Light Source Intensity Adjustment for Enhanced Feature Extraction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)


We explore the automatic adjustment of an artificial light source intensity for the purposes of image-based feature extraction and recognition. Two histogram-based criteria are proposed to achieve this adjustment: a two-class separation measure for 2D features and a Gaussian distribution measure for 2.5D features. To this end, the light source intensity is varied within a fixed interval as a camera captures one image for each intensity variation. The image that best satisfies the criteria for feature extraction is tested on a neural-network based recognition system. The network considers information related to both 2D (contour) and 2.5D shape (local surface curvature) of different objects. Experimental tests performed during different times of the day confirm that the proposed adjustment delivers improved feature extraction, extending the recognition capabilities of the system and adding robustness against changes in ambient light.


Object recognition neural networks feature extraction 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Robotics and Advanced Manufacturing GroupCentro de Investigación y de Estudios Avanzados del I.P.N.Ramos ArizpeMéxico

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