CN105928099A - Intelligent air purifier - Google Patents

Intelligent air purifier Download PDF

Info

Publication number
CN105928099A
CN105928099A CN201610234518.0A CN201610234518A CN105928099A CN 105928099 A CN105928099 A CN 105928099A CN 201610234518 A CN201610234518 A CN 201610234518A CN 105928099 A CN105928099 A CN 105928099A
Authority
CN
China
Prior art keywords
point
image
line segment
module
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201610234518.0A
Other languages
Chinese (zh)
Inventor
吴本刚
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Individual
Original Assignee
Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Priority to CN201610234518.0A priority Critical patent/CN105928099A/en
Publication of CN105928099A publication Critical patent/CN105928099A/en
Pending legal-status Critical Current

Links

Classifications

    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F3/00Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems
    • F24F3/12Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems characterised by the treatment of the air otherwise than by heating and cooling
    • F24F3/16Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems characterised by the treatment of the air otherwise than by heating and cooling by purification, e.g. by filtering; by sterilisation; by ozonisation
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
    • F24F11/30Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
    • F24F11/62Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/10Terrestrial scenes
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
    • F24F11/62Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63Electronic processing
    • F24F11/64Electronic processing using pre-stored data

Landscapes

  • Engineering & Computer Science (AREA)
  • Combustion & Propulsion (AREA)
  • General Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Signal Processing (AREA)
  • Fuzzy Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

The invention discloses an intelligent air purifier. The intelligent air purifier comprises an air purifier and a scene recognition device mounted on the air purifier, wherein the scene recognition device comprises an image preprocessing module, an image extreme point detecting module, an image feature point positioning module, a main direction determining module, a feature extracting module and a scene determining module; the image feature point positioning module is used for determining extreme points of feature points by removing low-contrast points sensitive to noise and unstable edge points among the extreme points; the main direction determining module is used for connecting any two adjacent peak values in a gradient direction histogram about the feature points to form a plurality of sub line segments, merging the adjacent sub line segments with similar slope in a length direction to form a line segment and taking a direction of an optimal line segment among a plurality of line segments as a main direction of the feature points. The intelligent air purifier has the advantages of high scene recognition accuracy and fast scene recognition speed.

Description

A kind of Intellectual air cleaner
Technical field
The present invention relates to air purification field, be specifically related to a kind of Intellectual air cleaner.
Background technology
The judgement of scene plays its maximum effect for any machine and plays an important role, if air cleaner can determine that self Residing scene and choose the pattern of correspondence and carry out air is purified, efficiency will be greatly improved.But, current air is clean Change machine does not has scene decision-making function.Additionally, in order to large-scale view data is processed, need to improve analyzing and processing Efficiency and precision.
Summary of the invention
For the problems referred to above, the present invention provides a kind of Intellectual air cleaner.
The purpose of the present invention realizes by the following technical solutions:
Provide a kind of Intellectual air cleaner, it is possible to scene is identified, including air cleaner be arranged on air cleaning Scene Recognition device on machine, scene Recognition device includes:
(1) image pre-processing module, it includes the image transform subblock for coloured image is converted into gray level image and is used for The image filtering submodule that described gray level image is filtered, the gradation of image conversion formula of described image transform subblock is:
I ( x , y ) = m a x ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) + m i n ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) 2 + 2 [ max ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) - min ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) ]
Wherein, (x, y), (x, y), (x, (x, y) the intensity red green blue value at place, (x y) represents I B G R y) to represent pixel respectively Pixel (x, y) gray value at place;
(2) image extreme point detection module, it by being carried out the Gauss of the image that convolution is created as by difference of Gaussian and image Difference scale space detects the position of each extreme point, when sampled point relative to it with 8 consecutive points of yardstick and neighbouring When the value of 18 points that yardstick is corresponding is the biggest, described sampled point is maximum point, when sampled point relative to it with 8 of yardstick When the value of 18 points that consecutive points are corresponding with neighbouring yardstick is the least, described sampled point is minimum point, described difference of Gaussian chi The reduced mechanical model in degree space is:
D (x, y, σ)=(G (x, k σ)-G (x, σ)) * I'(x, y)+(G (y, k σ)-G (y, σ)) * I'(x, y)
Herein
G ( x , σ ) = 1 2 π σ e - x 2 / 2 σ 2 , G ( y , σ ) = 1 2 π σ e - y 2 / 2 σ 2
Wherein, D (x, y, σ) represents Gaussian difference scale space function, I'(x, is y) by the image letter of image transformant module output Number, * represents that convolution algorithm, σ represent the Gaussian function that the metric space factor, G (x, σ), G (y, σ) they are the changeable scale defined, K is constant multiplication factor;
(3) image characteristic point locating module, it is by rejecting in described each extreme point the low contrast point of noise-sensitive and not Stable marginal point determines the extreme point as characteristic point, positions for extreme point pinpoint first including be sequentially connected with Submodule, for removing the second locator module of low contrast point and for removing the 3rd locator module of mobile rim point, Wherein:
A, described first locator module are by carrying out secondary Taylor expansion to described Gaussian difference scale space function and derivation obtains The exact position of extreme point, the metric space function of extreme point is:
D ( X ^ ) = D ( x , y , σ ) + ∂ D ( x , y , σ ) T ∂ x X ^
Wherein,Represent the metric space function of extreme point, D (x, y, σ)TFor the side-play amount relative to extreme point,Represent The exact position of extreme point;
B, described second locator module carry out grey level enhancement, normalized successively to the image exported soon by image conversion submodule Rear rejecting described low contrast point, enhanced gray value is:
Herein
Described low contrast point judge formula as:
D ( X ^ ) < T 1 , T 1 &Element; &lsqb; 0.01 , 0.06 &rsqb;
Wherein, I " (x, y) represents the enhanced image function of gray value,For comprising the correction coefficient of local message, M is The maximum gradation value of pixel, described maximum gradation value M=255, mHFor all pixels higher than 128 of the gray value in image Average, mLIt is the gray value average that is less than all pixels of 128, ψ (x, y) is the image after being processed by image filtering submodule, T1For the threshold value set;
C, described 3rd locator module obtain this extreme value by the Hessian matrix H that Location Scale is 2 × 2 calculating extreme point The principal curvatures of point, and by rejecting principal curvatures ratio more than threshold value T set2Extreme point reject described mobile rim point, Wherein threshold value T2Span be [10,15], described principal curvatures ratio is come really by the ratio between the eigenvalue of comparator matrix H Fixed;
Preferably, described Intellectual air cleaner, scene Recognition device also includes:
(1) principal direction determines module, including the connection sub module being sequentially connected with, merges submodule and processes submodule, described company Line is used for two peak value lines of the arbitrary neighborhood in the gradient orientation histogram about described characteristic point in module to form many height Line segment, described merging submodule is for merging formation one in the longitudinal direction by having close slope and adjacent sub-line section Line segment, described process submodule for using the direction of the optimum line segment in a plurality of line segment as the principal direction of characteristic point, described optimum Line segment judge formula as:
L Y = L g &OverBar; max , g &OverBar; max = max ( g &OverBar; L n ) , g &OverBar; L n = 1 k &Sigma; k = 1 k g k , L n &Element; L &upsi; )
Wherein, LYRepresent optimum line segment,For average gradient value it isLine segment,For nth bar in described a plurality of line segment The average gradient value of line segment, gkFor the kth strip line segment in described nth bar line segment, LυFor described a plurality of line segment middle conductor length Line segment aggregate more than average line segment length;
(2) characteristic extracting module, it is according to described main formula always hyperspin feature neighborhood of a point, and according to postrotational neighborhood to institute State characteristic point to be described, thus generate the descriptor of described characteristic point;
(3) scene determination module, uses the feature extracted to contrast with the scene characteristic in data base, completes scene and judges.
Further, the sub-line section described in close slope is that slope differences is less than predetermined threshold value T3Sub-line section, described threshold value T3's Span be (0,0.1].
The invention have the benefit that
1, the image pre-processing module arranged considers visual custom and the human eye perceptibility to different color with colouring intensity Non-linear relation, it is possible to describe image the most accurately;
2, propose the reduced mechanical model of Gaussian difference scale space, decrease operand, improve arithmetic speed, Jin Erti The high speed of graphical analysis;
3, the image characteristic point locating module arranged carries out low contrast point and the removal of mobile rim point to extreme point, it is ensured that special Levy effectiveness a little, wherein the gray value of image is strengthened, it is possible to be greatly increased the stability of image, the most right Low contrast point is removed, and then improves the accuracy of graphical analysis;
4, principal direction is set and determines module, it is proposed that the judgement formula of optimum line segment, with appointing in characteristic point gradient orientation histogram The direction of the optimum line segment in the line segment that adjacent two peak value lines of anticipating are formed is as the principal direction of characteristic point, and line segment is relative to point more Add stable so that the descriptor of image characteristic of correspondence point has repeatability, improves the accuracy of feature descriptor, and then More fast and accurately image can be identified detection, there is the highest robustness.
Accompanying drawing explanation
The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limitation of the invention, for Those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtains the attached of other according to the following drawings Figure.
Fig. 1 is the connection diagram of each module of the present invention.
Detailed description of the invention
The invention will be further described with the following Examples.
Embodiment 1
See Fig. 1, the present embodiment Intellectual air cleaner, know including air cleaner and the scene being arranged on air cleaner Other device, scene Recognition device includes:
(1) image pre-processing module, it includes the image transform subblock for coloured image is converted into gray level image and is used for The image filtering submodule that described gray level image is filtered, the gradation of image conversion formula of described image transform subblock is:
I ( x , y ) = m a x ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) + m i n ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) 2 + 2 &lsqb; max ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) - min ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) &rsqb;
Wherein, (x, y), (x, y), (x, (x, y) the intensity red green blue value at place, (x y) represents I B G R y) to represent pixel respectively Pixel (x, y) gray value at place;
(2) image extreme point detection module, it by being carried out the Gauss of the image that convolution is created as by difference of Gaussian and image Difference scale space detects the position of each extreme point, when sampled point relative to it with 8 consecutive points of yardstick and neighbouring When the value of 18 points that yardstick is corresponding is the biggest, described sampled point is maximum point, when sampled point relative to it with 8 of yardstick When the value of 18 points that consecutive points are corresponding with neighbouring yardstick is the least, described sampled point is minimum point, described difference of Gaussian chi The reduced mechanical model in degree space is:
D (x, y, σ)=(G (x, k σ)-G (x, σ)) * I'(x, y)+(G (y, k σ)-G (y, σ)) * I'(x, y)
Herein
G ( x , &sigma; ) = 1 2 &pi; &sigma; e - x 2 / 2 &sigma; 2 , G ( y , &sigma; ) = 1 2 &pi; &sigma; e - y 2 / 2 &sigma; 2
Wherein, D (x, y, σ) represents Gaussian difference scale space function, I'(x, is y) by the image letter of image transformant module output Number, * represents that convolution algorithm, σ represent the Gaussian function that the metric space factor, G (x, σ), G (y, σ) they are the changeable scale defined, K is constant multiplication factor;
(3) image characteristic point locating module, it is by rejecting in described each extreme point the low contrast point of noise-sensitive and not Stable marginal point determines the extreme point as characteristic point, positions for extreme point pinpoint first including be sequentially connected with Submodule, for removing the second locator module of low contrast point and for removing the 3rd locator module of mobile rim point, Wherein:
A, described first locator module are by carrying out secondary Taylor expansion to described Gaussian difference scale space function and derivation obtains The exact position of extreme point, the metric space function of extreme point is:
D ( X ^ ) = D ( x , y , &sigma; ) + &part; D ( x , y , &sigma; ) T &part; x X ^
Wherein,Represent the metric space function of extreme point, D (x, y, σ)TFor the side-play amount relative to extreme point,Represent The exact position of extreme point;
B, described second locator module carry out grey level enhancement, normalized successively to the image exported soon by image conversion submodule Rear rejecting described low contrast point, enhanced gray value is:
Herein
Described low contrast point judge formula as:
D ( X ^ ) < T 1 , T 1 &Element; &lsqb; 0.01 , 0.06 &rsqb;
Wherein, I " (x, y) represents the enhanced image function of gray value,For comprising the correction coefficient of local message, M is The maximum gradation value of pixel, described maximum gradation value M=255, mHFor all pixels higher than 128 of the gray value in image Average, mLIt is the gray value average that is less than all pixels of 128, ψ (x, y) is the image after being processed by image filtering submodule, T1For the threshold value set;
C, described 3rd locator module obtain this extreme value by the Hessian matrix H that Location Scale is 2 × 2 calculating extreme point The principal curvatures of point, and by rejecting principal curvatures ratio more than threshold value T set2Extreme point reject described mobile rim point, Wherein threshold value T2Span be [10,15], described principal curvatures ratio is come really by the ratio between the eigenvalue of comparator matrix H Fixed;
Preferably, described Intellectual air cleaner, scene Recognition device also includes:
(1) principal direction determines module, including the connection sub module being sequentially connected with, merges submodule and processes submodule, described company Line is used for two peak value lines of the arbitrary neighborhood in the gradient orientation histogram about described characteristic point in module to form many height Line segment, described merging submodule is for merging formation one in the longitudinal direction by having close slope and adjacent sub-line section Line segment, described process submodule for using the direction of the optimum line segment in a plurality of line segment as the principal direction of characteristic point, described optimum Line segment judge formula as:
L Y = L g &OverBar; max , g &OverBar; max = max ( g &OverBar; L n ) , g &OverBar; L n = 1 k &Sigma; k = 1 k g k , L n &Element; L &upsi; )
Wherein, LYRepresent optimum line segment,For average gradient value it isLine segment,For nth bar in described a plurality of line segment The average gradient value of line segment, gkFor the kth strip line segment in described nth bar line segment, LυFor described a plurality of line segment middle conductor length Line segment aggregate more than average line segment length;
(2) characteristic extracting module, it is according to described main formula always hyperspin feature neighborhood of a point, and according to postrotational neighborhood to institute State characteristic point to be described, thus generate the descriptor of described characteristic point;
(3) scene determination module, uses the feature extracted to contrast with the scene characteristic in data base, completes scene and judges.
Further, the sub-line section described in close slope is that slope differences is less than predetermined threshold value T3Sub-line section, described threshold value T3's Span be (0,0.1].
It is strong with color that the image pre-processing module that the present embodiment is arranged considers visual custom and the human eye perceptibility to different color The non-linear relation of degree, it is possible to describe image the most accurately;Propose the reduced mechanical model of Gaussian difference scale space, subtract Lack operand, improve arithmetic speed, and then improve the speed of graphical analysis;The image characteristic point locating module pair arranged Extreme point carries out low contrast point and the removal of mobile rim point, it is ensured that the effectiveness of characteristic point, the wherein gray value to image Strengthen, it is possible to be greatly increased the stability of image, the most accurate low contrast point is removed, and then improve image The accuracy analyzed;Principal direction is set and determines module, it is proposed that the judgement formula of optimum line segment, with characteristic point gradient direction Nogata The direction of the optimum line segment in the line segment that two peak value lines of the arbitrary neighborhood in figure are formed is as the principal direction of characteristic point, line segment phase More stable for point so that the descriptor of image characteristic of correspondence point has repeatability, improves the accurate of feature descriptor Property, and then can more fast and accurately image be identified detection, there is the highest robustness;The present embodiment takes threshold value T1=0.01, T2=10, T3=0.1, the precision of scene Recognition improves 2%, and speed improves 1%.
Embodiment 2
See Fig. 1, the present embodiment Intellectual air cleaner, know including air cleaner and the scene being arranged on air cleaner Other device, scene Recognition device includes:
(1) image pre-processing module, it includes the image transform subblock for coloured image is converted into gray level image and is used for The image filtering submodule that described gray level image is filtered, the gradation of image conversion formula of described image transform subblock is:
I ( x , y ) = m a x ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) + m i n ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) 2 + 2 &lsqb; max ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) - min ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) &rsqb;
Wherein, (x, y), (x, y), (x, (x, y) the intensity red green blue value at place, (x y) represents I B G R y) to represent pixel respectively Pixel (x, y) gray value at place;
(2) image extreme point detection module, it by being carried out the Gauss of the image that convolution is created as by difference of Gaussian and image Difference scale space detects the position of each extreme point, when sampled point relative to it with 8 consecutive points of yardstick and neighbouring When the value of 18 points that yardstick is corresponding is the biggest, described sampled point is maximum point, when sampled point relative to it with 8 of yardstick When the value of 18 points that consecutive points are corresponding with neighbouring yardstick is the least, described sampled point is minimum point, described difference of Gaussian chi The reduced mechanical model in degree space is:
D (x, y, σ)=(G (x, k σ)-G (x, σ)) * I'(x, y)+(G (y, k σ)-G (y, σ)) * I'(x, y)
Herein
G ( x , &sigma; ) = 1 2 &pi; &sigma; e - x 2 / 2 &sigma; 2 , G ( y , &sigma; ) = 1 2 &pi; &sigma; e - y 2 / 2 &sigma; 2
Wherein, D (x, y, σ) represents Gaussian difference scale space function, I'(x, is y) by the image letter of image transformant module output Number, * represents that convolution algorithm, σ represent the Gaussian function that the metric space factor, G (x, σ), G (y, σ) they are the changeable scale defined, K is constant multiplication factor;
(3) image characteristic point locating module, it is by rejecting in described each extreme point the low contrast point of noise-sensitive and not Stable marginal point determines the extreme point as characteristic point, positions for extreme point pinpoint first including be sequentially connected with Submodule, for removing the second locator module of low contrast point and for removing the 3rd locator module of mobile rim point, Wherein:
A, described first locator module are by carrying out secondary Taylor expansion to described Gaussian difference scale space function and derivation obtains The exact position of extreme point, the metric space function of extreme point is:
D ( X ^ ) = D ( x , y , &sigma; ) + &part; D ( x , y , &sigma; ) T &part; x X ^
Wherein,Represent the metric space function of extreme point, D (x, y, σ)TFor the side-play amount relative to extreme point,Represent The exact position of extreme point;
B, described second locator module carry out grey level enhancement, normalized successively to the image exported soon by image conversion submodule Rear rejecting described low contrast point, enhanced gray value is:
Herein
Described low contrast point judge formula as:
D ( X ^ ) < T 1 , T 1 &Element; &lsqb; 0.01 , 0.06 &rsqb;
Wherein, I " (x, y) represents the enhanced image function of gray value,For comprising the correction coefficient of local message, M is The maximum gradation value of pixel, described maximum gradation value M=255, mHFor all pixels higher than 128 of the gray value in image Average, mLIt is the gray value average that is less than all pixels of 128, ψ (x, y) is the image after being processed by image filtering submodule, T1For the threshold value set;
C, described 3rd locator module obtain this extreme value by the Hessian matrix H that Location Scale is 2 × 2 calculating extreme point The principal curvatures of point, and by rejecting principal curvatures ratio more than threshold value T set2Extreme point reject described mobile rim point, Wherein threshold value T2Span be [10,15], described principal curvatures ratio is come really by the ratio between the eigenvalue of comparator matrix H Fixed;
Preferably, described Intellectual air cleaner, scene Recognition device also includes:
(1) principal direction determines module, including the connection sub module being sequentially connected with, merges submodule and processes submodule, described company Line is used for two peak value lines of the arbitrary neighborhood in the gradient orientation histogram about described characteristic point in module to form many height Line segment, described merging submodule is for merging formation one in the longitudinal direction by having close slope and adjacent sub-line section Line segment, described process submodule for using the direction of the optimum line segment in a plurality of line segment as the principal direction of characteristic point, described optimum Line segment judge formula as:
L Y = L g &OverBar; max , g &OverBar; max = max ( g &OverBar; L n ) , g &OverBar; L n = 1 k &Sigma; k = 1 k g k , L n &Element; L &upsi; )
Wherein, LYRepresent optimum line segment,For average gradient value it isLine segment,For nth bar in described a plurality of line segment The average gradient value of line segment, gkFor the kth strip line segment in described nth bar line segment, LυFor described a plurality of line segment middle conductor length Line segment aggregate more than average line segment length;
(2) characteristic extracting module, it is according to described main formula always hyperspin feature neighborhood of a point, and according to postrotational neighborhood to institute State characteristic point to be described, thus generate the descriptor of described characteristic point;
(3) scene determination module, uses the feature extracted to contrast with the scene characteristic in data base, completes scene and judges.
Further, the sub-line section described in close slope is that slope differences is less than predetermined threshold value T3Sub-line section, described threshold value T3's Span be (0,0.1].
It is strong with color that the image pre-processing module that the present embodiment is arranged considers visual custom and the human eye perceptibility to different color The non-linear relation of degree, it is possible to describe image the most accurately;Propose the reduced mechanical model of Gaussian difference scale space, subtract Lack operand, improve arithmetic speed, and then improve the speed of graphical analysis;The image characteristic point locating module pair arranged Extreme point carries out low contrast point and the removal of mobile rim point, it is ensured that the effectiveness of characteristic point, the wherein gray value to image Strengthen, it is possible to be greatly increased the stability of image, the most accurate low contrast point is removed, and then improve image The accuracy analyzed;Principal direction is set and determines module, it is proposed that the judgement formula of optimum line segment, with characteristic point gradient direction Nogata The direction of the optimum line segment in the line segment that two peak value lines of the arbitrary neighborhood in figure are formed is as the principal direction of characteristic point, line segment phase More stable for point so that the descriptor of image characteristic of correspondence point has repeatability, improves the accurate of feature descriptor Property, and then can more fast and accurately image be identified detection, there is the highest robustness;The present embodiment takes threshold value T1=0.02, T2=11, T3=0.08, the precision of scene Recognition improves 1%, and speed improves 1.5%.
Embodiment 3
See Fig. 1, the present embodiment Intellectual air cleaner, know including air cleaner and the scene being arranged on air cleaner Other device, scene Recognition device includes:
(1) image pre-processing module, it includes the image transform subblock for coloured image is converted into gray level image and is used for The image filtering submodule that described gray level image is filtered, the gradation of image conversion formula of described image transform subblock is:
I ( x , y ) = m a x ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) + m i n ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) 2 + 2 &lsqb; max ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) - min ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) &rsqb;
Wherein, (x, y), (x, y), (x, (x, y) the intensity red green blue value at place, (x y) represents I B G R y) to represent pixel respectively Pixel (x, y) gray value at place;
(2) image extreme point detection module, it by being carried out the Gauss of the image that convolution is created as by difference of Gaussian and image Difference scale space detects the position of each extreme point, when sampled point relative to it with 8 consecutive points of yardstick and neighbouring When the value of 18 points that yardstick is corresponding is the biggest, described sampled point is maximum point, when sampled point relative to it with 8 of yardstick When the value of 18 points that consecutive points are corresponding with neighbouring yardstick is the least, described sampled point is minimum point, described difference of Gaussian chi The reduced mechanical model in degree space is:
D (x, y, σ)=(G (x, k σ)-G (x, σ)) * I'(x, y)+(G (y, k σ)-G (y, σ)) * I'(x, y)
Herein
G ( x , &sigma; ) = 1 2 &pi; &sigma; e - x 2 / 2 &sigma; 2 , G ( y , &sigma; ) = 1 2 &pi; &sigma; e - y 2 / 2 &sigma; 2
Wherein, D (x, y, σ) represents Gaussian difference scale space function, I'(x, is y) by the image letter of image transformant module output Number, * represents that convolution algorithm, σ represent the Gaussian function that the metric space factor, G (x, σ), G (y, σ) they are the changeable scale defined, K is constant multiplication factor;
(3) image characteristic point locating module, it is by rejecting in described each extreme point the low contrast point of noise-sensitive and not Stable marginal point determines the extreme point as characteristic point, positions for extreme point pinpoint first including be sequentially connected with Submodule, for removing the second locator module of low contrast point and for removing the 3rd locator module of mobile rim point, Wherein:
A, described first locator module are by carrying out secondary Taylor expansion to described Gaussian difference scale space function and derivation obtains The exact position of extreme point, the metric space function of extreme point is:
D ( X ^ ) = D ( x , y , &sigma; ) + &part; D ( x , y , &sigma; ) T &part; x X ^
Wherein,Represent the metric space function of extreme point, D (x, y, σ)TFor the side-play amount relative to extreme point,Represent The exact position of extreme point;
B, described second locator module carry out grey level enhancement, normalized successively to the image exported soon by image conversion submodule Rear rejecting described low contrast point, enhanced gray value is:
Herein
Described low contrast point judge formula as:
D ( X ^ ) < T 1 , T 1 &Element; &lsqb; 0.01 , 0.06 &rsqb;
Wherein, I " (x, y) represents the enhanced image function of gray value,For comprising the correction coefficient of local message, M is The maximum gradation value of pixel, described maximum gradation value M=255, mHFor all pixels higher than 128 of the gray value in image Average, mLIt is the gray value average that is less than all pixels of 128, ψ (x, y) is the image after being processed by image filtering submodule, T1For the threshold value set;
C, described 3rd locator module obtain this extreme value by the Hessian matrix H that Location Scale is 2 × 2 calculating extreme point The principal curvatures of point, and by rejecting principal curvatures ratio more than threshold value T set2Extreme point reject described mobile rim point, Wherein threshold value T2Span be [10,15], described principal curvatures ratio is come really by the ratio between the eigenvalue of comparator matrix H Fixed;
Preferably, described Intellectual air cleaner, scene Recognition device also includes:
(1) principal direction determines module, including the connection sub module being sequentially connected with, merges submodule and processes submodule, described company Line is used for two peak value lines of the arbitrary neighborhood in the gradient orientation histogram about described characteristic point in module to form many height Line segment, described merging submodule is for merging formation one in the longitudinal direction by having close slope and adjacent sub-line section Line segment, described process submodule for using the direction of the optimum line segment in a plurality of line segment as the principal direction of characteristic point, described optimum Line segment judge formula as:
L Y = L g &OverBar; max , g &OverBar; max = max ( g &OverBar; L n ) , g &OverBar; L n = 1 k &Sigma; k = 1 k g k , L n &Element; L &upsi; )
Wherein, LYRepresent optimum line segment,For average gradient value it isLine segment,For nth bar in described a plurality of line segment The average gradient value of line segment, gkFor the kth strip line segment in described nth bar line segment, LυFor described a plurality of line segment middle conductor length Line segment aggregate more than average line segment length;
(2) characteristic extracting module, it is according to described main formula always hyperspin feature neighborhood of a point, and according to postrotational neighborhood to institute State characteristic point to be described, thus generate the descriptor of described characteristic point;
(3) scene determination module, uses the feature extracted to contrast with the scene characteristic in data base, completes scene and judges.
Further, the sub-line section described in close slope is that slope differences is less than predetermined threshold value T3Sub-line section, described threshold value T3's Span be (0,0.1].
It is strong with color that the image pre-processing module that the present embodiment is arranged considers visual custom and the human eye perceptibility to different color The non-linear relation of degree, it is possible to describe image the most accurately;Propose the reduced mechanical model of Gaussian difference scale space, subtract Lack operand, improve arithmetic speed, and then improve the speed of graphical analysis;The image characteristic point locating module pair arranged Extreme point carries out low contrast point and the removal of mobile rim point, it is ensured that the effectiveness of characteristic point, the wherein gray value to image Strengthen, it is possible to be greatly increased the stability of image, the most accurate low contrast point is removed, and then improve image The accuracy analyzed;Principal direction is set and determines module, it is proposed that the judgement formula of optimum line segment, with characteristic point gradient direction Nogata The direction of the optimum line segment in the line segment that two peak value lines of the arbitrary neighborhood in figure are formed is as the principal direction of characteristic point, line segment phase More stable for point so that the descriptor of image characteristic of correspondence point has repeatability, improves the accurate of feature descriptor Property, and then can more fast and accurately image be identified detection, there is the highest robustness;The present embodiment takes threshold value T1=0.03, T2=12, T3=0.06, the precision of scene Recognition improves 2.5%, and speed improves 3%.
Embodiment 4
See Fig. 1, the present embodiment Intellectual air cleaner, know including air cleaner and the scene being arranged on air cleaner Other device, scene Recognition device includes:
(1) image pre-processing module, it includes the image transform subblock for coloured image is converted into gray level image and is used for The image filtering submodule that described gray level image is filtered, the gradation of image conversion formula of described image transform subblock is:
I ( x , y ) = m a x ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) + m i n ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) 2 + 2 &lsqb; max ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) - min ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) &rsqb;
Wherein, (x, y), (x, y), (x, (x, y) the intensity red green blue value at place, (x y) represents I B G R y) to represent pixel respectively Pixel (x, y) gray value at place;
(2) image extreme point detection module, it by being carried out the Gauss of the image that convolution is created as by difference of Gaussian and image Difference scale space detects the position of each extreme point, when sampled point relative to it with 8 consecutive points of yardstick and neighbouring When the value of 18 points that yardstick is corresponding is the biggest, described sampled point is maximum point, when sampled point relative to it with 8 of yardstick When the value of 18 points that consecutive points are corresponding with neighbouring yardstick is the least, described sampled point is minimum point, described difference of Gaussian chi The reduced mechanical model in degree space is:
D (x, y, σ)=(G (x, k σ)-G (x, σ)) * I'(x, y)+(G (y, k σ)-G (y, σ)) * I'(x, y)
Herein
G ( x , &sigma; ) = 1 2 &pi; &sigma; e - x 2 / 2 &sigma; 2 , G ( y , &sigma; ) = 1 2 &pi; &sigma; e - y 2 / 2 &sigma; 2
Wherein, D (x, y, σ) represents Gaussian difference scale space function, I'(x, is y) by the image letter of image transformant module output Number, * represents that convolution algorithm, σ represent the Gaussian function that the metric space factor, G (x, σ), G (y, σ) they are the changeable scale defined, K is constant multiplication factor;
(3) image characteristic point locating module, it is by rejecting in described each extreme point the low contrast point of noise-sensitive and not Stable marginal point determines the extreme point as characteristic point, positions for extreme point pinpoint first including be sequentially connected with Submodule, for removing the second locator module of low contrast point and for removing the 3rd locator module of mobile rim point, Wherein:
A, described first locator module are by carrying out secondary Taylor expansion to described Gaussian difference scale space function and derivation obtains The exact position of extreme point, the metric space function of extreme point is:
D ( X ^ ) = D ( x , y , &sigma; ) + &part; D ( x , y , &sigma; ) T &part; x X ^
Wherein,Represent the metric space function of extreme point, D (x, y, σ)TFor the side-play amount relative to extreme point,Represent The exact position of extreme point;
B, described second locator module carry out grey level enhancement, normalized successively to the image exported soon by image conversion submodule Rear rejecting described low contrast point, enhanced gray value is:
Herein
Described low contrast point judge formula as:
D ( X ^ ) < T 1 , T 1 &Element; &lsqb; 0.01 , 0.06 &rsqb;
Wherein, I " (x, y) represents the enhanced image function of gray value,For comprising the correction coefficient of local message, M is The maximum gradation value of pixel, described maximum gradation value M=255, mHFor all pixels higher than 128 of the gray value in image Average, mLIt is the gray value average that is less than all pixels of 128, ψ (x, y) is the image after being processed by image filtering submodule, T1For the threshold value set;
C, described 3rd locator module obtain this extreme value by the Hessian matrix H that Location Scale is 2 × 2 calculating extreme point The principal curvatures of point, and by rejecting principal curvatures ratio more than threshold value T set2Extreme point reject described mobile rim point, Wherein threshold value T2Span be [10,15], described principal curvatures ratio is come really by the ratio between the eigenvalue of comparator matrix H Fixed;
Preferably, described Intellectual air cleaner, scene Recognition device also includes:
(1) principal direction determines module, including the connection sub module being sequentially connected with, merges submodule and processes submodule, described company Line is used for two peak value lines of the arbitrary neighborhood in the gradient orientation histogram about described characteristic point in module to form many height Line segment, described merging submodule is for merging formation one in the longitudinal direction by having close slope and adjacent sub-line section Line segment, described process submodule for using the direction of the optimum line segment in a plurality of line segment as the principal direction of characteristic point, described optimum Line segment judge formula as:
L Y = L g &OverBar; max , g &OverBar; max = max ( g &OverBar; L n ) , g &OverBar; L n = 1 k &Sigma; k = 1 k g k , L n &Element; L &upsi; )
Wherein, LYRepresent optimum line segment,For average gradient value it isLine segment,For nth bar in described a plurality of line segment The average gradient value of line segment, gkFor the kth strip line segment in described nth bar line segment, LυFor described a plurality of line segment middle conductor length Line segment aggregate more than average line segment length;
(2) characteristic extracting module, it is according to described main formula always hyperspin feature neighborhood of a point, and according to postrotational neighborhood to institute State characteristic point to be described, thus generate the descriptor of described characteristic point;
(3) scene determination module, uses the feature extracted to contrast with the scene characteristic in data base, completes scene and judges.
Further, the sub-line section described in close slope is that slope differences is less than predetermined threshold value T3Sub-line section, described threshold value T3's Span be (0,0.1].
It is strong with color that the image pre-processing module that the present embodiment is arranged considers visual custom and the human eye perceptibility to different color The non-linear relation of degree, it is possible to describe image the most accurately;Propose the reduced mechanical model of Gaussian difference scale space, subtract Lack operand, improve arithmetic speed, and then improve the speed of graphical analysis;The image characteristic point locating module pair arranged Extreme point carries out low contrast point and the removal of mobile rim point, it is ensured that the effectiveness of characteristic point, the wherein gray value to image Strengthen, it is possible to be greatly increased the stability of image, the most accurate low contrast point is removed, and then improve image The accuracy analyzed;Principal direction is set and determines module, it is proposed that the judgement formula of optimum line segment, with characteristic point gradient direction Nogata The direction of the optimum line segment in the line segment that two peak value lines of the arbitrary neighborhood in figure are formed is as the principal direction of characteristic point, line segment phase More stable for point so that the descriptor of image characteristic of correspondence point has repeatability, improves the accurate of feature descriptor Property, and then can more fast and accurately image be identified detection, there is the highest robustness;The present embodiment takes threshold value T1=0.04, T2=13, T3=0.04, the precision of scene Recognition improves 1.5%, and speed improves 2%.
Embodiment 5
See Fig. 1, the present embodiment Intellectual air cleaner, know including air cleaner and the scene being arranged on air cleaner Other device, scene Recognition device includes:
(1) image pre-processing module, it includes the image transform subblock for coloured image is converted into gray level image and is used for The image filtering submodule that described gray level image is filtered, the gradation of image conversion formula of described image transform subblock is:
I ( x , y ) = m a x ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) + m i n ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) 2 + 2 &lsqb; max ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) - min ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) &rsqb;
Wherein, (x, y), (x, y), (x, (x, y) the intensity red green blue value at place, (x y) represents I B G R y) to represent pixel respectively Pixel (x, y) gray value at place;
(2) image extreme point detection module, it by being carried out the Gauss of the image that convolution is created as by difference of Gaussian and image Difference scale space detects the position of each extreme point, when sampled point relative to it with 8 consecutive points of yardstick and neighbouring When the value of 18 points that yardstick is corresponding is the biggest, described sampled point is maximum point, when sampled point relative to it with 8 of yardstick When the value of 18 points that consecutive points are corresponding with neighbouring yardstick is the least, described sampled point is minimum point, described difference of Gaussian chi The reduced mechanical model in degree space is:
D (x, y, σ)=(G (x, k σ)-G (x, σ)) * I'(x, y)+(G (y, k σ)-G (y, σ)) * I'(x, y)
Herein
G ( x , &sigma; ) = 1 2 &pi; &sigma; e - x 2 / 2 &sigma; 2 , G ( y , &sigma; ) = 1 2 &pi; &sigma; e - y 2 / 2 &sigma; 2
Wherein, D (x, y, σ) represents Gaussian difference scale space function, I'(x, is y) by the image letter of image transformant module output Number, * represents that convolution algorithm, σ represent the Gaussian function that the metric space factor, G (x, σ), G (y, σ) they are the changeable scale defined, K is constant multiplication factor;
(3) image characteristic point locating module, it is by rejecting in described each extreme point the low contrast point of noise-sensitive and not Stable marginal point determines the extreme point as characteristic point, positions for extreme point pinpoint first including be sequentially connected with Submodule, for removing the second locator module of low contrast point and for removing the 3rd locator module of mobile rim point, Wherein:
A, described first locator module are by carrying out secondary Taylor expansion to described Gaussian difference scale space function and derivation obtains The exact position of extreme point, the metric space function of extreme point is:
D ( X ^ ) = D ( x , y , &sigma; ) + &part; D ( x , y , &sigma; ) T &part; x X ^
Wherein,Represent the metric space function of extreme point, D (x, y, σ)TFor the side-play amount relative to extreme point,Represent The exact position of extreme point;
B, described second locator module carry out grey level enhancement, normalized successively to the image exported soon by image conversion submodule Rear rejecting described low contrast point, enhanced gray value is:
Herein
Described low contrast point judge formula as:
D ( X ^ ) < T 1 , T 1 &Element; &lsqb; 0.01 , 0.06 &rsqb;
Wherein, I " (x, y) represents the enhanced image function of gray value,For comprising the correction coefficient of local message, M is The maximum gradation value of pixel, described maximum gradation value M=255, mHFor all pixels higher than 128 of the gray value in image Average, mLIt is the gray value average that is less than all pixels of 128, ψ (x, y) is the image after being processed by image filtering submodule, T1For the threshold value set;
C, described 3rd locator module obtain this extreme value by the Hessian matrix H that Location Scale is 2 × 2 calculating extreme point The principal curvatures of point, and by rejecting principal curvatures ratio more than threshold value T set2Extreme point reject described mobile rim point, Wherein threshold value T2Span be [10,15], described principal curvatures ratio is come really by the ratio between the eigenvalue of comparator matrix H Fixed;
Preferably, described Intellectual air cleaner, scene Recognition device also includes:
(1) principal direction determines module, including the connection sub module being sequentially connected with, merges submodule and processes submodule, described company Line is used for two peak value lines of the arbitrary neighborhood in the gradient orientation histogram about described characteristic point in module to form many height Line segment, described merging submodule is for merging formation one in the longitudinal direction by having close slope and adjacent sub-line section Line segment, described process submodule for using the direction of the optimum line segment in a plurality of line segment as the principal direction of characteristic point, described optimum Line segment judge formula as:
L Y = L g &OverBar; max , g &OverBar; max = max ( g &OverBar; L n ) , g &OverBar; L n = 1 k &Sigma; k = 1 k g k , L n &Element; L &upsi; )
Wherein, LYRepresent optimum line segment,For average gradient value it isLine segment,For nth bar in described a plurality of line segment The average gradient value of line segment, gkFor the kth strip line segment in described nth bar line segment, LυFor described a plurality of line segment middle conductor length Line segment aggregate more than average line segment length;
(2) characteristic extracting module, it is according to described main formula always hyperspin feature neighborhood of a point, and according to postrotational neighborhood to institute State characteristic point to be described, thus generate the descriptor of described characteristic point;
(3) scene determination module, uses the feature extracted to contrast with the scene characteristic in data base, completes scene and judges.
Further, the sub-line section described in close slope is that slope differences is less than predetermined threshold value T3Sub-line section, described threshold value T3's Span be (0,0.1].
It is strong with color that the image pre-processing module that the present embodiment is arranged considers visual custom and the human eye perceptibility to different color The non-linear relation of degree, it is possible to describe image the most accurately;Propose the reduced mechanical model of Gaussian difference scale space, subtract Lack operand, improve arithmetic speed, and then improve the speed of graphical analysis;The image characteristic point locating module pair arranged Extreme point carries out low contrast point and the removal of mobile rim point, it is ensured that the effectiveness of characteristic point, the wherein gray value to image Strengthen, it is possible to be greatly increased the stability of image, the most accurate low contrast point is removed, and then improve image The accuracy analyzed;Principal direction is set and determines module, it is proposed that the judgement formula of optimum line segment, with characteristic point gradient direction Nogata The direction of the optimum line segment in the line segment that two peak value lines of the arbitrary neighborhood in figure are formed is as the principal direction of characteristic point, line segment phase More stable for point so that the descriptor of image characteristic of correspondence point has repeatability, improves the accurate of feature descriptor Property, and then can more fast and accurately image be identified detection, there is the highest robustness;The present embodiment takes threshold value T1=0.05, T2=14, T3=0.02, the precision of scene Recognition improves 1.8%, and speed improves 1.5%.
Last it should be noted that, above example is only in order to illustrate technical scheme, rather than to scope Restriction, although having made to explain to the present invention with reference to preferred embodiment, it will be understood by those within the art that, Technical scheme can be modified or equivalent, without deviating from the spirit and scope of technical solution of the present invention.

Claims (3)

1. an Intellectual air cleaner, it is possible to be identified scene around, it is characterized in that, including air cleaner and be arranged on sky Scene Recognition device on gas clearing machine, scene Recognition device includes:
(1) image pre-processing module, it includes the image transform subblock for coloured image is converted into gray level image and is used for The image filtering submodule that described gray level image is filtered, the gradation of image conversion formula of described image transform subblock is:
I ( x , y ) = m a x ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) + m i n ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) 2 + 2 &lsqb; m a x ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) - m i n ( R ( x , y ) , G ( x , y ) , B ( x , y ) ) &rsqb;
Wherein, (x, y), (x, y), (x, (x, y) the intensity red green blue value at place, (x y) represents I B G R y) to represent pixel respectively Pixel (x, y) gray value at place;
(2) image extreme point detection module, it by being carried out the Gauss of the image that convolution is created as by difference of Gaussian and image Difference scale space detects the position of each extreme point, when sampled point relative to it with 8 consecutive points of yardstick and neighbouring chi When the value of 18 points that degree is corresponding is the biggest, described sampled point is maximum point, when sampled point relative to it with 8 phases of yardstick When the value of 18 points that adjoint point is corresponding with neighbouring yardstick is the least, described sampled point is minimum point, described Gaussian difference scale The reduced mechanical model in space is:
D (x, y, σ)=(G (x, k σ)-G (x, σ)) * I'(x, y)+(G (y, k σ)-G (y, σ)) * I'(x, y)
Herein
G ( x , &sigma; ) = 1 2 &pi; &sigma; e - x 2 / 2 &sigma; 2 , G ( y , &sigma; ) = 1 2 &pi; &sigma; e - y 2 / 2 &sigma; 2
Wherein, D (x, y, σ) represents Gaussian difference scale space function, I'(x, is y) by the image letter of image transformant module output Number, * represents that convolution algorithm, σ represent the Gaussian function that the metric space factor, G (x, σ), G (y, σ) they are the changeable scale defined, K is constant multiplication factor;
(3) image characteristic point locating module, it is by rejecting in described each extreme point the low contrast point of noise-sensitive and not Stable marginal point determines the extreme point as characteristic point, including be sequentially connected with for pinpoint first locator of extreme point Module, for removing the second locator module of low contrast point and for removing the 3rd locator module of mobile rim point, its In:
A, described first locator module are by carrying out secondary Taylor expansion to described Gaussian difference scale space function and derivation obtains The exact position of extreme point, the metric space function of extreme point is:
D ( X ^ ) = D ( x , y , &sigma; ) + &part; D ( x , y , &sigma; ) T &part; x X ^
Wherein,Represent the metric space function of extreme point, D (x, y, σ)TFor the side-play amount relative to extreme point,Represent The exact position of extreme point;
B, described second locator module carry out grey level enhancement, normalization successively to the image exported soon by image conversion submodule Rejecting described low contrast point after reason, enhanced gray value is:
Herein
Described low contrast point judge formula as:
D ( X ^ ) < T 1 , T 1 &Element; &lsqb; 0.01 , 0.06 &rsqb;
Wherein, I " (x, y) represents the enhanced image function of gray value,For comprising the correction coefficient of local message, M is The maximum gradation value of pixel, described maximum gradation value M=255, mHFor all pixels equal higher than 128 of the gray value in image Value, mLBeing the gray value average that is less than all pixels of 128, (x y) is the image after being processed by image filtering submodule, T to ψ1 For the threshold value set;
C, described 3rd locator module obtain this extreme value by the Hessian matrix H that Location Scale is 2 × 2 calculating extreme point The principal curvatures of point, and by rejecting principal curvatures ratio more than threshold value T set2Extreme point reject described mobile rim point, Wherein threshold value T2Span be [10,15], described principal curvatures ratio is come really by the ratio between the eigenvalue of comparator matrix H Fixed.
A kind of Intellectual air cleaner the most according to claim 1, is characterized in that, scene Recognition device also includes:
(1) principal direction determines module, including the connection sub module being sequentially connected with, merges submodule and processes submodule, described company Line is used for two peak value lines of the arbitrary neighborhood in the gradient orientation histogram about described characteristic point in module to form many height Line segment, described merging submodule is for merging formation one in the longitudinal direction by having close slope and adjacent sub-line section Line segment, described process submodule for using the direction of the optimum line segment in a plurality of line segment as the principal direction of characteristic point, described optimum Line segment judge formula as:
L Y = L g &OverBar; max , g &OverBar; max = m a x ( g &OverBar; L n ) , g &OverBar; L n = 1 k &Sigma; k = 1 k g k , L n &Element; L &upsi; )
Wherein, LYRepresent optimum line segment,For average gradient value it isLine segment,For nth bar in described a plurality of line segment The average gradient value of line segment, gkFor the kth strip line segment in described nth bar line segment, LυFor described a plurality of line segment middle conductor length Line segment aggregate more than average line segment length;
(2) characteristic extracting module, it is according to described main formula always hyperspin feature neighborhood of a point, and according to postrotational neighborhood to institute State characteristic point to be described, thus generate the descriptor of described characteristic point;
(3) scene determination module, uses the feature extracted to contrast with the scene characteristic in data base, completes scene and judges.
A kind of Intellectual air cleaner the most according to claim 1, is characterized in that, described in have the sub-line section of close slope be oblique Rate variance is less than predetermined threshold value T3Sub-line section, described threshold value T3Span be (0,0.1].
CN201610234518.0A 2016-04-14 2016-04-14 Intelligent air purifier Pending CN105928099A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610234518.0A CN105928099A (en) 2016-04-14 2016-04-14 Intelligent air purifier

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610234518.0A CN105928099A (en) 2016-04-14 2016-04-14 Intelligent air purifier

Publications (1)

Publication Number Publication Date
CN105928099A true CN105928099A (en) 2016-09-07

Family

ID=56838271

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610234518.0A Pending CN105928099A (en) 2016-04-14 2016-04-14 Intelligent air purifier

Country Status (1)

Country Link
CN (1) CN105928099A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107588511A (en) * 2017-09-21 2018-01-16 四川长虹电器股份有限公司 Air conditioner energy source management system and method based on contour and HOG human testings
US10926210B2 (en) 2018-04-04 2021-02-23 ACCO Brands Corporation Air purifier with dual exit paths
USD913467S1 (en) 2018-06-12 2021-03-16 ACCO Brands Corporation Air purifier
CN113486756A (en) * 2021-06-29 2021-10-08 哈尔滨蛐宝科技有限公司 Intelligent air disinfection system and method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100111416A1 (en) * 2007-04-23 2010-05-06 Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. Device and method for determining an edge histogram, device and method for storing an image in an image database, device and method for finding two similar images and computer program
CN104180486A (en) * 2014-08-14 2014-12-03 南通博云物联网技术有限公司 Intelligent home system and control method
CN104978709A (en) * 2015-06-24 2015-10-14 北京邮电大学 Descriptor generation method and apparatus
CN105301982A (en) * 2015-11-23 2016-02-03 广东美的暖通设备有限公司 Control method and system for intelligent household electrical appliance

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100111416A1 (en) * 2007-04-23 2010-05-06 Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. Device and method for determining an edge histogram, device and method for storing an image in an image database, device and method for finding two similar images and computer program
CN104180486A (en) * 2014-08-14 2014-12-03 南通博云物联网技术有限公司 Intelligent home system and control method
CN104978709A (en) * 2015-06-24 2015-10-14 北京邮电大学 Descriptor generation method and apparatus
CN105301982A (en) * 2015-11-23 2016-02-03 广东美的暖通设备有限公司 Control method and system for intelligent household electrical appliance

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
吴京辉: "视频监控目标的跟踪与识别研究", 《中国博士学位论文全文数据库 信息科技辑》 *
张建兴: "基于注意力的目标识别算法及在移动机器人的应用研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107588511A (en) * 2017-09-21 2018-01-16 四川长虹电器股份有限公司 Air conditioner energy source management system and method based on contour and HOG human testings
US10926210B2 (en) 2018-04-04 2021-02-23 ACCO Brands Corporation Air purifier with dual exit paths
USD913467S1 (en) 2018-06-12 2021-03-16 ACCO Brands Corporation Air purifier
USD927671S1 (en) 2018-06-12 2021-08-10 ACCO Brands Corporation Air purifier
CN113486756A (en) * 2021-06-29 2021-10-08 哈尔滨蛐宝科技有限公司 Intelligent air disinfection system and method
CN113486756B (en) * 2021-06-29 2022-07-22 哈尔滨蛐宝科技有限公司 Intelligent air disinfection system and method

Similar Documents

Publication Publication Date Title
CN107679520B (en) Lane line visual detection method suitable for complex conditions
CN105844337A (en) Intelligent garbage classification device
CN108182383B (en) Vehicle window detection method and device
CN105928099A (en) Intelligent air purifier
CN110717872B (en) Method and system for extracting characteristic points of V-shaped welding seam image under laser-assisted positioning
CN106446952A (en) Method and apparatus for recognizing score image
CN109993154B (en) Intelligent identification method for single-pointer sulfur hexafluoride instrument of transformer substation
CN107665348B (en) Digital identification method and device for digital instrument of transformer substation
CN106650567B (en) Safety belt detection method and device
CN106709518A (en) Android platform-based blind way recognition system
CN113674206B (en) Extraction method suitable for characteristic parameters of deep-melting K-TIG welding molten pool and keyhole entrance
CN110288571B (en) High-speed rail contact net insulator abnormity detection method based on image processing
CN106778754A (en) A kind of industrial ammeter digit recognition method of robust
CN105844260A (en) Multifunctional smart cleaning robot apparatus
CN113888446A (en) Intelligent detection method for bending line of sheet metal structural part
CN106875430A (en) Single movement target method for tracing and device based on solid form under dynamic background
Wei et al. Detection of lane line based on Robert operator
WO2020114134A1 (en) Visual processing method for identifying emery particles
CN105844651A (en) Image analyzing apparatus
CN104732530A (en) Image edge detection method
CN102313740B (en) Solar panel crack detection method
CN109146863A (en) A kind of pavement marker line defect detection device
CN105933698A (en) Intelligent satellite digital TV program play quality detection system
CN105930853A (en) Automatic image capturing device for content generation
CN105930779A (en) Image scene mode generation device

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20160907

RJ01 Rejection of invention patent application after publication