Li Peilin, Lee Sang-Heon, Hsu Hung-Yao, Park Jae-Sam
School of Engineering, University of South Australia, Mawson Lakes 5095, Australia.
Department of Electronics Engineering, Incheon National University, 119 Academy Road, Yeon Su Gu, Incheon 22012, Korea.
Sensors (Basel). 2017 Jan 13;17(1):142. doi: 10.3390/s17010142.
The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions.
基于视觉的自动收获机械手的主要问题在于，在自然光照条件下的图像中难以正确识别水果。大多数情况下，解决方案是基于多光谱图像中颜色分量的线性组合。然而，结果并未达到令人满意的水平。为克服这一问题，本文提出一种鲁棒的非线性融合方法，用同步近红外图像增强原始彩色图像。这两幅图像采用多尺度分解方法通过Daubechies小波变换（DWT）进行融合。通过DWT，通过融合颜色分量的颜色对比度和近红外（NIR）分量的均匀性来降低背景噪声并增强必要的图像特征。对得到的融合彩色图像使用C均值算法进行分类以进行重建。与一些使用颜色分量线性组合的现有方法相比，使用统计量对所提方法的性能进行评估。结果表明，不同光谱分量中的信息融合具有提高图像质量的优势，从而提高了自然光照条件下柑橘类水果识别的分类准确率。
