Empirical mode decomposition for saliency detection
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In this paper we present a scene exploration method for the identification of interest regions in unknown indoor environments and the position estimation of the objects located in those regions. Our method consists of two stages: First, we generate a saliency map of the scene based on the spectral residual of three color channels and interest points are detected in this map. Second, we propose and evaluate a method for the clustering of neighboring interest regions, the rejection of outliers and the estimation of the positions of potential objects. Once the location of objects in the scene is known, recognition of objects/object classes can be performed or the locations can be used for grasping the object. The main contribution of this paper lies in a computationally inexpensive method for the localization of multiple salient objects in a scene. The performance obtained on a dataset of indoor scenes shows that our method performs good, is very fast and hence highly suitable for real-world applications, such as mobile robots and surveillance.