Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
What can two images tell us about a third one?
International Journal of Computer Vision
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Knowledge engineering and management: the CommonKADS methodology
Knowledge engineering and management: the CommonKADS methodology
A Cognitive Vision Platform for Automatic Recognition of Natural Complex Objects
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
A Semantic Web Primer
Towards ontology based cognitive vision
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Bounded transparency for automated inspection in agriculture
Computers and Electronics in Agriculture
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
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One of the major challenges in computer vision is to create automated systems that perform tasks with at least the same competences as human experts. In particular for automated inspection of natural objects this is not easy to achieve. The task is hampered by large in-class variations and complex 3D-morphology of the objects and subtle argumentations of experts. For example, in our horticultural case we deal with quality assessment of young tomato plants, which requires experienced specialists. We submit that automation of such a task employing an explicit model of the objects and their assessment is preferred over a black-box model obtained from modelling input-output relations only. We propose to employ ontologies for representing the geometrical shapes, object parts and quality classes associated with the explicit models. Our main contribution is the description of a method to develop a white-box computer vision application in which the needed expert knowledge is defined by: (i) decomposing the task of the inspection system into subtasks and (ii) identifying the algorithms that execute the subtasks. This method describes the interaction between the task decomposition and the needed task-specific knowledge, and studies the delicate balance between general domain knowledge and task-specific details. As a proof of principle of this methodology, we work through a horticultural case study and argue that the method leads to a robust, well-performing, and extendable computer vision system.