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This paper aims to develop a transparent object recognition algorithm which can integrate with visual cues of transparent objects to enhance the retrieval result. Due to the special characteristics of transparent objects, very few vision methods exist to identify them. To achieve our goal, we first use active depth sensor combining with image pre-processing techniques to retrieve transparent candidates...
Gender recognition for interactive functions becomes essential topic in terms of service robotics applications. Ensemble learning which combines multiple classifiers prediction is now an active area of research in Machine Learning and Pattern Recognition. We propose an ensemble learning to facilitate gender classification. The features which we use are raw data (image pixels as input), Local binary...
In this paper, we propose a hybrid visual tracking algorithm that combines two discriminative trackers. Discriminative trackers treat tracking as a classification problem, that is, they try to distinguish targets from backgrounds, and usually the trackers incorporate classifiers. The two trackers collectively determine the new object location via a process called confidence fusion. The two trackers...
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