TY - GEN
T1 - Contrast and distribution based saliency detection in infrared images
AU - Li, Lu
AU - Zheng, Yu
AU - Zhou, Fugen
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/30
Y1 - 2015/11/30
N2 - Saliency-based approaches has been well studied and successfully used in object detection for visible images. However, few researches have been done for saliency detection in infrared images, which are characterized with low resolution, SNR and contrast, fuzzy edge and lack of color features. In this paper, a contrast and distribution based saliency detection approach is proposed for infrared images. First, we develop an enhanced multi-scale saliency feature by improving the quality and contrast of the image in frequency domain. Second, luminance-distribution and gradient feature are explored to highlight the object with great gradient and compact distribution. Finally, by integrating the above two features, the final saliency map for infrared image were obtained. Experimental results on real infrared images demonstrate the effectiveness of the proposed approach against the state-of-the-art algorithms.
AB - Saliency-based approaches has been well studied and successfully used in object detection for visible images. However, few researches have been done for saliency detection in infrared images, which are characterized with low resolution, SNR and contrast, fuzzy edge and lack of color features. In this paper, a contrast and distribution based saliency detection approach is proposed for infrared images. First, we develop an enhanced multi-scale saliency feature by improving the quality and contrast of the image in frequency domain. Second, luminance-distribution and gradient feature are explored to highlight the object with great gradient and compact distribution. Finally, by integrating the above two features, the final saliency map for infrared image were obtained. Experimental results on real infrared images demonstrate the effectiveness of the proposed approach against the state-of-the-art algorithms.
KW - Detection algorithms
KW - Feature extraction
KW - Image color analysis
KW - Image edge detection
KW - Image resolution
KW - Object detection
KW - Visualization
UR - https://www.scopus.com/pages/publications/84960456221
U2 - 10.1109/MMSP.2015.7340825
DO - 10.1109/MMSP.2015.7340825
M3 - 会议稿件
AN - SCOPUS:84960456221
T3 - 2015 IEEE 17th International Workshop on Multimedia Signal Processing, MMSP 2015
BT - 2015 IEEE 17th International Workshop on Multimedia Signal Processing, MMSP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Workshop on Multimedia Signal Processing, MMSP 2015
Y2 - 19 October 2015 through 21 October 2015
ER -