Sub-pixel Edge Fitting Using B-Spline

Bouchara, Frédéric; Bertrand, Marc; Ramdani, Sofiane; Haydar, Mahmoud

doi:10.1007/978-3-540-71457-6_32

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…In the majority of previous works about the simulation of 1D edge detection, e.g. [4], [5] and [6], it is simply assumed that the noise follows a Gaussian distribution. But this assumption could not conform to the real radiometric sensor characteristic according to the camera model in EMVA 1288.…”

Section: Simulation Modelmentioning

confidence: 99%

EMVA 1288 Camera characterisation and the influences of radiometric camera characteristics on geometric measurements

et al. 2016

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<p>Over the past decades, a large number of imaging sensors based mostly on CCD or CMOS technology were developed. Datasheets provided by their developers are usually written on their own standards and no universal figure of merit can be drawn from them for comparison purposes. The EMVA 1288 is a standard aims to overcome this problem by setting parameters and experimental setup for radiometric characterisation of cameras. An implementation of an experimental setup and software environment for radiometric characterisation of imaging sensors following the guidelines of the EMVA 1288 is presented here. Using simulations, the influences and impact of several EMVA 1288 parameters on geometric measurements can be estimated. This paper also presents a signal model and image acquisition chain; measurements of radiometric characteristics of an image sensor; and sensor evaluation for geometric measurements, where the aforementioned influences on geometric measurements are discussed.</p>

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Section: Simulation Modelmentioning

confidence: 99%

EMVA 1288 Camera characterisation and the influences of radiometric camera characteristics on geometric measurements

et al. 2016

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“…1 moment-based (Da and Zhang, 2010;Lyvers et al, 1989;Frei and Chen, 1977); 2 interpolating-based (Hwang and Lee, 2004;Zhang and Wu, 2006); and 3 fitting-based (Bouchara et al, 2007;Ye et al, 2005).…”

Section: Edge Detection To Sub-pixel Resolution Using Invariant Momentmentioning

confidence: 99%

Machine vision method for non-contact measurement of surface roughness of a rotating workpiece

Kumar

Ratnam

2015

Sensor Review

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Purpose -This paper aims to propose a non-contact method using machine vision for measuring the surface roughness of a rotating workpiece at speeds of up to 4,000 rpm. Design/methodology/approach -A commercial digital single-lens-reflex camera with high shutter speed and backlight was used to capture a silhouette of the rotating workpiece profile. The roughness profile was extracted at sub-pixel accuracy from the captured images using the moment invariant method of edge detection. The average (Ra), root-mean square (Rq) and peak-to-valley (Rt) roughness parameters were measured for ten different specimens at spindle speeds of up to 4,000 rpm. The roughness values measured using the proposed machine vision system were verified using the stylus profilometer. Findings -The roughness values measured using the proposed method show high correlation (up to 0.997 for Ra) with those determined using the profilometer. The mean differences in Ra, Rq and Rt between the two methods were only 4.66, 3.29 and 3.70 per cent, respectively. Practical implications -The proposed method has significant potential for application in the in-process roughness measurement and tool condition monitoring from workpiece profile signature during turning, thus, obviating the need to stop the machine. Originality/value -The machine vision method combined with sub-pixel edge detection has not been applied to measure the roughness of a rotating workpiece.

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“…At first glance, visible damage and more particularly cracks implies a local intensity variation on the structure surface. Algorithms exist to detect the contours of objects on images at pixel resolutions [9,10], and sub-pixel scales [11][12][13]. These algorithms have inspired the development of methods for automatic recognition of surface defects on concrete samples [14,15] or on bridges [16] for instance.…”

Section: Introductionmentioning

confidence: 99%

An algorithm for structural health monitoring by digital image correlation: Proof of concept and case study

Curt

Capaldo

Hild

et al. 2022

Optics and Lasers in Engineering

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In the field of structural health monitoring, the use of Digital Image Correlation (DIC) on relevant surfaces offers remarkable advantages. Such a method is presented herein. The statistically stationary nature of usual loads makes it possible to determine, by training with DIC, a reduced kinematic basis (composed of "modes") and a statistical amplitude distribution for each mode. Further, a specific DIC technique is proposed to deal with such particular kinematics by introducing an extractor per mode that operates on images after a filtering step. This per-mode DIC measurement is reduced to a simple scalar product between the image and the corresponding extractor, which allows for very fast and noniterative processing. As an illustration, this methodology is deployed on a test case of fatigue crack propagation.

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Sub-pixel Edge Fitting Using B-Spline

Cited by 6 publications

References 20 publications

EMVA 1288 Camera characterisation and the influences of radiometric camera characteristics on geometric measurements

EMVA 1288 Camera characterisation and the influences of radiometric camera characteristics on geometric measurements

Machine vision method for non-contact measurement of surface roughness of a rotating workpiece

An algorithm for structural health monitoring by digital image correlation: Proof of concept and case study

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