Online learning and object recognition for AUV optical vision

Author(s):  
Xiaohai Yuan ◽  
Zhen Hu ◽  
Jianping Chen ◽  
Rongsheng Chen ◽  
Peilin Liu
Author(s):  
Holger Bekel ◽  
Ingo Bax ◽  
Gunther Heidemann ◽  
Helge Ritter

Author(s):  
Apostolos Galanopoulos ◽  
Jose A. Ayala-Romero ◽  
George Iosifidis ◽  
Douglas J. Leith

Object detection and recognition are the meta-heuristic problems in computer vision. Practically usable dynamic object recognition methods are still unavailable. A new method was proposed which improves over existing methods in every stage. In that addition features like geometric shapes, ellipsis are added. An heuristic codebook was proposed of good generalization and discriminative properties, enabling multipath interferences mechanisms on propagation of1 conditional livelihood. A new learning method also proposed which is capable of online learning


Author(s):  
YANG WU ◽  
NANNING ZHENG ◽  
YUANLIU LIU ◽  
ZEJIAN YUAN

This paper presents a novel research on promoting the performance and enriching the functionalities of object recognition. Instead of simply fitting various data to a few predefined semantic object categories, we propose to generate proper results for different object instances based on their actual visual appearances. The results can be fine-grained and layered categorization along with absolute or relative localization. We present a generic model based on structured prediction and an efficient online learning algorithm to solve it. Experiments on a new benchmark dataset demonstrate the effectiveness of our model and its superiority against traditional recognition methods.


Author(s):  
Heiko Wersing ◽  
Stephan Kirstein ◽  
Bernd Schneiders ◽  
Ute Bauer-Wersing ◽  
Edgar Körner

2007 ◽  
Vol 17 (04) ◽  
pp. 219-230 ◽  
Author(s):  
HEIKO WERSING ◽  
STEPHAN KIRSTEIN ◽  
MICHAEL GÖTTING ◽  
HOLGER BRANDL ◽  
MARK DUNN ◽  
...  

We present a biologically motivated architecture for object recognition that is capable of online learning of several objects based on interaction with a human teacher. The system combines biological principles such as appearance-based representation in topographical feature detection hierarchies and context-driven transfer between different levels of object memory. Training can be performed in an unconstrained environment by presenting objects in front of a stereo camera system and labeling them by speech input. The learning is fully online and thus avoids an artificial separation of the interaction into training and test phases. We demonstrate the performance on a challenging ensemble of 50 objects.


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