensions of choice box
It is whole), i.e., the choice box box (a, b, c, d) that area is greater than preset area threshold value is replaced are as follows:
box(a-λ(b-a),b+λ(b-a),c-λ(d-c),d+λ(d-c)).And the choice box after extension is determined as target
Choice box.
After being extended, intersection judgement can also be carried out to remaining all choice boxs again, by the choosing of all intersections
It selects frame to merge, it is ensured that be not overlapped again between final target selection frame region.
S306, Precise imaging is carried out to the corresponding target area of target selection frame, obtains precise image.
S307, superposition precise image and stochastical sampling image, obtain terahertz image.
Example IV:
Referring to FIG. 5, Fig. 5 is the flow chart of another terahertz imaging method in the embodiment of the present invention, this method includes
Following steps:
S401, stochastical sampling imaging is carried out using back-projection algorithm, obtains stochastical sampling image.
S402, the maximum point that gray value is determined in stochastical sampling image, and choice box is constructed for maximum point.
S403, the pixel that the gray value in stochastical sampling image is greater than default gray threshold is determined as pixel to be selected
Point.
S404, expand choice box, form choice box undetermined.
S405, when the pixel number to be selected or percentage covered in the diff area between choice box undetermined and choice box
When greater than corresponding default judgment threshold, choice box is replaced with into choice box undetermined.
It is illustrated for ease of description, below combining tri- steps of above-mentioned steps S403, S404 and S405.
In view of the gray value of the corresponding pixel in the region of dangerous material is greater than the gray scale of the pixel in non-dangerous article region
Value, so choosing the pixel that gray value is greater than default gray threshold.Choose all pixels for meeting following formula:
F (x, y) >-W1[max (f (x, y))-min (f (x, y))]+max (f (x, y)), that is to say, that select
Pixel f (x, y) to be selected is signal strength W1Preceding pixel.
Then expand choice box, form choice box undetermined, choice box undetermined can be that each boundary value of choice box is past
Choice box after one unit of outer expansion, for example, then choice box undetermined is box (a-1, b+ if choice box is box (a, b, c, d)
1,c-1,d+1)；It is undetermined select frame can for by one of boundary of choice box toward the choice box after one unit of external expansion, example
If choice box is that then choice box undetermined is box (a, b, c, d+1) to box (a, b, c, d).Certainly, the degree for expanding or extending is also
It can be multiple parasangs.
Then, the corresponding pixel in region of expansion or dilation is counted.Specifically, if choice box box1 is passed through
Choice box box2 to be selected is obtained after crossing extension or expansion, then the corresponding region box2-box1 is counted.Work as box2-box1
The number or percentage of pixel to be selected in pixel in region meet corresponding threshold value, then replace with choice box box1
Pixel box2 to be selected, specifically, if choice box box1 be box1 (a1, b1, c1, d1), choice box undetermined be box2 (a2, b2,
C2, d2), then replaced choice box box1 is box1 (a2, b2, c2, d2).
Preferably, in above-mentioned step two, expansion choice box, during forming choice box undetermined, there is the following two kinds expansion
One such or two kinds of modes combined can be used in practical applications and expand choice box for big mode.
The first expands mode, and choice box is expanded to surrounding, forms choice box undetermined.
Specifically, being then box to the choice box box2 undetermined after surrounding expansion if choice box box1 is box (a, b, c, d)
(a-1,b+1,c-1,d+1)。
Then after obtaining choice box undetermined, all choice box box (a, b, c, d) are judged whether to meet following formula:
Pxpercent (box (a-1, b+1, c-1, d+1)-box (a, b, c, d)) > W2, wherein pxpercent (box) be
Selected pixels point accounts for the percentage of selection frame region whole pixel, (box (a-1, b+1, c-1, d+ in the region choice box box
1)-box (a, b, c, d)) indicate that box2-box1 is the part that the region box1 is removed in the region choice box box2, W2To preset
Percentage threshold.If it is satisfied, then substituting former choice box with the choice box box (a-1, b+1, c-1, d+1) undetermined after expansion
box(a,b,c,d).That is, replaced choice box box2 is box (a-1, b+1, c-1, d+1).
Second of expansion mode, orientation expand choice box, form choice box undetermined.
Specifically, if choice box box1 be box (a, b, c, d), then to orientation expand it is one unit greater after choice box undetermined
Box2 has following 4 kinds of forms of expression:
1,box(a-1,b,c,d)；
2,box(a,b+1,c,d)；
3,box(a,b,c-1,d)；
4、box(a,b,c,d+1)。
After obtaining choice box undetermined, following judgement is done:
Judgement formula 1, pxMaxNum (box (a-1, b, c, d)-box (a, b, c, d)) >=W3；
Judgement formula 2, pxMaxNum (box (a, b+1, c, d)-box (a, b, c, d)) >=W3；
Judgement formula 3, pxMaxNum (box (a, b, c-1, d)-box (a, b, c, d)) >=W3；
Judgement formula 4, pxMaxNum (box (a, b, c, d+1)-box (a, b, c, d)) >=W3；
Wherein, pxMaxNum (box) is the pixel number that the continuous selected pixels point in a direction is most in the region choice box box
Amount, box2-box1 are the part that the region box1 is removed in the region choice box box2, W3For the number threshold value of pixel to be selected.
When judging that formula 1 is set up, former choice box box is substituted with the choice box box (a-1, b, c, d) undetermined after growing up
(a,b,c,d)。
It can similarly obtain, when corresponding above formula is set up, respectively correspondingly with choice box box (a, b+1, c, d) undetermined, box (a,
B, c-1, d), box (a, b, c, d+1) substitutes former choice box.Circulation is until growth is not satisfied in all boundaries of all choice boxs
Condition.
S406, will be enlarged by after choice box merge processing.
S407, the choice box that area is greater than preset area threshold value is determined as target selection frame.
S408, Precise imaging is carried out to the corresponding target area of target selection frame, obtains precise image.
S409, superposition precise image and stochastical sampling image, obtain terahertz image.
Embodiment five:
Referring to FIG. 6, Fig. 6 is the flow chart of another terahertz imaging method in the embodiment of the present invention, this method includes
Following steps:
S501, stochastical sampling imaging is carried out using back-projection algorithm, obtains stochastical sampling image.
S502, the maximum point that gray value is determined in stochastical sampling image, and choice box is constructed for maximum point.
S503, expand choice box according to default choice box growing strategy.
The shortest distance between every two choice box after S504, calculating expansion, and the choice box for being zero by the shortest distance
It merges.
The shortest distance between every two choice box is the nearest direct range of choice box frame, when two choice boxs intersect
Or when adjacent, the shortest distance is 0.Judge the method that whether two choice boxs intersect (containing adjacent): for choice box box (a1,
b1,c1,d1) and box (a2,b2,c2,d2), judge whether following formula is true respectively:
|c1+d1-c2-d2|≤|d1+d2-c1-c2|；
|a1+b1-a2-b2|≤|b1+b2-a1-a2|；
When at least meeting one in above-mentioned two formula, two choice box intersections, particularly when any formula equal sign is set up two
A choice box is adjacent, and two choice boxs are adjacent in embodiments of the present invention is considered as intersection.
When two choice boxs are non-intersecting, the shortest distance between choice box is calculated using coordinate quadrant method.It is right
In calculating choice box box1 (a1,b1,c1,d1) and box2 (a2,b2,c2,d2) distance dis (box1, box2), calculate step
It is as follows:
Step 1: taking the central point of choice box box2One 2 dimension coordinate system xy is established for origin, is sentenced
Break the central point of another choice box box1In which quadrant of this coordinate system.
Step 2: taking a different points to be compared on choice box frame respectively according to the different quadrant in place, i.e.,
Take two choice boxs respectively closest to the endpoint of another choice box；If two points got are respectively (x1,y1) and (x2,y2)。
Step 3: the vector when the central point of choice box box1 is in first and third quadrantOtherwise
Step 4: by judging vectorTo calculate the distance of two choice boxs.Calculation formula is as follows:
Choice box merging method: for choice box box (a1,b1,c1,d1) and box (a2,b2,c2,d2), then the choosing after merging
Selecting frame is box (min (a1,a2),max(b1,b2),min(c1,c2),max(d1,d2)).Specifically, reference can be made to merging shown in Fig. 7
After box1 and box2, box3 is obtained, it should be noted that, box1 and box2 in Fig. 7 do not intersect, which is only to illustrate to merge
Mode.It should be noted that can also be merged when the distance between two choice boxs is not 0.For example, if merging it
The number of the pixel to be selected in the region that choice box newly covers afterwards can reach certain numerical value and can also merge.
S505, the choice box by area greater than preset area threshold value will extend to each boundary extension predetermined coefficient times
Choice box afterwards is determined as target selection frame.
S506, Precise imaging is carried out to the corresponding target area of target selection frame, obtains precise image.
S507, superposition precise image and stochastical sampling image, obtain terahertz image.
Embodiment six:
For convenient for those skilled in the art understand that technical solution provided by the embodiment of the present invention, below with specific specific
Example is provided for the embodiments of the invention technical solution and is described in detail:
As shown in figure 8, method provided by the embodiment of the present invention is a kind of a wide range of Terahertz choice box growth district choosing
Select fast imaging method.Existing, the image handled is in perfectly fine imaging as shown in figure 9, photo resolution is
400*400。
The specific implementation steps are as follows:
S601: first with traditional radar imaging method-back-projection algorithm and sample boxes method carry out stochastical sampling at
Picture.If sample boxes are the rectangle frame of 3*3 size, entire image is divided into 17689 such sample boxes.In each sample boxes
2 points are chosen to be imaged.Then the signal strength maximum value for calculating each sample boxes, the signal strength as the sample boxes
Value.In the process, current imaging results are recorded and whether each pixel carried out the state of imaging.
S602；Two dimensional image Wavelet Denoising Method is carried out, to result (the Terahertz echo-signal of each pixel after denoising
Intensity) it is counted, obtain the gray value of SAR (Synthetic Aperture Radar, synthetic aperture radar) image most
Big value and minimum value.If the image grayscale Value Data after Wavelet Denoising Method is f (x, y), x, y are respectively horizontal, ordinate, then grey
Angle value maximum value is max (f (x, y)), and minimum value is min (f (x, y)).
S603: and then the gray value maximum point on lookup X-Y scheme, it is alternatively that the seed point of frame growth algorithm.Then
Each seed point is framed with choice box in two-dimensional imaging result figure.The choice box is box.If choice box it is upper,
Under, left and right boundary be respectively a, b, c, d, then choice box is denoted as box (a, b, c, d), area S=(b-a+1) * (d-c+
1).The side length for defining choice box is unit length 1.The distance between every two choice box be choice box frame it is nearest it is direct away from
From when two choice boxs intersect or is adjacent, distance is 0.Judge the method that whether two choice boxs intersect (containing adjacent): for
Choice box box (a1,b1,c1,d1) and box (a2,b2,c2,d2), judge respectively
|c1+d1-c2-d2|≤|d1+d2-c1-c2|；
|a1+b1-a2-b2|≤|b1+b2-a1-a2|；
It is whether true.Two choice box intersections when at least meeting one in above-mentioned two formula, particularly when any formula equal sign
Two choice boxs are adjacent when establishment, and choice box is adjacent in the method is considered as intersection.
When two choice boxs are non-intersecting, the distance between choice box is calculated using coordinate quadrant method.For meter
Calculate choice box box1 (a1,b1,c1,d1) and box2 (a2,b2,c2,d2) distance dis (box1, box2), calculate steps are as follows:
Step 1: taking the central point of choice box box2One 2 dimension coordinate system is established (respectively for origin
Represented with x and y), judge the central point of another choice box box1This coordinate system which as
Limit.
Step 2: taking a different points to be compared on choice box frame respectively according to the different quadrant in place, i.e.,
Take two choice boxs respectively closest to the endpoint of another choice box；If two points got are respectively (x1,y1) and (x2,y2)。
Step 3: the vector when the central point of choice box box1 is in first and third quadrantOtherwise
Step 4: by judging vectorTo calculate the distance of two choice boxs.Calculation formula is as follows:
Choice box merging method: for choice box box (a1,b1,c1,d1) and box (a2,b2,c2,d2), then the choosing after merging
Selecting frame is box (min (a1,a2),max(b1,b2),min(c1,c2),max(d1,d2))。
S604: W before all signal strengths is chosen1=30% pixel.Choose all pixels for meeting following formula:
F (x, y) >-W1·[max(f(x,y))-min(f(x,y))]+max(f(x,y))；Wherein, pxpercent
(box) percentage of selection frame region whole pixel is accounted for for selected pixels point in the region choice box box, box2-box1 is choosing
The part that the region box1 is removed in the region frame box2 is selected, then following formula is judged to all choice box box (a, b, c, d)
Pxpercent (box (a-1, b+1, c-1, d+1)-box (a, b, c, d)) > W2It is whether true, if so, then use
Choice box box (a-1, b+1, c-1, d+1) after expansion substitutes original choice box box (a, b, c, d).W in above formula2=80% is root
The threshold value being arranged according to experience and actual conditions.
S605: the pixel of selection is identical as step S604.PxMaxNum (box) is a direction in the region choice box box
The most pixel quantity of continuous selected pixels point, box2-box1 is the part that the region box1 is removed in the region choice box box2, then right
All choice box box (a, b, c, d) successively judge
pxMaxNum(box(a-1,b,c,d)-box(a,b,c,d))≥W3, if it sets up, if so, then with raw upwards
Choice box box (a-1, b, c, d) after length substitutes original choice box box (a, b, c, d).W in above formula3=3 be rule of thumb and real
The threshold value of border situation setting.Similarly, then successively judge whether following three formula is true:
pxMaxNum(box(a,b+1,c,d)-box(a,b,c,d))≥W3；
pxMaxNum(box(a,b,c-1,d)-box(a,b,c,d))≥W3；
pxMaxNum(box(a,b,c,d+1)-box(a,b,c,d))≥W3；
If so, box (a, b+1, c, d) respectively correspondingly then is used, box (a, b, c-1, d), box (a, b, c, d+1) substitution
Former choice box.Circulation is until growth conditions is not satisfied in all boundaries of all choice boxs.Each pair of choice box carries out growth expansion
Afterwards, distance between any two is calculated to all choice boxs, distance is merged for 0 two choice boxs.
S606: condition judgement is merged to all existing choice boxs between any two, meets merging for condition.It closes
And condition is as follows: the pixel of selection is identical as step S603, if two original choice boxs are respectivelyWith
box2(a2,b2,c2,d2), area S1、S2, the region after merging is box3 (a3,b3,c3,d3), area S2, region 4
There is box4=box3-box2-box1.As shown in fig. 7, choice box box1 and choice box box 2, the then new choice box merged
Box3, region 4 are the white portion region in Fig. 7.Then merge condition:
(S1+S2)(1+pxpercent(box4)-0.5*lg(dis(box1,box2)))≥S3.Wherein S3To merge condition
Threshold value.Traversal is re-started if having choice box to merge in the process, until all existing choice boxs two-by-two it
Between merging condition is not satisfied.
S607: remove all areas less than area threshold W4=25 choice box.Image quality in order to balance avoids losing
Part edge region, to all existing each boundary extension of choice box λ=0.05 times, i.e., by all choice box box (a, b, c, d)
Replacement are as follows:
Box (a- λ (b-a), b+ λ (b-a), c- λ (d-c), d+ λ (d-c)) then carries out intersection to all choice boxs again and sentences
It is disconnected, the choice box of all intersections is merged, it is ensured that be not overlapped again between all selection frame regions.
S608: finally there are the regions of all choice boxs be required interesting target region.Using traditional
Back-projection algorithm carries out Precise imaging to all area-of-interests, has carried out the pixel of imaging in step s 601 at this time
Point is not repeated to calculate.
S609: Precise imaging is superimposed to obtain final imaging results by pixel with the imaging results of stochastical sampling.Finally
Imaging results are as shown in Figure 10.I.e. imaging is as a result, terahertz image namely as described above, black wire frame are by choosing
Select the target selection frame obtained after frame is handled.As can be seen that using terahertz imaging side provided by the embodiment of the present invention
Imaging results of the terahertz image that method finally obtains in dangerous material i.e. target area are consistent, that is, are not influenced
Subsequent identification judging result.And Precise imaging is then reduced for nontarget area, reduce the waste of computer resource, can add
Fast image taking speed.
Corresponding to above method embodiment, the embodiment of the invention also provides a kind of terahertz imaging devices, hereafter retouch
The terahertz imaging device stated can correspond to each other reference with above-described terahertz imaging method.
Shown in Figure 11, which comprises the following modules:
Stochastical sampling module 101 obtains stochastical sampling figure for carrying out stochastical sampling imaging using back-projection algorithm
Picture；
Frame construction module 102 is selected, for determining the maximum point of gray value in stochastical sampling image, and is very big
Value point building choice box；
Target selection frame obtains module 103, for handling according to preset rules choice box, obtains target selection
Frame；
Precise imaging module 104 obtains fine figure for carrying out Precise imaging to the corresponding target area of target selection frame
Picture；
Terahertz image obtains module 105, for being superimposed precise image and stochastical sampling image, obtains terahertz image.
Using device provided by the embodiment of the present invention, carry out stochastical sampling imaging using back-projection algorithm, obtain with
Machine sampled images；The maximum point of gray value is determined in stochastical sampling image, and constructs choice box for maximum point；According to
Preset rules handle choice box, obtain target selection frame；To the corresponding target area of target selection frame carry out finely at
Picture obtains precise image；It is superimposed precise image and stochastical sampling image, obtains terahertz image.When carrying out terahertz imaging,
Stochastical sampling imaging is carried out first with back-projection algorithm, stochastical sampling image can be obtained.Wherein, in stochastical sampling image
The gray value of each pixel has respectively represented the Terahertz echo signal intensity of corresponding position.The Terahertz of usual dangerous material
Echo signal intensity is relatively high.That is, the pole of gray value can occur in stochastical sampling image in dangerous material whereabouts
Big value point.Then, choice box is established for the maximum point determined, and choice box is handled, it is final to obtain target choosing
Select frame.The corresponding target area of target selection frame is subjected to Precise imaging, precise image can be obtained, by precise image and terahertz
Hereby random image is overlapped, and can get terahertz image, i.e. completion terahertz imaging.In this way, can either be to key area (danger
Dangerous product region) high-resolution imaging is carried out, guarantee the accuracy of identification of dangerous material, and the region because reducing Precise imaging due to accelerates
Image taking speed reduces the waste of computer resource.When being applied in real-time safety check, safety check speed can be accelerated, promote safety check quality.
In a kind of specific embodiment of the invention, target selection frame obtains module 103, specifically includes:
Expanding unit, for expanding choice box according to default choice box growing strategy；
Combining unit merges processing for the choice box after will be enlarged by；
Object selection frame determination unit, the choice box for area to be greater than preset area threshold value are determined as target selection
Frame.
In a kind of specific embodiment of the invention, Object selection frame determination unit is specifically used for for area being greater than pre-
If the choice box after extension is determined as target selection to each boundary extension predetermined coefficient times by the choice box of area threshold
Frame.
In a kind of specific embodiment of the invention, expanding unit is specifically included:
Pixel to be selected determines subelement, for the gray value in stochastical sampling image to be greater than to the picture of default gray threshold
Vegetarian refreshments is determined as pixel to be selected；
Expand subelement and forms choice box undetermined for expanding choice box；
Choice box replaces subelement, for when the picture to be selected covered in the diff area between choice box undetermined and choice box
When vegetarian refreshments number or percentage are greater than accordingly default judgment threshold, choice box is replaced with into choice box undetermined.
In a kind of specific embodiment of the invention, expand subelement, is specifically used for expanding choice box to surrounding, shape
At choice box undetermined.
In a kind of specific embodiment of the invention, expand subelement, be specifically used for orientation expand choice box, formed to
Determine choice box.
In a kind of specific embodiment of the invention, combining unit is selected specifically for calculating the every two after expanding
The shortest distance between frame, and the choice box that the shortest distance is zero is merged.
Corresponding to above method embodiment, the embodiment of the invention also provides a kind of terahertz imaging equipment, hereafter retouch
A kind of terahertz imaging equipment stated can correspond to each other reference with a kind of above-described terahertz imaging method.
Shown in Figure 12, which includes:
Memory D1, for storing computer program；
Processor D2 realizes the step of the terahertz imaging method of above method embodiment when for executing computer program
Suddenly.
Corresponding to above method embodiment, the embodiment of the invention also provides a kind of readable storage medium storing program for executing, are described below
A kind of readable storage medium storing program for executing can correspond to each other reference with a kind of above-described terahertz imaging method.
A kind of readable storage medium storing program for executing is stored with computer program on readable storage medium storing program for executing, and computer program is held by processor
The step of terahertz imaging method of above method embodiment is realized when row.
The readable storage medium storing program for executing be specifically as follows USB flash disk, mobile hard disk, read-only memory (Read-Only Memory,
ROM), the various program storage generations such as random access memory (Random Access Memory, RAM), magnetic or disk
The readable storage medium storing program for executing of code.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with it is other
The difference of embodiment, same or similar part may refer to each other between each embodiment.For being filled disclosed in embodiment
For setting, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is referring to method part
Explanation.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Profession
Technical staff can use different methods to achieve the described function each specific application, but this realization is not answered
Think beyond the scope of this invention.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand technical solution of the present invention and its core concept.It should be pointed out that for the common of the art
, without departing from the principle of the present invention, can be with several improvements and modifications are made to the present invention for technical staff, these
Improvement and modification are also fallen within the protection scope of the claims of the present invention.
Claims (10)
1. a kind of terahertz imaging method characterized by comprising
Stochastical sampling imaging is carried out using back-projection algorithm, obtains stochastical sampling image；
The maximum point of gray value is determined in the stochastical sampling image, and constructs choice box for the maximum point；
The choice box is handled according to preset rules, obtains target selection frame；
Precise imaging is carried out to the corresponding target area of the target selection frame, obtains precise image；
It is superimposed the precise image and the stochastical sampling image, obtains terahertz image.
2. terahertz imaging method according to claim 1, which is characterized in that it is described according to preset rules to the selection
Frame is handled, and target selection frame is obtained, comprising:
Expand the choice box according to default choice box growing strategy；
Choice box after will be enlarged by merges processing；
The choice box that area is greater than preset area threshold value is determined as target selection frame.
3. terahertz image imaging method according to claim 2, which is characterized in that described that area is greater than preset area
The choice box of threshold value is determined as target selection frame, comprising:
Area is greater than the choice box of preset area threshold value to each boundary extension predetermined coefficient times, and by the choice box after extension
It is determined as target selection frame.
4. terahertz imaging method according to claim 2, which is characterized in that described according to default choice box growing strategy
Expand the choice box, comprising:
The pixel that gray value in the stochastical sampling image is greater than default gray threshold is determined as pixel to be selected；
Expand the choice box, forms choice box undetermined；
When the pixel number to be selected covered in the diff area between the choice box undetermined and the choice box or hundred
When dividing than being greater than corresponding default judgment threshold, the choice box is replaced with into the choice box undetermined.
5. terahertz imaging method according to claim 4, which is characterized in that it is described to expand the choice box, formed to
Determine choice box, comprising:
The choice box is expanded to surrounding, forms choice box undetermined.
6. terahertz imaging method according to claim 4, which is characterized in that it is described to expand the choice box, formed to
Determine choice box, comprising:
Orientation expands the choice box, forms choice box undetermined.
7. according to the described in any item terahertz imaging methods of claim 2 to 6, which is characterized in that it is described will be enlarged by after choosing
It selects frame and merges processing, comprising:
Calculate the shortest distance between the every two choice box after expanding；
The choice box that the shortest distance is zero is merged.
8. a kind of terahertz imaging device characterized by comprising
Stochastical sampling module obtains stochastical sampling image for carrying out stochastical sampling imaging using back-projection algorithm；
Frame construction module is selected, for determining the maximum point of gray value in the stochastical sampling image, and is the pole
Big value point building choice box；
Target selection frame obtains module, for handling according to preset rules the choice box, obtains target selection frame；
Precise imaging module obtains precise image for carrying out Precise imaging to the corresponding target area of the target selection frame；
Terahertz image obtains module, for being superimposed the precise image and the stochastical sampling image, obtains terahertz image.
9. a kind of terahertz imaging equipment characterized by comprising
Memory, for storing computer program；
Processor realizes the terahertz imaging method as described in any one of claim 1 to 7 when for executing the computer program
The step of.
10. a kind of readable storage medium storing program for executing, which is characterized in that be stored with computer program, the meter on the readable storage medium storing program for executing
It is realized when calculation machine program is executed by processor as described in any one of claim 1 to 7 the step of terahertz imaging method.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040062441A1 (en) * | 2000-12-06 | 2004-04-01 | Jerome Meniere | Method for detecting new objects in an illuminated scene |
| US20040095484A1 (en) * | 2002-11-14 | 2004-05-20 | Beardsley Paul A. | Object segmentation from images acquired by handheld cameras |
| CN101286233A (en) * | 2008-05-19 | 2008-10-15 | 重庆邮电大学 | Fuzzy edge detection method based on object cloud |
| CN101556695A (en) * | 2009-05-15 | 2009-10-14 | 广东工业大学 | Image matching method |
| CN101916373A (en) * | 2010-08-11 | 2010-12-15 | 西安电子科技大学 | Road semiautomatic extraction method based on wavelet detection and ridge line tracking |
| CN102621548A (en) * | 2012-04-17 | 2012-08-01 | 中南大学 | Multi-scale backward projection imaging method for ground penetrating radar |
| CN102629386A (en) * | 2012-03-28 | 2012-08-08 | 浙江大学 | Region segmentation method for colorful textile texture images |
| CN105335960A (en) * | 2014-08-13 | 2016-02-17 | 温州大学 | Image segmentation method combining edge detection algorithm with watershed algorithm |
| CN106250895A (en) * | 2016-08-15 | 2016-12-21 | 北京理工大学 | A kind of remote sensing image region of interest area detecting method |
| CN106308795A (en) * | 2016-08-31 | 2017-01-11 | 北京农业信息技术研究中心 | Warning device for foot-and-mouth diseases of animals based on terahertz imaging technology and system and method thereof |
| CN106447659A (en) * | 2016-09-27 | 2017-02-22 | 电子科技大学 | Region growth detection method based on multiple judgments |
| CN106683076A (en) * | 2016-11-24 | 2017-05-17 | 南京航空航天大学 | Texture feature clustering-based locomotive wheelset tread damage detection method |
| CN106952315A (en) * | 2017-03-22 | 2017-07-14 | 广东工业大学 | A kind of method that image quick reconfiguration is carried out to Terahertz complex-valued data based on BFGS |
| CN107092040A (en) * | 2017-06-01 | 2017-08-25 | 上海理工大学 | Terahertz imaging rays safety detection apparatus and video procession method |
| CN107255641A (en) * | 2017-06-06 | 2017-10-17 | 西安理工大学 | A kind of method that Machine Vision Detection is carried out for GRIN Lens surface defect |
| CN107665486A (en) * | 2017-09-30 | 2018-02-06 | 深圳绰曦互动科技有限公司 | A kind of method for automatically split-jointing, device and terminal device applied to radioscopic image |
-
2018
- 2018-05-18 CN CN201810479352.8A patent/CN108957572B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040062441A1 (en) * | 2000-12-06 | 2004-04-01 | Jerome Meniere | Method for detecting new objects in an illuminated scene |
| US20040095484A1 (en) * | 2002-11-14 | 2004-05-20 | Beardsley Paul A. | Object segmentation from images acquired by handheld cameras |
| CN101286233A (en) * | 2008-05-19 | 2008-10-15 | 重庆邮电大学 | Fuzzy edge detection method based on object cloud |
| CN101556695A (en) * | 2009-05-15 | 2009-10-14 | 广东工业大学 | Image matching method |
| CN101916373A (en) * | 2010-08-11 | 2010-12-15 | 西安电子科技大学 | Road semiautomatic extraction method based on wavelet detection and ridge line tracking |
| CN102629386A (en) * | 2012-03-28 | 2012-08-08 | 浙江大学 | Region segmentation method for colorful textile texture images |
| CN102621548A (en) * | 2012-04-17 | 2012-08-01 | 中南大学 | Multi-scale backward projection imaging method for ground penetrating radar |
| CN105335960A (en) * | 2014-08-13 | 2016-02-17 | 温州大学 | Image segmentation method combining edge detection algorithm with watershed algorithm |
| CN106250895A (en) * | 2016-08-15 | 2016-12-21 | 北京理工大学 | A kind of remote sensing image region of interest area detecting method |
| CN106308795A (en) * | 2016-08-31 | 2017-01-11 | 北京农业信息技术研究中心 | Warning device for foot-and-mouth diseases of animals based on terahertz imaging technology and system and method thereof |
| CN106447659A (en) * | 2016-09-27 | 2017-02-22 | 电子科技大学 | Region growth detection method based on multiple judgments |
| CN106683076A (en) * | 2016-11-24 | 2017-05-17 | 南京航空航天大学 | Texture feature clustering-based locomotive wheelset tread damage detection method |
| CN106952315A (en) * | 2017-03-22 | 2017-07-14 | 广东工业大学 | A kind of method that image quick reconfiguration is carried out to Terahertz complex-valued data based on BFGS |
| CN107092040A (en) * | 2017-06-01 | 2017-08-25 | 上海理工大学 | Terahertz imaging rays safety detection apparatus and video procession method |
| CN107255641A (en) * | 2017-06-06 | 2017-10-17 | 西安理工大学 | A kind of method that Machine Vision Detection is carried out for GRIN Lens surface defect |
| CN107665486A (en) * | 2017-09-30 | 2018-02-06 | 深圳绰曦互动科技有限公司 | A kind of method for automatically split-jointing, device and terminal device applied to radioscopic image |
Non-Patent Citations (2)
| Title |
|---|
| 张馨 等: ""被动式人体太赫兹成像系统的图像重构算法研究"", 《中国激光》 * |
| 王林华 等: ""太赫兹安检系统人体图像边缘物体识别"", 《红外与激光工程》 * |
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