A novel multisource pig-body multifeature fusion method based on Gabor features

Abstract

The multi-source image fusion has been a hot topic during recent years because of its higher segmentation accuracy rate. However, the traditional multi-source image fusion methods could not obtain better contrast and more details of the fused image. To better detect the pig-body feature, a novel infrared and visible image fusion method for pig-body segmentation and temperature detection is proposed in non-subsampled contourlet transform (NSCT) domain, named as NSCT-GF. Firstly, the visible and infrared images were decomposed into a series of multi-scale and multi-directional sub-bands using NSCT. Then, to better represent the fine-scale of texture information, the Gabor energy map was extracted by Gabor filter with even-symmetry, and the low-frequency coefficients were fused by the maximum of Ordinal encoding. Then, to preserve the more coarse-scale and edge detail information, Gabor filter with odd-symmetry was employed to fuse high-frequency NSCT sub-bands and the fused coefficients were reconstructed into a final fusion image by inverse NSCT. Next, the pig-body shape was obtained by Ostu automatic threshold segmentation and optimized by morphological processing. Finally, the pig-body temperature was extracted based on shape segmentation. Experimental results showed that the proposed segmentation method was capable of achieving 1.84–3.89% higher average segmentation accuracy rate than the prevailing conventional methods and also improved efficiency in terms of time consumption. It lays a foundation for accurately measuring the temperature of pig-body.