HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, Singapore 639798, Singapore.
HP Security Lab, HP Inc., Bristol BS1 6NP, UK.
Sensors (Basel). 2023 Mar 22;23(6):3361. doi: 10.3390/s23063361.
Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one's focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background-foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve.
阈值化是许多计算机视觉算法的前提。通过抑制图像中的背景，可以去除不必要的信息，将注意力转移到要检查的对象上。我们提出了一种基于图像像素色度的两阶段基于直方图的背景抑制技术。该方法是无监督的，完全自动化的，不需要任何训练或真实数据。使用印刷电路板组件 (PCA) 板数据集和滑铁卢大学皮肤癌数据集评估了所提出方法的性能。在 PCA 板上准确执行背景抑制有助于检查具有小目标的数字图像，例如 PCA 板上的文本或微控制器。皮肤癌病变的分割将帮助医生实现皮肤癌的自动检测。结果表明，在不同的相机或照明条件下，各种样本图像的背景-前景分离清晰而稳健，而现有的最先进的阈值化方法的裸实现无法实现这一点。
