"On-line Evolving Image Classifiers and Their Application to Surface Inspection"
, in Image and Vision Computing, Vol. 28, Nummer 7, Elsevier, Seite(n) 1065-1079, 7-2010
On-line Evolving Image Classifiers and Their Application to Surface Inspection
Sprache des Titels:
In this paper, we present image classifiers which are able to
adapt and evolve themselves at an on-line machine vision system.
These classifiers are initially trained on some pre-labelled
training data and further updated based on newly recorded samples,
for instance during a production process. The evolution and
adaptation mechanism is necessary in order to guarantee a
process-save on-line system as usually the pre-labelled data does
not cover all possible operating conditions, system states or
image classes. It is also recommended for a refinement of the
classifiers during the on-line mode in order to boost predictive
performance with more loaded samples. We will present two types of
on-line evolving image classifiers: The first one is a
clustering-based classification approach, which exploits
conventional vector quantization, forming an incremental evolving
variant around it and extending it to the supervised
classification case. The second one is an evolving fuzzy
classifier approach which comes with two model architectures,
classical single model and a novel multi-model architecture, the
later exploiting indicator matrices/vectors for training. The
approaches are evaluated in three different on-line surface
inspection systems dealing with CD imprint inspection, egg
inspection and inspection of metal rotor parts. The evaluation
will show the impact of on-line evolved versus 'static'
classifiers kept fixed during the whole on-line process.