Abstract
The development of image interpretation systems is concerned with tricky problems such as a limited number of observations, environmental influence, and noise. Recent systems lack robustness, accuracy, and flexibility. The introduction of case-based reasoning (CBR) strategies can help to overcome these drawbacks. The special type of information (i.e., images) and the problems mentioned above provide special requirements for CBR strategies. In this paper we review what has been achieved so far and research topics concerned with case-based image interpretation. We introduce a new approach for an image interpretation system and review its components.
Cite
CITATION STYLE
Perner, P. (2001). Why case-based reasoning is attractive for image interpretation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2080, pp. 27–43). Springer Verlag. https://doi.org/10.1007/3-540-44593-5_3
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
