This paper discusses the accuracy improvement of automatic analysis of construction and demolition waste (CDW) by using the combination of image analysis and spectral information. This means using the combination of methods of image processing, methods of spectral analysis and methods of supervised learning....
In this paper a new method for the automatic visual inspection of metallic surfaces is proposed by using Convolutional Neural Networks (CNN). The different combinations of network parameters were developed and tested. The obtained results of CNN were analysed and compared with the results of our previous...
Quality assessment is an important step in production processes of metal parts. This step is required in order to check whether surface quality meets the requirements. Progress in the field of computing technologies and computer vision gives the possibility of visual surface quality control with industrial...
Braunschweig: International Measurement Confederation (IMEKO), 2016-11-30
This paper discusses the possibility of automatic classifying of construction and demolition waste (CDW) by using methods of spectral analysis and supervised classifiers. The classification performances in colour images shown, that we have touse additional spectral information to solve the recognition...
This paper discusses two analysis activities in the construction material industry, which could be solved by intelligent image processing algorithms. The first task is the optical identification of recycled aggregates of construction and demolition waste (CDW) as basis of an innovative sorting method...
Braunschweig: International Measurement Confederation (IMEKO), 2013-08
Natural products are exposed to various environmental
influences resulting in a high phenotypical variability. This
makes it very difficult to develop automatic recognition algorithms
in contrast to the recognition of manufactured products.
Recent developments in the field of computer science, especially
the...