CN105740818A - Artificial mark detection method applied to augmented reality - Google Patents

Artificial mark detection method applied to augmented reality Download PDF

Info

Publication number
CN105740818A
CN105740818A CN201610065210.8A CN201610065210A CN105740818A CN 105740818 A CN105740818 A CN 105740818A CN 201610065210 A CN201610065210 A CN 201610065210A CN 105740818 A CN105740818 A CN 105740818A
Authority
CN
China
Prior art keywords
line segment
edge
quadrilateral
point
edge line
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.)
Granted
Application number
CN201610065210.8A
Other languages
Chinese (zh)
Other versions
CN105740818B (en
Inventor
赵子健
马帅依凡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong University
Original Assignee
Shandong University
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 Shandong University filed Critical Shandong University
Priority to CN201610065210.8A priority Critical patent/CN105740818B/en
Publication of CN105740818A publication Critical patent/CN105740818A/en
Application granted granted Critical
Publication of CN105740818B publication Critical patent/CN105740818B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/20Scenes; Scene-specific elements in augmented reality scenes

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

本发明涉及一种应用于增强现实的人工标志检测方法,具体步骤包括:S1:采集帧图像,对帧图像粗采样,采用斜向网格扫描,检测得到帧图像边缘像素;S2:基于RANSAC算法,检测得到帧图像中的边缘线段;S3:对边缘线段进行融合;S4:对边缘线段进行延伸、筛选;S5:检测四边形边角点,并根据四边形边角点,构造四边形。本发明运算前对帧图像预处理,进行粗网格采样,对每个网格区域进行边缘检测,大大减小了程序运算时间,提高了检测速度,实时性好,满足实时检测要求。本发明采用基于边缘的检测方法,首先进行线段测试,然后根据线段测试得到的线段,重构标志的四边形边框,对光照变化和遮挡情况有很好的鲁棒性。The present invention relates to an artificial sign detection method applied to augmented reality, and the specific steps include: S1: collecting a frame image, sampling the frame image roughly, using oblique grid scanning, and detecting edge pixels of the frame image; S2: based on the RANSAC algorithm , to detect the edge segments in the frame image; S3: fuse the edge segments; S4: extend and filter the edge segments; S5: detect the corner points of the quadrilateral, and construct a quadrilateral according to the corner points of the quadrilateral. The invention preprocesses frame images before operation, performs coarse grid sampling, and performs edge detection on each grid area, greatly reduces program operation time, improves detection speed, has good real-time performance, and meets real-time detection requirements. The invention adopts an edge-based detection method, firstly performs a line segment test, and then reconstructs a quadrilateral frame of a sign according to the line segment obtained by the line segment test, and has good robustness to illumination changes and occlusion conditions.

Description

一种应用于增强现实的人工标志检测方法An Artificial Landmark Detection Method Applied to Augmented Reality

技术领域technical field

本发明涉及一种应用于增强现实的人工标志检测方法,属于增强现实应用的技术领域。The invention relates to an artificial mark detection method applied in augmented reality, and belongs to the technical field of augmented reality applications.

背景技术Background technique

增强现实技术,是一种将真实世界信息和虚拟世界信息“无缝”集成的新技术,是把原本在现实世界的一定时间空间范围内很难体验到的实体信息(视觉信息、声音、味道、触觉等),通过电脑等科学技术,模拟仿真后再叠加,将虚拟的信息应用到真实世界,被人类感官所感知,从而达到超越现实的感官体验。真实的环境和虚拟的物体实时地叠加到了同一个画面或空间同时存在。Augmented reality technology is a new technology that "seamlessly" integrates real world information and virtual world information. , touch, etc.), through computer and other science and technology, simulate and then superimpose, apply virtual information to the real world, and be perceived by human senses, so as to achieve a sensory experience beyond reality. The real environment and virtual objects are superimposed on the same screen or space in real time.

基准标志系统,广泛应用于增强现实、机器人导航、位置跟踪、图像建模等通用工程,主要通过图像处理技术检测识别置于环境中的已知2D人造标志,提取标志信息,来计算摄像机和物体相对位置关系。基准标志系统最重要的参数就是误检率、混码率、最小检测像素和光照抗扰度。基准标志系统的关键技术是人工标志的设计和相对应的识别定位方法。现有的人工标志设计单一,使得在图像识别时易受到光线和复杂物体的干扰,内部没有存储信息,使得虚拟物体的资料必须储存在识别设备上且无法灵活改变。The fiducial mark system is widely used in augmented reality, robot navigation, position tracking, image modeling and other general projects. It mainly uses image processing technology to detect and identify known 2D artificial signs placed in the environment, extract sign information, and calculate cameras and objects. Relative positional relationship. The most important parameters of the fiducial marking system are false detection rate, mixed code rate, minimum detection pixel and light immunity. The key technology of the fiducial marker system is the design of artificial markers and the corresponding identification and positioning methods. The existing artificial signs have a single design, which makes image recognition vulnerable to interference from light and complex objects. There is no internal storage information, so the data of virtual objects must be stored on the recognition device and cannot be changed flexibly.

ARTag标志,是一种二值平面标志,每个标志有自己的ID数,通过二值数值编码于标志内部。相比之前的ARToolkit标志,ARTag解决了高错误率,误检率和混码率等问题。基准标志系统性能取决于对于2D标志的检测性能,除了检测速度,错误率等考量外,标志检测算法也需要对光照条件,遮挡等状况有很好的鲁棒性。The ARTag logo is a binary flat logo, and each logo has its own ID number, which is encoded inside the logo by a binary value. Compared with the previous ARToolkit logo, ARTag solves the problems of high error rate, false detection rate and mixed code rate. The performance of the benchmark marker system depends on the detection performance of 2D markers. In addition to considerations such as detection speed and error rate, the marker detection algorithm also needs to be robust to lighting conditions and occlusions.

发明内容Contents of the invention

针对现有技术的不足,本发明提供了一种应用于增强现实的人工标志检测方法;Aiming at the deficiencies of the prior art, the present invention provides an artificial marker detection method applied to augmented reality;

本发明提高了人工标志的识别速度、抗干扰力及准确度。The invention improves the recognition speed, anti-interference ability and accuracy of artificial signs.

术语解释Terminology Explanation

RANSAC算法,RANdomSAmpleConsensus的缩写,是根据一组包含异常数据的样本数据集,计算出数据的数学模型参数,得到有效样本数据的算法。RANSAC algorithm, the abbreviation of RANdomSAmpleConsensus, is an algorithm that calculates the mathematical model parameters of the data based on a set of sample data sets containing abnormal data, and obtains effective sample data.

本发明的技术方案为:Technical scheme of the present invention is:

一种应用于增强现实的人工标志检测方法,具体步骤包括:A method for detecting artificial signs applied to augmented reality, the specific steps comprising:

S1:采集帧图像,对帧图像粗采样,采用斜向网格扫描,检测得到帧图像边缘像素;S1: Collect the frame image, roughly sample the frame image, use oblique grid scanning, and detect the edge pixels of the frame image;

S2:基于RANSAC算法,根据步骤S1得到的边缘像素,检测得到帧图像中的边缘线段;S2: Based on the RANSAC algorithm, according to the edge pixels obtained in step S1, the edge line segment in the frame image is detected;

S3:对步骤S2检测到的边缘线段进行融合;S3: Fusing the edge line segments detected in step S2;

S4:对步骤S3融合后的边缘线段进行延伸、筛选;S4: Extending and screening the edge line segments fused in step S3;

S5:根据步骤S4延伸、筛选后的边缘线段,检测四边形边角点,并根据四边形边角点,构造四边形。S5: Detect the corner points of the quadrilateral according to the extended and screened edge segments in step S4, and construct a quadrilateral according to the corner points of the quadrilateral.

根据本发明优选的,所述步骤S1,具体包括:Preferably according to the present invention, the step S1 specifically includes:

S11:将帧图像分为若干个区域,每个区域包含m×m像素,m∈(20,60);进一步优选的,m=40;将帧图像分为若干个区域,检测加快了速度,提高了实时性。S11: divide the frame image into several regions, each region contains m×m pixels, m∈(20, 60); further preferably, m=40; divide the frame image into several regions, the detection speed is accelerated, Improved real-time performance.

S12:对步骤S11获取的每个区域,采用斜向x°及(180-x)°的扫描线进行网格扫描,x∈(22.5,67.5),每个网格的对边距离为y个像素,y∈(3,9);进一步优选的,x=45,y=5;S12: For each area obtained in step S11, use oblique x° and (180-x)° scanning lines to perform grid scanning, x∈(22.5,67.5), and the distance across each grid is y Pixel, y∈(3,9); further preferably, x=45, y=5;

S13:步骤S12的每条扫描线与高斯一阶导卷积,计算像素点沿每条扫描线方向的梯度强度分量;S13: each scanning line in step S12 is convolved with the Gaussian first-order derivation, and the gradient intensity component of the pixel along the direction of each scanning line is calculated;

S14:根据步骤S13得到的像素点沿每条扫描线方向的梯度强度分量,计算像素点的梯度强度值,梯度强度值中的局部极值对应的像素点即边缘像素梯度强度局部极值点,提取该边缘像素,同时根据梯度强度分量,计算该边缘像素的方向。S14: According to the gradient strength component of the pixel point obtained in step S13 along the direction of each scanning line, calculate the gradient strength value of the pixel point, the pixel point corresponding to the local extremum in the gradient strength value is the local extremum point of the edge pixel gradient strength, The edge pixel is extracted, and the direction of the edge pixel is calculated according to the gradient intensity component.

根据本发明优选的,所述步骤S3,具体包括:Preferably according to the present invention, the step S3 specifically includes:

S31:选取一条通过步骤S2得到的边缘线段,命为线段a,从剩余边缘线段中任意选取另一条线段,命为线段b,对线段a、b进行融合判定,即:如果线段a、b满足:|θab|∈Δθ且Lab∈Δl,则将线段a、b融合,得到新线段;否则,继续在剩余边缘线段中选取线段,继续与线段a进行融合判定,直至线段a与除线段a外所有边缘线段完成融合判定;θa、θb为线段a、b的方向,Δθ为待融合线段方向误差的阈值,θab、Lab为线段a、b连线ab的方向和长度,Δl为线段a、b连线ab允许长度的阈值;S31: Select an edge line segment obtained by step S2, and name it line segment a, randomly select another line segment from the remaining edge line segments, and call it line segment b, and perform fusion judgment on line segments a and b, that is, if line segments a and b satisfy : |θ ab |∈Δ θ , And Lab ∈ Δ l , then merge the line segments a and b to get a new line segment; otherwise, continue to select a line segment from the remaining edge line segments, and continue to fuse with line segment a until line segment a and all edge line segments except line segment a are completed Fusion judgment; θ a and θ b are the direction of line segments a and b, Δ θ is the threshold value of the direction error of the line segment to be fused, θ ab and Lab are the direction and length of line ab connecting line segment a and b, and Δ l is line segment a , The threshold of the allowable length of b connection ab;

S32:重复步骤S31,至步骤S2得到的所有边缘线段完成融合。S32: Step S31 is repeated until all edge line segments obtained in step S2 are fused.

根据本发明优选的,所述步骤S4,具体包括:Preferably according to the present invention, said step S4 specifically includes:

S41:根据步骤S3得到的边缘线段,任意选取一根边缘线段并选取其一个端点,判断沿该边缘线段的方向与该端点相邻的像素点的方向是否一致,如果一致,则将该像素点添加到该边缘线段中,继续检查与该像素点相邻的下一个像素点,直至某像素点方向与该边缘线段方向不一致,则该像素点更新为该边缘线段的一个新端点;S41: According to the edge line segment obtained in step S3, arbitrarily select an edge line segment and select an end point thereof, and judge whether the direction along the edge line segment is consistent with the direction of the pixel adjacent to the end point, if they are consistent, then the pixel point Add it to the edge line segment, and continue to check the next pixel adjacent to the pixel point until the direction of a pixel point is inconsistent with the direction of the edge line segment, then the pixel point is updated as a new endpoint of the edge line segment;

S42:对步骤S3得到的所有边缘线段的两个端点执行步骤S41,得到延伸后的边缘线段及其新端点;S42: Execute step S41 on the two endpoints of all the edge segments obtained in step S3, to obtain the extended edge segments and their new endpoints;

S43:对步骤S42得到的延伸后的边缘线段进行初步筛选,删除长度值小于n个像素的边缘线段,n∈(15,25),进一步优选的,n=20;长度值过小的边缘线段不可能是标志边框,即便是,说明该标志边框畸变过大或者场景尺度太大,已经没有检测的意义,因此删除;S43: Preliminarily screen the extended edge line segment obtained in step S42, and delete the edge line segment whose length value is less than n pixels, n∈(15, 25), further preferably, n=20; the edge line segment whose length value is too small It cannot be the logo border, even if it is, it means that the logo border is too distorted or the scene scale is too large, it is no longer meaningful to detect, so it is deleted;

S44:对步骤S43初步筛选后的边缘线段进行线段测试,沿边缘线段的方向选取一个距离其端点2—4个像素的像素点,检测该像素点的灰度值,如果该像素点的灰度值处于128—255范围内,则判定该端点合格,该边缘线段符合标志边框特征,否则,则判定该端点不合格,该边缘线段不符合标志边框特征;当边缘线段的两个端点都判定为不合格时,则删除该边缘线段;S44: Carry out a line segment test to the edge line segment after the preliminary screening of step S43, select a pixel point 2-4 pixels away from its end point along the direction of the edge line segment, detect the gray value of the pixel point, if the gray value of the pixel point If the value is in the range of 128-255, it is judged that the endpoint is qualified, and the edge line segment conforms to the feature of the frame of the sign; If unqualified, delete the edge segment;

S45:重复步骤S44,直至所有边缘线段都进行了线段测,得到所有符合标志边框特征的边缘线段。S45: Step S44 is repeated until all edge line segments have been measured, and all edge line segments conforming to the characteristics of the sign frame are obtained.

根据本发明优选的,步骤S5中,根据步骤S4延伸、筛选后的边缘线段,检测四边形边角点,具体包括:Preferably according to the present invention, in step S5, according to the edge line segment after step S4 extension, screening, detect quadrilateral corner point, specifically include:

S51:从步骤S45得到的所有符合标志边框特征的边缘线段中,任意选取一条边缘线段,设定为线段cd,作为四边形的第一条边,从剩余的符合标志边框特征的边缘线段中,选取与所述线段cd相交的线段ef,线段cd、ef满足:θcd!≈θef,min{ce,cf,de,df}≤Δ且线段cd、线段ef满足四边形邻边特征,线段cd、ef的相交点即四边形的一个边角点;S51: From all the edge line segments that meet the characteristics of the frame of the sign obtained in step S45, arbitrarily select an edge line segment, set as line segment cd, as the first side of the quadrilateral, and select from the remaining edge line segments that meet the feature of the frame of the sign The line segment ef intersecting the line segment cd, the line segments cd and ef satisfy: θ cd ! ≈θ ef , min{ce, cf, de, df}≤Δ and the line segment cd and line segment ef satisfy the characteristics of the adjacent sides of the quadrilateral, and the intersection point of the line segment cd and ef is a corner point of the quadrilateral;

S52:利用步骤S51所述方法,得到四边形的边角点序列;S52: Using the method described in step S51, obtain the corner point sequence of the quadrilateral;

S53:遍历所有步骤S45得到的所有符合标志边框特征的边缘线段,得到所有的四边形的边角点序列。S53: Go through all the edge line segments obtained in step S45 that meet the characteristics of the frame of the logo, and obtain all the corner point sequences of the quadrilaterals.

根据本发明优选的,根据四边形边角点,构造四边形,具体包括:Preferably according to the present invention, according to the corner points of the quadrilateral, constructing the quadrilateral specifically includes:

S54:根据步骤S53得到的所有的边角点序列包含的边角点个数构造四边形:若边角点序列的边角点个数为4,连接4个边角点,直接构造四边形;S54: Construct a quadrilateral according to the number of corner points contained in all the corner point sequences obtained in step S53: if the number of corner points in the corner point sequence is 4, connect the 4 corner points to directly construct a quadrilateral;

若边角点序列的边角点个数为3,延长未构成第4个边角点的2条边缘线段,交点得到第4个边角点,连接4个边角点,构造四边形;If the number of corner points in the corner point sequence is 3, extend the 2 edge line segments that do not constitute the 4th corner point, and obtain the 4th corner point at the intersection point, connect the 4 corner points to construct a quadrilateral;

若边角点序列的边角点个数为2,延长仅有1个边角点的2条边缘线段,如果与第3条边缘线段相交,交点即为边角点,连接4个边角点,构造四边形;否则,无法构造四边形;If the number of corner points in the corner point sequence is 2, extend the 2 edge line segments with only 1 corner point, if it intersects with the third edge line segment, the intersection point is the corner point, connecting 4 corner points , to construct a quadrilateral; otherwise, a quadrilateral cannot be constructed;

若边角点序列的边角点个数为1,无法构造四边形;If the number of corner points in the corner point sequence is 1, a quadrilateral cannot be constructed;

S55:遍历所有边角点序列,得到所有的四边形,即检测所有的人工标志。S55: Traverse all corner point sequences to obtain all quadrilaterals, that is, detect all artificial signs.

本发明的有益效果为:The beneficial effects of the present invention are:

1、本发明运算前对帧图像预处理,进行粗网格采样,对每个网格区域进行边缘检测,大大减小了程序运算时间,提高了检测速度,实时性好,满足实时检测要求。1. The present invention preprocesses the frame image before operation, performs coarse grid sampling, and performs edge detection for each grid area, which greatly reduces the program operation time, improves the detection speed, and has good real-time performance and meets the real-time detection requirements.

2、本发明采用基于边缘的检测方法,首先进行线段测试,然后根据线段测试得到的线段,重构标志的四边形边框,对光照变化和遮挡情况有很好的鲁棒性。2. The present invention adopts an edge-based detection method. Firstly, the line segment test is performed, and then the quadrilateral frame of the sign is reconstructed according to the line segment obtained from the line segment test, which has good robustness to illumination changes and occlusion conditions.

附图说明Description of drawings

图1为本发明的流程图;Fig. 1 is a flowchart of the present invention;

图2为RANSAC算法示意图;Figure 2 is a schematic diagram of the RANSAC algorithm;

RANSAC算法是一种常用的线段拟合方法。图2中,黑白点即是检测到的边缘像素点,c1、d1点为被任意选中的边缘像素点,作为假设边缘线段的端点,满足c1、d1点以及c1、d1点连线方向一致,与假设边缘线段c1d1距离足够近且方向与线段c1d1方向一致的点被视为线段c1d1的支持点。图2中,线段c1d1的支持点为12个,线段e1f1的支持点为3个,重复上述步骤,得到支持点最多的线段,即该线段被认定为存在的线段。The RANSAC algorithm is a commonly used line segment fitting method. In Fig. 2, the black and white points are the detected edge pixels, c1 and d1 are the edge pixels selected arbitrarily, as the end points of the hypothetical edge line segment, satisfying that c1, d1 and c1, d1 are connected in the same direction, Points that are close enough to the hypothetical edge line segment c1d1 and whose direction is consistent with the direction of the line segment c1d1 are regarded as support points of the line segment c1d1. In Figure 2, the line segment c1d1 has 12 support points, and the line segment e1f1 has 3 support points. Repeat the above steps to obtain the line segment with the most support points, that is, the line segment is identified as an existing line segment.

图3为对融合后的边缘线段进行延伸、筛选的示意图。Fig. 3 is a schematic diagram of extending and screening the fused edge segments.

具体实施方式detailed description

下面结合说明书附图和实施例对本发明作进一步限定,但不限于此。The present invention will be further limited below in conjunction with the accompanying drawings and embodiments, but not limited thereto.

实施例Example

一种应用于增强现实的人工标志检测方法,具体步骤包括:A method for detecting artificial signs applied to augmented reality, the specific steps comprising:

S1:采集帧图像,对帧图像粗采样,采用斜向网格扫描,检测得到帧图像边缘像素;S1: Collect the frame image, roughly sample the frame image, use oblique grid scanning, and detect the edge pixels of the frame image;

S2:基于RANSAC算法,根据步骤S1得到的边缘像素,检测得到帧图像中的边缘线段;S2: Based on the RANSAC algorithm, according to the edge pixels obtained in step S1, the edge line segment in the frame image is detected;

S3:对步骤S2检测到的边缘线段进行融合;S3: Fusing the edge line segments detected in step S2;

S4:对步骤S3融合后的边缘线段进行延伸、筛选;S4: Extending and screening the edge line segments fused in step S3;

S5:根据步骤S4延伸、筛选后的边缘线段,检测四边形边角点,并根据四边形边角点,构造四边形。如图1所示。S5: Detect the corner points of the quadrilateral according to the extended and screened edge segments in step S4, and construct a quadrilateral according to the corner points of the quadrilateral. As shown in Figure 1.

所述步骤S1,具体包括:The step S1 specifically includes:

S11:将帧图像分为若干个区域,每个区域包含40×40像素,将帧图像分为若干个区域,检测加快了速度,提高了实时性。S11: Divide the frame image into several regions, each region contains 40×40 pixels, divide the frame image into several regions, speed up the detection, and improve the real-time performance.

S12:对步骤S11获取的每个区域,采用斜向45°及135°的扫描线进行网格扫描,每个网格的对边距离为5个像素;S12: For each area obtained in step S11, use oblique 45° and 135° scanning lines to perform grid scanning, and the distance across the sides of each grid is 5 pixels;

采用斜向扫描线进行网格扫描主要原因是:一般情况下,标志正置于图片较少,标志一般斜置于图片中,故采用斜向扫描线进行网格扫描。The main reason for adopting oblique scanning lines for grid scanning is: generally, there are few pictures where signs are placed upright, and signs are generally placed obliquely in pictures, so oblique scanning lines are used for grid scanning.

S13:步骤S12的每条扫描线与高斯一阶导卷积,计算像素点沿每条扫描线方向的梯度强度分量;S13: each scanning line in step S12 is convolved with the Gaussian first-order derivation, and the gradient intensity component of the pixel along the direction of each scanning line is calculated;

S14:根据步骤S13得到的像素点沿每条扫描线方向的梯度强度分量,计算像素点的梯度强度值,梯度强度值中的局部极值对应的像素点即边缘像素梯度强度局部极值点,提取该边缘像素,提取边缘像素的阈值为30/256像素值,同时根据梯度强度分量,计算该边缘像素的方向θ=tan-1(gy/gx),gy是Y分量的梯度强度,gx是X分量的梯度强度。S14: According to the gradient strength component of the pixel point obtained in step S13 along the direction of each scanning line, calculate the gradient strength value of the pixel point, the pixel point corresponding to the local extremum in the gradient strength value is the local extremum point of the edge pixel gradient strength, Extract the edge pixel, the threshold of extracting the edge pixel is 30/256 pixel value, and calculate the direction θ=tan -1 (g y /g x ) of the edge pixel according to the gradient intensity component, g y is the gradient intensity of the Y component , g x is the gradient strength of the X component.

S2:基于RANSAC算法,根据步骤S1得到的边缘像素,检测得到帧图像中的边缘线段;S2: Based on the RANSAC algorithm, according to the edge pixels obtained in step S1, the edge line segment in the frame image is detected;

所述步骤S2,具体包括:The step S2 specifically includes:

S21:在步骤S11已划分的40×40像素的所有区域中,随机选取两个边缘像素,若两个边缘像素的方向与它们连线的方向一致,则假定它们的连线存在一条边缘线段;S21: In all the regions of 40×40 pixels that have been divided in step S11, randomly select two edge pixels, and if the direction of the two edge pixels is consistent with the direction of their connection, it is assumed that there is an edge line segment between them;

S22:计算支持步骤S21所述的假定的边缘线段的边缘像素的数量,如果边缘像素满足:γ∈(0.1,0.25),边缘像素的方向θ1与边缘线段方向一致,则认为该边缘像素支持该假定的边缘线段,Count加1;其中,γ为边缘像素距离假定的边缘线段的距离,θ1为边缘像素的方向,Count为支持所述的假定的边缘线段的边缘像素的数量;S22: Calculate the number of edge pixels supporting the assumed edge line segment described in step S21, if the edge pixel satisfies: γ∈(0.1,0.25), and the direction θ1 of the edge pixel is consistent with the direction of the edge line segment, then it is considered that the edge pixel supports the Assumed edge line segment, Count plus 1; wherein, γ is the distance from the edge pixel to the assumed edge line segment, θ1 is the direction of the edge pixel, and Count is the number of edge pixels supporting the assumed edge line segment;

S23:Count达到12的假定的边缘线段被认为是存在的,从图像中移除支持它们的边缘像素;S23: The assumed edge line segments whose Count reaches 12 are considered to exist, and the edge pixels supporting them are removed from the image;

S24:重复步骤S21至步骤S23,直至图像中大多数边缘像素被移除,并且找到所有的边缘线段。S24: Repeat steps S21 to S23 until most edge pixels in the image are removed and all edge segments are found.

图2中,黑白点即是检测到的边缘像素,c1、d1点为被任意选取的边缘像素点,作为假定边缘线段的端点,满足:c1、d1点方向以及c1、d1点连线方向一致,与假定边缘线段c1d1距离足够近且方向与c1、d1点连线方向一致的点被视为假定边缘线段c1d1的支持点。图2中,假定边缘线段c1d1的支持点为12个,假定边缘线段c1d1的支持点为3个,边缘线段c1d1被认为是存在的,假定边缘线段c1d1被排除。In Figure 2, the black and white points are the detected edge pixels, and the c1 and d1 points are arbitrarily selected edge pixel points as the end points of the hypothetical edge line segment, satisfying that the directions of c1 and d1 points and the direction of the connecting line of c1 and d1 points are consistent , the point that is close enough to the hypothetical edge segment c1d1 and whose direction is consistent with the direction of the line connecting points c1 and d1 is regarded as the support point of the hypothetical edge segment c1d1. In Fig. 2, it is assumed that the support points of the edge line segment c1d1 are 12, and the support points of the edge line segment c1d1 are assumed to be 3, the edge line segment c1d1 is considered to exist, and the edge line segment c1d1 is assumed to be excluded.

所述步骤S3,具体包括:The step S3 specifically includes:

选取一条通过步骤S2得到的边缘线段,命为线段a,从剩余边缘线段中任意选取另一条线段,命为线段b,对线段a、b进行融合判定,即:如果线段a、b满足:|θab|∈Δθ且Lab∈Δl,则将线段a、b融合,得到新线段;否则,继续在剩余边缘线段中选取线段,继续与线段a进行融合判定,直至线段a与除线段a外所有边缘线段完成融合判定;θa、θb为线段a、b的方向,Δθ为待融合线段方向误差的阈值,θab、Lab为线段a、b连线ab的方向和长度,Δl为线段a、b连线ab允许长度的阈值;Select an edge line segment obtained through step S2, call it line segment a, choose another line segment arbitrarily from the remaining edge line segments, call it line segment b, and make a fusion judgment on line segment a and b, that is: if line segment a and b satisfy: | θ a −θ b |∈Δ θ , And Lab ∈ Δ l , then merge the line segments a and b to get a new line segment; otherwise, continue to select a line segment from the remaining edge line segments, and continue to fuse with line segment a until line segment a and all edge line segments except line segment a are completed Fusion judgment; θ a and θ b are the direction of line segments a and b, Δ θ is the threshold value of the direction error of the line segment to be fused, θ ab and Lab are the direction and length of line ab connecting line segment a and b, and Δ l is line segment a , The threshold of the allowable length of b connection ab;

S32:重复步骤S31,至步骤S2得到的所有边缘线段完成融合。S32: Step S31 is repeated until all edge line segments obtained in step S2 are fused.

所述步骤S4,具体包括:The step S4 specifically includes:

S41:根据步骤S3得到的边缘线段,任意选取一根边缘线段并选取其一个端点,判断沿该边缘线段的方向与该端点相邻的像素点的方向是否一致,如果一致,则将该像素点添加到该边缘线段中,继续检查与该像素点相邻的下一个像素点,直至某像素点方向与该边缘线段方向不一致,则该像素点更新为该边缘线段的一个新端点;S41: According to the edge line segment obtained in step S3, arbitrarily select an edge line segment and select an end point thereof, and judge whether the direction along the edge line segment is consistent with the direction of the pixel adjacent to the end point, if they are consistent, then the pixel point Add it to the edge line segment, and continue to check the next pixel adjacent to the pixel point until the direction of a pixel point is inconsistent with the direction of the edge line segment, then the pixel point is updated as a new endpoint of the edge line segment;

图3中,像素点1为选取的边缘线段的像素点,像素点2为添加到边缘线段的像素点,像素点3为新端点;In Fig. 3, pixel point 1 is the pixel point of the selected edge line segment, pixel point 2 is the pixel point added to the edge line segment, and pixel point 3 is the new endpoint;

S42:对步骤S3得到的所有边缘线段的两个端点执行步骤S41,得到延伸后的边缘线段及其新端点;S42: Execute step S41 on the two endpoints of all the edge segments obtained in step S3, to obtain the extended edge segments and their new endpoints;

S43:对步骤S42得到的延伸后的边缘线段进行初步筛选,删除长度值小于20个像素的边缘线段;长度值过小的边缘线段不可能是标志边框,即便是,说明该标志边框畸变过大或者场景尺度太大,已经没有检测的意义,因此删除;S43: Preliminarily screen the extended edge line segment obtained in step S42, and delete the edge line segment whose length value is less than 20 pixels; the edge line segment whose length value is too small cannot be the logo frame, even if it is, it means that the logo frame distortion is too large Or the scale of the scene is too large to be meaningful for detection, so it is deleted;

S44:对步骤S43初步筛选后的边缘线段进行线段测试,沿边缘线段的方向选取一个距离其端点3个像素的像素点,检测该像素点的灰度值,如果该像素点的灰度值处于250范围内,则判定该端点合格,该边缘线段符合标志边框特征,否则,则判定该端点不合格,该边缘线段不符合标志边框特征;当边缘线段的两个端点都判定为不合格时,则删除该边缘线段;S44: Carry out a line segment test on the edge line segment after the preliminary screening in step S43, select a pixel point 3 pixels away from its end point along the direction of the edge line segment, and detect the gray value of the pixel point, if the gray value of the pixel point is in 250, it is judged that the endpoint is qualified, and the edge line segment conforms to the feature of the sign frame; otherwise, it is judged that the end point is unqualified, and the edge line segment does not meet the feature of the sign frame; Then delete the edge segment;

图3中,像素点4为测试像素点,可以知道,如果检测线段符合标志边框特征,像素点4应该是相对明亮点。In Figure 3, pixel 4 is a test pixel, and it can be known that if the detected line segment conforms to the feature of the logo frame, pixel 4 should be a relatively bright point.

S45:重复步骤S44,直至所有边缘线段都进行了线段测,得到所有符合标志边框特征的边缘线段。S45: Step S44 is repeated until all edge line segments have been measured, and all edge line segments conforming to the characteristics of the sign frame are obtained.

步骤S5中,根据步骤S4延伸、筛选后的边缘线段,检测四边形边角点,具体包括:In step S5, according to the edge line segment after step S4 extension, screening, detect quadrilateral corner point, specifically include:

S51:从步骤S45得到的所有符合标志边框特征的边缘线段中,任意选取一条边缘线段,设定为线段cd,作为四边形的第一条边,从剩余的符合标志边框特征的边缘线段中,选取与所述线段cd相交的线段ef,线段cd、ef满足:θcd!≈θet,min{ce,cf,de,df}≤Δ且线段cd、线段ef满足四边形邻边特征,线段cd、ef的相交点即四边形的一个边角点;S51: From all the edge line segments that meet the characteristics of the frame of the sign obtained in step S45, arbitrarily select an edge line segment, set as line segment cd, as the first side of the quadrilateral, and select from the remaining edge line segments that meet the feature of the frame of the sign The line segment ef intersecting the line segment cd, the line segments cd and ef satisfy: θ cd ! ≈θ et , min{ce, cf, de, df}≤Δ and the line segment cd and line segment ef satisfy the characteristics of the adjacent sides of the quadrilateral, and the intersection point of the line segment cd and ef is a corner point of the quadrilateral;

S52:利用步骤S51所述方法,得到四边形的边角点序列;S52: Using the method described in step S51, obtain the corner point sequence of the quadrilateral;

S53:遍历所有步骤S45得到的所有符合标志边框特征的边缘线段,得到所有的四边形的边角点序列。S53: Go through all the edge line segments obtained in step S45 that meet the characteristics of the frame of the logo, and obtain all the corner point sequences of the quadrilaterals.

根据四边形边角点,构造四边形,具体包括:Construct a quadrilateral according to the corner points of the quadrilateral, including:

S54:根据步骤S53得到的所有的边角点序列包含的边角点个数构造四边形:若边角点序列的边角点个数为4,连接4个边角点,直接构造四边形;S54: Construct a quadrilateral according to the number of corner points contained in all the corner point sequences obtained in step S53: if the number of corner points in the corner point sequence is 4, connect the 4 corner points to directly construct a quadrilateral;

若边角点序列的边角点个数为3,延长未构成第4个边角点的2条边缘线段,交点得到第4个边角点,连接4个边角点,构造四边形;If the number of corner points in the corner point sequence is 3, extend the 2 edge line segments that do not constitute the 4th corner point, and obtain the 4th corner point at the intersection point, connect the 4 corner points to construct a quadrilateral;

若边角点序列的边角点个数为2,延长仅有1个边角点的2条边缘线段,如果与第3条边缘线段相交,交点即为边角点,连接4个边角点,构造四边形;否则,无法构造四边形;If the number of corner points in the corner point sequence is 2, extend the 2 edge line segments with only 1 corner point, if it intersects with the third edge line segment, the intersection point is the corner point, connecting 4 corner points , to construct a quadrilateral; otherwise, a quadrilateral cannot be constructed;

若边角点序列的边角点个数为1,无法构造四边形;If the number of corner points in the corner point sequence is 1, a quadrilateral cannot be constructed;

S55:遍历所有边角点序列,得到所有的四边形,即检测所有的人工标志。S55: Traverse all corner point sequences to obtain all quadrilaterals, that is, detect all artificial signs.

Claims (8)

1.一种应用于增强现实的人工标志检测方法,其特征在于,具体步骤包括:1. A kind of artificial mark detection method that is applied to augmented reality is characterized in that, concrete steps comprise: S1:采集帧图像,对帧图像粗采样,采用斜向网格扫描,检测得到帧图像边缘像素;S1: Collect the frame image, roughly sample the frame image, use oblique grid scanning, and detect the edge pixels of the frame image; S2:基于RANSAC算法,根据步骤S1得到的边缘像素,检测得到帧图像中的边缘线段;S2: Based on the RANSAC algorithm, according to the edge pixels obtained in step S1, the edge line segment in the frame image is detected; S3:对步骤S2检测到的边缘线段进行融合;S3: Fusing the edge line segments detected in step S2; S4:对步骤S3融合后的边缘线段进行延伸、筛选;S4: Extending and screening the edge line segments fused in step S3; S5:根据步骤S4延伸、筛选后的边缘线段,检测四边形边角点,并根据四边形边角点,构造四边形。S5: Detect the corner points of the quadrilateral according to the extended and screened edge segments in step S4, and construct a quadrilateral according to the corner points of the quadrilateral. 2.根据权利要求1所述的一种应用于增强现实的人工标志检测方法,其特征在于,所述步骤S1,具体包括:2. A method for detecting artificial signs applied to augmented reality according to claim 1, wherein said step S1 specifically comprises: S11:将帧图像分为若干个区域,每个区域包含m×m像素,m∈(20,60);S11: divide the frame image into several regions, each region contains m×m pixels, m∈(20,60); S12:对步骤S11获取的每个区域,采用斜向x°及180°-x°的扫描线进行网格扫描,x∈(22.5,67.5),每个网格的对边距离为y个像素,y∈(3,9);S12: For each area obtained in step S11, use oblique x° and 180°-x° scanning lines to perform grid scanning, x∈(22.5,67.5), and the distance across each grid is y pixels , y∈(3,9); S13:步骤S12的每条扫描线与高斯一阶导卷积,计算像素点沿每条扫描线方向的梯度强度分量;S13: each scanning line in step S12 is convolved with the Gaussian first-order derivation, and the gradient intensity component of the pixel along the direction of each scanning line is calculated; S14:根据步骤S13得到的像素点沿每条扫描线方向的梯度强度分量,计算像素点的梯度强度值,梯度强度值中的局部极值对应的像素点即边缘像素梯度强度局部极值点,提取该边缘像素,同时根据梯度强度分量,计算该边缘像素的方向。S14: According to the gradient strength component of the pixel point obtained in step S13 along the direction of each scanning line, calculate the gradient strength value of the pixel point, the pixel point corresponding to the local extremum in the gradient strength value is the local extremum point of the edge pixel gradient strength, The edge pixel is extracted, and the direction of the edge pixel is calculated according to the gradient intensity component. 3.根据权利要求2所述的一种应用于增强现实的人工标志检测方法,其特征在于,m=40;x=45,y=5。3. A method for detecting artificial signs applied in augmented reality according to claim 2, characterized in that m=40; x=45, y=5. 4.根据权利要求1所述的一种应用于增强现实的人工标志检测方法,其特征在于,所述步骤S3,具体包括:4. A kind of artificial mark detection method applied to augmented reality according to claim 1, is characterized in that, described step S3, specifically comprises: S31:选取一条通过步骤S2得到的边缘线段,命为线段a,从剩余边缘线段中任意选取另一条线段,命为线段b,对线段a、b进行融合判定,即:如果线段a、b满足:|θab|∈Δθ且Lab∈Δl,则将线段a、b融合,得到新线段;否则,继续在剩余边缘线段中选取线段,继续与线段a进行融合判定,直至线段a与除线段a外所有边缘线段完成融合判定;θa、θb为线段a、b的方向,Δθ为待融合线段方向误差的阈值,θab、Lab为线段a、b连线ab的方向和长度,Δl为线段a、b连线ab允许长度的阈值;S31: Select an edge line segment obtained by step S2, and name it line segment a, randomly select another line segment from the remaining edge line segments, and call it line segment b, and perform fusion judgment on line segments a and b, that is, if line segments a and b satisfy : |θ ab |∈Δ θ , And Lab ∈ Δ l , then merge the line segments a and b to get a new line segment; otherwise, continue to select a line segment from the remaining edge line segments, and continue to fuse with line segment a until line segment a and all edge line segments except line segment a are completed Fusion judgment; θ a and θ b are the direction of line segments a and b, Δ θ is the threshold value of the direction error of the line segment to be fused, θ ab and Lab are the direction and length of line ab connecting line segment a and b, and Δ l is line segment a , The threshold of the allowable length of b connection ab; S32:重复步骤S31,至步骤S2得到的所有边缘线段完成融合。S32: Step S31 is repeated until all edge line segments obtained in step S2 are fused. 5.根据权利要求1所述的一种应用于增强现实的人工标志检测方法,其特征在于,所述步骤S4,具体包括:5. A kind of artificial mark detection method applied to augmented reality according to claim 1, is characterized in that, described step S4, specifically comprises: S41:根据步骤S3得到的边缘线段,任意选取一根边缘线段并选取其一个端点,判断沿该边缘线段的方向与该端点相邻的像素点的方向是否一致,如果一致,则将该像素点添加到该边缘线段中,继续检查与该像素点相邻的下一个像素点,直至某像素点方向与该边缘线段方向不一致,则该像素点更新为该边缘线段的一个新端点;S41: According to the edge line segment obtained in step S3, arbitrarily select an edge line segment and select an end point thereof, and judge whether the direction along the edge line segment is consistent with the direction of the pixel adjacent to the end point, if they are consistent, then the pixel point Add it to the edge line segment, and continue to check the next pixel adjacent to the pixel point until the direction of a pixel point is inconsistent with the direction of the edge line segment, then the pixel point is updated as a new endpoint of the edge line segment; S42:对步骤S3得到的所有边缘线段的两个端点执行步骤S41,得到延伸后的边缘线段及其新端点;S42: Execute step S41 on the two endpoints of all the edge segments obtained in step S3, to obtain the extended edge segments and their new endpoints; S43:对步骤S42得到的延伸后的边缘线段进行初步筛选,删除长度值小于n个像素的边缘线段,n∈(15,25);S43: Preliminarily screen the extended edge segments obtained in step S42, and delete the edge segments whose length is less than n pixels, n∈(15, 25); S44:对步骤S43初步筛选后的边缘线段进行线段测试,沿边缘线段的方向选取一个距离其端点2—4个像素的像素点,检测该像素点的灰度值,如果该像素点的灰度值处于128—255范围内,则判定该端点合格,该边缘线段符合标志边框特征,否则,则判定该端点不合格,该边缘线段不符合标志边框特征;当边缘线段的两个端点都判定为不合格时,则删除该边缘线段;S44: Carry out a line segment test to the edge line segment after the preliminary screening of step S43, select a pixel point 2-4 pixels away from its end point along the direction of the edge line segment, detect the gray value of the pixel point, if the gray value of the pixel point If the value is in the range of 128-255, it is judged that the endpoint is qualified, and the edge line segment conforms to the feature of the frame of the sign; If unqualified, delete the edge segment; S45:重复步骤S44,直至所有边缘线段都进行了线段测,得到所有符合标志边框特征的边缘线段。S45: Step S44 is repeated until all edge line segments have been measured, and all edge line segments conforming to the characteristics of the sign frame are obtained. 6.根据权利要求5所述的一种应用于增强现实的人工标志检测方法,其特征在于,n=20。6. A method for detecting artificial signs applied in augmented reality according to claim 5, characterized in that n=20. 7.根据权利要求5所述的一种应用于增强现实的人工标志检测方法,其特征在于,步骤S5中,根据步骤S4延伸、筛选后的边缘线段,检测四边形边角点,具体包括:7. A kind of artificial mark detection method that is applied to augmented reality according to claim 5, it is characterized in that, in step S5, according to the edge segment after step S4 extension, screening, detect quadrilateral corner point, specifically comprise: S51:从步骤S45得到的所有符合标志边框特征的边缘线段中,任意选取一条边缘线段,设定为线段cd,作为四边形的第一条边,从剩余的符合标志边框特征的边缘线段中,选取与所述线段cd相交的线段ef,线段cd、ef满足:θcd!≈θef,min{ce,cf,de,df}≤Δ且线段cd、线段ef满足四边形邻边特征,线段cd、ef的相交点即四边形的一个边角点;S51: From all the edge line segments that meet the characteristics of the frame of the sign obtained in step S45, arbitrarily select an edge line segment, set as line segment cd, as the first side of the quadrilateral, and select from the remaining edge line segments that meet the feature of the frame of the sign The line segment ef intersecting the line segment cd, the line segments cd and ef satisfy: θ cd ! ≈θ ef , min{ce, cf, de, df}≤Δ and the line segment cd and line segment ef satisfy the characteristics of the adjacent sides of the quadrilateral, and the intersection point of the line segment cd and ef is a corner point of the quadrilateral; S52:利用步骤S51所述方法,得到四边形的边角点序列;S52: Using the method described in step S51, obtain the corner point sequence of the quadrilateral; S53:遍历所有步骤S45得到的所有符合标志边框特征的边缘线段,得到所有的四边形的边角点序列。S53: Go through all the edge line segments obtained in step S45 that meet the characteristics of the frame of the logo, and obtain all the corner point sequences of the quadrilaterals. 8.根据权利要求7所述的一种应用于增强现实的人工标志检测方法,其特征在于,根据四边形边角点,构造四边形,具体包括:8. A kind of artificial sign detection method that is applied to augmented reality according to claim 7, is characterized in that, according to quadrilateral corner point, constructs quadrilateral, specifically comprises: S54:根据步骤S53得到的所有的边角点序列包含的边角点个数构造四边形:若边角点序列的边角点个数为4,连接4个边角点,直接构造四边形;S54: Construct a quadrilateral according to the number of corner points contained in all the corner point sequences obtained in step S53: if the number of corner points in the corner point sequence is 4, connect the 4 corner points to directly construct a quadrilateral; 若边角点序列的边角点个数为3,延长未构成第4个边角点的2条边缘线段,交点得到第4个边角点,连接4个边角点,构造四边形;If the number of corner points in the corner point sequence is 3, extend the 2 edge line segments that do not constitute the 4th corner point, and obtain the 4th corner point at the intersection point, connect the 4 corner points to construct a quadrilateral; 若边角点序列的边角点个数为2,延长仅有1个边角点的2条边缘线段,如果与第3条边缘线段相交,交点即为边角点,连接4个边角点,构造四边形;否则,无法构造四边形;If the number of corner points in the corner point sequence is 2, extend the 2 edge line segments with only 1 corner point, if it intersects with the third edge line segment, the intersection point is the corner point, connecting 4 corner points , to construct a quadrilateral; otherwise, a quadrilateral cannot be constructed; 若边角点序列的边角点个数为1,无法构造四边形;If the number of corner points in the corner point sequence is 1, a quadrilateral cannot be constructed; S55:遍历所有边角点序列,得到所有的四边形,即检测所有的人工标志。S55: Traverse all corner point sequences to obtain all quadrilaterals, that is, detect all artificial signs.
CN201610065210.8A 2016-01-29 2016-01-29 A kind of artificial target's detection method applied to augmented reality Expired - Fee Related CN105740818B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610065210.8A CN105740818B (en) 2016-01-29 2016-01-29 A kind of artificial target's detection method applied to augmented reality

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610065210.8A CN105740818B (en) 2016-01-29 2016-01-29 A kind of artificial target's detection method applied to augmented reality

Publications (2)

Publication Number Publication Date
CN105740818A true CN105740818A (en) 2016-07-06
CN105740818B CN105740818B (en) 2018-11-06

Family

ID=56247015

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610065210.8A Expired - Fee Related CN105740818B (en) 2016-01-29 2016-01-29 A kind of artificial target's detection method applied to augmented reality

Country Status (1)

Country Link
CN (1) CN105740818B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106682652A (en) * 2017-02-27 2017-05-17 上海大学 Structure surface disease inspection and analysis method based on augmented reality
CN108428250A (en) * 2018-01-26 2018-08-21 山东大学 A kind of X angular-point detection methods applied to vision positioning and calibration
CN110136159A (en) * 2019-04-29 2019-08-16 辽宁工程技术大学 A Line Segment Extraction Method for High Resolution Remote Sensing Images
CN110310279A (en) * 2019-07-09 2019-10-08 苏州梦想人软件科技有限公司 Rectangle and curl rectangle corner image-recognizing method

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
YONG-JOONG KIM等: "Business Card Region Segmentation by Block-based Line Fitting and Largest Quadrilateral Search with Constraints", 《INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND ANALYSIS》 *
沈铁: "高精度手术导航的研究与应用", 《中国优秀硕士学位论文全文数据库》 *
郑银强: "红外手术导航仪的高精度定位理论与方法", 《中国优秀硕士学位论文全文数据库》 *
郭俊芳: "纸币面额与序列号识别算法的设计与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106682652A (en) * 2017-02-27 2017-05-17 上海大学 Structure surface disease inspection and analysis method based on augmented reality
CN106682652B (en) * 2017-02-27 2020-06-23 上海大学 Structure surface disease inspection and analysis method based on augmented reality
CN108428250A (en) * 2018-01-26 2018-08-21 山东大学 A kind of X angular-point detection methods applied to vision positioning and calibration
CN108428250B (en) * 2018-01-26 2021-09-21 山东大学 X-corner detection method applied to visual positioning and calibration
CN110136159A (en) * 2019-04-29 2019-08-16 辽宁工程技术大学 A Line Segment Extraction Method for High Resolution Remote Sensing Images
CN110136159B (en) * 2019-04-29 2023-03-31 辽宁工程技术大学 Line segment extraction method for high-resolution remote sensing image
CN110310279A (en) * 2019-07-09 2019-10-08 苏州梦想人软件科技有限公司 Rectangle and curl rectangle corner image-recognizing method

Also Published As

Publication number Publication date
CN105740818B (en) 2018-11-06

Similar Documents

Publication Publication Date Title
CN104361353B (en) A kind of application of localization method of area-of-interest in instrument monitoring identification
JP5699788B2 (en) Screen area detection method and system
CN104809689B (en) A kind of building point cloud model base map method for registering based on profile
CN106960208B (en) Method and system for automatically segmenting and identifying instrument liquid crystal number
CN111784576A (en) An Image Mosaic Method Based on Improved ORB Feature Algorithm
CN105740818B (en) A kind of artificial target's detection method applied to augmented reality
CN108257146A (en) Movement locus display methods and device
CN103871072B (en) Automatic extraction method of orthophoto mosaic line based on projection digital elevation model
CN111383204A (en) Video image fusion method, fusion device, panoramic monitoring system and storage medium
CN109190742B (en) A Decoding Method of Encoded Feature Points Based on Grayscale Features
CN106952312B (en) A logo-free augmented reality registration method based on line feature description
CN103902953A (en) Screen detection system and method
Hadjigeorgiou et al. An evaluation of image analysis algorithms for constructing discontinuity trace maps
CN108007345A (en) Measuring method of excavator working device based on monocular camera
CN107705254A (en) A kind of urban environment appraisal procedure based on streetscape figure
CN104851089A (en) Static scene foreground segmentation method and device based on three-dimensional light field
CN105447489B (en) A kind of character of picture OCR identifying system and background adhesion noise cancellation method
CN111861866A (en) A panorama reconstruction method of substation equipment inspection image
CN101814138B (en) Method for identifying and classifying types of damage of sealants of cement concrete pavement based on images
CN109187548A (en) A kind of rock cranny recognition methods
CN110263778A (en) A kind of meter register method and device based on image recognition
CN105184317A (en) License plate character segmentation method based on SVM classification
Adu-Gyamfi et al. Functional evaluation of pavement condition using a complete vision system
CN108492306A (en) A kind of X-type Angular Point Extracting Method based on image outline
CN112037192A (en) Method for collecting burial depth information in town gas public pipeline installation process

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20181106