Serge Samper

Syntactic texture and perception for a new generic visual anomalies classification

The research purpose is to improve aesthetic anomalies detection and evaluation based on what is perceived by human eye and on the 2006 CIE report.1 It is therefore important to define parameters able to discriminate surfaces, in accordance with the perception of human eye. Our starting point in assessing aesthetic anomalies is geometric description such as defined by ISO standard,2 i.e. traduce anomalies description with perception words about texture divergence impact. However, human controllers observe (detect) the aesthetic anomaly by its visual effect and interpreter for its geometric description. The research question is how define generic parameters for discriminating aesthetic anomalies, from enhanced information of visual texture such as recent surface visual rendering approach. We propose to use an approach from visual texture processing that quantify spatial variations of pixel for translating changes in color, material and relief. From a set of images from different angles of light which gives us access to the surface appearance, we propose an approach from visual effect to geometrical specifications as the current standards have identified the aesthetic anomalies.

Discrete Modal Decomposition for surface appearance modelling and rendering

Controlling surface appearance has become essential in the supplier/customer relationship. In this context, many industries have implemented new methods to improve the sensory inspection, particularly in terms of variability. A trend is to develop both hardware and methods for moving towards the automation of appearance inspection and analysis. If devices inspired from dimensional control solutions generally allow to identify defects far apart the expected quality of products, it do not allow to quantify finely appearance anomalies, and decide on their acceptance. To address this issue, new methods devoted to appearance modelling and rendering have been implemented, such as the Reflectance Transformation Imaging (RTI) technique. By varying the illumination positions, the RTI technique aims at enriching the classical information conveyed by images. Thus each pixel is described by a set of values rather than one value classically; each value corresponding to a specific illumination position. This set of values could be interpolated or approximated by a continuous model (function), associated to the reflectance of the pixel, generally based on a second order polynomial (namely, Polynomial Texture Mapping Technique). This paper presents a new approach to evaluate this information from RTI acquisitions. A modal projection based on dynamics (Discrete Modal Decomposition) is used to estimate surface reflectance on each measurement point. After presenting the acquisition device, an application on an industrial surface is proposed in order to validate the approach, and compare it to the more classical polynomial transformation. Results show that the proposed projection basis not only provides closer assessment of surface reflectance (modelling) but also yields to a more realistic rendering.

Influence of additive laser manufacturing parameters on surface using density of partially melted particles

Mastering the additive laser manufacturing surface is a real challenge and would allow functional surfaces to be obtained without finishing. Direct Metal Deposition (DMD) surfaces are composed by directional and chaotic textures that are directly linked to the process principles. The aim of this work is to obtain surface topographies by mastering the operating process parameters. Based on experimental investigation, the influence of operating parameters on the surface finish has been modeled. Topography parameters and multi-scale analysis have been used in order to characterize the DMD obtained surfaces. This study also proposes a methodology to characterize DMD chaotic texture through topography filtering and 3D image treatment. In parallel, a new parameter is proposed: density of particles ( D p ). Finally, this study proposes a regression modeling between process parameters and density of particles parameter.