Segmentation of Depth-of-Field Images Based on the Response of ICA Filters

Andre CAVALCANTE  Allan Kardec BARROS  Yoshinori TAKEUCHI  Noboru OHNISHI  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.4   pp.1170-1173
Publication Date: 2012/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.1170
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
image segmentation,  depth-of-field,  kurtosis,  

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In this letter, a new approach to segment depth-of-field (DoF) images is proposed. The methodology is based on a two-stage model of visual neuron. The first stage is a retinal filtering by means of luminance normalizing non-linearity. The second stage is a V1-like filtering using filters estimated by independent component analysis (ICA). Segmented image is generated by the response activity of the neuron measured in terms of kurtosis. Results demonstrate that the model can discriminate image parts in different levels of depth-of-field. Comparison with other methodologies and limitations of the proposed methodology are also presented.