Textiles are mainly used for decoration and protection. However, further research must be conducted for blurring correction since the scenes at night are commonly blurred due to artificial lighting. The extracted features can be used in a further step to determine patterns, identify objects or detect background. Our approach is tested on videos of four different urban scenes with mobilities captured during day and night. We present a CUDA implementation of our proposed algorithm. In this paper we compute the adaptive threshold using the parameters of the GGD. The intensity residuals distribution can be modelled by a Generalized Gaussian Distribution (GGD). We propose to compute an automatic threshold based on the distribution of the intensity residuals resulting from the pairwise comparisons when using LBP techniques. Currently, Local Binary Pattern (LBP) techniques are widely used for extracting features due to their robustness to illumination changes and the low requirements for implementation. Furthermore, robust algorithms for outdoor visual scenes may retrieve correspondent features along the day where a challenge is to deal with lighting changes. Video analysis in real time requires fast and efficient algorithms to extract relevant information from a considerable number, commonly 25, of frames per second.
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