TY - JOUR
T1 - Assessment of the influence of adaptive components in trainable surface inspection systems
AU - Eitzinger, Christian
AU - Heidl, Wolfgang
AU - Lughofer, Edwin
AU - Raiser, Stefan
AU - Smith, Jim
AU - Tahir, Muhammad
AU - Sannen, Davy
AU - van Brussel, Hendrik
PY - 2010
Y1 - 2010
N2 - In this paper,we present a framework for the classification of images in surface inspection tasks and address several key aspects of the processing chain from the original image to the final classification result. A major contribution of this paper is a quantitative assessment of how incorporating adaptivity into the feature calculation, the feature pre-processing, and into the classifiers themselves, influences the final image classification performance. Hereby, results achieved on a range of artificial and real-world test data from applications in printing, die-casting, metal processing and food production are presented.
AB - In this paper,we present a framework for the classification of images in surface inspection tasks and address several key aspects of the processing chain from the original image to the final classification result. A major contribution of this paper is a quantitative assessment of how incorporating adaptivity into the feature calculation, the feature pre-processing, and into the classifiers themselves, influences the final image classification performance. Hereby, results achieved on a range of artificial and real-world test data from applications in printing, die-casting, metal processing and food production are presented.
KW - Pattern recognition
KW - image processing and computer vision
KW - communications engineering and networks
U2 - 10.1007/s00138-009-0211-1
DO - 10.1007/s00138-009-0211-1
M3 - Article
SN - 0932-8092
VL - 21
SP - 613
EP - 626
JO - Machine Vision and Applications
JF - Machine Vision and Applications
IS - 5
ER -
