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This thesis addresses the ¿gure-ground segmentation problem in the context of complex systems for automatic object recognition. Firstly the problem of image segmentation in general terms is introduced, followed by a discussion about its importance for online and interactive acquisition of visual representations. Secondly a machine learning approach using arti¿cial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time ¿gure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to ful¿ll these requirements characterize the novelty of the approach compared to state-of-the-art methods. Finally the proposed technique is extended in several aspects, which yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition.
| Autorius: | Alexander Denecke |
| Leidėjas: | Südwestdeutscher Verlag für Hochschulschriften AG Co. KG |
| Išleidimo metai: | 2015 |
| Knygos puslapių skaičius: | 164 |
| ISBN-10: | 3838133714 |
| ISBN-13: | 9783838133713 |
| Formatas: | 220 x 150 x 11 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Hypothesis-based image segmentation: A Machine Learning Approach“
