One of the main goals of vision systems is to recognize objects in real world to perform appropriate actions. This implies the ability of handling objects and, moreover, to know the relations between these objects and their environment in what we call scenes. Most of the time, navigation in unknown environments is difficult due to a lack of easily identifiable landmarks. Hence, in this work, some geometric features to identify objects are considered. Firstly, a Markov random field segmentation approach is implemented. Then, the key factor for the recognition is the calculation of the so‐called distance histograms, which relate the distances between the border points to the mass center for each object in a scene.
This work, first discusses the features to be analyzed in order to create a reliable database for a proper recognition of the objects in a scene. Then, a robust classification system is designed and finally some experiments are completed to show that the recognition system can be utilized in a real‐world operation.
The results of the experiments show that including this distance information improves significantly the final classification process.
This paper describes an object recognition scheme, where a set of histograms is included to the features vector. As is shown, the incorporation of this feature improves the robustness of the system and the recognition rate.
Arques, P., Pujol, F., Llorens, F., Pujol, M. and Rizo, R. (2007), "Applying distance histograms for robust object recognition", Kybernetes, Vol. 36 No. 1, pp. 42-51. https://doi.org/10.1108/03684920710741134Download as .RIS
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