Designing Illuminant Spectral Power Distributions for Surface Classification
Designing Illuminant Spectral Power Distributions for Surface Classification
复制标题
设计用于表面分类的光源光谱功率分布
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
B. Wandell
中科院分区：
文献类型：
--
作者：
H. Blasinski;J. Farrell;B. Wandell
在许多科学、医学和工业成像应用中，用户可以完全控制场景照明，并且颜色再现不是主要目标。例如，可以共同设计传感器和光谱照明，以便分类和检测生物组织、有机和无机材料以及对象表面属性的变化。在本文中，我们提出了两种不同的表面分类光源光谱选择方法。在监督框架中，我们描述了一个双凸优化问题，我们在优化支持向量分类器权重和优化光源之间交替进行。我们还描述了一种可用于未标记数据的稀疏主成分分析(PCA)降维方法。我们使用一种凸松弛交替方向乘子方法(ADMM)有效地解决了非凸主成分分析问题。我们将优化光源的单色成像传感器的分类精度与自然宽带照明的传统RGB相机的分类精度进行了比较。
There are many scientific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties. In this paper, we propose two different approaches to illuminant spectrum selection for surface classification. In the supervised framework we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal illuminants. We also describe a sparse Principal Component Analysis (PCA) dimensionality reduction approach that can be used with unlabeled data. We efficiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classification accuracy of a monochrome imaging sensor with optimized illuminants to the classification accuracy of conventional RGB cameras with natural broadband illumination.
DOI：
10.1364/josaa.3.000029
发表时间：
1986-01-01
影响因子：
1.9
作者：
MALONEY, LT;WANDELL, BA
通讯作者：
WANDELL, BA