Surface inspection of flat products by means of texture analysis: on-line implementation using neural networks

Carlos Fernandez; Carlos Platero; Pascual Campoy; Rafael Aracil

doi:10.1117/12.196077

23 November 1994 Surface inspection of flat products by means of texture analysis: on-line implementation using neural networks

Carlos Fernandez, Carlos Platero, Pascual Campoy, Rafael Aracil

Author Affiliations +

Proceedings Volume 2249, Automated 3D and 2D Vision; (1994) https://doi.org/10.1117/12.196077
Event: Optics for Productivity in Manufacturing, 1994, Frankfurt, Germany

Abstract

This paper describes some texture-based techniques that can be applied to quality assessment of flat products continuously produced (metal strips, wooden surfaces, cork, textile products, ...). Since the most difficult task is that of inspecting for product appearance, human-like inspection ability is required. A common feature to all these products is the presence of non- deterministic texture on their surfaces. Two main subjects are discussed: statistical techniques for both surface finishing determination and surface defect analysis as well as real-time implementation for on-line inspection in high-speed applications. For surface finishing determination a Gray Level Difference technique is presented to perform over low resolution images, that is, no-zoomed images. Defect analysis is performed by means of statistical texture analysis over defective portions of the surface. On-line implementation is accomplished by means of neural networks. When a defect arises, textural analysis is applied which result in a data-vector, acting as input of a neural net, previously trained in a supervised way. This approach tries to reach on-line performance in automated visual inspection applications when texture is presented in flat product surfaces.

Citation Download Citation

Carlos Fernandez, Carlos Platero, Pascual Campoy, and Rafael Aracil "Surface inspection of flat products by means of texture analysis: on-line implementation using neural networks", Proc. SPIE 2249, Automated 3D and 2D Vision, (23 November 1994); https://doi.org/10.1117/12.196077

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