scholarly journals MTF Measurement Based on Interactive Live-Wire Edge Extraction

2007 ◽  
pp. 523-530
Author(s):  
Peng Liu ◽  
Dingsheng Liu ◽  
Fang Huang
Keyword(s):  
Edge Extraction

scholarly journals A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3416
Author(s):  
Pawel Burdziakowski ◽  
Angelika Zakrzewska
Keyword(s):  
Data Integration ◽  
Point Cloud ◽  
Laser Scanning ◽  
Detection Threshold ◽  
Measurement Data ◽  
Steel Structures ◽  
Adaptive Method ◽  
Edge Extraction ◽  
Geometric Structures ◽  
Redundant Data

The continuous and intensive development of measurement technologies for reality modelling with appropriate data processing algorithms is currently being observed. The most popular methods include remote sensing techniques based on reflected-light digital cameras, and on active methods in which the device emits a beam. This research paper presents the process of data integration from terrestrial laser scanning (TLS) and image data from an unmanned aerial vehicle (UAV) that was aimed at the spatial mapping of a complicated steel structure, and a new automatic structure extraction method. We proposed an innovative method to minimize the data size and automatically extract a set of points (in the form of structural elements) that is vital from the perspective of engineering and comparative analyses. The outcome of the research was a complete technology for the acquisition of precise information with regard to complex and high steel structures. The developed technology includes such elements as a data integration method, a redundant data elimination method, integrated photogrammetric data filtration and a new adaptive method of structure edge extraction. In order to extract significant geometric structures, a new automatic and adaptive algorithm for edge extraction from a random point cloud was developed and presented herein. The proposed algorithm was tested using real measurement data. The developed algorithm is able to realistically reduce the amount of redundant data and correctly extract stable edges representing the geometric structures of a studied object without losing important data and information. The new algorithm automatically self-adapts to the received data. It does not require any pre-setting or initial parameters. The detection threshold is also adaptively selected based on the acquired data.

Application of Image Processing to the Vehicle License Plate Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1638-1641 ◽  
Author(s):  
Chun Yu Chen ◽  
Bao Zhi Cheng ◽  
Xin Chen ◽  
Fu Cheng Wang ◽  
Chen Zhang
Keyword(s):  
Traffic Engineering ◽  
Character Recognition ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Edge Extraction ◽  
Recognition Method ◽  
License Plate Location ◽  
Vehicle License Plate Recognition ◽  
Plate Location

At present, the traffic engineering and automation have developed, and the vehicle license plate recognition technology need get a corresponding improvement also. In case of identifying a car license picture, the principle of automatic license plate recognition is illustrated in this paper, and the processing is described in detail which includes the pre-processing, the edge extraction, the license plate location, the character segmentation, the character recognition. The program implementing recognition is edited by Matlab. The example result shows that the recognition method is feasible, and it can be put into practice.

Camera Calibration Method Based on Circle Plane Board

2013 ◽  
Vol 475-476 ◽  
pp. 184-187
Author(s):  
Wen Guo Li ◽  
Shao Jun Duan
Keyword(s):  
Camera Calibration ◽  
Calibration Method ◽  
Lens Distortion ◽  
Edge Extraction ◽  
Circle Plane ◽  
Circle Centre ◽  
Extraction Algorithm ◽  
Calibration Algorithm ◽  
Characteristic Points ◽  
Distortion Parameters

We present a camera calibration method based on circle plane board. The centres of circles on plane are regarded as the characteristic points, which are used to implement camera calibration. The proposed calibration is more accurate than many previous calibration algorithm because of the merit of the coordinate of circle centre being obtained from thousand of of edge pionts of ellipse, which is very reliable to image noise caused by edge extraction algorithm. Experiments shows the proposed algorithm can obtain high precise inner parameters, and lens distortion parameters.

Edge extraction of optical subaperture based on differential box-counting dimension method with window merge replication

2016 ◽  
Vol 55 (9) ◽  
pp. 093107 ◽  
Author(s):  
Yunqi Wang ◽  
Mei Hui ◽  
Ming Liu ◽  
Liquan Dong ◽  
Xiaohua Liu ◽  
...  
Keyword(s):  
Edge Extraction ◽  
Box Counting ◽  
Box Counting Dimension

Feature Edge Extraction Via Angle-Based Edge Collapsing and Recovery

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Soji Yamakawa ◽  
Kenji Shimada
Keyword(s):  
Adverse Effects ◽  
Computer Aided Design ◽  
Region Growing ◽  
New Method ◽  
Small Radius ◽  
Edge Extraction ◽  
Line Segments ◽  
Computer Aided ◽  
Downstream Processes ◽  
Aided Design

This paper presents a new method for extracting feature edges from computer-aided design (CAD)-generated triangulations. The major advantage of this method is that it tends to extract feature edges along the centroids of the fillets rather than along the edges where fillets are connected to nonfillet surfaces. Typical industrial models include very small-radius fillets between relatively large surfaces. While some of those fillets are necessary for certain types of analyses, many of them are irrelevant for many other types of applications. Narrow fillets are unnecessary details for those applications and cause numerous problems in the downstream processes. One solution to the small-radius fillet problem is to divide the fillets along the centroid and then merge each fragment of the fillet with nonfillet surfaces. The proposed method can find such fillet centroids and can substantially reduce the adverse effects of such small-radius fillets. The method takes a triangulated geometry as input and first simplifies the model so that small-radius, or “small,” fillets are collapsed into line segments. The simplification is based on the normal errors and therefore is scale-independent. It is particularly effective for a shape that is a mix of small and large features. Then, the method creates segmentation in the simplified geometry, which is then transformed back to the original shape while maintaining the segmentation information. The groups of triangles are expanded by applying a region-growing technique to cover all triangles. The feature edges are finally extracted along the boundaries between the groups of triangles.

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