CN110378867A - By prospect background pixel to and grayscale information obtain transparency mask method - Google Patents

By prospect background pixel to and grayscale information obtain transparency mask method Download PDF

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Publication number
CN110378867A
CN110378867A CN201910444633.4A CN201910444633A CN110378867A CN 110378867 A CN110378867 A CN 110378867A CN 201910444633 A CN201910444633 A CN 201910444633A CN 110378867 A CN110378867 A CN 110378867A
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pixel
image
transparency
transparency mask
mask
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蔡昭权
蔡映雪
黄思博
陈伽
胡松
梁椅辉
黄翰
陈广财
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Huizhou University
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Huizhou University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20036—Morphological image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30232—Surveillance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory

Abstract

A method of by prospect background pixel to and grayscale information obtain the first image transparency mask, transparency estimated value is recalculated to obtain the first transparency mask of the first image by measuring the confidence level of prospect background pixel pair first, then new picture is generated by superposition grayscale information and obtain the second transparency mask of the first image, and further correct the first transparency mask of the first image.The disclosure is capable of the confidence level and grayscale information of Prospects of Comprehensive Utilization background pixel pair, provides a kind of scheme of new acquisition transparency mask.

Description

By prospect background pixel to and grayscale information obtain transparency mask method
Technical field
The disclosure belongs to field of image processing, in particular to it is a kind of by prospect background pixel to and grayscale information schemed The method of the transparency mask of picture.
Background technique
In image domains, scratches diagram technology and realized based on the estimation of transparency mask.Can by selection color gamut come Different transparency masks is generated for image.
However, in the prior art, the preparation method of transparency mask is although enough, but on how to Utilization prospects back Scene element to and grayscale information obtain transparency mask, there has been no the implementation method of related novel.
Summary of the invention
Present disclose provides it is a kind of by prospect background pixel to and grayscale information obtain the first image transparency mask Method, include the following steps:
S100 divides all foreground pixel set F, all background pixel set B and all unknown pictures in the first image Plain set Z；
S200 gives certain prospect background pixels to (Fi, Bj), each unknown pixel Z is measured according to the following formulakIt is saturating Lightness
Wherein, IkFor unknown pixel ZkRGB color value, the foreground pixel FiFor apart from unknown pixel ZkNearest m Foreground pixel, the background pixel BjAlso for apart from unknown pixel ZkM nearest background pixel, the prospect background pixel pair (Fi, Bj) amount to m2Group；
S300, for the m2Each group of prospect background pixel in group is to (Fi, Bj) and its it is correspondingAccording to as follows Formula measures prospect background pixel to (Fi, Bj) confidence level nij:
Wherein, σ value 0.1, and choose the highest MAX (n of confidence levelij) corresponding to that group prospect background pixel to for (FiMAX, BjMAX)；
S400 calculates each unknown pixel Z according to the following formulakTransparency estimated value
S500, according to each unknown pixel ZkTransparency estimated valuePrimarily determine the of the first image One transparency mask；
S600, to the first image superposition grayscale information to generate the second image, and it is all to divide it to second image Foreground pixel set, all background pixel set and all unknown pixel set；
S700 executes step S200 to S500 for second image, to determine that the first transparency of the second image hides Cover, and using the first transparency mask of second image as the second transparency mask of the first image；
S800, using the second transparency mask of the first image, the first transparency for correcting the first image is hidden Cover.
By the method, the disclosure is capable of the confidence level and grayscale information of Prospects of Comprehensive Utilization background pixel pair, is provided A kind of scheme of new acquisition transparency mask.
Detailed description of the invention
Fig. 1 is the schematic diagram of one embodiment the method in the disclosure.
Specific embodiment
In order to make those skilled in the art understand that technical solution disclosed by the disclosure, below in conjunction with embodiment and related The technical solution of each embodiment is described in attached drawing, and described embodiment is a part of this disclosure embodiment, without It is whole embodiments.Term " first " used by the disclosure, " second " etc. rather than are used for for distinguishing different objects Particular order is described.In addition, " comprising " and " having " and their any deformation, it is intended that covering and non-exclusive packet Contain.Such as contain the process of a series of steps or units or method or system or product or equipment are not limited to arrange Out the step of or unit, but optionally further include the steps that not listing or unit, or further includes optionally for these mistakes Other intrinsic step or units of journey, method, system, product or equipment.
Referenced herein " embodiment " is it is meant that a particular feature, structure, or characteristic described can wrap in conjunction with the embodiments It is contained at least one embodiment of the disclosure.Each position in the description occur the phrase might not each mean it is identical Embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.It will be appreciated by those skilled in the art that , embodiment described herein can combine with other embodiments.
Referring to Fig. 1, Fig. 1 be in the disclosure one embodiment provide one kind by prospect background pixel to and grayscale information Obtain the flow diagram of the method for the transparency mask of the first image.As shown, described method includes following steps:
S100 divides all foreground pixel set F, all background pixel set B and all unknown pictures in the first image Plain set Z；
It is understood that there are many means for dividing foreground pixel, background pixel and unknown pixel to image, can be artificial Mark, can also by way of machine learning or data-driven, can also be according to corresponding prospect threshold value, background threshold come Mark off all foreground and background pixels and its corresponding set；If after foreground and background pixel divides, unknown pixel, its is right It should gather also with regard to being divided out naturally；
S200 gives certain prospect background pixels to (Fi, Bj), each unknown pixel Z is measured according to the following formulakIt is saturating Lightness
Wherein, IkFor unknown pixel ZkRGB color value, the foreground pixel FiFor apart from unknown pixel ZkNearest m Foreground pixel, the background pixel BjAlso for apart from unknown pixel ZkM nearest background pixel, the prospect background pixel pair (Fi, Bj) amount to m2Group；
To those skilled in the art, theoretically, the selection of m can make corresponding prospect background pixel to being Part sample, can also be with exhaustive whole image；For step S200, it is intended to the color and prospect background by unknown pixel The color relationship of pixel pair estimates the transparency of unknown pixel；In addition, the selection of m can also further combined with neighborhood territory pixel with Between unknown pixel color, texture, gray scale, brightness, in terms of feature；
S300, for the m2Each group of prospect background pixel in group is to (Fi, Bj) and its it is correspondingAccording to such as Lower formula measurement prospect background pixel is to (Fi, Bj) confidence level nij:
Wherein, σ value 0.1, and choose the highest MAX (n of confidence levelij) corresponding to that group prospect background pixel to for (FiMAX, BjMAX)；
It is understood that the value of σ is empirical value or statistical value or simulation value, step S300 is further screened using confidence level Prospect background pixel pair, and for subsequent step by the prospect background pixel further screened to estimating that unknown pixel is transparent Degree；
S400 calculates each unknown pixel Z according to the following formulakTransparency estimated value
S500, according to each unknown pixel ZkTransparency estimated valuePrimarily determine the of the first image One transparency mask；
This is to say, after the transparency estimated value of each unknown pixel obtains, the present embodiment with regard to primarily determining naturally First transparency mask of the first image；Why say be naturally, be because transparency mask can be considered as by By selected those respective pixels composition of certain value (or value range)；
S600, to the first image superposition grayscale information to generate the second image, and it is all to divide it to second image Foreground pixel set, all background pixel set and all unknown pixel set；
For the step, the present embodiment is contemplated that gray scale is believed in view of each pixel is in addition to the effect of RGB color Cease the influence to pixel；Therefore, after being superimposed grayscale information, transparency mask is modified using following steps.
S700 executes step S200 to S500 for second image, to determine that the first transparency of the second image hides Cover, and using the first transparency mask of second image as the second transparency mask of the first image；
S800, using the second transparency mask of the first image, the first transparency for correcting the first image is hidden Cover.
So far, the confidence level and grayscale information of disclosure Prospects of Comprehensive Utilization background pixel pair provides a kind of new acquisition The scheme of transparency mask.It is understood that the acquisition of transparency mask, is the process infinitely approached, at present it's hard to say certain Kind method transparency mask obtained is unique correct.
In another embodiment, in step S600, in the following way to the first image superposition grayscale information to generate Second image:
S601 carries out mean filter to the first image and obtains third image；
S602, the first image and third image generate the second image by following formula:
Wherein, IM2Indicate the gray value of k-th of pixel on the second image after being superimposed, xrIndicate k-th of picture on the first image Plain xkNeighborhood territory pixel, NkIt indicates with xkCentered on neighborhood in number of pixels,It indicates to the first image The pixel value of k-th of pixel on the resulting third image of mean filter is carried out, β takes 0.5.
The mode of specific superposition grayscale information is given by empirical value and related formula for above-described embodiment.
In another embodiment, step S800 further include:
S801 is found respectively according to the first transparency mask of the second transparency mask of the first image and the first image The edge at the edge of its second transparency mask, the first transparency mask；
S802 obtains the position of all pixels at the edge of the second transparency mask and the edge of the first transparency mask All pixels position, and determine position and the first transparency mask of all pixels at the edge of the second transparency mask The region that the position of all pixels at edge is overlapped, and then determine the identical pixel Z in positionsp；
S803 searches pixel Z respectivelyspThe transparency estimated value of the first transparency mask corresponding to the first image and right It should be in the transparency estimated value of the second transparency mask of the first image, and using the average value of the two as pixel ZspIt is revised Transparency estimated value；
S804, with pixel ZspRevised transparency estimated value corrects the first transparency mask of the first image.
For above-described embodiment, it is intended to find, compares the identical pixel in position in two kinds of transparency masks, and utilize Transparency estimated value of the identical pixel in the position in respective transparency mask is averaged to correct the of the first image One transparency mask.
In another embodiment, the step S802 further comprises:
S8021, according to the position of all pixels at the edge of the second transparency mask of judgement and the first transparency mask Edge all pixels position be overlapped region, further determine that the different pixel Z in positiondp, comprising: it is transparent to be located at second Spend the pixel Z at the edge of maskdp2With the pixel Z at the edge for being located at the first transparency maskdp1；
Unlike previous embodiment, edge that two transparency masks of the present embodiment additional attention are determined The different pixel in middle position, and find out these pixels of position different from each other；
S8022 utilizes the different pixel Z in the positiondpPixel Z identical with positionsp, obtain the second transparency mask Edge and the first transparency mask edge determined by: the closed enclosed region of institute and described between edge and edge The position of all closing pixels of enclosed region；
For the step, the edge as corresponding to each mask can be considered as a connection or closure to a certain degree Curve, then no matter closed curve corresponding to two masks is what kind of overlapping or nonoverlapping relationship: two are hidden Those of on the corresponding edge of cover for the pixel of position not corresponding (i.e. position is different or position is not overlapped), jointly really All closing pixels of the closed enclosed region of institute and the enclosed region between the edge and edge of two masks are determined Position；
S8023 executes following sub-step:
(1) pixel Z is searcheddp1Position corresponding to pixel estimate in the transparency of the first transparency mask of the first image Evaluation, and the transparence value of the corresponding pixel in the second image is searched, and using the average value of the two as pixel Zdp1Amendment Transparency estimated value afterwards；
(2) pixel Z is searcheddp2Position corresponding to pixel estimate in the transparency of the second transparency mask of the first image Evaluation, and the transparence value of the corresponding pixel first transparency mask in the first image is searched, and with the average value of the two As pixel Zdp2Revised transparency estimated value；
For the step, it is transparent under two different systems to be intended to find each pixel in aforementioned enclosed region Estimated value or transparence value are spent, and using the average value of the two as the revised transparency estimated value of respective pixel, then under For correcting the first transparency mask of the first image in one step S8024.That is, the present embodiment is similar to previous reality The amendment thinking for applying example is such, and what only the present embodiment solved is the corresponding edge of two masks closed region jointly.
S8024, in conjunction with pixel Zdp1Revised transparency estimated value and pixel Zdp2Revised transparency estimated value, is repaired First transparency mask of positive the first image.
Step in embodiment of the disclosure method can be sequentially adjusted, merged and deleted according to actual needs.
It should be noted that for the various method embodiments described above, for simple description, therefore, it is stated as a series of Combination of actions, but those skilled in the art should understand that, the present invention is not limited by the sequence of acts described because According to the present invention, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know It knows, the embodiments described in the specification are all preferred embodiments, and related movement, module, unit are not necessarily originally Necessary to invention.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment Point, reference can be made to the related descriptions of other embodiments.
In several embodiments provided by the disclosure, it should be understood that disclosed method is, it can be achieved that be corresponding function Energy unit, processor or even system may be distributed over multiple wherein each section of the system both can be located in one place In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme 's.In addition, each functional unit can integrate in one processing unit, it is also possible to each unit individualism, it can also two A or more than two units are integrated in one unit.Above-mentioned integrated unit both can take the form of hardware realization, can also To realize in the form of software functional units.If the integrated unit is realized in the form of SFU software functional unit and conduct Independent product when selling or using, can store in a computer readable storage medium.Based on this understanding, originally The disclosed technical solution substantially all or part of the part that contributes to existing technology or the technical solution in other words It can be embodied in the form of software products, which is stored in a storage medium, including several fingers It enables and using so that a computer equipment (can be smart phone, personal digital assistant, wearable device, laptop, plate Computer) execute the disclosure each embodiment the method all or part of the steps.And storage medium above-mentioned include: USB flash disk, Read-only memory (ROM, Read-Only Memory), is moved random access memory (RAM, Random Access Memory) The various media that can store program code such as dynamic hard disk, magnetic or disk.
The above, above embodiments are only to illustrate the technical solution of the disclosure, rather than its limitations；Although referring to before Embodiment is stated the disclosure is described in detail, it should be understood by those skilled in the art that: it still can be to aforementioned each reality Technical solution documented by example is applied to modify or equivalent replacement of some of the technical features；And these modification or Person's replacement, the range for the presently disclosed embodiments technical solution that it does not separate the essence of the corresponding technical solution.

Claims (4)

1. it is a kind of by prospect background pixel to and grayscale information obtain the first image transparency mask method, including it is as follows Step:
S100 divides all foreground pixel set F, all background pixel set B and all unknown pixel collection in the first image Close Z；
S200 gives certain prospect background pixels to (Fi, Bj), each unknown pixel Z is measured according to the following formulakTransparency
Wherein, IkFor unknown pixel ZkRGB color value, the foreground pixel FiFor apart from unknown pixel ZkM nearest prospect Pixel, the background pixel BjAlso for apart from unknown pixel ZkM nearest background pixel, the prospect background pixel is to (Fi, Bj) amount to m2Group；
S300, for the m2Each group of prospect background pixel in group is to (Fi, Bj) and its it is correspondingAccording to the following formula Prospect background pixel is measured to (Fi, Bj) confidence level nij:
Wherein, σ value 0.1, and choose the highest MAX (n of confidence levelij) corresponding to that group prospect background pixel to for (FiMAX, BjMAX)；
S400 calculates each unknown pixel Z according to the following formulakTransparency estimated value
S500, according to each unknown pixel ZkTransparency estimated valuePrimarily determine the first transparent of the first image Spend mask；
S600 to the first image superposition grayscale information to generate the second image, and divides its all prospect to second image Pixel set, all background pixel set and all unknown pixel set；
S700 executes step S200 to S500 for second image, to determine the first transparency mask of the second image, And using the first transparency mask of second image as the second transparency mask of the first image；
S800 corrects the first transparency mask of the first image using the second transparency mask of the first image.
2. according to the method described in claim 1, wherein, it is preferred that folded to the first image in the following way in step S600 Add grayscale information to generate the second image:
S601 carries out mean filter to the first image and obtains third image；
S602, the first image and third image generate the second image by following formula:
Wherein, IM2Indicate the gray value of k-th of pixel on the second image after being superimposed, xrIndicate k-th of pixel x on the first imagek Neighborhood territory pixel, NkIt indicates with xkCentered on neighborhood in number of pixels,It indicates to carry out mean value to the first image The pixel value of k-th of pixel on resulting third image is filtered, β takes 0.5.
3. according to the method described in claim 1, wherein, step S800 further include:
S801, according to the first transparency mask of the second transparency mask of the first image and the first image, find respectively its The edge at the edge of two transparency masks, the first transparency mask；
S802 obtains the institute of the position of all pixels at the edge of the second transparency mask and the edge of the first transparency mask There is the position of pixel, and determines the position of all pixels at the edge of the second transparency mask and the edge of the first transparency mask All pixels the region that is overlapped of position, and then determine the identical pixel Z in positionsp；
S803 searches pixel Z respectivelyspThe transparency estimated value of the first transparency mask corresponding to the first image, and correspond to The transparency estimated value of second transparency mask of the first image, and using the average value of the two as pixel ZspIt is revised transparent Spend estimated value；
S804, with pixel ZspRevised transparency estimated value corrects the first transparency mask of the first image.
4. according to the method described in claim 3, wherein, the step S802 further comprises:
S8021, according to the side of the position of all pixels at the edge of the second transparency mask of judgement and the first transparency mask The region that the position of all pixels of edge is overlapped, further determines that the different pixel Z in positiondp, comprising: it is located at the second transparency and hides The pixel Z at the edge of coverdp2With the pixel Z at the edge for being located at the first transparency maskdp1；
S8022 utilizes the different pixel Z in the positiondpPixel Z identical with positionsp, obtain the edge of the second transparency mask Determined by edge with the first transparency mask: the closed enclosed region of institute and the closed area between edge and edge The position of all closing pixels in domain；
S8023 executes following sub-step:
(1) pixel Z is searcheddp1Position corresponding to pixel in the transparency estimated value of the first transparency mask of the first image, And the transparence value of the corresponding pixel in the second image is searched, and using the average value of the two as pixel Zdp1It is revised Transparency estimated value；
(2) pixel Z is searcheddp2Position corresponding to pixel in the transparency estimated value of the second transparency mask of the first image, And the transparence value of the corresponding pixel first transparency mask in the first image is searched, and using the average value of the two as picture Plain Zdp2Revised transparency estimated value；
S8024, in conjunction with pixel Zdp1Revised transparency estimated value and pixel Zdp2Revised transparency estimated value corrects institute State the first transparency mask of the first image.
CN201910444633.4A 2018-09-26 2019-05-24 By prospect background pixel to and grayscale information obtain transparency mask method Withdrawn CN110378867A (en)

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Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112989962B (en) * 2021-02-24 2024-01-05 上海商汤智能科技有限公司 Track generation method, track generation device, electronic equipment and storage medium

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001057496A2 (en) * 2000-02-03 2001-08-09 Applied Materials, Inc. Straight line defect detection
CN101621615A (en) * 2009-07-24 2010-01-06 南京邮电大学 Self-adaptive background modeling and moving target detecting method
CN102999892A (en) * 2012-12-03 2013-03-27 东华大学 Intelligent fusion method for depth images based on area shades and red green blue (RGB) images
CN106204567A (en) * 2016-07-05 2016-12-07 华南理工大学 A kind of natural background video matting method
US20170116481A1 (en) * 2015-10-23 2017-04-27 Beihang University Method for video matting via sparse and low-rank representation
CN107516319A (en) * 2017-09-05 2017-12-26 中北大学 A kind of high accuracy simple interactive stingy drawing method, storage device and terminal
CN108391118A (en) * 2018-03-21 2018-08-10 惠州学院 A kind of display system for realizing 3D rendering based on projection pattern

Family Cites Families (41)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6870945B2 (en) * 2001-06-04 2005-03-22 University Of Washington Video object tracking by estimating and subtracting background
US7466842B2 (en) * 2005-05-20 2008-12-16 Mitsubishi Electric Research Laboratories, Inc. Modeling low frame rate videos with bayesian estimation
US8508546B2 (en) * 2006-09-19 2013-08-13 Adobe Systems Incorporated Image mask generation
US8520972B2 (en) * 2008-09-12 2013-08-27 Adobe Systems Incorporated Image decomposition
CN101686338B (en) * 2008-09-26 2013-12-25 索尼株式会社 System and method for partitioning foreground and background in video
CN101588459B (en) * 2009-06-26 2011-01-05 北京交通大学 Video keying processing method
US8625888B2 (en) * 2010-07-21 2014-01-07 Microsoft Corporation Variable kernel size image matting
US8386964B2 (en) * 2010-07-21 2013-02-26 Microsoft Corporation Interactive image matting
CN102456212A (en) * 2010-10-19 2012-05-16 北大方正集团有限公司 Separation method and system for visible watermark in numerical image
CN102163216B (en) * 2010-11-24 2013-02-13 广州市动景计算机科技有限公司 Picture display method and device thereof
CN102236901B (en) * 2011-06-30 2013-06-05 南京大学 Method for tracking target based on graph theory cluster and color invariant space
US8744123B2 (en) * 2011-08-29 2014-06-03 International Business Machines Corporation Modeling of temporarily static objects in surveillance video data
US8731315B2 (en) * 2011-09-12 2014-05-20 Canon Kabushiki Kaisha Image compression and decompression for image matting
US9305357B2 (en) * 2011-11-07 2016-04-05 General Electric Company Automatic surveillance video matting using a shape prior
CN102651135B (en) * 2012-04-10 2015-06-17 电子科技大学 Optimized direction sampling-based natural image matting method
US8792718B2 (en) * 2012-06-29 2014-07-29 Adobe Systems Incorporated Temporal matte filter for video matting
CN103366364B (en) * 2013-06-07 2016-06-29 太仓中科信息技术研究院 A kind of stingy drawing method based on color distortion
AU2013206597A1 (en) * 2013-06-28 2015-01-22 Canon Kabushiki Kaisha Depth constrained superpixel-based depth map refinement
US9898856B2 (en) * 2013-09-27 2018-02-20 Fotonation Cayman Limited Systems and methods for depth-assisted perspective distortion correction
US20150091891A1 (en) * 2013-09-30 2015-04-02 Dumedia, Inc. System and method for non-holographic teleportation
CN104112144A (en) * 2013-12-17 2014-10-22 深圳市华尊科技有限公司 Person and vehicle identification method and device
US10089740B2 (en) * 2014-03-07 2018-10-02 Fotonation Limited System and methods for depth regularization and semiautomatic interactive matting using RGB-D images
CN104952089B (en) * 2014-03-26 2019-02-15 腾讯科技（深圳）有限公司 A kind of image processing method and system
CN103903230A (en) * 2014-03-28 2014-07-02 哈尔滨工程大学 Video image sea fog removal and clearing method
CN105590307A (en) * 2014-10-22 2016-05-18 华为技术有限公司 Transparency-based matting method and apparatus
CN104573688B (en) * 2015-01-19 2017-08-25 电子科技大学 Mobile platform tobacco laser code intelligent identification Method and device based on deep learning
CN104680482A (en) * 2015-03-09 2015-06-03 华为技术有限公司 Method and device for image processing
CN104935832B (en) * 2015-03-31 2019-07-12 浙江工商大学 For the video keying method with depth information
CN105100646B (en) * 2015-08-31 2018-09-11 北京奇艺世纪科技有限公司 Method for processing video frequency and device
CN105809679B (en) * 2016-03-04 2019-06-18 李云栋 Mountain railway side slope rockfall detection method based on visual analysis
US10275892B2 (en) * 2016-06-09 2019-04-30 Google Llc Multi-view scene segmentation and propagation
CN117864918A (en) * 2016-07-29 2024-04-12 奥的斯电梯公司 Monitoring system for passenger conveyor, passenger conveyor and monitoring method thereof
CN107872644B (en) * 2016-09-23 2020-10-09 亿阳信通股份有限公司 Video monitoring method and device
CN106778810A (en) * 2016-11-23 2017-05-31 北京联合大学 Original image layer fusion method and system based on RGB feature Yu depth characteristic
US10198621B2 (en) * 2016-11-28 2019-02-05 Sony Corporation Image-Processing device and method for foreground mask correction for object segmentation
CN106952276A (en) * 2017-03-20 2017-07-14 成都通甲优博科技有限责任公司 A kind of image matting method and device
CN107194867A (en) * 2017-05-14 2017-09-22 北京工业大学 A kind of stingy picture synthetic method based on CUDA
CN107273905B (en) * 2017-06-14 2020-05-08 电子科技大学 Target active contour tracking method combined with motion information
CN107230182B (en) * 2017-08-03 2021-11-09 腾讯科技（深圳）有限公司 Image processing method and device and storage medium
CN108399361A (en) * 2018-01-23 2018-08-14 南京邮电大学 A kind of pedestrian detection method based on convolutional neural networks CNN and semantic segmentation
CN108320298B (en) * 2018-04-28 2022-01-28 亮风台（北京）信息科技有限公司 Visual target tracking method and equipment

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001057496A2 (en) * 2000-02-03 2001-08-09 Applied Materials, Inc. Straight line defect detection
CN101621615A (en) * 2009-07-24 2010-01-06 南京邮电大学 Self-adaptive background modeling and moving target detecting method
CN102999892A (en) * 2012-12-03 2013-03-27 东华大学 Intelligent fusion method for depth images based on area shades and red green blue (RGB) images
US20170116481A1 (en) * 2015-10-23 2017-04-27 Beihang University Method for video matting via sparse and low-rank representation
CN106204567A (en) * 2016-07-05 2016-12-07 华南理工大学 A kind of natural background video matting method
CN107516319A (en) * 2017-09-05 2017-12-26 中北大学 A kind of high accuracy simple interactive stingy drawing method, storage device and terminal
CN108391118A (en) * 2018-03-21 2018-08-10 惠州学院 A kind of display system for realizing 3D rendering based on projection pattern

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
NING XU 等: "Deep Image Matting", 《COMPUTER VISION FOUNDATION》 *
SHUTAO LI 等: "Image matting for fusion of multi-focus images in dynamic scenes", 《INFORMATION FUSION》 *
ZHAOQUAN CAI 等: "Improving sampling-based image matting with cooperative coevolution differential evolution algorithm", 《SOFT COMPUTING》 *
谢斌 等: "基于雾气遮罩理论的图像去雾算法", 《计算机工程与科学》 *

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