Light Source Intensity Adjustment for Enhanced Feature Extraction
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.
KeywordsObject recognition neural networks feature extraction
Unable to display preview. Download preview PDF.
- 3.Horn, B.K.P.: Shape from Shading: A Method for Obtaining the Shape of a Smooth Opaque Object from One View. PhD thesis, MIT (1970)Google Scholar
- 4.Brooks, M.: Two results concerning ambiguity in shape from shading. In: AAAI-83, pp. 36–39 (1983)Google Scholar
- 8.Collewet, C.: Modeling complex luminance variations for target tracking. In: Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 1–7 (2008)Google Scholar
- 9.Moses, Y., Adini, Y., Ullman, S.: Face Recognition: the Problem of Compensating for Changes in Illumination Direction. In: Proc. European Conference on Computer Vision, pp. 286–296 (1994)Google Scholar
- 10.Liao, M., Wang, L., Yang, R.: Gong. M, Light Fall-off Stereo. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
- 12.Woodham, R.J.: Photometric method for determining surface orientation from multiple images. Optical Enginnering 19(1), 139–144 (1980)Google Scholar