CN107301650A - Four connect chess checkerboard image processing method, system and man-machine chess's system - Google Patents
Four connect chess checkerboard image processing method, system and man-machine chess's system Download PDFInfo
- Publication number
- CN107301650A CN107301650A CN201710507424.0A CN201710507424A CN107301650A CN 107301650 A CN107301650 A CN 107301650A CN 201710507424 A CN201710507424 A CN 201710507424A CN 107301650 A CN107301650 A CN 107301650A
- Authority
- CN
- China
- Prior art keywords
- chess
- chessboard
- image
- grid
- color
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 12
- 239000011159 matrix material Substances 0.000 claims description 36
- 238000000034 method Methods 0.000 claims description 16
- 230000011218 segmentation Effects 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 6
- 230000003628 erosive effect Effects 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000000877 morphologic effect Effects 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 239000003086 colorant Substances 0.000 abstract description 3
- 241000209202 Bromus secalinus Species 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F3/00—Board games; Raffle games
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Processing (AREA)
Abstract
Connect chess checkerboard image processing method, system and man-machine chess's system the invention discloses one kind four, the chess piece that different colours are respectively held by game player and robot is played chess in turn, tow-armed robot connects chess chessboard and the general image of background by calling the camera of robot left arm to obtain comprising four, and general image is sent to image processing system, responded by image processing system and judged, tow-armed robot unit is returned result to, allows tow-armed robot control machine people's right arm to go to realize crawl and the action of correct placement chess piece.The checkerboard image processing method of the present invention can judge position of most preferably playing chess exactly, so that man-machine chess's system possesses plays four abilities for connecting chess game under true environment, allow game player to have experience of really playing chess, drastically increase the interactivity of man-machine chess.
Description
Technical field
The present invention relates to field of human-computer interaction, particularly one kind four connects chess checkerboard image processing method, system and man-machine right
Play chess system.
Background technology
Traditional robot is all to use to calculate result using PC when playing chess to play with the mankind and playing, and then the mankind help
The specific action of implementation is helped, or is played chess using virtual simulator robot on the media such as mobile phone, computer or television
Mode, these modes interactivity in game is played chess is strong, it is impossible to really play chess experience to game player.
The content of the invention
The present invention is intended to provide one kind four connects chess checkerboard image processing method, system and man-machine chess's system, in true ring
Border is played plays the chess game of four companies with the real-time man-machine chess of people, allows game player to have experience of really playing chess.
In order to solve the above technical problems, the technical solution adopted in the present invention is：One kind four connects chess checkerboard image processing side
Method, comprises the following steps：
1）It is logical to S by the general image comprising chessboard and background got by BGR color space conversions to hsv color space
The general image in road is filtered processing, is partitioned into the chessboard profile of the only bianry image comprising chessboard and the bianry image,
The chessboard profile is area-of-interest；
2）Sub-zone dividing is carried out to the area-of-interest, each one grid of the subregion correspondence；
3）Threshold segmentation is fixed to any color of chess piece in the H passages of the general image, obtains in the subregion
There are all profiles of gray value, in any subregion, if the contour area that Threshold segmentation comes out is more than the subregion area
Half, then judge there is the color chess piece in the corresponding grid of the subregion；
4）Chessboard situation is converted into two-dimensional matrix using following methods：If the chess piece color in grid is the first color, should
The element of the corresponding two-dimensional matrix of grid is set to i；If the chess piece color in grid is the second color, by the grid corresponding two
The element of dimension matrix is set to j；If not having chess piece in grid, the element of the corresponding two-dimensional matrix of the grid is set to 0；
5）Optimal position of playing chess is calculated using the two-dimensional matrix.
Position of most preferably playing chess can be accurately judged to by above-mentioned image processing method, it is more real right to game player
Play chess experience.
Step 5）Afterwards, in addition to：The optimal position of playing chess is sent to intelligent robot, facilitates robot to obtain most
Good position of playing chess.
Step 1）In, processing is filtered to the general image using gaussian filtering method, then with fixed threshold
Dividing method obtains the bianry image for only including chessboard, and bianry image is handled with morphologic expansion and erosion algorithm,
Obtain the chessboard profile in bianry image.Preprocessing process is simple, further improves image procossing precision.
Step 5）In, two-dimensional matrix is judged using the minimax algorithms of artificial intelligence field, under calculating most preferably
Chess position.Implementation process is simple.
Correspondingly, chess checkerboard image processing system is connected present invention also offers one kind four, it includes：
Pretreatment unit：For the general image comprising chessboard and background will to be got by BGR color space conversions to HSV face
The colour space, processing is filtered to the general image of channel S, is partitioned into the bianry image and the bianry image for only including chessboard
Chessboard profile, the chessboard profile is area-of-interest；Sub-zone dividing unit：For what is exported to the pretreatment unit
Area-of-interest carries out sub-zone dividing, each one grid of the subregion correspondence；
Cutting unit：Threshold segmentation is fixed to any color of chess piece for the H passages in general image, the son is obtained
There are all profiles of gray value in region, in any subregion, if the contour area that Threshold segmentation comes out is more than the sub-district
The half of domain area, then judge there is the color chess piece in the corresponding grid of the subregion；
Two-dimensional matrix acquiring unit：For chessboard situation to be converted into two-dimensional matrix using following methods：If the chess piece in grid
Color is the first color, then the element of the corresponding two-dimensional matrix of the grid is set into i；If the chess piece color in grid is second
Color, then be set to j by the element of the corresponding two-dimensional matrix of the grid；If there is no chess piece in grid, by the grid corresponding two
The element of dimension matrix is set to 0；
Computing unit：Two-dimensional matrix for being exported according to the two-dimensional matrix acquiring unit calculates optimal position of playing chess.
The pretreatment unit includes：
Converting unit：For the general image comprising chessboard and background will to be got by BGR color space conversions to hsv color
Space；
Filter unit：The general image of hsv color space S passage for being exported to converting unit is filtered processing；
Bianry image acquiring unit：For the filtered general image exported according to filter unit, with the segmentation of fixed threshold
Method is partitioned into the bianry image for only including chessboard；
Chessboard profile acquiring unit：For the bianry image exported according to the bianry image acquiring unit, using morphologic
Expansion and erosion algorithm are handled the bianry image, obtain the chessboard profile of bianry image, i.e. area-of-interest.
Said system also includes communication unit, and the optimal position of playing chess for the computing unit to be exported is sent to intelligence
Robot.
Accordingly, man-machine chess's system of the invention includes tow-armed robot and above-mentioned four and connects chess checkerboard image processing system
System；Camera for gathering general image is installed on the arm of the intelligent robot；The Study of Intelligent Robot Control system
Unite after the optimal position of playing chess of communication unit transmission is received, control intelligence machine human arm captures and places chess piece described in
Optimal position of playing chess.
Or, man-machine chess's system of the invention includes intelligent robot, camera and above-mentioned four and connected at chess checkerboard image
Reason system；The general image of the camera collection comprising chessboard and background, and the general image is transferred to pretreatment list
Member；The control system of the intelligent robot controls intelligence machine after the optimal position of playing chess of communication unit transmission is received
Human arm captures and places chess piece to the optimal position of playing chess.
Compared with prior art, the advantageous effect of present invention is that：The checkerboard image processing method of the present invention can be with
Position of most preferably playing chess is judged exactly, so that man-machine chess's system possesses plays four energy for connecting chess game under true environment
Power, allows game player to have experience of really playing chess, drastically increases the interactivity of man-machine chess.
Brief description of the drawings
Fig. 1 is dimensional matrix data structure chart of the present invention；
Fig. 2 is man-machine chess's implementation process figure of the present invention.
Embodiment
The game rules of four company's chesses be chessboard row or column or it is diagonally opposed on there are the connected feelings of the same chess piece of color 4
Condition then game over, holds the color side for winner.Four checkerboard configurations for connecting chess are that 6 rows 7 are arranged, i.e., a height of 6 row, a width of 7 row, altogether
42 grids, the 7 of cross direction is classified as entrance of playing chess.Record 7 plays chess entry position with capturing chess piece to tow-armed robot in advance
Position, then calling the camera of robot left arm to obtain the general image comprising chessboard and background and carry out that analysis judges should
Which is played walk chess.Note observing the image got when calling the camera of robot left arm, chessboard in image is made as far as possible
Position is not tilted.Image procossing related algorithm is all the operator using OpenCV, and algorithm part is concretely comprised the following steps：（1）Extract
Chessboard target：First by the general image got by BGR color space conversions to hsv color space, individually to the figure of channel S
As carrying out gaussian filtering process, the bianry image for only including chessboard is then obtained with the dividing method of fixed threshold, shape is then used
The expansion of state and erosion algorithm are handled bianry image, then obtain the chessboard profile of bianry image, the chessboard profile
It is exactly the chessboard target that we need to extract, referred to as area-of-interest.（2）Sub-zone dividing is carried out to area-of-interest：Cause
It is that 6 rows 7 arrange totally 42 grids for chessboard grid distribution in kind, therefore we will also carry out similar division in the picture, will
Area-of-interest also carries out the division of 6 rows 7 row, separates 42 sub-regions, 42 grids of correspondence.If 42 chesses during chessboard is in kind
There is no chess piece in lattice, then corresponding 42 sub-regions are also sky in the region of interest, white is shown as in image；If
Some grid has chess piece during chessboard is in kind, then can also show gray value in corresponding subregion in the region of interest.
（3）Split chess piece position：The color distinction of two kinds of chess pieces and chessboard so visually will could be distinguished all than larger.
General chessboard color is all using blueness, and chess piece color uses red, yellow or green substantially.And red, yellow, green and blue these four colors
H passages in the picture have specific span, therefore can carry out accurate color differentiation.Using chess piece as red and yellow
Exemplified by, if there is the chess piece of red in some grid, red is fixed after Threshold segmentation with the image of H passages, in figure
As showing as that gray value is had in corresponding subregion in area-of-interest, all profiles for having gray value are obtained, if
In certain sub-regions, the contour area split is more than the half of subregion area（Contained entirely because subregion is actual
Chess piece and the grid edge for containing chess piece, this mode of half for being more than subregion area using contour area, which is avoided that, to be caused to miss
Sentence）, then it is assumed that there are red pawns in the corresponding grid of the subregion.The position of the subregion and content are carried out into mark to preserve
Come；The chess piece of segmentation yellow is also to use same logic.（4）Chessboard situation is switched into two-dimensional matrix：Above by chess piece
Position and content has all been recorded, it is necessary to which chessboard situation is switched into two-dimensional matrix again could carry out judgement of playing chess.Chessboard situation
Corresponding relation with two-dimensional matrix is：Position is one-to-one, the numeral 1 in red pawns correspondence two-dimensional matrix, yellow chess
Numeral 2 in son correspondence two-dimensional matrix, if not having chess piece in some grid, then in two-dimensional matrix in opposite position
Hold for 0.（5）Calculate optimal position of playing chess：Get after the two-dimensional matrix corresponding with chessboard situation, just using artificial intelligence neck
The minimax algorithms in domain are judged two-dimensional matrix, calculate optimal position of playing chess.（6）Send result：By position of most preferably playing chess
Put and be sent to tow-armed robot, tow-armed robot carries out action of playing chess.
Arranged as shown in figure 1, two-dimensional matrix of the present invention has 6 rows 7, altogether 42 lattice.Numeral 0 in figure is represented at this without chess
Son；Numeral 1, which is represented, red pawns at this；Numeral 2, which is represented at this, yellow chess piece.
Man-machine chess's flow of the present invention is shown in Fig. 2, and tow-armed robot control machine people's left arm calls camera to observe chessboard office
Gesture, is then sent to graphics processing unit by image information（PC ends in corresponding diagram 2）, graphics processing unit return most preferably play chess
Position is to tow-armed robot, and tow-armed robot control system control machine people's right arm realizes chess piece crawl and placement action.
The chess piece that different colours are respectively held by game player and robot is played chess in turn, and tow-armed robot is by calling robot left
The camera of arm is obtained connects chess chessboard and the image of background comprising four, and general image is sent into image processing system, by scheming
As processing system is responded and is judged, tow-armed robot unit is returned result to, makes tow-armed robot control machine people right
Arm goes to realize crawl and the action of correct placement chess piece.This mode can make game player have substitution feel, truly feel be
Play chess in real time with robot.
Claims (9)
1. one kind four connects chess checkerboard image processing method, it is characterised in that comprise the following steps：
1）The general image comprising chessboard and background will be got by BGR color space conversions to hsv color space, to channel S
General image be filtered processing, be partitioned into only comprising chessboard bianry image and the bianry image chessboard profile, institute
State chessboard profile i.e. area-of-interest；
2）Sub-zone dividing is carried out to the area-of-interest, each one grid of the subregion correspondence；
3）Threshold segmentation is fixed to any color of chess piece in the H passages of the general image, obtains in the subregion
There are all profiles of gray value, in any subregion, if the contour area that Threshold segmentation comes out is more than the subregion area
Half, then judge there is the color chess piece in the corresponding grid of the subregion；
4）Chessboard situation is converted into two-dimensional matrix using following methods：If the chess piece color in grid is the first color, should
The element of the corresponding two-dimensional matrix of grid is set to i；If the chess piece color in grid is the second color, by the grid corresponding two
The element of dimension matrix is set to j；If not having chess piece in grid, the element of the corresponding two-dimensional matrix of the grid is set to 0；
5）Optimal position of playing chess is calculated using the two-dimensional matrix.
2. according to claim 1 four connect chess checkerboard image processing method, it is characterised in that step 5）Afterwards, in addition to：
The optimal position of playing chess is sent to intelligent robot.
3. according to claim 1 four connect chess checkerboard image processing method, it is characterised in that step 1）In, using Gauss
Filtering method is filtered processing to the general image, is then obtained with the dividing method of fixed threshold and only includes the two of chessboard
It is worth image, the bianry image is handled with morphologic expansion and erosion algorithm, the chessboard wheel in bianry image is obtained
It is wide.
4. according to claim 1 four connect chess checkerboard image processing method, it is characterised in that step 5）In, use
Minimax algorithms are judged two-dimensional matrix, calculate optimal position of playing chess.
5. one kind four connects chess checkerboard image processing system, it is characterised in that including：
Pretreatment unit：For the general image comprising chessboard and background will to be got by BGR color space conversions to HSV face
The colour space, processing is filtered to the general image of channel S, is partitioned into the bianry image and the bianry image for only including chessboard
Chessboard profile, the chessboard profile is area-of-interest；
Sub-zone dividing unit：Area-of-interest for being exported to the pretreatment unit carries out sub-zone dividing, each
One grid of the subregion correspondence；
Cutting unit：Threshold segmentation is fixed to any color of chess piece for the H passages in the general image, institute is obtained
All profiles for having gray value in subregion are stated, in any subregion, if the contour area that Threshold segmentation comes out is more than described
The half of subregion area, then judge there is the color chess piece in the corresponding grid of the subregion；
Two-dimensional matrix acquiring unit：For chessboard situation to be converted into two-dimensional matrix using following methods：If the chess piece in grid
Color is the first color, then the element of the corresponding two-dimensional matrix of the grid is set into i；If the chess piece color in grid is second
Color, then be set to j by the element of the corresponding two-dimensional matrix of the grid；If there is no chess piece in grid, by the grid corresponding two
The element of dimension matrix is set to 0；
Computing unit：Two-dimensional matrix for being exported according to the two-dimensional matrix acquiring unit calculates optimal position of playing chess.
6. according to claim 5 four connect chess checkerboard image processing system, it is characterised in that the pretreatment unit bag
Include：
Converting unit：For the general image comprising chessboard and background will to be got by BGR color space conversions to hsv color
Space；
Filter unit：The general image of hsv color space S passage for being exported to converting unit is filtered processing；
Bianry image acquiring unit：For the filtered general image exported according to filter unit, with the segmentation of fixed threshold
Method obtains the bianry image for only including chessboard；
Chessboard profile acquiring unit：For the bianry image exported according to the bianry image acquiring unit, using morphologic
Expansion and erosion algorithm are handled the bianry image, obtain the chessboard profile of bianry image, i.e. area-of-interest.
7. four according to claim 5 or 6 connect chess checkerboard image processing system, it is characterised in that also including communication unit,
Optimal position of playing chess for the computing unit to be exported is sent to intelligent robot.
8. a kind of man-machine chess's system, it is characterised in that connect chess chessboard figure including four described in intelligent robot and claim 7
As processing system；Camera for gathering general image is installed on the arm of the intelligent robot；Intelligent robot control
System processed is after the optimal position of playing chess of communication unit transmission is received, and control intelligence machine human arm, which captures and places chess piece, to be arrived
The optimal position of playing chess.
9. a kind of man-machine chess's system, it is characterised in that including four companies described in intelligent robot, camera and claim 7
Chess checkerboard image processing system；The general image of the camera collection comprising chessboard and background, and the general image is passed
It is defeated by pretreatment unit；The control system of the intelligent robot after the optimal position of playing chess of communication unit transmission is received,
Control intelligence machine human arm captures and places chess piece to the optimal position of playing chess.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710507424.0A CN107301650A (en) | 2017-06-28 | 2017-06-28 | Four connect chess checkerboard image processing method, system and man-machine chess's system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710507424.0A CN107301650A (en) | 2017-06-28 | 2017-06-28 | Four connect chess checkerboard image processing method, system and man-machine chess's system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107301650A true CN107301650A (en) | 2017-10-27 |
Family
ID=60135197
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710507424.0A Pending CN107301650A (en) | 2017-06-28 | 2017-06-28 | Four connect chess checkerboard image processing method, system and man-machine chess's system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107301650A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107813321A (en) * | 2017-11-27 | 2018-03-20 | 安徽工业大学 | A kind of quintet game robot control method |
| CN109227555A (en) * | 2018-09-18 | 2019-01-18 | 浙江工业大学 | A kind of intelligent robot played chess for Quoridor |
| WO2023178920A1 (en) * | 2022-03-23 | 2023-09-28 | 上海商汤智能科技有限公司 | Chess placement method and apparatus, electronic device, chess playing robot, storage medium, and program |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW400506B (en) * | 1998-12-03 | 2000-08-01 | Inventec Corp | A method of executing black-white chess in computer |
| US20120242041A1 (en) * | 2007-04-19 | 2012-09-27 | Buza Donald E Buza | Casino Table Game |
| CN102799318A (en) * | 2012-08-13 | 2012-11-28 | 深圳先进技术研究院 | Human-machine interaction method and system based on binocular stereoscopic vision |
| US20130175760A1 (en) * | 2012-01-07 | 2013-07-11 | Steven Jon Halasz | Storytelling Strategy Board Game Method of Playing and Apparatus |
| CN104091324A (en) * | 2014-06-16 | 2014-10-08 | 华南理工大学 | Quick checkerboard image feature matching algorithm based on connected domain segmentation |
| CN104932683A (en) * | 2015-05-28 | 2015-09-23 | 重庆大学 | Game motion sensing control method based on vision information |
| CN103252077B (en) * | 2012-10-26 | 2015-10-07 | 上海未来伙伴机器人有限公司 | Chess playing robot |
| CN106426165A (en) * | 2016-09-29 | 2017-02-22 | 陕西科技大学 | Man-machine five-in-a-row playing robot control method |
| CN106484081A (en) * | 2015-08-24 | 2017-03-08 | 吕广兴 | A kind of solution of the quintet game man-machine chess based on computer vision |
| CN106485329A (en) * | 2016-09-27 | 2017-03-08 | 珠海格力智能装备有限公司 | The processing method and processing device of man-machine chess |
| CN106863322A (en) * | 2017-03-01 | 2017-06-20 | 江苏哈工海渡工业机器人有限公司 | Man-machine skill in playing chess game playing system and game all-in-one |
-
2017
- 2017-06-28 CN CN201710507424.0A patent/CN107301650A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW400506B (en) * | 1998-12-03 | 2000-08-01 | Inventec Corp | A method of executing black-white chess in computer |
| US20120242041A1 (en) * | 2007-04-19 | 2012-09-27 | Buza Donald E Buza | Casino Table Game |
| US20130175760A1 (en) * | 2012-01-07 | 2013-07-11 | Steven Jon Halasz | Storytelling Strategy Board Game Method of Playing and Apparatus |
| CN102799318A (en) * | 2012-08-13 | 2012-11-28 | 深圳先进技术研究院 | Human-machine interaction method and system based on binocular stereoscopic vision |
| CN103252077B (en) * | 2012-10-26 | 2015-10-07 | 上海未来伙伴机器人有限公司 | Chess playing robot |
| CN104091324A (en) * | 2014-06-16 | 2014-10-08 | 华南理工大学 | Quick checkerboard image feature matching algorithm based on connected domain segmentation |
| CN104932683A (en) * | 2015-05-28 | 2015-09-23 | 重庆大学 | Game motion sensing control method based on vision information |
| CN106484081A (en) * | 2015-08-24 | 2017-03-08 | 吕广兴 | A kind of solution of the quintet game man-machine chess based on computer vision |
| CN106485329A (en) * | 2016-09-27 | 2017-03-08 | 珠海格力智能装备有限公司 | The processing method and processing device of man-machine chess |
| CN106426165A (en) * | 2016-09-29 | 2017-02-22 | 陕西科技大学 | Man-machine five-in-a-row playing robot control method |
| CN106863322A (en) * | 2017-03-01 | 2017-06-20 | 江苏哈工海渡工业机器人有限公司 | Man-machine skill in playing chess game playing system and game all-in-one |
Non-Patent Citations (3)
| Title |
|---|
| CYNTHIA MATUSZEK等: "Gambit: An autonomous chess-playing robotic system", 《2011 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION》 * |
| 付世斌: "手持移动终端平台上的围棋比赛图像识别研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 梁澄宇等: "围棋比赛图像识别算法研究", 《现代计算机 图形图像》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107813321A (en) * | 2017-11-27 | 2018-03-20 | 安徽工业大学 | A kind of quintet game robot control method |
| CN109227555A (en) * | 2018-09-18 | 2019-01-18 | 浙江工业大学 | A kind of intelligent robot played chess for Quoridor |
| WO2023178920A1 (en) * | 2022-03-23 | 2023-09-28 | 上海商汤智能科技有限公司 | Chess placement method and apparatus, electronic device, chess playing robot, storage medium, and program |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107301650A (en) | Four connect chess checkerboard image processing method, system and man-machine chess's system | |
| CN107019901B (en) | Method for establishing chess and card game automatic gaming robot based on image recognition and automatic control | |
| CN104835164B (en) | A kind of processing method and processing device of binocular camera depth image | |
| CN101719015B (en) | Method for positioning finger tips of directed gestures | |
| CN101587591B (en) | Visual accurate tracking technique based on double parameter thresholds dividing | |
| CN107648833B (en) | A kind of contactless man-machine chess's control method and system based on machine vision | |
| CN109308711A (en) | Object detection method, device and image processing equipment | |
| CN105761202B (en) | A kind of color image color moving method | |
| CN105138975B (en) | A kind of area of skin color of human body dividing method based on degree of depth conviction network | |
| CN106097354A (en) | A kind of combining adaptive Gauss Face Detection and the hand images dividing method of region growing | |
| CN106471523A (en) | Colour code using infrared imaging | |
| CN106022327A (en) | Identification and processing method for physical chess | |
| Lei et al. | An algorithm of skin detection based on texture | |
| CN106203451A (en) | A kind of image area characteristics extracts and the method for characteristic matching | |
| CN107194870A (en) | A kind of image scene reconstructing method based on conspicuousness object detection | |
| CN113657175A (en) | Intelligent identification method, system, storage medium and equipment for switch state of power distribution cabinet | |
| CN102918559B (en) | Image processor and method and the golf emulator thereof of performance fruit ridge landforms | |
| CN107193384A (en) | Based on Kinect coloured images in mouse and the switching method of keyboard emulation behavior | |
| CN107194306A (en) | Sportsman's method for tracing and device in video | |
| CN112652063B (en) | Method and system for generating dynamic area boundary in game map and computer readable storage medium | |
| CN107497117B (en) | Image processing method and system for turnover chess chessboard and man-machine chess playing system | |
| CN107292239A (en) | A kind of parking offense detection method based on triple context updates | |
| CN109934159A (en) | A kind of gesture identification method of multiple features fusion | |
| CN106778537A (en) | A kind of collection of animal social network structure and analysis system and its method based on image procossing | |
| CN108710433A (en) | A kind of scene application artificial intelligence exchange method and its system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171027 |