24 November 2014 A simple and efficient object detection method based on saliency measure for infrared radiation image
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Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 930130 (2014) https://doi.org/10.1117/12.2073112
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China
Abstract
Detection of visually salient objects plays an important role in applications such as object segmentation, adaptive compression, object recognition, etc. A simple and computationally efficient method is presented in this paper for detecting visually salient objects in Infrared Radiation images. The proposed method can be divided into three steps. Firstly, the infrared image is pre-processed to increase the contrast between objects and background. Secondly, the spectral residual of the pre-processed image is extracted in the log spectrum, then via corresponding inverse transform and threshold segmentation we can get the rough regions of the salient objects. Finally, we apply a sliding window to acquire the explicit position of the salient objects using the probabilistic interpretation of the semi-local feature contrast which is estimated by comparing the gray level distribution of the object and the surrounding area in the original image. And as we change the size of the sliding window, different size of objects can be found out. In our proposed method, the first two steps combined together to play a role in narrowing the searching region and thus accelerating computation. The third procedure is applied to extract the salient objects. We test our method on abundant amount of Infrared Radiation images, and the results show that our saliency detection based object detection method is effective and robust.
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Zhaolei Sun, Bin Hui, "A simple and efficient object detection method based on saliency measure for infrared radiation image", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 930130 (24 November 2014); doi: 10.1117/12.2073112; https://doi.org/10.1117/12.2073112
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