Tool combination for the description of steelsurface image and defect classification

This paper extends our previous work (Perronnin and Dance, 2007, Perronnin et al, 2010c, Sánchez and Perronnin, 2011) with: (1) a more detailed description of the FK framework

The advantage of the method we propose consist in the fact that, by using ones determined dependence q ( ~ ) for Qzao, it enables us to perform the calculations even under

Experimental results on Berg dataset show that the proposed image feature representation method together with the constructed image classifier, SVM-MK, can achieve

In this paper, we give a detailed scheme of building discriminant descriptors of defect images, based on the combination of Gabor wavelets (GW), known as efficient in

The problems as well as the objective of this realized work are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect -

The problems as well as the objective of this realized work are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect -

For image segmentation, the Mumford-Shah model needs to find regions and constant values of the regions, for it we use a variational approach based on the extraction

Peaks were found, which according to the impurity content could be attributed to lattice derects and to their complexes and the activa- tion energies obtained