Showing papers on "Illumination problem published in 2014"

Illumination normalization of non-uniform images based on double mean filtering

Abstract: In segmentation process, non-uniform illumination problem can affect the segmentation result. In this paper, a new method is proposed to solve the problem based on double mean filtering. By applying a combination between mean and threshold value, the varying background is normalized. This proposed method had been experimented with a few badly illuminated images and the result is evaluated by using Misclassification Error (ME), Sensitivity and Specificity. Based on the ME results, proposed method increases the segmentation correction to 88.27%. Besides that, the sensitivity and specificity of proposed method obtained is 94.56190% and 98.57924% and for classical Otsu is 90.30550% and 61.85435%

Cross-band ear recognition in low or variant illumination environments

Abstract: Ear biometric is slowly gaining its position in biometric studies. Just like fingerprint and iris, the ears are unique and have other advantages over current regular biometric methods. Besides those advantages, there are some issues arising for ear recognition. One of those is regarding the illumination. Low illumination may result in low quality image acquired resulting in low recognition rate. Based on this situation, we proposed a cross-band ear recognition to overcome the variant illumination problem. This method starts by measuring the environments illumination which will determine which type of images (i.e.: thermal or visible) acquired to be processed. Once determined, the images will undergo pre-processing before the ear region is being localized using Viola-Jones approach with Haar-like feature. The ear features will be extracted using local binary patterns operator. Euclidean distance of the feature of test image and database images will be calculated. The lowest Euclidean value will determine the individual identity (intra- and inter-variance).