Surface Reflection: Physical and Geometrical Perspectives
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reflection-area-based feature descriptor for solder joint inspection
Machine Vision and Applications
Visual inspection of machined metallic high-precision surfaces
EURASIP Journal on Applied Signal Processing
Adaptive Lighting for Machine Vision Applications
CRV '11 Proceedings of the 2011 Canadian Conference on Computer and Robot Vision
Specular-Reduced Imaging for Inspection of Machined Surfaces
CRV '12 Proceedings of the 2012 Ninth Conference on Computer and Robot Vision
Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
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In many industrial applications, it is important to identify defects on specular surfaces. On machined surfaces, defect identification may be further complicated by the presence of marks from a machining process. These marks may dramatically and unpredictably change the appearance of the surface, while not altering its ability to function. To differentiate between surface characteristics that constitute a defect and those that do not, we propose a system that directly illuminates specular machined surfaces with a programmable array of high-power light-emitting diodes that allows the angle of the incident light to be varied over a series of images. A reflection model is used to predict the reflected intensity as a function of incident lighting angle for each point on the imaged surface. A surface defect causes the observed reflected intensity as a function of incident lighting angle to differ from that predicted by the reflection model. Such differences between the observations and the reflection model are shown to identify surface defects such as porosities, dents and scratches in the presence of marks from the machining process.