Low level saliency feature extraction method based on Hessian threshold

The local invariant feature extraction algorithm SRUF (Speeded Up Robust Features) is introduced firstly. Then the new method of finding low level visual saliency feature based on SURF is deduced. The new method pay attention to Hessian matrix threshold and extract image features through changing the Hessian threshold. The number of saliency feature points change with the change of Hessian threshold. The visual saliency feature points will become sparser when Hessian threshold becomes larger. When some certain extreme thresholds which are defined as Hessian threshold Nodes are reached, the retained feature points are remarkable discriminative and stable feature points which make up the best sparse saliency features set. The feature extraction, matching and object recognition experiments of robot vision are finished to verify the new method. Experiment results show that the method is very effective.