TWI263944B - Naked body image detection method - Google Patents

Naked body image detection method Download PDF

Info

Publication number
TWI263944B
TWI263944B TW93117330A TW93117330A TWI263944B TW I263944 B TWI263944 B TW I263944B TW 93117330 A TW93117330 A TW 93117330A TW 93117330 A TW93117330 A TW 93117330A TW I263944 B TWI263944 B TW I263944B
Authority
TW
Taiwan
Prior art keywords
image
color
skin color
skin
naked
Prior art date
Application number
TW93117330A
Other languages
Chinese (zh)
Other versions
TW200601178A (en
Inventor
Chien-Shu Lee
Pau-Choo Chung
Yung-Ming Kuo
E-Liang Chen
Original Assignee
Chien-Shu Lee
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 Chien-Shu Lee filed Critical Chien-Shu Lee
Priority to TW93117330A priority Critical patent/TWI263944B/en
Publication of TW200601178A publication Critical patent/TW200601178A/en
Application granted granted Critical
Publication of TWI263944B publication Critical patent/TWI263944B/en

Links

Abstract

The present invention relates to a naked body image detection method, which relates to a technology of determining a skin-color chromatic distribution of an image by utilizing a neural network, and further determining whether the image is a naked body image by utilizing a post-process. An input vector is firstly generated from an input image, and then inputted to the neural network for determining whether there's a skin-color element. If the image does not have a skin-color element, the image is regarded as a non-naked body image. If the image has a skin-color element, the neural network would determine a skin-color chromatic set which the image belongs to, and further detect the skin-color according to the skin-color chromatic distribution of the set. Next, a maximum smooth skin-color block would be determined according to the characteristic of human skin-color area smoothness. The central position, area measurement and length/width ratio of the maximum smooth skin-color block are then calculated for determining whether the image is a potential naked body image. If yes, the present invention further detects a face within the maximum smooth skin-color block, and calculates the area measurement ratio of the face to the maximum smooth skin-color block, so as to determine whether the image is a portrait photo. If not, the image is identified as a naked body image. Accordingly, the present invention determines whether the image is a naked body image based on the above steps.

Description

1263944 玖, invention description: [Technical field to which the invention belongs] - 锸 if lip! There is a method for detecting the naked person _ image, the user buckle Λ y it with _ software before:: $, lower the user to make learning and automatic side effect: the possibility of automatic 5 error _; ί people's skin color i? 2fi non-skin color, such as the back also covers part of the center of the image, and choose f, the main body of the road 々 body shirt is usually the most exposed image of the main image, for the naked body position Whether the miscellaneous image is a point or not because the subject is a human iH' Bu 3 image of another very large headshot in line with iliiiiij, the probability, in addition, because it is handled to avoid mistakes, too judged, so the face debt test must also be Minimize false positives: 3⁄4 like = layer is too complicated '[Previous technology] Because the strong network of government and private institutions has integrated the tools of the public's lack of life. In particular, now the network is 20%, 1 hanging life can not be 5 隹, 罔 频 frequency is seen from the former data machine upgrade to ADSLi 1263944 coaxial cable (CABLE), the bandwidth of the two is more than tens of times, Greatly improve the bandwidth limit so that. The convenience of the Internet, while giving people a privilege of $ , ϊ , , , 法 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 对于 。 。 。 。 。 。 Nowadays, the erotic shells detect the _ word line, and almost all of them use the words to judge the motive, starting point and purpose of the main ii.兀 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋 疋For the main reason is because of the erotic image shooting two "difficult to come high, the character is marked with special lights, or: 5: no nuclear or protagonist and the color change on the background ^ ^ ^ 士 镜 镜 ' According to previous research, only the color space caused by the color space of the skin caused similar sub-human skin color and background color classification. 3 This we use this feature as the skin 1263944 [Invention content]: ϊ : Image _ 妓, its step spoon:, the collection of the shadow of the human skin / color image ^ Input: ί5 ί Jingjing network round: ginseng? f) 33 into the feature "by the ^ ^ 2 into: Zhang vector 'The complex j is composed of the automatic and Hm color components of the neural network; (4), the composition of the skin color, like the information has a combination, then the gamma to the A total color color privately distributed to the subset of which belongs to 1^ Take the dare to color the smooth block 'and calculate the Pro =, ίϊ ίί; ^ 2;? If the most f-color smooth block is larger than the soil i-light smooth block is located in the center of the image, if the continuation of the large skin level The second f, block y can be a bare body image, and then the 疋 is less than a certain threshold, if , the maximum skin color smoothing block can be imaged, and the subsequent image processing is continued; and (8) determining whether the maximum skin color smoothing block is a large head photo by the face of the j, and determining the maximum skin color smoothing if The block is a bare body image. Preferably, the step of the manual pre-processing operation of the human skin color image comprises: Step 1: manually creating a bare body image skin color sub-collection data f; step; step two · · the naked body image skin color sub-set database The skin color of each image in the inside is separated by artificial circle selection. Step 3: The f/point on the skin color block separated by manual circle is composed of RGB. The pixel is transferred to the corresponding UV color space, so that the skin color region in each image corresponds to a CCHOJroma Chart Histogram); Step 4: Each UV color is determined by the Closing of the morphology The skin color distribution set 1263944 is closed, so that each of the uv color space skin color confinement areas is to be the first step of the third image of the image household 2 = binarization (10) is B-cc, and then adopts -na rSiiβ-cc; Step 5: According to the " ίί 3 mother - Zhang Lian Lai Sheng's test: 敝 做 _ — fff two images of the ΓΗ 其 其 其 其 其 其 其 其 其 总 总 总 总 = = = = ? ? ? ? ? ? ? ? ? ? ?池ν) ' Using the corresponding CB-CC to find the Υ-overlapping weZveZ Γ> 〇·8, step six············································ Covering more than 80%, then Α, β, (4) will be obtained from each other after a group of skin color sub-collections ^f body image skin color database, J爻J as the naked person - class is learning materials, another
CCH CCH:A^CCH) (Accumulated, color collection will get coffee (^ thousand sentences jf=' group out f ]'x] 256x256 indicates that its distribution and space "true ^ up ^ sampling, can not make the probability of the error, Α τ and representative purposes" resulting in the judgment
The action of interpolating is additionally an error, especially for each A-CCH. The difference is the same as the interpolation. The result of the interpolating and strengthening is the result of accumulating color space. Chart IEA-CCH (Interpolated 1263944 and Enhanced A_CCH), Xl, 2, ..., N ^ h\ x = VV /7 / ' ΛΑ ^ - y丄I~U value is the more representative, is the skin color is zero Represents a part of non-skinning (background), a TFA Γ^Τ and '" part of the I value is set to 1 ' and the produced 4 ί; Γ 肤色 车 车 车 ' 其中 其中 其中 其中 其中 其中 其中 其中To classify and judge _ group of human skin color information "funding: 5 recorded and automatic original 妒ϊ ί ί ίίϊ neural network input vector layer is used to capture the judgment and r background feature vector composed of its scene ί =
匕j, the skin of the block, Tao, and the background feature vector is the heart of the two, the background is obtained. Therefore, the image will generate a network model test of the U-type neural network to obtain a possible color distribution of the skin color. % bamboo ΐ ί ί 其 ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti ti χ(ΐ6Ν) pixel size, where μ and N are positive integers, and then the image is cut into 16χΐ6 blocks; step 2, a normal image is calculated, and the foreground skin color feature vector and the background are calculated. Feature inward, y second, correlation feature operation: the similarity distribution is represented by a two-dimensional space to obtain the correlation feature matrix representing the skin color and the background relative to a group of AEA-CCH; step four Simplified features
Retc^ion): Since the correlation feature matrix has 256x256 data, if this number is used as input, it will cause a serious burden on the neural network in training. In order to take into account the calculation amount and retain relative spatial information, it can be 256x256 The array is reduced to a 32x32 array, and the input vectors representing the skin color and the background are represented by two matrices, each of which is 32x32; and step 5, the input is generated: 1263944 Each of the skin color module layers in the neural Sii construct 5 The skin color module is composed of a class of good fathers and fathers. Among them, one of the skin color module layers, the sheep 2, including the m training samples, and the practice steps (4), the positive and partial weights (four) correction amount; Value matrix ί : Steps until convergence or ί ^: ί ί ΐ - - , , , , , ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ , , , , Each skin phase, sex, has a value between [〇, 丨], and the higher the value, the input of the N rounds of the skin color to the decision, and then the group of skin colors. According to the five sets of skin color 4, the color of the skin color attributed to the best image is generated to generate a group of the genus, to find the vector of f, and select the one or several of the strongest values of the processing round. Oh, as the decision-making layer, and where * is its value
Yl, p1, 2, 3, . . . , N, whose expression is ^: 1 is the output of spectroscopy as η-1, if fk > f, and fk > 〇e5 ? vj^k 〇, otherwise 10 1263944 For Puding, only one set of values is closest, and the other degree (4) begins to bribe the body color of the human body. The body color of the human body is measured by the group of skin color that belongs to the image. IEA-CCH (Interpolated and EnhanrP^: the image produced by the white shirt
Chart Histogram) to detect the C=u=d Chroma image in the image, and then set the skin color portion to the image and the binary color value can be obtained - the corresponding skin color portion is white and the back color is color ϊ ϊ ί ί ; ί people? (d) in order to reduce the operation (Opening): right / white '^理; · Steps · · break the image outline, and cut the narrow neck and eliminate the fine c break, calculate to smooth the break After the operation, many sounds can be included, and the large area will be divided after the background is finished. The connected fμ color surface _ block sequence indicates the image example ί i 骤 ☆ color The rectangle of the aspect ratio block, ii covers the maximum smooth skin color experimental data and the drum is obtained: Step 2, according to the Rgn- job, the maximum flat area is 5, the area is fen-max, and - Aspect ratio Ratio, and use the average width hvg of f to smooth the brain 1263944%n-max/L, long barren r r ϊ ίίί length and width (10) ati 〇 critical value, such as according to ^ human body image, such as: two loyal It is too narrow, but is classified as non-naked smooth skin coffee A may be naked i body scene; like: = 33 table the biggest other party ^ ίί package £ Zhongkou, Loyalty: The face features of the block are judged to be the largest smooth skin color block. The two eyes and the mouth are obtained by the face test. I close the outline of the part, and then find it; the face close-up image, not the bare knife No, it can be determined that the image is determined to be a naked human body image. Body image, otherwise, the image can be recognized [Embodiment], J5 Gong Guan, the other inventions of the invention... x _ has the advantages of power, the step-by-step description is as follows: will pick up, first with color information element, therefore It's hard to 'and the posture changes quite a lot. The color of the skin color changes with these changes. The guide will be a big problem. If: skin color for accurate detection representative 5:; ==: 5 These 2 are measured by a skin color space. However, if all of the people can use this skin color space to make a skin color distribution as _=|2, the skin color and the earthy life of the skin color will be wider, because the skin color distribution range becomes wider, 12 1263944 plus ^2 is violent! 'Detected by the color of the palm of the hand', thus increasing the degree, with ί Γ ί = We can first learn and adjust the mechanism according to the color of the color
Type) is recorded in (4) the distribution prototype (Proto's purpose. In the network link, to achieve accurate detection of skin color network iri£ti2S: i lack of time because of the neural method, please refer to the figure - map: Ming One proposes to use a neural network to determine: st T knows that the invention uses post-processing to add "尨" to "color" and the image belongs to that - a neural network will determine the skin color to be detected, If the color space of the skin color used for n is not established, then the image will be processed by the step-by-item effective management website and the anti-blocking network ϋίϊί wire will enable the HtS fruit or domain mirror to be created. Yes, that is, the color information of the effect of the color shift of the 'involvement of the neural network of the class: the species' can be directly regarded as the class _ c row, and the input shadow is not removed in order to remove the influence of brightness. In the case of color images, 13 1263944 will transfer RGB to another color space to make the color smash, and the degree will be hurt. For example: TSL, HIS, Yuv, L b, ^ have a knife away ^, color Yi "observable _ skin color, = square m L Li Nei Valley, the following will select the foot m color space as a turn The interpretation of the book is based on the manual method, the pre-classification work of the ancient I shadow i, thinking that the neural network is ii. Please consider the second figure of the figure. The flow chart and the following steps, such as Jingshen, the pre-classification of the skin color by hand, can be manually extracted to capture the skin color of the original image: the skin color of each image first, and the skin color part is separated by Lee. (Please refer to the attached work). ^Skin color part of the color space chart (aircma Chart I^istogram · CCH) · Each of the separated skin blocks on the mother, the pixels consisting of RGB are transferred to the corresponding Claw color space. Because the skin color area in the image will correspond to a CCH (refer to Appendix 2). Morphology closure (C1〇sing) operation: Close the treatment of each UV color space, the purpose It is to obtain a closed area of each claw color space. First, the CCH obtained by each image is binarized, and the binarized CCH is called B-CC, as shown in Annex 3. Then a 3* is adopted. 1 structural element (st Ru ru 对 对 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B 四 四 四 四 四 四As shown in Annex 4, x and y are CCHs of any two images. The sum of CCH values is as follows) and ί^ΣΣ池v), and then the overlapping area z is found by using CB-CC corresponding to its UV u phase. V If x and y are simultaneously established, x, y are classified into the same set. ', 14 1263944 X5] x (m, v) / JT > 〇. 8
weZ veZ
ΣΣ·^, ν)/η8 ueZ veZ Grouping: According to the previous two comparisons, more than 80% of the CCH collection, each 隹ϋ 到 to all two, / 斤 is grouping, we will The motion of the surface is that the image A and the image B cover each other by 8 〇 μ. For example, set 1 like C covers more than 80% of each other, 〇β, ’ = s 2 is the image Β and shadow. Finally, we will get several skin colors as the same group,
The skin color; come: "5! 15 Mai 2?? 苎!, the temple has the highest probability of color collection, after the tKiii database. It is difficult to group skin, - is the learning material, one is the measurement; like the representative color distribution divided into two sets, the group skin I color space chart (Accumulated plus color in the average effect) to explain, cut Gas 'two will ^: 4 for example X two 1,2, 3 4. X — 256 χ 256 不 , , 正 正 正 正 正 正 ί ί ί ί ί ί ί ί ί ί ί 目 城 分布 分布 分布 分布 分布 分布 分布 分布 分布 分布 分布 分布 分布 真 真 真 真 真 真 真 真 真 真 真Error on the occurrence. The probability of occurrence, to indicate the error of the color, but the round sampling on s: sr: (four) inside = two = 15 1263944 The value of the neighbor point of the Sunshine is added to the value of the central coordinate point, assimilation purpose. The result of interpolation and enhancement is called interpolation Α (ΤΗ,······················· The higher the value of t, the more representative of the skin color ί ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ ̄ For the _ ΙΕΑ Η Η Η Η Η Η Η Η Η qu qu qu qu qu qu qu qu qu qu qu qu qu qu Ot;" J (Multi-Layer Feed-Forward Neural) to learn and classify the color of each group. In fact, other types of neural networks can also be applied. i ϋ ϋ ϋ ϊ 输 输 输 输 输 输 输 输 输 输 输 输 输The sound of the sound is combined with the information of the background and the information of the background. The group/system is a three-layered forward neural network (Three-Layer ^Srward Neural Network) 5 to achieve automatic skin color classification. The effect. This should be, when the number of color groups is 'can be easily learned and adapted to the model ΐ ίί= will explain the neural network to the vector shown in the third figure of the figure, that is, how to get from a raw image,撷 Take out the user, like the input feature vector of which group of skin colors belongs to. According to the view is the back' and the four corners of the image do not contain human objects, and the vector of the 系 is from the foreground skin color features inward, month, The composition of the 肤色 罝 罝 , , , , , , 肤色 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 Size 'where the coffee is a positive integer, Then calculate the amount of 5: as shown in Annex 6 (A), respectively, to obtain the possible skin color of the center and the surrounding, respectively, and the middle of the image is shown as a normalized image of the image ^, d ^ /7,. Production of a beautiful scene color feature vector and background features iM =, 2, 3, 4, is a possible skin color block (Possible Back = 1 〇 Hnp), Bl,, 1 = 1, 2, 3, 4 ' is the possible background block (Possible is the color space chart of pregnancy, Shanghai (10), furnace is the sum of the color space chart of the &, calculated as follows:
S
ACCH 2XCi k^l txch βΑΟΟΗ __ jyCCH k=lf sudden loss, similarity measurement: traditional similarity measurement, mostly with a single scalar, indicating the similarity of the two objects, in order to preserve the relative space of the color space A similarity distribution is represented by a one-dimensional space. Assume that ^ represents the correlation between skin color and background relative to AEA-CCH of group X. ^ Array: m ij\x ^ij ^acch vj · s;; x h. ij,x where mii,x,mij,x The matrix elements ' of SACCH, B-1, Mx, and Mx are matrix elements of x group AEA-CCH ( //x), and wherein 0$i, j$255, X is 1, 2, 3, 4. Step 4: Feature simplification: due to the correlation feature matrix Mx, Μ: 256x256
yACCH 17 1263944 Poor material, if this number I is used as a loser, it will cause a serious burden on training, in order to take into account the amount of calculation and retention 2;;: In the case of ^, the matrix element is ~,,. The different process is as follows /ΓΧ64+63 /x64+63 Σ i=kx64 /=/χ64 left x64+63/χ64+63 Σ Σ%,. i=kx64 /=1x64 : The input vector is the eigenvector, which is the foreground Ϊ Jingte! The vector consists of 疋, 以, and the face of the skin layer of the gods 1 network architecture w〇rk). The test road (Back plus Qing this on number if, is 2048, hidden layer her job, output layer group skin color _ color model, group of skin color. For a certain ^ ^ two ^ ^ training process *, in order to let the network 2 交 样本 C , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , (A), and the heart is the X group < matrix Si, and the four blocks most likely to be skin color are as follows: · Set 16 blocks in the center of the x group image, how u=6 Ghost, I6's (R, G, B) vector set is r 7 "I) for the prime point respectively. The relative coordinates of the A block are (i, j), 3⁄4 household pf, G, B: value, f For a 3-dimensional vector, then x α2)'·Ά6,15),χΡ((616)). According to the RGB to YUV formula, for each pixel point (i, j·) of 18 1263944 7 < 16 , ( ί ϊ 可 Ϊ 相对 相对 相对 相对 相对 相对 相对 相对 相对 相对 — — — — — — — — — For, ~, 'for its matrix coordinates, get the value of (in, V") in the < in the (~, V"). So, every possibility like the collection of 辛%·η skin color. Therefore, every big one The block 'will get 16 lakes - < 16 big and then add 256 values to get - total value, expressed by X, transport, lower: 』 = group & four areas most likely to be skin color Block: two; the first four of t, the corresponding: ί4ϊ^ίί: «Ιίχ%Τ/χ^ϊΐί The input vector corresponding to the image. The parent of the HA砰 (2) Calculate the gap between the output layer and the hidden layer, calculate - Concealed layer weighted value matrix correction amount and partial weight vector correction amount. Calculate (3) = ^ value _: more gambling out of the layer value scale and hidden layer weighted by the road (4) retracting and re-enacting the uranium surface step, Until arrogance or a certain amount of learning (5) test: As mentioned above, a test image will get yCC", both
ACCH (take the central four blocks), $ cumulative color space statistics capture, get, 4 input vectors, "enter four skin color 、, and test, and the mother a skin color module will get a value second generation The correlation between the image and the skin color of the group, the value is between [0 U,
The Sit correlation then enters four output vectors into the decision layer to determine which group of skin colors the image should belong to. 19 1263944 Case Competition 型 type = via the network (Competitive ί, ^ ίΐί: 4^ The formula is as follows: rib is the output of fl, LW, 2, 3, 4, transport rk=i 1, if fk - fj And fk ^0.5 ,vj^k ^ 〇, otherwise. Near, and the similarity is super i〇. 5 Ο ' = The skin color is the most. Because of the naked body image, the human body h bare bSHH object = human body image of the human body area has Three characteristics, the second sacrifice, the bare ttit' system. Therefore, after we have completed the smoothness of the debt test, the ratio of the long 觅 ratio is off. According to the experimental results, even if: j? sex 3 has: calculate the face area Area and skin color surface ^ have |= domain. If a critical value, then this image is a face photo instead of "^= rate = a certain 20 1263944 love shadow and easy to be misjudged as the color of the fourth map of the lake and below 5 The scale method is used to deal with it. Please discriminate the Ren = road of the schema as belonging to the four groups of 1 __ learning ^ Π valued image. If the value of < is greater than G, the money is Etr. ·, For white, the background is black, and the color of the skin part is ^^Fig.4. The process of the process is to quantify the rough chain of the skin. We take the leisure of whether, in addition, if a block of eight surrounding blocks are ^ 'This block color blocks are also considered. This finish is obtained from the processing HA'c
Ig is the same in the coordinate E (x, y) of the record E, and the block is = f, t =, the block of 8x8' is then added to the individual blocks, 6 = plus, right and less than 63⁄4. , this block is regarded as a skin color block, anti 21 1263944 严f like 1s, then the image 1 read image is done after the skin color smoothness detection, the result is 2! Or become the sudden extension of the human skin color, Even if the harmony is huge, the skin color image I obtained after the detection is calculated to be larger than the continuous area. In order to reduce the skin area iiiss program "尥 = division, the purpose of the dog branch, the structural unit is a 6x6 two-dimensional matrix;; master /, the second step, the maximum area: after the disconnection operation, The background of the skin color will be divided into 'the next block from the household = the largest area. From the known experimental data can be, ¥, if the largest skin color area is larger, then the sound shirt looks like In order to expose the human body image, and continue to the main skin '^ Otherwise, the job shows that the image is non-naked, and the special S is before; Ϊ: iiifff, after the above processing, has the following two-core area 一 范 以 D: Set 5 Most of the seats in the image have their own circumference: 吏ί: too ===13⁄4 2 ϊΐΐ ϊΐΐ ί = the long axis of the block, and the long axis of the Z sister area 5 is found along this long axis, and The length L and the width W of the rectangle are known. The picture above is the block of the skin tone after the smoothness detection. The figure below shows the rectangle covering the skin color block. According to the fact, we can get the skin color block. Get the average width of this skin block [avg and aspect ratio ^·= I 22 1263944
They use the value of Ratio to determine the aspect ratio of the skin color block. Equation gentleman W__avg = Rgn_max/L
Ratio = L/W_avg The threshold of this Ratio can be found from the experiment. This skin color block is too narrow and is classified as a non-naked body shadow ^: The value represents 乂 if we only use the skin area to occupy the whole area ^ you are 'and the color block position and aspect ratio as bare Cl Proportion is off, the big head is based on the face of the face and the proportion of the face is determined by the tube. If the image of the big head passes through the genus of the idol, the image will be mistakenly judged as a naked human body image. Therefore, :, this, the class photo to the side, will reduce the chance of misjudgment, you can take two master photos, we know that the face area of the headshot accounted for ^ Zhang $ detection circle to select the face range If this 23 1263944 is necessary, please apply in accordance with the law, please give permission. The description of the above embodiments is purely for the purpose of explaining the principles of the present invention and its best practical mode, so that those skilled in the art can grasp the changes of their different applications for their specific purposes. It will be apparent to those skilled in the art that a number of modifications and variations can be made to achieve the same effect, and are intended to be within the scope of the invention. 24 1263944 Simple description of the schema] The first figure is how the invention uses the neural network to determine the skin color space of the second image, and then uses the post-processing centering, shadow, and a flow chart of the exposed human body image; 】 疋 为 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • The neural network model architecture diagram of the invention can automatically learn and record the classification and color of the group; the mouth group; ί = four diagrams: the touch flow chart of the processing of the New Zealand; The smoothness of the skin is processed by the flow chart to remove the moon's view and the maximum skin tone smoothing block is processed. 25

Claims (1)

1263944 Picking up, applying for a patent range · · L types of naked human body image detection methods, the steps include: (a) one color space per Chroma Chart; (b) color S image artificial pre-processing work, its system Make a pre-separation of human skin color images through human = ϊ 乍 乍 乍 乍 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体 人体The reference of the network input is compared with the skin color information of the human body; ", whether the image has the closest image of the human skin color (4) atmosphere from 5, classification, _ arbitrary image, this iSilg image terminates all subsequent image processing, judgment (e) By the automatic neural network of this type of neural network, the color phase = total 2 Decheng # ra 3 will be measured by the 5 Hai's human skin color area and then disconnected to the most U, 益, 5 benefits 2 The threshold value may be bare body image, soil or, if it is, Bei I] Guhai's largest production, no position' is placed in the center of the image, then ώContinue ί like the lit block may be bare body shadow Whether the ratio can be J, at the preset threshold, if 杲, 目 — -; 疋 裸 裸 裸 裸 裸 ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ 26 By the characteristics of the face, the method of the person's method, the step-------the bare body image skin color collection database; the step of knowing the naked body image S skin color 'utility person' step three (four) consumption Every point on the turn is transferred from the Hi to the corresponding color space. So CCHCChro;:Ch^fog^)^ # ^ ^ "J - ^ Step 4: By means of Morphology, the closed-end transport f, the per-color space = read; That is, the third step of the '- Zhang rif t:, the CCH binarization' of the binarization is ^CC a 3*3 structural element (Structure B-: ie) cfSC to do the closing operation, so that - The closed step 5 is based on the area of the CB-CC, and the S3 generated for each image is compared by two or two. For example, x and y are CCHs of any two images, and the sum of CCH values is respectively z. = n coffee, v) and hZZwe, then use its corresponding idee to find a suitable area Z ' If the following two conditions are true, then the same sub-set; Hx(w,v)/H8 weZ veZ wgZ vgZ ΣΣ>^,ν)/}^0·8 — 7—7 27 1263944 Image, then all pairs are done ^1, image B covers 8G% or more of two images A and more than %, then A, B, shirts like 0 cover each other 80 will eventually ^ number of groups 8 skin ^ _ - group ' and so on, step seven: for the six sounds of step six, the largest skin color group to ili nude ^ ^N=Now, cloth 'can be used to contain τ tiring force [_ ' get - ί plus ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί ί Hij,x] 256x256 means that χ=1,2,丄, difference 'for the difference: (3)=the opposite sex' also does the enhanced action; the difference between the bar and the color is step 9: by interpolation And reinforcement, combined into a = 2 = to: on the 5 redundant two ϋ m n interpolation 舆 enhanced operation results are jinterpolated and touch (4) A-CCH), the system can ^ ^ nij \ xil56x256 table, which two 1 of 34 'JX hhmn, x, ~, the higher the value of x, the greater the likelihood that the skin color is more, and the value of zero is the part of the non-skin & background. 28 1263944 In order to train the network The process t can simultaneously distinguish the relationship between the background and the background, and the background is bitter; ^^ light JJ is a kind of reference input to the human neural network. j 3. The bare body as described in claim 1 Among them, m butterfly system is a network model architecture used to learn to record the skin color information of various groups.曰 刀 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ The layer of the network 'Cang 5 · as claimed in the fourth paragraph of the patent scope of the naked body shadow silly, the feature-oriented orientation consists of a Lie skin color feature vector and a back brother feature, wherein the foreground skin color feature vector can ^ Second, the central and surrounding areas of the five skin color areas are solid. The eigenvectors of the 3 scenes are from the four corners of the original wheeled image # it Scope method of the bare body image described in item 5, ^ middle side, τ, skin color feature vector and the background feature vector The step of obtaining the package is as follows: the image is normalized: all the input image size is repeated and the image is cut into 16x16 blocks; the transformed image is calculated to obtain the foreground skin color feature and correlation feature. : A two-dimensional space represents the skin color and the background relative to a group of AEA-CCH 29 1263944,, the serious burden of training, ten ΐΐ盥 32x32; 匕 January jin, the input vector, the size of each is two The wheeled vector························································································· The group is used to learn different skin groups separately, and the layer is 'the detection method of each image, the steps - the training county takes the =:::: steps include ·· Let the network recognize the X group image盥Practice the needle, in order to be the same, The sample package includes the unscheduled input network of the color and background of the xiiUfi image to train it, and the 'two groups of image intersections and the corresponding wheel-in vector are included; Μ the non-X group of each image--step two Calculate the error and repair quantity, calculate the output layer plus "array hidden layer gap and calculate the surface plus amount to the is weight matrix: update the round-out layer weight matrix and the inner hidden ' until convergence or do - A fixed number of 23⁄4 loses zssi?:, vector, one at [〇,1], the higher the value (four) 1263944 to the mosquito layer to determine that the image should belong to that-group color group. It is the same nerve in the same sound. The network, its competitive output system - the strongest value of two processing ί, so that the vector composed of ί J5f value is 'selected' _, and its "'5^ group = number = decision ^ patent application scope 4th The 裰 裰 在 在 同 If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If Number of mouths = V is the most recent connection table ======================================= The use: Zhang two m can get a corresponding skin color part is white and the background color tit Sf two to do the AND operation, to remove the background part and the part of the interference, you can carry out the following steps, ^ ^ ^ ^ Decrease skin color background 31 1263944 Break SSi with liSi0:;1 Widely for black and white image type with maximum area: After completing the disconnection operation, the second product A can be obtained from the static data. If the most is otherwise, then the table is the accumulation area, and the area A is in the object's image, the image may be smashed, and the image is continued to be displayed in Dejing. The image is a non-naked human body image H iiSfii item The side of the bare body image step _ block average width and length and width ratio calculation ϊ ί axis to find the long axis of the skin color smooth block, and extend the rectangle of the long smooth skin block, and ask for The surface of the largest smooth skin color block obtained by the experimental data is = gn - max ' According to Rgnjax, the maximum smooth skin color area 求 J can be obtained to determine the silk ratio of the maximum smooth skin color block, and the average width W - Avg = Rgn_max/L Long See Ratio Ratio = L/W Avg £巧三, according to the experimental data, the Newtonian ratio threshold value, the value is that the maximum smooth skin color block is too narrow and the boundary is exposed to the human body image, such as the critical fine system represents the most naked bond test Judging whether it is a big wish, if not, ^ face human body image 'If it is, Hu Dingzhi · The scales are exposed as 稞露16. If you apply for patent scope! Exposed human body image as described in the item = detection side 32 1263944 The facial features in the packet g and 4 take the large skin color smooth block, the close-up method of distinguishing the face, the face is turned into the skin color block, and the second part is obtained - the most suitable for 4 The center point of :=3⁄4, according to the center j, the image is a special part, which can be judged. Otherwise, the image can be identified as a non-naked body shadow 33 1263944 柒, designated representative figure: (1) The representative representative of the case is: (1). (2) A brief description of the symbol of the symbol of the representative figure: (none) 捌 If there is a chemical formula in this case, please disclose the chemical formula that best shows the characteristics of the invention:
TW93117330A 2004-06-16 2004-06-16 Naked body image detection method TWI263944B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
TW93117330A TWI263944B (en) 2004-06-16 2004-06-16 Naked body image detection method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
TW93117330A TWI263944B (en) 2004-06-16 2004-06-16 Naked body image detection method

Publications (2)

Publication Number Publication Date
TW200601178A TW200601178A (en) 2006-01-01
TWI263944B true TWI263944B (en) 2006-10-11

Family

ID=37967218

Family Applications (1)

Application Number Title Priority Date Filing Date
TW93117330A TWI263944B (en) 2004-06-16 2004-06-16 Naked body image detection method

Country Status (1)

Country Link
TW (1) TWI263944B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI471755B (en) * 2010-01-13 2015-02-01 Chao Lieh Chen Device for operation and control of motion modes of electrical equipment
TWI676136B (en) * 2018-08-31 2019-11-01 雲云科技股份有限公司 Image detection method and image detection device utilizing dual analysis
US10959646B2 (en) 2018-08-31 2021-03-30 Yun yun AI Baby camera Co., Ltd. Image detection method and image detection device for determining position of user

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI471755B (en) * 2010-01-13 2015-02-01 Chao Lieh Chen Device for operation and control of motion modes of electrical equipment
TWI676136B (en) * 2018-08-31 2019-11-01 雲云科技股份有限公司 Image detection method and image detection device utilizing dual analysis
US10959646B2 (en) 2018-08-31 2021-03-30 Yun yun AI Baby camera Co., Ltd. Image detection method and image detection device for determining position of user
US11087157B2 (en) 2018-08-31 2021-08-10 Yun yun AI Baby camera Co., Ltd. Image detection method and image detection device utilizing dual analysis

Also Published As

Publication number Publication date
TW200601178A (en) 2006-01-01

Similar Documents

Publication Publication Date Title
CN108520219A (en) A kind of multiple dimensioned fast face detecting method of convolutional neural networks Fusion Features
CN109508669B (en) Facial expression recognition method based on generative confrontation network
CN106778788B (en) The multiple features fusion method of aesthetic evaluation is carried out to image
CN104794693B (en) A kind of portrait optimization method of face key area automatic detection masking-out
CN107578418B (en) Indoor scene contour detection method fusing color and depth information
CN106874929B (en) Pearl classification method based on deep learning
Zhang et al. Content-adaptive sketch portrait generation by decompositional representation learning
CN109815893B (en) Color face image illumination domain normalization method based on cyclic generation countermeasure network
Johnson et al. Sparse codes as Alpha Matte.
CN109376582A (en) A kind of interactive human face cartoon method based on generation confrontation network
CN109614996A (en) The recognition methods merged based on the weakly visible light for generating confrontation network with infrared image
CN109446922B (en) Real-time robust face detection method
CN107463920A (en) A kind of face identification method for eliminating partial occlusion thing and influenceing
CN109583321A (en) The detection method of wisp in a kind of structured road based on deep learning
CN108537239A (en) A kind of method of saliency target detection
TWI263944B (en) Naked body image detection method
CN111178208A (en) Pedestrian detection method, device and medium based on deep learning
CN110032925A (en) A kind of images of gestures segmentation and recognition methods based on improvement capsule network and algorithm
CN109543602A (en) A kind of recognition methods again of the pedestrian based on multi-view image feature decomposition
CN109886153A (en) A kind of real-time face detection method based on depth convolutional neural networks
Luo et al. Bi-GANs-ST for perceptual image super-resolution
Yu et al. Improving face sketch recognition via adversarial sketch-photo transformation
CN109544694A (en) A kind of augmented reality system actual situation hybrid modeling method based on deep learning
CN109993072A (en) The low resolution pedestrian weight identifying system and method generated based on super resolution image
CN110110648A (en) Method is nominated in view-based access control model perception and the movement of artificial intelligence