论文标题
使用2-D CEPSTRAL特征提取和多类分类方法的医学带识别
Medicine Strip Identification using 2-D Cepstral Feature Extraction and Multiclass Classification Methods
论文作者
论文摘要
医学的错误分类对患者的健康危险，如果该患者在视觉上受损或根本无法识别出医药条的颜色，形状或类型，则更是如此。本文提出了一种通过2-D Cepstral分析其图像来识别医学条的方法，然后进行分类，该分类使用了使用K-Near-neign（KNN），支持向量机（SVM）和Logistic Remistion（LR）分类器进行的分类。提取的二维cepstral特征与药物条完全不同，因此使它们异常准确。本文还提出了颜色梯度和药丸形状特征（CGPF）提取程序，并讨论了二进制稳健不变的可扩展关键点（Brisk）算法。实施了上述算法，并比较了其识别结果。
Misclassification of medicine is perilous to the health of a patient, more so if the said patient is visually impaired or simply did not recognize the color, shape or type of medicine strip. This paper proposes a method for identification of medicine strips by 2-D cepstral analysis of their images followed by performing classification that has been done using the K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Logistic Regression (LR) Classifiers. The 2-D cepstral features extracted are extremely distinct to a medicine strip and consequently make identifying them exceptionally accurate. This paper also proposes the Color Gradient and Pill shape Feature (CGPF) extraction procedure and discusses the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm as well. The mentioned algorithms were implemented and their identification results have been compared.
