CN107992875A - A kind of well-marked target detection method based on image bandpass filtering - Google Patents
A kind of well-marked target detection method based on image bandpass filtering Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Abstract
The present invention relates to a kind of well-marked target detection method based on image bandpass filtering, original image is pre-processed；By image zoom to being sized, and floating-point conversion is carried out, obtain floating-point image；Multiple bandpass filtering is carried out using iir filter to floating-point image, bandpass filtering is carried out using IIR digital filter, bandpass filter is obtained by two low-pass filter difference, further bandpass filtering is carried out to filter result again after a bandpass filtering is carried out to floating-point image, obtains bandpass filtering result；Bandpass filtering result is split, Morphological scale-space is carried out to segmentation result, cancelling noise influences；Result after Morphological scale-space is clustered, cluster result is screened according to target priori, in Screening Treatment, is screened according to known characteristic, and obtain final testing result.The present invention realizes that simply, operation efficiency is high, can effectively detect target.
Description
Technical field
The present invention relates to a kind of well-marked target detection method based on image bandpass filtering, suitable for high-definition image complicated field
Well-marked target detection field under scape.
Background technology
The mankind can quickly select a part for image to carry out depth before high-level vision processing analysis visual scene is carried out
Analysis, to reduce the complexity of overall calculation, the mechanism that this selected section key area is handled is that vision is shown
Work property.The data volume of processing can be greatly reduced by this method, have great significance for follow-up analysis.
Vision significance includes top-down and two kinds of mechanism from bottom to top.It is by the aobvious of view data driving from bottom to top
Work property, i.e. the image attraction to human eye notice in itself；Top-down system is then to image section region by purpose driving
Concern.Usually in Digital Image Processing, mode from bottom to top is more paid close attention to, that is, considers image in itself to degree of concern
Influence.
Target detection technique is Digital Image Processing and one important technology of artificial intelligence field, it is directed to from complexity
Candidate target is partitioned into background, the processing such as further to be tracked, identified.Target detection seeks to fixed in the background
Position goes out position and the size of interesting target.For target motion conditions, it is big with quiet target detection two that moving-target detection can be divided into
Class.
The common method of moving-target detection has：Inter-frame difference, background difference and motion segmentation etc..Inter-frame difference utilizes interframe
Change realize detection, common method has two frames or three-frame difference method.Background subtraction is using Background or passes through model weight
Structure background, obtains motion target area, common method is mixed Gauss model background modeling method with current frame difference.Movement point
Segmentation method is mainly using light stream extraction motion vector, and split.
Target detection technique can be divided mainly into two classes in static image：Spy is searched in the picture using feature and sorter network
Set the goal, and possible interesting target in image is found using the notable method of vision.
It is common to search for specific objective method in the picture using feature and sorter network and be divided into two classes：Traditional classifier side
Method and deep learning frame method.In conventional method it is common including：Target identification based on HOG features and SVM classifier, base
In Haar features and the target identification of Adaboost graders.Using the target identification of deep learning frame method, including
SSD algorithms of proposition such as the Faster RCNN algorithms that R.Girshick is proposed, W.Liu etc..
In recent years, the notable method of numerous visions is suggested, for detecting to the significant target of human eye in image, such as early stage
ITTI models, the spectrum residual error method that Hou Xiaodi is proposed etc..Conspicuousness method does not differentiate between target type, only considers target visually
Significance degree, its recall rate is high, but it has more flase drop accordingly.
But above-mentioned existing method there are the shortcomings that be mainly reflected in：
(1) for static object detection method, the characteristics of due to grader, it can only identify the target of particular category,
When target is not in identification range, target can not be partitioned into.And due to the limitation of characteristic mass and grader generalization ability, its
Detection capability is poor, when there are during difference, possibly can not being detected between target shape and training sample, omission factor and false alarm rate compared with
It is high.Deep learning frame method in static object detection, it possesses compared with high detection rate, but detection speed is slower, it is difficult in reality
Used in the real-time application on border.
(2) notable figure that common vision significance algorithm obtains is ineffective, and the time of complicated conspicuousness method disappears
Consume huge, can not be applied in real time.
The content of the invention
Present invention solves the technical problem that it is：What deficiency of the prior art overcome, for image object in complex scene
Detection, there is provided a kind of method based on conspicuousness detection, improves the speed of target detection, realizes simple, and operation efficiency is high,
Target can effectively be detected and be easy to realize on a hardware platform.
The present invention technical solution be：A kind of well-marked target detection method based on image bandpass filtering, step is such as
Under：
(1) original image is pre-processed, by the original image zoom to being sized, and carries out floating-point conversion,
Obtain floating-point image；
(2) bandpass filtering twice is carried out using iir filter to floating-point image；
(3) bandpass filtering result is split, obtains segmentation result；
(4) Morphological scale-space is carried out to the segmentation result, cancelling noise point influences；
(5) result after Morphological scale-space is clustered, obtains cluster result；
(6) cluster result is screened according to target priori, in Screening Treatment, is carried out according to known characteristic
Screening, and obtain final testing result.
In the step (1), if described image is coloured image, gray processing is first carried out, obtains gray level image, then will figure
As zoom is to being sized, and floating-point conversion is carried out, obtain floating-point image.
In the step (2), bandpass filtering is carried out using IIR digital filter, bandpass filter is by two low-pass filtering
Device difference obtains, and digital IIR low-pass filters are made of positive and negative filtering operation twice, and successively to horizontal direction and vertical direction
Carry out, the forward filtering recurrence formula of low-pass filter is as follows：
x′n=(1-a) × x 'n-1+a×xn
X in formulanIt is nth point grey scale pixel value, x 'nIt is the forward filtering of nth point as a result, a is filtering parameter；
Inverse filtering recurrence formula is as follows：
x″n=(1-a) x "n+1+a×x′n
Second of bandpass filtering is carried out to filter result again after first time bandpass filtering is carried out to floating-point image, obtains band logical
Filter result.
The value of first time bandpass filtering parameter a is respectively 0.6 and 0.2, and the value of second of bandpass filtering parameter a is respectively
0.3 and 0.1, it can be adjusted according to practical application scene.
In the step (3) binary segmentation is carried out using fixed threshold.
The fixed threshold is 1.0, can be adjusted according to practical application scene.
The step (5), is clustered, progressive scanning picture using breadth first algorithm, by be not classified and segmentation
As a result for 1 point be used as seed point, using 4 neighborhood region-growing methods carry out breadth first search cluster, will cluster put be labeled as
Classify a little, and continued to scan on, finally obtained cluster result.
In the step (6), it is known that characteristic include：Target sizes, target aspect ratio, the known characteristic can basis
Practical application scene is adjusted.
The present invention compared with prior art the advantages of be：
(1) present invention can effectively detect well-marked target all in image, with existing grader learning algorithm phase
Than it can adapt in a variety of different targets.Algorithm missing inspection proposed by the present invention is few, and target can be detected effectively.
(2) iir filter that the present invention uses, it realizes very high effect, and only carrying out 4 traversals to image can complete to filter
Ripple calculates, and the relatively used conspicuousness algorithm having, computational efficiency is obviously improved, and processing water in real time can be reached on hardware system
It is flat.
(3) algorithm that the present invention uses is realized simple, can quickly be developed, it is only necessary to slightly adjusting parameter, i.e. a
Value can adjust on demand, and threshold value can adjust on demand, and characteristic can adjust on demand, you can suitable for different scenes.
Brief description of the drawings
Fig. 1 is a kind of FB(flow block) of the well-marked target detection method based on image bandpass filtering of the present invention；
Fig. 2 is the original image of input；
Fig. 3 is the binary saliency figure after the segmentation that step (3) obtains；
Fig. 4 is the output result finally detected.
Embodiment
The present invention is described in detail with reference to the accompanying drawings and embodiments.
A set of target detection software, its input image resolution are 1280x720, RGB color image.
(1) image is pre-processed.Gray processing is carried out to colour, obtains gray level image, it is down-sampled to 320x180, and
Gray level image is subjected to floating-point conversion.Gray level image is as shown in Figure 2.
(2) multiple bandpass filtering is carried out using iir filter to floating-point image.The present invention using IIR digital filter into
Row bandpass filtering.The bandpass filter is obtained by two low-pass filter difference.
Digital IIR low-pass filters are made of positive and negative filtering operation twice, and successively to horizontal direction and vertical direction into
OK, forward filtering recurrence formula is as follows：
x′n=(1-a) × x 'n-1+a×xn
X in formulanIt is nth point grey scale pixel value, x 'nIt is the forward filtering of nth point as a result, a is filtering parameter.
Inverse filtering recurrence formula is as follows：
x″n=(1-a) x "n+1+a×x′n
X in formula "nIt is the output of nth point horizontal filtering result, its filtering parameter a is identical with forward filtering.
After carrying out horizontal direction low-pass filtering operation line by line, vertical direction low-pass filtering operation is carried out by column.
Two wave filters have different filtering parameters, make the cutoff frequency of two filter low pass filtering different, are dividing
It is other poor to making after image filtering, obtain bandpass filtering result.
FBP=| b × (FH-FL)|
In formula, FHIt is off the higher low-pass filter filter result of frequency, FLIt is off the relatively low low-pass filter of frequency
Filter result is higher here to refer to opposite FLIt is higher, it is relatively low here to refer to opposite FLIt is higher, FBPIt is that bandpass filter is defeated
Go out, b is amplification factor.
In the present invention, a bandpass filtering is carried out to original image, then bandpass filtering is further carried out to filter result
Obtain final vision significance figure.
During first time bandpass filtering, the parameter a of two low-pass filters is respectively 0.6 and 0.2, and amplification factor b is
50, carry out a bandpass filtering again to result after obtaining result, filtering parameter is respectively 0.3 and 0.1, and amplification factor b is 1, is obtained
It is distributed to conspicuousness, parameter can adjust according to the actual requirements.
(3) bandpass filtering result is split, carries out binary segmentation using fixed threshold, obtain segmentation result：
T is threshold value in formula, and threshold value is decided to be 1.0 by experiment and experience, can adjust according to the actual requirements.
(4) opening operation is used to operate the image used, for filtering out the less influence of noise of scale.Filtered knot
Fruit is as shown in Figure 3.
(5) segmentation result is clustered, is clustered using breadth first algorithm.Progressive scanning picture, will not divided
Class and segmentation result be 1 point be used as seed point, using 4 neighborhood region-growing methods carry out breadth first search cluster, will gather
Class point is labeled as having classified a little, and continues to scan on, and obtains the centre coordinate and outer rim of each target.
(6) each target obtained to step (5) is screened according to target priori.In Screening Treatment, according to
The characteristic known, such as：Target sizes, target aspect ratio etc. are screened, and export final testing result.The embodiment of the present invention
In, use following priori：
(1) target size is more than 5x5；
(2) target size is less than 60x60；
(3) when target length is more than 20, aspect ratio should be less than 4.
Screening analysis is carried out to result according to this three, ineligible target is screened out, obtains final detection knot
Fruit, prior information can adjust according to the actual requirements.
Detection block is drawn on original image after detection block length and width are expanded 4 times, obtained image is as shown in Figure 4.
Two cars in figure are at the same time it is also desirable that the target searched, it is effectively split to the significant target of human eye
And Overlapping display frame.In figure 3, most of flase drop target is all screened out in subsequent screening process, is only had in Fig. 4
4 flase drop objects.The present invention handles frame per second up to 30 frames/second on DSP architecture, reaches real-time processing requirement.
Above example is provided just for the sake of the description purpose of the present invention, and is not intended to limit the scope of the present invention.This
The scope of invention is defined by the following claims.The various equivalent substitutions that do not depart from spirit and principles of the present invention and make and repair
Change, should all cover within the scope of the present invention.
Claims (8)
1. a kind of well-marked target detection method based on image bandpass filtering, it is characterised in that comprise the following steps：
(1) original image is pre-processed, by the original image zoom to being sized, and carries out floating-point conversion, obtain
Floating-point image；
(2) bandpass filtering twice is carried out using iir filter to floating-point image；
(3) bandpass filtering result is split, obtains segmentation result；
(4) Morphological scale-space is carried out to the segmentation result, cancelling noise point influences；
(5) result after Morphological scale-space is clustered, obtains cluster result；
(6) cluster result is screened according to target priori, in Screening Treatment, is sieved according to known characteristic
Choosing, and obtain final testing result.
2. the well-marked target detection method according to claim 1 based on image bandpass filtering, it is characterised in that：The step
Suddenly in (1), if described image is coloured image, gray processing is first carried out, obtains gray level image, then by image zoom to setting ruler
It is very little, and floating-point conversion is carried out, obtain floating-point image.
3. the well-marked target detection method according to claim 1 based on image bandpass filtering, it is characterised in that：The step
Suddenly in (2), bandpass filtering is carried out using IIR digital filter, bandpass filter is obtained by two low-pass filter difference, numeral
IIR low-pass filters are made of positive and negative filtering operation twice, and horizontal direction and vertical direction are carried out successively, low-pass filter
Forward filtering recurrence formula it is as follows：
x′n=(1-a) × x 'n-1+a×xn
X in formulanIt is nth point grey scale pixel value, x 'nIt is the forward filtering of nth point as a result, a is filtering parameter；
Inverse filtering recurrence formula is as follows：
x″n=(1-a) x "n+1+a×x′n
Second of bandpass filtering is carried out to filter result again after first time bandpass filtering is carried out to floating-point image, obtains bandpass filtering
As a result.
4. the well-marked target detection method according to claim 3 based on image bandpass filtering, it is characterised in that：For the first time
The value of bandpass filtering parameter a is respectively 0.6 and 0.2, and the value of second of bandpass filtering parameter a is respectively 0.3 and 0.1, can basis
Practical application scene is adjusted.
5. the well-marked target detection method according to claim 1 based on image bandpass filtering, it is characterised in that：The step
Suddenly in (3) binary segmentation is carried out using fixed threshold.
6. the well-marked target detection method according to claim 5 based on image bandpass filtering, it is characterised in that：It is described solid
It is 1.0 to determine threshold value, can be adjusted according to practical application scene.
7. the well-marked target detection method according to claim 1 based on image bandpass filtering, it is characterised in that：The step
Suddenly (5), are clustered using breadth first algorithm, progressive scanning picture, using be not classified and segmentation result be 1 point as
Seed point, breadth first search cluster is carried out using 4 neighborhood region-growing methods, cluster point is labeled as having classified a little, and continue
Scanning, finally obtains cluster result.
8. the well-marked target detection method according to claim 1 based on image bandpass filtering, it is characterised in that：The step
Suddenly in (6), it is known that characteristic include：Target sizes, target aspect ratio, the known characteristic can according to practical application scene into
Row adjustment.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110910421A (en) * | 2019-11-11 | 2020-03-24 | 西北工业大学 | Weak and small moving object detection method based on block characterization and variable neighborhood clustering |
| CN111145156A (en) * | 2019-12-27 | 2020-05-12 | 创新奇智(南京)科技有限公司 | Rapid screw surface defect detection method |
| CN111784630A (en) * | 2020-05-18 | 2020-10-16 | 广州信瑞医疗技术有限公司 | Method and device for segmenting components of pathological image |
| CN111784698A (en) * | 2020-07-02 | 2020-10-16 | 广州信瑞医疗技术有限公司 | Image self-adaptive segmentation method and device, electronic equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714537A (en) * | 2013-12-19 | 2014-04-09 | 武汉理工大学 | Image saliency detection method |
| CN104103082A (en) * | 2014-06-06 | 2014-10-15 | 华南理工大学 | Image saliency detection method based on region description and priori knowledge |
| CN104217438A (en) * | 2014-09-19 | 2014-12-17 | 西安电子科技大学 | Image significance detection method based on semi-supervision |
| CN104992183A (en) * | 2015-06-25 | 2015-10-21 | 中国计量学院 | Method for automatic detection of substantial object in natural scene |
| CN105574534A (en) * | 2015-12-17 | 2016-05-11 | 西安电子科技大学 | Significant object detection method based on sparse subspace clustering and low-order expression |
| CN105975911A (en) * | 2016-04-28 | 2016-09-28 | 大连民族大学 | Energy perception motion significance target detection algorithm based on filter |
| CN106447699A (en) * | 2016-10-14 | 2017-02-22 | 中国科学院自动化研究所 | High-speed rail overhead contact line equipment object detection and tracking method based on Kalman filtering |
| CN107229917A (en) * | 2017-05-31 | 2017-10-03 | 北京师范大学 | A kind of several remote sensing image general character well-marked target detection methods clustered based on iteration |
-
2017
- 2017-12-25 CN CN201711422362.XA patent/CN107992875B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714537A (en) * | 2013-12-19 | 2014-04-09 | 武汉理工大学 | Image saliency detection method |
| CN104103082A (en) * | 2014-06-06 | 2014-10-15 | 华南理工大学 | Image saliency detection method based on region description and priori knowledge |
| CN104217438A (en) * | 2014-09-19 | 2014-12-17 | 西安电子科技大学 | Image significance detection method based on semi-supervision |
| CN104992183A (en) * | 2015-06-25 | 2015-10-21 | 中国计量学院 | Method for automatic detection of substantial object in natural scene |
| CN105574534A (en) * | 2015-12-17 | 2016-05-11 | 西安电子科技大学 | Significant object detection method based on sparse subspace clustering and low-order expression |
| CN105975911A (en) * | 2016-04-28 | 2016-09-28 | 大连民族大学 | Energy perception motion significance target detection algorithm based on filter |
| CN106447699A (en) * | 2016-10-14 | 2017-02-22 | 中国科学院自动化研究所 | High-speed rail overhead contact line equipment object detection and tracking method based on Kalman filtering |
| CN107229917A (en) * | 2017-05-31 | 2017-10-03 | 北京师范大学 | A kind of several remote sensing image general character well-marked target detection methods clustered based on iteration |
Non-Patent Citations (1)
| Title |
|---|
| LINZHANG 等: "SDSP: A novel saliency detection method by combining simple priors", 《2013 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110910421A (en) * | 2019-11-11 | 2020-03-24 | 西北工业大学 | Weak and small moving object detection method based on block characterization and variable neighborhood clustering |
| CN111145156A (en) * | 2019-12-27 | 2020-05-12 | 创新奇智(南京)科技有限公司 | Rapid screw surface defect detection method |
| CN111784630A (en) * | 2020-05-18 | 2020-10-16 | 广州信瑞医疗技术有限公司 | Method and device for segmenting components of pathological image |
| CN111784698A (en) * | 2020-07-02 | 2020-10-16 | 广州信瑞医疗技术有限公司 | Image self-adaptive segmentation method and device, electronic equipment and storage medium |
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