生猪轮廓红外与光学图像的融合算法

An image fusion algorithm of infrared thermal and optical images for pig contour

该文针对生猪红外热图像和光学图像的融合,提出一种基于非子采样轮廓波的图像融合算法。在图像多尺度、多方向分解的基础上,设计了基于邻域平均能量和邻域方差的低频子带系数加权融合规则,以及基于邻域能量最大的带通系数融合规则。针对亮度-色度-饱和度变换法(intensity-hue-saturation transform,IHS)、小波变换法(discrete wavelet transform,DWT)、轮廓波变换法(contourlet transform,CT)等融合方法以及非子采样轮廓波变换(nonsubsampled contourlet transform,NSCT)域下不同融合规则进行了对比试验,试验结果表明该文算法具有较好的融合效果。定量融合评价指标中,平均梯度指标高于IHS、DWT、CT等方法25%以上,边缘信息保持指标高于其他3种方法23%以上。该文方法的提出对于改善生猪异常视觉监测中的前景轮廓提取具有较大意义;同时,对进一步开展猪体部位区域温度特征提取,建立生猪多源特征融合的计算机视觉异常监测系统,提高生猪异常预警可靠性具有积极意义。

Recently, given the new trends to higher efficiency and automation in livestock farming, research of livestock health monitoring through computer vision has been an active area. Our team has concentrated on pig health monitoring for some time. It was found that pig contour segmentation and feature extraction are unstable and disturbed by pig manure and uneven illumination distribution in the rough environment of a pig house. In this paper, an image fusion method based on the nonsubsampled contourlet transform （NSCT） is presented to improve the stability and accuracy of pig contour segmentation. First, the infrared thermal image and the optical image of a pig, which have been registered, are decomposed by NSCT. After that, a group of low frequency sub-band coefficients and multi-directional band-pass sub-band coefficients of each source image could be obtained. Secondly, different fusion rules for low frequency sub-band coefficients and band-pass sub-band coefficients were proposed. For the fusion of low frequency sub-band coefficients, both the factors of average energy and variance of neighbor area were considered to compute a combined value first. Then, weighted values were obtained based on it. The weighted average results of the coefficients of each image were selected as the final low frequency sub-band coefficients of fusion image. For the band-pass sub-band coefficients, the fusion coefficients were selected based on the rule of maximum energy of a neighbor area. Finally, the fusion image was obtained through inverse NSCT. In experiments, a FLIR T250 infrared thermal imager was used to acquire IR thermal image and optical image at Xima animal husbandry corporation in Zhenjiang city, Jiangsu Province. Before fusing, a pair of IR and optical experiment images with resolution of 452×339 were obtained, which are registered by using the method of contour matching of radial line feature points. Then, a group of tests were completed by using different image fusion methods, including IHS, DWT, contourlet transform and the proposed algorithm. The comparative results show that the proposed algorithm gives the better fusion effect, the average gradient value is about 25% and the quality of edge information remained about 23% higher than the other three methods. The contour segmentation results of fusion images by using Otsu method also demonstrate the good performance of the proposed algorithm. Furthermore, to contrast with different fusion rules in NSCT field, another group of tests illustrated the better segmentation result compared with the other three rules. All the experimental results demonstrated that the proposed algorithm improved the stability and accuracy of pig contour segmentation, which provides a basis for the further research of multi-senor image feature extraction for pig health monitoring.