Hypothesis-based image segmentation: A Machine Learning Approach - Alexander Denecke
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Piegāde 15-21 darba dienu laikā
30 dienu atgriešanas politika
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 ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Alexander Denecke |
|---|---|
| Izdevējs | Südwestdeutscher Verlag für Hochschulschriften AG Co. KG |
| Izlaides gads | 2015 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783838133713 |