An automated vision based on-line novel percept detection method for a mobile robot
It is generally agreed that learning, either supervised or unsupervised, can provide the best possible specification of known classes and offer inference for outlier detection by a dissimilarity threshold from the nominal feature space. Novel percept detection can take a step further by investigating whether these outliers form new dense clusters in both the feature space and the image space. By defining a novel percept to be a pattern group that has not been seen before in the feature space and the image space, in this paper, a non-conventional approach is proposed for multiple-novel-percept detection problem in robotic applications. Based on a computer vision system inspired loosely by neurobiological evidence, our approach can work in near real time for highly sparse high-dimensional feature vectors extracted from image patches while maintaining robustness to image transformations. Experiments conducted in an indoor environment and an outdoor environment demonstrate the efficacy of our method.
clustering computer vision on-line novelty detection statistical approaches classification tree