Open AccessLow light level image target detection based on texture saliency
Author(s) -
Jin Zhang,
Jing Han,
Yi Zhang,
Lianfa Bai
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.069501
Subject(s) - robustness (evolution) , artificial intelligence , computer science , pixel , computer vision , pattern recognition (psychology) , fractal dimension , salient , fractal , feature extraction , noise (video) , image (mathematics) , image texture , image processing , algorithm , mathematics , mathematical analysis , biochemistry , chemistry , gene
Owing to its low contrast, the target of low light level (LLL) image is not very salient, and it is difficult to detect automatically. Aimed at this problem, this paper proposes a noise robustness algorithm for computing the local texture coarseness (LTC) of textured images, and provides a texture saliency (TS) calculation method that is applicable to saliency analysis of LLL image. Firstly, we present a novel LTC algorithm, by which the LTC around a pixel using the best size of the pixel. Compared with coarseness measure based on local fractal dimension, the LTC algorithm shows much better noise robustness in the experiments of noised textured images. Then, a TS algorithm is given based on the extraction of texture coarseness feature map. Finally, we apply the TS algorithm to LLL image target detection, which is efficient proved by experimental results.