CN106529432A - Hand area segmentation method deeply integrating significance detection and prior knowledge - Google Patents

Abstract

The invention relates to a hand area segmentation method deeply integrating significance detection and prior knowledge. This method combines the significant pattern of a hand area at pixel level with the significant pattern of a hand area at the regional level to make the detection algorithm for the entire hand area achieve higher robustness and accuracy. The method comprises the following steps: using an introduced Bayesian framework to obtain the confidence degree of each pixel in the hand area; combining the relevant techniques such as threshold segmentation, and finally obtaining a hand area segmentation result with high accuracy. The invention overcomes the shortcomings of a traditional hand area method which can only be applied to relatively simple background and non-near-skin color interference scene, and the method can obtain a hand area segmentation image accurately even in the case of various disturbances such as non-uniform illumination, near skin color background and face noise, therefore, making it applied with great prospects.

Description

A kind of depth integration conspicuousness detection and the hand region dividing method of priori

Technical field

The present invention relates to a kind of depth integration conspicuousness detection and the hand region dividing method of priori, belong to calculating The art such as machine vision, image procossing, pattern-recognition field

Background technology

The gesture identification of view-based access control model refers to that using all kinds of cameras form, displacement to hand etc. carry out continuous collecting, The sequence frame of a model information is formed, then converts them to corresponding instruction, for some operations of control realization.The skill Art obtains quite varied application in numerous scenes such as man-machine interaction, robot control and virtual reality.Common gesture Technology of identification generally involves hand region segmentation, the tracking of hand Shape Feature Extraction, hand and the several aspects of gesture identification.Its In, hand region cutting techniques are exactly the interference that other elements are excluded in picture, and the hand region of people is partitioned into exactly Come, while ensureing the definition of hand profile.Hand region splits the initial step as gesture identification, and the quality of its effect is straight Connect the quality for determining that gesture recognition system recognizes accuracy.

Traditional hand region cutting techniques mainly include following several：1. Face Detection method, i.e., build skin face in advance Color model, such as Hsu R L exist《Face detection in color images》The YCrCb color space skins proposed in one text Color model etc., and colour of skin judgement is carried out to each pixel in image using complexion model, select the maximum region of the nearly colour of skin to make For hand region, so as to realize the segmentation of hand region.But under complex background condition, there are numerous nearly colour of skin objects Interference, carries out hand Segmentation robustness by the method merely poor；2. template matching method, i.e., by collecting a large amount of hand shapes Sample is traveled through building a hand shape database, then in altimetric image to be checked or random template matches are finding Hand region.The method high cost, speed are slow, accuracy is low, are progressively eliminated at present；3. background subtraction method, that is, pass through Background is modeled, and corresponding background is reduced in original image, to obtain foreground target.This kind of method requirement is right Background has accurately estimation, and also needs to the hand region that the subsequent treatment of complexity can obtain estimation, changeable in scene Under the conditions of be difficult to；4. rim detection, i.e., carry out rim detection, and exclude non-hand by certain technological means to image The edge in region, so as to obtain the estimation of hand region.This method effect under uniform background or simple background is preferable, but It is difficult to apply under complex background；5. increase and limit, i.e., by limiting backcolor, wearing the means such as exclusive data gloves, so as to Exclude the interference that complex background is particularly nearly colour of skin background.Although the method effectively, significantly limit answering for gesture identification With scope, not with higher practicality.In a word, above-mentioned conventional method all has certain lacking in accuracy, robustness Fall into, it is difficult to be directly used in actual scene.

In recent years, emerged the new method of many hand regions segmentation, mostly by traditional hand region dividing method with Combine carries out the judgement of hand region to other image processing methods (such as motion detection, Threshold segmentation etc.).As Zou Jie China exists《Base In the dynamic gesture track identification system research of monocular vision》In one text, with reference to moving object detection, Face Detection and texture The methods such as detection are completing the detection of hand region.Since Laurent Itti exist《A Model of Saliency-Based Visual Attention for Rapid Scene Analysis》After proposing saliency detection algorithm earliest in one text, A large amount of conspicuousness detection methods emerge in an endless stream, in view of unique advantage of the saliency detection in display foreground Objective extraction, Some articles start to introduce the supplementary means that image significance detection method is detected as hand region.As a Yang Wen Ji exists《Towards The gesture interaction technical research of home-services robot》In one text, obtained using multiple dimensioned color contrast and texture contrast The Saliency maps of image are taken, then Saliency maps are carried out with skin color probability map, objectivity attribute tolerance contour level priori Fusion, row threshold division of going forward side by side, so as to obtain final hand region testing result, this method is divided under relatively easy background Cut accuracy high, but due to excessively considering the other contrast of region class, cause which and still imitate under complex background condition Fruit is not good；Chuang Yuelong exist《Saliency-guided improvement for hand posture detectionand recognition》In one text, it is proposed that a kind of conspicuousness detection method for not combining any prior information, It is mainly used in the coarse localization of hand, then the notable figure for obtaining and skin color probability map is merged, is estimated so as to obtain hand region Meter, this method can exclude the interference of large stretch of nearly colour of skin background, but for being all more significant region and the face for the nearly colour of skin Portion lacks elimination ability.In a word, it is compared with conventional method, existing that conspicuousness detection is incorporated in hand region split sence A series of schemes come, be enhanced in the ambient interferences ability of part in accuracy and overcoming, but big due to which Mostly conspicuousness detection is each carried out with the detection based on prioris such as the colours of skin, is finally merged again so that its algorithm Robustness it is poor, it is difficult to overcome the interference of face and other complex backgrounds completely, still cannot veritably in actual scene Used.

In a word, traditional hand region dividing method technological means is single, and depends on Face Detection technology unduly, most of Hand region segmentation under simple background is can be only used for, once and in image to be split, there is complex texture, nearly area of skin color, the back of the body During the disturbing factor such as scape is changeable, uneven illumination is even, these methods cannot nearly all realize accurate hand region segmentation.It is existing Hand region detection method fails to make full use of priori, is particularly nearly colour of skin ambient interferences energy complex background interference is overcome There is larger defect in power.

The content of the invention

For the deficiencies in the prior art, the invention provides the hand of a kind of detection of depth integration conspicuousness and priori Region segmentation method；

The present invention successfully eliminates the interference that various complex backgrounds are particularly nearly colour of skin background, finally obtains accurate hand Portion region.

The technical scheme is that：

A kind of depth integration conspicuousness detection and the hand region dividing method of priori, concrete steps include：

(1) original image is divided into into N number of region；100≤N≤400

(2) by measuring the hand region detection side that conspicuousness detection is merged with colour of skin priori based on color distribution degree Method, realizes the Preliminary detection to hand region, specifically includes：

A, in RGB color, t different value is obtained to the color quantizing of each passage, subtracts color total quantity T is arrived less³Kind；

B, the significance for calculating each color respectively；

C, traversal each color, form color significance look-up table, and the color significance look-up table includes every kind of quantization Color afterwards and its corresponding significance；

D, the significance to each color are normalized；

Each pixel in image after e, the process of traversal step a, according to color significance look-up table to the pixel Significance carry out assignment, obtain hand region notable figure image1；Each pixel significance is higher, and the point belongs to hand area The possibility in domain is bigger.

Based on " backcolor generally spreads relatively broad and well-balanced in the picture, and as the forecolor of hand region Be distributed in the picture and relatively more concentrate " and the priori of " being more likely to be hand region closer to the pixel of the colour of skin " know Know, define a kind of new hand region that conspicuousness detection and colour of skin priori depth integration are measured based on color distribution degree Detection method, realizes the Preliminary detection to hand region.The method merges priori Skin Color Information, while make use of color of image Space spreads situation, has carried out the detection of hand region conspicuousness in pixel scale to image, preferably eliminates the non-colour of skin back of the body The interference that scape and the nearly colour of skin background area of fritter are detected to hand.

(3) by based on skin color probability distance and the hand region detection method of region contrast, realizing to hand region Preliminary detection, specifically include：

F, the N number of region for splitting original image acquisition to step (1), each area in the image after calculation procedure d is processed respectively The significance in domain；

G, traversal regional, forming region significance look-up table, the region significance look-up table include regional And its corresponding significance；

H, the significance to regional are normalized；

I, according to region significance look-up table, assignment is carried out to the significance of each pixel in original image, hand is obtained Region notable figure image2；Each pixel significance is higher, and the possibility that the point belongs to hand region is bigger.

Based on " being more likely to be hand region closer to the pixel of the colour of skin " and " concentrate in entire image aggregation and More likely belong to hand region closer to the region of the colour of skin than peripheral region " priori, definition is a kind of general based on the colour of skin The hand region detection method of rate distance and region contrast, by according to the difference of interregional skin color probability come between definition region Contrast, so as to enhance the significance in the high region of the nearly colour of skin, concentration class, realize the Preliminary detection to hand region. The method merges priori Skin Color Information, hand region conspicuousness detection is carried out to image, preferably eliminate in the rank of region Interference of the nearly colour of skin background area of bulk to hand region detection, at the same time enhances the edge of hand region, after being Region segmentation provides good condition.

(4) the hand region notable figure that hand region notable figure image1 and step (3) that fusion steps (2) are obtained is obtained Image2, obtains the hand region confidence map image3 after excluding complex background interference；

(5) according to the hand region confidence map image3 for obtaining, calculate the prior probability of each pixel and observe seemingly So probability, according to Bayesian formula, further calculates the posterior probability that the pixel belongs to hand region, obtains final accurate Hand region confidence map image4.

The hand region after excluding complex background interference can be obtained to put after above two hand region notable figure is merged Letter figure.Thus, in order to further improve hand region segmentation accuracy, this patent according to Bayes principle, using hand The information provided by region confidence map, has carried out more accurate Pixel-level detection using Bayesian frame to hand region.Will A Bayesian inference problem, i.e., the hand region for being obtained according to technological means 1 and technological means 2 are regarded in hand region detection as Confidence map come calculate each pixel prior probability and observation likelihood probability, then further calculated according to Bayesian formula The pixel belongs to the posterior probability of hand region, so as to obtain final accurate hand region confidence map.

According to currently preferred, step b, for random color i, i ∈ 1,2...t³, its significance Se (i) Ask for formula (I), (II) as follows：

S_e(i)=P_skin(i)exp(-E(i)/σ_e) (Ⅰ)

In formula (I), (II), parameter σ_eRefer to influence degree of the color dispersion level of control color i to its significance, σ_e Bigger, influence degree is less, σ_eSpan be (0,5.0]；

P_skinThe skin color probability of (i) for color i；Work as L_skin(i)<When 12, P_skin(i)=0；Otherwise, L_skinI () refers to color i obtained by Oconaire skin similarity models Belong to the log-likelihood of skin points；L_maxRefer to the L in original image_skinThe maximum of (i)；H (i) refers to color i in skin region Shared ratio in the color histogram H in domain, it is shared in the color histogram h in non-skin region that h (i) refers to color i Ratio；

The present invention adopts Oconaire skin similarity models, and the model is by counting a large amount of colour of skin samples and non-colour of skin sample This, so as to obtain the color histogram h of color histogram H in RGB color inner skin region and non-skin region, most The log-likelihood L that color i belongs to skin points is obtained eventually_skin(i)。

E (i) is color i space measure of spread function, equal for measure that color i spread in whole image regional Even degree and the spatial distribution dispersion degree in whole image；Spatial distribution is more extensive in the picture for color i, E's (i) Value is bigger；

D_s(R_k,R_j) for region R_kWith region R_jSpace length；K ∈ 1,2...N and k ≠ j；

p_ikRefer to region R_kMiddle color accounts for ratio of the color for the pixel quantity of i in whole image for the pixel quantity of i Example；

p_ijRefer to region R_jMiddle color accounts for ratio of the color for the pixel quantity of i in whole image for the pixel quantity of i Example.

According to currently preferred, step f, for arbitrary region R_k, its significance S_r(R_k) ask for formula (III), (IV) is as follows：

In formula (III), (IV), N_RiFor region R_iPixel quantity；

Parameter σ_cRefer to that space length is for the influence degree of region significance, σ between control area_cSuggestion span For (0,5.0], σ_cBigger, distant region is just bigger for one's respective area conspicuousness influence degree；

D_s(R_k,R_j) for region R_kWith region R_jSpace length；

D_c(R_k,R_j) for region R_kWith region R_jSkin color probability distance；

f(c_km) refer in region R_kIn probability of color m appeared in one's respective area；m∈1,2...t³And m ≠ i；

f(c_ij) refer in region R_jIn probability of color i appeared in one's respective area；

P_skin(c_km) refer to region R_kIn m kind colors c_kmSkin color probability；

P_skin(c_ij) refer to region R_jIn i-th kind of color c_ijSkin color probability；

According to currently preferred, in the step (5), concrete steps include：

J, any pixel v of calculating belong to the prior probability p (hand) and arbitrarily pixel v of hand region and belong to background area The prior probability p (bk) in domain：Arbitrarily pixel v belongs to the prior probability p (hand) of hand region equal to hand region confidence map Significances of the image3 at the pixel；Arbitrarily pixel v belongs to the computing formula of the prior probability p (bk) of background area such as Shown in formula (V)：

P (bk)=1-p (hand) (V)；

K, original image is transformed in CIELab color spaces, it is for any pixel v, empty in CIELab colors according to which In l, a, b tri- Color Channel components value, calculate any pixel v belong to hand region observation likelihood probability p (v | Hand) and pixel v belongs to observation likelihood probability p (v | bk) of background area, as shown in formula (VI), formula (VII)：

In formula (VI), formula (VII),Refer in hand region R_handIn, there is same color passage with pixel v The pixel quantity of component l；Refer in hand region R_handIn, there are same color channel components a with pixel v Pixel quantity；Refer in hand region R_handIn, there is the pixel number of same color channel components b with pixel v Amount；Represent region R_handIn total pixel quantity；

Refer in background area R_bkIn, there is the pixel quantity of same color channel components l with pixel v；Refer in background area R_bkIn, there is the pixel quantity of same color channel components a with pixel v；It is Refer in background area R_bkIn, there is the pixel quantity of same color channel components b with pixel v；Represent background area R_bkIn total pixel quantity；

L, p (hand), p (bk), p (v | hand), p (v | bk) are substituted in Bayesian formula, obtain any pixel v category In posterior probability p (hand | v) of gesture area, shown in computing formula such as formula (VIII)：

The posterior probability that each pixel belongs to gesture area is obtained by formula (VIII)；Accurate hand region confidence is obtained Figure image4.In accurate hand region confidence map image4, the gray value of each pixel has been weighed the pixel and has belonged to hand region Possibility size.

According to currently preferred, the step (4), the hand region obtained by formula (Ⅸ) fusion steps (2) are notable Hand region notable figure image2 that figure image1 and step (3) are obtained, obtains the hand region after excluding complex background interference Confidence map image3, for hand region confidence map image3 any point, if its coordinate (x, y), then the pixel confidence Image3 (x, y) computational methods are：

In formula (Ⅸ), image1 (x, y) represents in hand region notable figure image1 coordinate as the pixel of (x, y) Significance；Image2 (x, y) represents significance of the coordinate as the pixel of (x, y) in hand region notable figure image2.

Image co-registration mode of the present invention be by image1 and two width images of image2 same coordinate point it is aobvious Work degree is multiplied and evolution, while realizing that two kinds of notable figures merge, effectively enhances middle confidence pixels, improves fusion Effect, so as to finally obtain image3 with the confidence level at coordinate points.

Beneficial effects of the present invention are：

The invention overcome traditional hand region method can be only applied to relatively easy background, without nearly colour of skin interference scene Shortcoming, in the case of the various interference such as inhomogeneous illumination, nearly colour of skin background, face noise, still sufficiently accurately can obtain The segmentation figure picture of hand region is obtained, thus is had broad application prospects.

Description of the drawings

Fig. 1 is original image schematic diagram in embodiment；

Fig. 2 is the schematic diagram of hand region notable figure image1 in embodiment；

Fig. 3 is the schematic diagram of hand region notable figure image2 in embodiment；

Fig. 4 is the schematic diagram of hand region confidence map image3 in embodiment；

Fig. 5 is the schematic diagram of hand region binary map image4 in embodiment；

Fig. 6 is the schematic diagram of the final confidence map image5 of hand region in embodiment；

Fig. 7 is the schematic diagram of final binary map image6 of hand region in embodiment；

Fig. 8 is the schematic diagram of hand region segmentation result in embodiment；

Fig. 9 is the stream of a kind of depth integration conspicuousness detection of the present invention and the hand region dividing method of priori Journey schematic diagram.

Specific embodiment

The present invention is further qualified with reference to Figure of description and embodiment, but not limited to this.

Embodiment

A kind of depth integration conspicuousness detection and the hand region dividing method of priori, as shown in figure 9, concrete steps Including：

(1) SLIC super-pixel segmentations are carried out to original image, R1, the common N number of regions of R2 ... RN, original image such as Fig. 1 institutes is obtained Show：

(2) by measuring the hand region detection side that conspicuousness detection is merged with colour of skin priori based on color distribution degree Method, realizes the Preliminary detection to hand region, specifically includes：

A, in RGB color, t different value is obtained to the color quantizing of each passage, subtracts color total quantity T is arrived less³Kind；

B, the significance for calculating each color respectively；For random color i, i ∈ 1,2...t³, its significance Se (i) Ask for formula (I), (II) as follows：

S_e(i)=P_skin(i)exp(-E(i)/σ_e) (Ⅰ)

In formula (I), (II), parameter σ_eRefer to influence degree of the color dispersion level of control color i to its significance, σ_e Bigger, influence degree is less, σ_eSpan be (0,5.0]；

P_skinThe skin color probability of (i) for color i；Work as L_skin(i)<When 12, P_skin(i)=0；Otherwise,

L_skinI () refers to that color i obtained by Oconaire skin similarity models belongs to skin The log-likelihood of point；L_maxRefer to the L in original image_skinThe maximum of (i)；H (i) refers to color of color i in skin area Shared ratio in histogram H, h (i) refer to the shared ratio in the color histogram h in non-skin region of color i；This Bright employing Oconaire skin similarity models, the model pass through to count a large amount of colour of skin samples and non-colour of skin sample, so as to obtain The color histogram H in the RGB color inner skin region and color histogram h in non-skin region, finally gives color i category In the log-likelihood L of skin points_skin(i)。

E (i) is color i space measure of spread function, equal for measure that color i spread in whole image regional Even degree and the spatial distribution dispersion degree in whole image；Spatial distribution is more extensive in the picture for color i, E's (i) Value is bigger；

D_s(R_k,R_j) for region R_kWith region R_jSpace length；K ∈ 1,2...N and k ≠ j；

p_ikRefer to region R_kMiddle color accounts for ratio of the color for the pixel quantity of i in whole image for the pixel quantity of i Example；

p_ijRefer to region R_jMiddle color accounts for ratio of the color for the pixel quantity of i in whole image for the pixel quantity of i Example.

C, traversal each color, form color significance look-up table, and the color significance look-up table includes every kind of quantization Color afterwards and its corresponding significance；

D, the significance to each color are normalized；

Each pixel in image after e, the process of traversal step a, according to color significance look-up table to the pixel Significance carry out assignment, obtain hand region notable figure image1；As shown in Figure 2；Each pixel significance is higher, the point The possibility for belonging to hand region is bigger.

Based on " backcolor generally spreads relatively broad and well-balanced in the picture, and as the forecolor of hand region Be distributed in the picture and relatively more concentrate " and the priori of " being more likely to be hand region closer to the pixel of the colour of skin " know Know, define a kind of new hand region that conspicuousness detection and colour of skin priori depth integration are measured based on color distribution degree Detection method, realizes the Preliminary detection to hand region.The method merges priori Skin Color Information, while make use of color of image Space spreads situation, has carried out the detection of hand region conspicuousness in pixel scale to image, preferably eliminates the non-colour of skin back of the body The interference that scape and the nearly colour of skin background area of fritter are detected to hand.

(3) by based on skin color probability distance and the hand region detection method of region contrast, realizing to hand region Preliminary detection, specifically include：

F, the N number of region for splitting original image acquisition to step (1), each area in the image after calculation procedure d is processed respectively The significance in domain；For arbitrary region R_k, its significance S_r(R_k) to ask for formula (III), (IV) as follows：

In formula (III), (IV),For region R_iPixel quantity；

Parameter σ_cRefer to that space length is for the influence degree of region significance, σ between control area_cSuggestion span For (0,5.0], σ_cBigger, distant region is just bigger for one's respective area conspicuousness influence degree；

D_s(R_k,R_j) for region R_kWith region R_jSpace length；

D_c(R_k,R_j) for region R_kWith region R_jSkin color probability distance；

f(c_km) refer in region R_kIn probability of color m appeared in one's respective area；m∈1,2...t³And m ≠ i；

f(c_ij) refer in region R_jIn probability of color i appeared in one's respective area；

P_skin(c_km) refer to region R_kIn m kind colors c_kmSkin color probability；

P_skin(c_ij) refer to region R_jIn i-th kind of color c_ijSkin color probability；

G, traversal regional, forming region significance look-up table, the region significance look-up table include regional And its corresponding significance；

H, the significance to regional are normalized；

I, according to region significance look-up table, assignment is carried out to the significance of each pixel in original image, hand is obtained Region notable figure image2；As shown in Figure 3；Each pixel significance is higher, and the possibility that the point belongs to hand region is bigger.

Based on " being more likely to be hand region closer to the pixel of the colour of skin " and " concentrate in entire image aggregation and More likely belong to hand region closer to the region of the colour of skin than peripheral region " priori, definition is a kind of general based on the colour of skin The hand region detection method of rate distance and region contrast, by according to the difference of interregional skin color probability come between definition region Contrast, so as to enhance the significance in the high region of the nearly colour of skin, concentration class, realize the Preliminary detection to hand region. The method merges priori Skin Color Information, hand region conspicuousness detection is carried out to image, preferably eliminate in the rank of region Interference of the nearly colour of skin background area of bulk to hand region detection, at the same time enhances the edge of hand region, after being Region segmentation provides good condition.

(4) the hand region notable figure that hand region notable figure image1 and step (3) that fusion steps (2) are obtained is obtained Image2, obtains the hand region confidence map image3 after excluding complex background interference；As shown in Figure 4；Merged by formula (Ⅸ) Hand region notable figure image2 that hand region notable figure image1 and step (3) that step (2) is obtained is obtained, is excluded Hand region confidence map image3 after complex background interference, for hand region confidence map image3 any point, if its seat Mark (x, y), then pixel confidence image3 (x, y) computational methods are：

In formula (Ⅸ), image1 (x, y) represents in hand region notable figure image1 coordinate as the pixel of (x, y) Significance；Image2 (x, y) represents significance of the coordinate as the pixel of (x, y) in hand region notable figure image2.

Image co-registration mode of the present invention be by image1 and two width images of image2 same coordinate point it is aobvious Work degree is multiplied and evolution, while realizing that two kinds of notable figures merge, effectively enhances middle confidence pixels, improves fusion Effect, so as to finally obtain image3 with the confidence level at coordinate points.

(5) Da-Jin algorithm is utilized, enters row threshold division to hand region confidence map image3, obtain hand region binary map Image4, in binary map image4, highlight regions are gesture area R according to a preliminary estimate_hand, black region is according to a preliminary estimate Background area R_bk, binary map image4 is as shown in Figure 5；

(6) according to the hand region confidence map image4 for obtaining, calculate the prior probability of each pixel and observe seemingly So probability, according to Bayesian formula, further calculates the posterior probability that the pixel belongs to hand region, obtains final accurate Hand region confidence map image5, as shown in Figure 6.

The hand region after excluding complex background interference can be obtained to put after above two hand region notable figure is merged Letter figure.Thus, in order to further improve hand region segmentation accuracy, this patent according to Bayes principle, using hand The information provided by region confidence map, has carried out more accurate Pixel-level detection using Bayesian frame to hand region.Will A Bayesian inference problem, i.e., the hand region for being obtained according to technological means 1 and technological means 2 are regarded in hand region detection as Confidence map come calculate each pixel prior probability and observation likelihood probability, then further calculated according to Bayesian formula The pixel belongs to the posterior probability of hand region, so as to obtain final accurate hand region confidence map.

In the step (6), concrete steps include：

J, any pixel v of calculating belong to the prior probability p (hand) and arbitrarily pixel v of hand region and belong to background area The prior probability p (bk) in domain：Arbitrarily pixel v belongs to the prior probability p (hand) of hand region equal to hand region confidence map Significances of the image4 at the pixel；Arbitrarily pixel v belongs to the computing formula of the prior probability p (bk) of background area such as Shown in formula (V)：

P (bk)=1-p (hand) (V)；

K, original image is transformed in CIELab color spaces, it is for any pixel v, empty in CIELab colors according to which In l, a, b tri- Color Channel components value, calculate any pixel v belong to hand region observation likelihood probability p (v | Hand) and pixel v belongs to observation likelihood probability p (v | bk) of background area, as shown in formula (VI), formula (VII)：

In formula (VI), formula (VII),Refer in hand region R_handIn, there is same color passage with pixel v The pixel quantity of component l；Refer in hand region R_handIn, there are same color channel components a with pixel v Pixel quantity；Refer in hand region R_handIn, there is the pixel number of same color channel components b with pixel v Amount；Represent region R_handIn total pixel quantity；

Refer in background area R_bkIn, there is the pixel quantity of same color channel components l with pixel v；Refer in background area R_bkIn, there is the pixel quantity of same color channel components a with pixel v；It is Refer in background area R_bkIn, there is the pixel quantity of same color channel components b with pixel v；Represent background area R_bkIn total pixel quantity；

L, p (hand), p (bk), p (v | hand), p (v | bk) are substituted in Bayesian formula, obtain any pixel v category In posterior probability p (hand | v) of gesture area, shown in computing formula such as formula (VIII)：

The posterior probability that each pixel belongs to gesture area is obtained by formula (VIII)；Accurate hand region confidence is obtained Figure image5.In accurate hand region confidence map image4, the gray value of each pixel has been weighed the pixel and has belonged to hand region Possibility size.

(7) Da-Jin algorithm is utilized, row threshold division is entered to final confidence map image5, and carried out eliminating non-maximum region, fill out Mend regional void etc. to process, obtain final binary map image6 of hand region, as shown in Figure 7：

(8) using binary map image6 as mask, image segmentation is carried out to original image, you can obtain hand region segmentation knot Really, as shown in Figure 8.

Claims (5)

1. a kind of depth integration conspicuousness detects the hand region dividing method with priori, it is characterised in that concrete steps Including：

(1) original image is divided into into N number of region；100≤N≤400；

(2) by measuring the hand region detection method that conspicuousness detection is merged with colour of skin priori based on color distribution degree, The Preliminary detection to hand region is realized, is specifically included：

A, in RGB color, t different value is obtained to the color quantizing of each passage, makes color total quantity be reduced to t³ Kind；

B, the significance for calculating each color respectively；

C, traversal each color, form color significance look-up table, after the color significance look-up table includes every kind of quantization Color and its corresponding significance；

D, the significance to each color are normalized；

Each pixel in image after e, the process of traversal step a, shows to the pixel according to color significance look-up table Work degree carries out assignment, obtains hand region notable figure image1；

(3) by based on skin color probability distance and the hand region detection method of region contrast, realizing to the first of hand region Step detection, specifically includes：

F, the N number of region for splitting original image acquisition to step (1), regional in the image after calculation procedure d is processed respectively Significance；

G, traversal regional, forming region significance look-up table, the region significance look-up table include regional and its Corresponding significance；

H, the significance to regional are normalized；

I, according to region significance look-up table, assignment is carried out to the significance of each pixel in original image, hand region is obtained Notable figure image2；

(4) the hand region notable figure that hand region notable figure image1 and step (3) that fusion steps (2) are obtained is obtained Image2, obtains the hand region confidence map image3 after excluding complex background interference；

(5) according to the hand region confidence map image3 for obtaining, prior probability and the observation likelihood for calculating each pixel is general Rate, according to Bayesian formula, further calculates the posterior probability that the pixel belongs to hand region, obtains final accurate hand Region confidence map image4.

2. a kind of depth integration conspicuousness according to claim 1 detects the hand region dividing method with priori, Characterized in that, step b, for random color i, i ∈ 1,2...t³, its significance S_eI () asks for formula (I), (II) It is as follows：

S_e(i)=P_skin(i)exp(-E(i)/σ_e) (Ⅰ)

$E\left(i\right)=-{\mathrm{\&Sigma;}}_{R_j,j\&Element;1,2...N}\&lsqb;p_{ij}{\log }_2{p}_{ij}{\mathrm{\&Sigma;}}_{R_k\&NotEqual;R_j}{D}_s(R_k,R_j)p_{ik}\&rsqb;---\left(II\right)$

In formula (I), (II), parameter σ_eRefer to influence degree of the color dispersion level of control color i to its significance, σ_eTake Value scope be (0,5.0]；

P_skinThe skin color probability of (i) for color i；Work as L_skin(i)<When 12, P_skin(i)=0；Otherwise, L_skinI () refers to that color i obtained by Oconaire skin similarity models belongs to the logarithm of skin points Likelihood；L_maxRefer to the L in original image_skinThe maximum of (i)；H (i) refers to color histogram H of color i in skin area In shared ratio, h (i) refers to the shared ratio in the color histogram h in non-skin region of color i；

E (i) is color i space measure of spread function, for measuring the uniform journey that color i is spread in whole image regional Degree and the spatial distribution dispersion degree in whole image；Spatial distribution is more extensive in the picture for color i, and the value of E (i) is got over Greatly；

D_s(R_k,R_j) for region R_kWith region R_jSpace length；K ∈ 1,2...N and k ≠ j；

p_ikRefer to region R_kMiddle color accounts for ratio of the color for the pixel quantity of i in whole image for the pixel quantity of i；

p_ijRefer to region R_jMiddle color accounts for ratio of the color for the pixel quantity of i in whole image for the pixel quantity of i.

3. a kind of depth integration conspicuousness according to claim 1 detects the hand region dividing method with priori, Characterized in that, step f, for arbitrary region R_k, its significance S_r(R_k) to ask for formula (III), (IV) as follows：

$S_r\left(R_k\right)={\mathrm{\&Sigma;}}_{R_k\&NotEqual;R_i}\&lsqb;N_{R_i}{D}_s(R_k,R_j)\exp (-D_c(R_k,R_j)/{\mathrm{\&sigma;}}_c)\&rsqb;---\left(III\right)$

$D_c(R_k,R_j)={\mathrm{\&Sigma;}}_{m=1}^{n_1}{\mathrm{\&Sigma;}}_{j=1}^{n_2}f\left(c_{km}\right)f\left(c_{ij}\right)\&lsqb;P_{skin}\left(c_{km}\right)-P_{skin}\left(c_{ij}\right)\&rsqb;---\left(IV\right)$

In formula (III), (IV),For region R_iPixel quantity；

Parameter σ_cRefer to that space length is for the influence degree of region significance, σ between control area_cSuggestion span for (0, 5.0]；

D_s(R_k,R_j) for region R_kWith region R_jSpace length；

D_c(R_k,R_j) for region R_kWith region R_jSkin color probability distance；

F (ckm) refers to the probability of color m in the Rk of region appeared in one's respective area；m∈1,2...t³And m ≠ i；

f(c_ij) refer in region R_jIn probability of color i appeared in one's respective area；

P_skin(c_km) refer to region R_kIn m kind colors c_kmSkin color probability；

P_skin(c_ij) refer to region R_jIn i-th kind of color c_ijSkin color probability.

4. a kind of depth integration conspicuousness according to claim 1 detects the hand region dividing method with priori, Characterized in that, in the step (5), concrete steps include：

J, any pixel v of calculating belong to the prior probability p (hand) and arbitrarily pixel v of hand region and belong to background area Prior probability p (bk)：Arbitrarily pixel v belongs to the prior probability p (hand) of hand region equal to hand region confidence map Significances of the image3 at the pixel；Arbitrarily pixel v belongs to the computing formula of the prior probability p (bk) of background area such as Shown in formula (V)：

P (bk)=1-p (hand) (V)；

K, original image is transformed in CIELab color spaces, for any pixel v, according to which in CIELab color spaces The value of tri- Color Channel components of l, a, b, calculates observation likelihood probability p (v | hand) that any pixel v belongs to hand region And pixel v belongs to observation likelihood probability p (v | bk) of background area, as shown in formula (VI), formula (VII)：

$p\left(v\right|hand)=\frac{N_{R_{hand},v_l}\&CenterDot;N_{R_{hand},v_a}\&CenterDot;N_{R_{hand},v_b}}{N_{R_{hand}}^3}---\left(VI\right)$

$p\left(v\right|bk)=\frac{N_{R_{bk},v_l}\&CenterDot;N_{R_{bk},v_a}\&CenterDot;N_{R_{bk},v_b}}{N_{R_{bk}}^3}---\left(VII\right)$

In formula (VI), formula (VII),Refer in hand region R_handIn, there are same color channel components l with pixel v Pixel quantity；Refer in hand region R_handIn, there is the pixel of same color channel components a with pixel v Point quantity；Refer in hand region R_handIn, there is the pixel quantity of same color channel components b with pixel v；Represent region R_handIn total pixel quantity；

Refer in background area R_bkIn, there is the pixel quantity of same color channel components l with pixel v；Refer in background area R_bkIn, there is the pixel quantity of same color channel components a with pixel v；It is Refer in background area R_bkIn, there is the pixel quantity of same color channel components b with pixel v；Represent background area R_bkIn total pixel quantity；

L, p (hand), p (bk), p (v | hand), p (v | bk) are substituted in Bayesian formula, obtain any pixel v and belong to hand Posterior probability p (hand | v) in gesture region, shown in computing formula such as formula (VIII)：

$p\left(hand\right|v)=\frac{p\left(hand\right)p\left(v\right|hand)}{p\left(hand\right)p\left(v\right|hand)+p\left(bk\right)p\left(v\right|bk)}---\left(VIII\right)$

The posterior probability that each pixel belongs to gesture area is obtained by formula (VIII)；Accurate hand region confidence map is obtained image4。

5. a kind of depth integration conspicuousness according to claim 1 detects the hand region dividing method with priori, Characterized in that, the step (4), hand region notable figure image1 obtained by formula (Ⅸ) fusion steps (2) and step (3) hand region notable figure image2 for obtaining, obtains the hand region confidence map image3 after excluding complex background interference, right In hand region confidence map image3 any point, if its coordinate (x, y), then pixel confidence image3 (x, y) computational methods For：

$image3(x,y)=\sqrt{image1(x,y)*image2(x,y)}---\left(IX\right)$

In formula (Ⅸ), image1 (x, y) represents in hand region notable figure image1 coordinate as the notable of the pixel of (x, y) Degree；Image2 (x, y) represents significance of the coordinate as the pixel of (x, y) in hand region notable figure image2.