scholarly journals Planar Contour Primitive Recognition of Thin Sheet Part Dimensional Inspection System

2011 ◽  
Vol 2-3 ◽  
pp. 463-468
Author(s):  
Ji Gang Wu ◽  
Kuan Fang He ◽  
Bin Qin
Keyword(s):  
Hough Transform ◽  
Thin Sheet ◽  
Detection Methods ◽  
Inspection System ◽  
Threshold Method ◽  
Dimensional Inspection ◽  
Straight Line ◽  
Dominant Point ◽  
Contour Point ◽  
Point Detection

According to the two indices of inspection accuracy and inspection speed, a planar contour primitive recognition method of thin sheet part dimensional inspection system based on curvature and HOUGH transform is proposed. A contour point classification algorithm based on neighborhood values is developed, and a curvature threshold method is selected to filter the contour points, and a projection height method is selected to distinguish the property of the primitive and classify the contour points, and the straight line primitive and arc primitive segmentation and merging algorithms are constructed respectively by HOUGH transform. The inspection accuracy and inspection speed of the proposed method are compared and analyzed by contrast experiments between the proposed method and four dominant point detection methods such as Chung & Tsai method and so on. The dominant point detection ability of the proposed method is tested by a simulation planar contour which includes all kinds of dominant points. The experimental results indicate that the proposed method can recognize primitives exactly, the inspection speed is fast and the universality is good.

Robot-handling fabrics with curved edges based on visual servoing and polygonal approximation

2008 ◽  
Vol 222 (10) ◽  
pp. 1263-1274 ◽  
Author(s):  
P Zacharia ◽  
I G Mariolis ◽  
N Aspragathos ◽  
E S Dermatas
Keyword(s):  
Visual Servoing ◽  
Detection Method ◽  
Experimental Tests ◽  
Polygonal Approximation ◽  
Curve Section ◽  
Robot Controller ◽  
Straight Line ◽  
Dominant Point ◽  
Point Detection ◽  
Work Table

The main scope of this work is the development of a robot controller for the manipulation of fabrics lying on a work table towards the sewing process. A fuzzy visual servoing manipulator controller is developed to guide the fabric along the sewing line. The paper focuses on handling fabrics with curved edges by locally approximating the curve section with straight-line segments. An innovative algorithm based on the dominant point detection method and the genetic-based method is incorporated in the robot visual servoing system to achieve sewing of the curved edges. Simulation and experimental tests are conducted to evaluate the performance of the proposed algorithm. The results demonstrate that this approach is efficient and effective for the polygonal approximation, and the proposed robotic system is proved to be robust and efficient, achieving acceptable seam accuracy in the minimum time.

A novel framework for making dominant point detection methods non-parametric

2012 ◽  
Vol 30 (11) ◽  
pp. 843-859 ◽  
Author(s):  
Dilip K. Prasad ◽  
Maylor K.H. Leung ◽  
Chai Quek ◽  
Siu-Yeung Cho
Keyword(s):  
Detection Methods ◽  
Dominant Point ◽  
Point Detection ◽  
Non Parametric

scholarly journals Research on Dimensional Inspection System of Thin Sheet Part Based on Machine Vision

2011 ◽  
Vol 2-3 ◽  
pp. 469-474
Author(s):  
Ji Gang Wu ◽  
Xue Jun Li ◽  
Bin Qin
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Spline Interpolation ◽  
Thin Sheet ◽  
Optimization Method ◽  
Inspection Time ◽  
Inspection System ◽  
Adaptive Optimization ◽  
Dimensional Inspection ◽  
Vision Inspection

Key technologies of dimensional inspection system of thin sheet part based on machine vision are investigated, and an entire machine vision inspection system is developed. A cad information-based line scanning step adaptive optimization method used for image grabbing of inspected part is proposed. A rectangle lens subpixel edge detection method based on cubic spline interpolation used for edge detection is advanced. A planar contour primitive recognition method based on curvature and HOUGH transform used for image recognition is raised. The inspection accuracy of the inspection system can reach to 1μm, and the inspection time can satisfy the requirements of on-line real-time inspection, so the inspection system is feasible.

Coarse-To-Fine 3D Randomized Hough Transform for Dim Target Detection

2014 ◽  
Vol 519-520 ◽  
pp. 1040-1045
Author(s):  
Ling Fan
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Constant Velocity ◽  
Three Dimensional ◽  
Randomized Hough Transform ◽  
Straight Line ◽  
Memory Complexity ◽  
Coarse To Fine

This paper makes some improvements on Roberts representation for straight line in space and proposes a coarse-to-fine three-dimensional (3D) Randomized Hough Transform (RHT) for the detection of dim targets. Using range, bearing and elevation information of the received echoes, 3D RHT can detect constant velocity target in space. In addition, this paper applies a coarse-to-fine strategy to the 3D RHT, which aims to solve both the computational and memory complexity problems. The validity of the coarse-to-fine 3D RHT is verified by simulations. In comparison with the 2D case, which only uses the range-bearing information, the coarse-to-fine 3D RHT has a better practical value in dim target detection.

scholarly journals Daylight Discomfort Glare Evaluation with Evalglare: Influence of Parameters and Methods on the Accuracy of Discomfort Glare Prediction

Buildings ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 94 ◽  
Author(s):  
Clotilde Pierson ◽  
Jan Wienold ◽  
Magali Bodart
Keyword(s):  
Visual Scene ◽  
Detection Methods ◽  
Clear Definition ◽  
Source Detection ◽  
Threshold Method ◽  
Area Method ◽  
Discomfort Glare ◽  
Definition Of ◽  
Statistical Indicators ◽  
Lack Of Knowledge

Nowadays, discomfort glare indices are frequently calculated by using evalglare. Due to the lack of knowledge on the implications of the methods and parameters of evalglare, the default settings are often used. But wrong parameter settings can lead to inappropriate glare source detection and therefore to invalid glare indices calculations and erroneous glare classifications. For that reason, this study aims to assess the influence of several glare source detection methods and parameters on the accuracy of discomfort glare prediction for daylight. This analysis uses two datasets, representative of the two types of discomfort glare: saturation and contrast glare. By computing three different statistical indicators to describe the accuracy of discomfort glare prediction, 63 different settings are compared. The results suggest that the choice of an evalglare method should be done when considering the type of glare that is most likely to occur in the visual scene: the task area method should be preferred for contrast glare scenes, and the threshold method for saturation glare scenes. The parameters that should be favored or avoided are also discussed, although a deeper understanding of the discomfort glare mechanism and a clear definition of a glare source would be necessary to reliably interpret these results.

scholarly journals A Study on Pre-processing Algorithms for Metal Parts Inspection

2011 ◽  
Vol 7 (1) ◽  
pp. 1-4
Author(s):  
Haider Hashim ◽  
Anton Prabuwono ◽  
Siti Norul Huda Abdullah
Keyword(s):  
Image Processing ◽  
Morphological Analysis ◽  
Image Understanding ◽  
Detection Methods ◽  
Experimental Result ◽  
Inspection System ◽  
Metal Parts ◽  
Analysis Task ◽  
Processing Step ◽  
Filter Noise

Pre-processing is very useful in a variety of situations since it helps to suppress information that is not related to the exact image processing or analysis task. Mathematical morphology is used for analysis, understanding and image processing. It is an influential method in the geometric morphological analysis and image understanding. It has befallen a new theory in the digital image processing domain. Edges detection and noise reduction are a crucial and very important pre-processing step. The classical edge detection methods and filtering are less accurate in detecting complex edge and filtering various types of noise. This paper proposed some useful mathematic morphological techniques to detect edge and to filter noise in metal parts image. The experimental result showed that the proposed algorithm helps to increase accuracy of metal parts inspection system.

scholarly journals Taxonomy of Object Detection methods: A Survey

2022 ◽  
Vol 10 (1) ◽  
pp. 20-25
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Real Life ◽  
Object Motion ◽  
Detection Methods ◽  
Illumination Variation ◽  
Human Machine Interaction ◽  
Research Areas ◽  
To Come ◽  
Point Detection

Object detection (OD) within a video is one of the relevant and critical research areas in the computer vision field. Due to the widespread of Artificial Intelligence, the basic principle in real life nowadays and its exponential growth predicted in the epochs to come, it will transmute the public. Object Detection has been extensively implemented in several areas, including human-machine Interaction, autonomous vehicles, security with video surveillance, and various fields that will be mentioned further. However, this augmentation of OD tackles different challenges such as occlusion, illumination variation, object motion, without ignoring the real-time aspect that can be quite problematic. This paper also includes some methods of application to take into account these issues. These techniques are divided into five subcategories: Point Detection, segmentation, supervised classifier, optical flow, a background modeling. This survey decorticates various methods and techniques used in object detection, as well as application domains and the problems faced. Our study discusses the cruciality of deep learning algorithms and their efficiency on future improvement in object detection topics within video sequences.

scholarly journals A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2732 ◽  
Author(s):  
Xinman Zhang ◽  
Jiayu Zhang ◽  
Mei Ma ◽  
Zhiqi Chen ◽  
Shuangling Yue ◽  
...  
Keyword(s):  
Machine Vision ◽  
High Precision ◽  
Absolute Error ◽  
Detection Methods ◽  
Inspection System ◽  
Quality Inspection ◽  
Construction Site ◽  
High Definition ◽  
Steel Bar ◽  
Steel Bars

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site.

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