We propose a needle-shape quality-control method. To this end, we have devised a new acquisition system that combines a camera and a backlight. Needle measurements are carried out at a micrometric scale using shadowgraphic image processing. Our method not only distinguishes good needles from bad ones, but also allows classifying flawed needles into various categories of defects. This classification is important because some categories of defects can affect the entire production, whereas others do not. The results of our needle-shape quality-control method are validated using real samples directly off the manufacturing line. Needles are correctly classified at >97%, and accurate measurements on global shape characteristics such as straightness and sharpness are obtained.
This paper describes a methodology for thin spikes characterization. Nowadays, its evaluation is performed by visual
control. We propose a method to measure these spikes at a micrometric scale by using ombroscopic image processing.
A spike needs to be mainly conic and its tip must be ogival. The first aspect is evaluated by comparing the spike with an
ideal cone based on spike's contour. To find lines supported by contours, we use the Radon transform. However, due to
irregular contour, we develop an improvement of this transform based on morphological operators. This way, real
segments are found and a correct estimation of an ideal cone can be done.
The second aspect is controlled by measuring the radius of the tip which gives both sharpness and regularity of the tip.
As the following of the curvature is problematic, we use a morphological skeleton on the contour to obtain a structure
similar to a Y. The intersection of these three branches leads to a correct estimation of the circular gauge. An additional
filling criterion validates the result.
This study is successful as the production is correctly classified and precise measures were obtained both in terms of
global characteristics and sharpness.
In this article, a new implementation of active curves algorithms is proposed. It is question of an active region algorithm
based on stationary states of a nonlinear diffusion principle. Its originality is to obtain a set of geometric envelopes in
one pass, with a correspondence between level threshold of the grayscale result and a regularity scale, close to the
original shape. This set of geometric envelopes gives a multiscale representation, from a very regular approximation to a
full detailed and roughest representation. This property is used in a new subpixel circle center estimator developed in the
purpose of distorted contours. Results are very promising as precision is noticeably improved compared to a least mean
squares estimator. This estimator is then pretty well adapted to limit distortions caused by industrial processes.
The subject of this paper is the improvement of measures on imperfect circular forms. Indeed, simple geometric forms
have been well studied in image processing. Thus, articles describing circles on a discrete framework are numerous but
the case of imperfect geometric forms, in return, is hardly ever deepen. However, it is a classical problem in industrial
vision control process to not have a perfect, or perfectly discretized, geometric object due to, notably, manufacturing
process, industrial environment (dust, vibrations, objects displacement, etc.), interferences on acquisition chain
(electronic noise, lenses imperfections, etc. ). The authors present a comparison of measurement methods of circles
characteristics subpixel estimation (center's coordinates and radius) for several distortions (geometric or not). The
estimators proposed are classic least mean squares, 3D Hough algorithms and a method combining a Radon transform
based estimator and a FitzHugh-Nagumo partial differential equation based active region algorithm. The originality of
the method is to furnish a set of geometric envelopes in a single pass from a roughest to a full detailed representation.
Moreover, this multiple active region principle also offers interesting electronic implementation possibilities for real
time image processing for metrology on production chains.
This article deals with the problem of the determination of characteristics of imperfect circular objects on discrete images mainly the radius and center's coordinates. Imperfections are provided by discretization, noise and interior distortions present in some production processes. To this end, a multi-level method based on active contours was developed and tested on, noisy or not, misshaped or not, simulated circles whom centers and radius were known. The adequacy of this approach was tested with several methods, among them several Radon based ones. More particularly, this study indicates the relevance of the use of active contours combined with a Radon transform based method, using a description of circles from their tangents, improved thanks to a fitting considering the discrete implementation of Radon transform. Through this study, an active region algorithm based on stationary states of a non linear diffusion principle is proposed. Its originality is to obtain a set of geometric envelopes in one pass, with a correspondence between level threshold of the grayscale result and a regularity scale, more or less close to the original shape. This set of geometric envelopes gives a multiscale representation, from a very regular approximation to a full detailed and roughest representation. Then, a more robust measure of the circle parameters can be computed.