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Received：12 August 2022，
Revised：18 October 2022，
Published：16 October 2023
移动端阅览
目的
2
油画区别于其他绘画形式的重要特征之一是其颜料图层存在厚度变化。因此，油画相似度鉴别不仅关注油画的纹理色彩细节，也需考虑其表面颜料厚度的差异。针对颜料厚度的细微变化，提出一种油画相似度的表面光场特征点分布鉴别方法。采用光场成像技术观测由于油画表面厚度起伏导致的不同角度成像的差异，以量化计算油画表面光场的相似性。
方法
2
该方法采用一块微透镜阵列板对油画表面的角度域变化进行光场编码，利用光场相机采集编码后的油画表面光场。在此基础上，选取油画表面光场中角度域差异大的特征点集合，采用K-Means方法对该特征点集合的二维分布进行多边形向量化描述。进而提出计算关键点连接线的欧氏距离与线段夹角，以度量表面光场特征点分布多边形的相似性。
结果
2
采用Illum光场相机拍摄了多组真实油画的表面光场实验数据，实验结果表明本文方法可对存在细微颜料厚度差别的油画相似度进行鉴别。在对油画表面光场的识别区分度以及检测精度方面，本文方法显著优于现有图像特征匹配鉴别方法。
结论
2
实验分析表明，相比于经典交并比及相似度系数，所提出油画相似性度量具有更优的相似性度量精度。通过调整编码板与测试油画表面距离的反复实验，验证所提方法能够有效检测0.5 mm以上厚度变化的表面光场差异。
Objective
2
Painting-specific layer thickness variation of oil painting can be used to reflect the shape， depth， and texture of the painted objects. Since the layer thickness is not be replicated easily， it is beneficial to identify the similarity between two sort of oil paintings. Thanks to its layer thickness， the surface of an oil painting will theoretically appear non-Lambertian feature， i.e.， the reflectance light of a targeted point will have obvious variation at a different angle. This kind of non-Lambertian feature is beneficial for oil painting identification. However， since the thickness variation of the oil painting surface is often less than 1mm， this sort of non-Lambertian effect is illustrated as a stronger weakness and it is still challenging to be captured using a traditional camera， even though for its higher spatial resolution there. To identify the weak angular variation better， we develop a compact plenoptic camera-based acquisition system further for capturing the oil painting surface. We facilitate a surface light field of the target oil paintings using a micro-lens array， which amplifies the non-Lambertian feature. It can activate sensitive identification of the oil painting in a non-contactable measurement.
Method
2
First， a surface light field is constructed via a micro-lens array in front of the oil painting surface at a distance of one focal length of the elemental micro-lens. In this case， the non-Lambertian effect of an oil painting can be significantly amplified based on the micro-lens array. Then， a compactable light field camera is used to capture a surface light field of the painting at 0.2 m away. A captured light field is composed of multiple angular samples （i.e.， the sub-aperture images）， which can refer to the speciality of the painting surfaces. For surface thickness variation， the angular samples of the captured light field are distributed deliberately， especially for some angular variation sensitive feature points. Therefore， this kind of non-Lambertian feature can be calculated to identify oil painting similarities. A theoretical analysis is used to demonstrate the optical path design of this amplification of angular differences. After capturing the surface light fields rather than applying the feature points matching， we try to measure the similarity of oil painting surfaces using a multiple angular views-derived polygonal similarity computation. The similarity metric is proposed， and the global distribution of those large angular variational feature points is concerned about simultaneously. The feature points are detected and gathered using the K-Means clustering. The
K
central points-related polygon can represent the spatial geometrical distribution for the surface-related undulation， which can be treated as a unique pattern for a given oil painting. Compared to feature point extraction and matching based schemes， the proposed similarity metric is more robust to image noises and feature point outliers.
Result
2
In the experiments， an oil painting surface light field generation and acquisition system is built up via such multiple tools like an Illum light field camera， a micro-lens array board， a step-motor translation stage， a positioner， a motion controller， and illumination devices. The surface light fields of some real world oil paintings are generated and captured using the proposed acquisition system. The proposed solution has its potential to clarify slight differences in the thickness of these samples to some extent. It is verified that the proposed solution can be used to detect the surface light field variation through multiple experiments， and micro-lens array and the target oil painting surface-between distance adjustment， in which surface undulation is greater than 0.5 mm. The proposed surface light field similarity metric is recognized to measure the region’s geometrical shape similarity. Comparative analysis is also carried out with scale-invariant feature transform （SIFT）， light field features （LiFF）， and Fourier disparity layer-based Harris-SIFT feature （FDL-HSIFT） feature extraction and its contexts. We also analyze the similarity of the computation results in terms of a different number of the K-Means centers. The results demonstrate that a feasible parameter
K
is essential for the distinguishability of the polygonal computation-based similarity metric
.
Since the surface light field capturing system is constructed based on a micro-lens array， the back-end capturing camera can be placed in front of the micro-lens array in a limited range only.
Conclusion
2
The surface light field generation and acquisition system is illustrated and demonstrated in oil painting identification. The proposed solution of surface light field feature extraction and polygonal similarity computation can be optimized in terms of its distinguishability. Additionally， the surface light field capturing system can be further optimized through illumination optimization and more accurate fixtures-added application. It can be extended to detect the variations of other related types of non-Lambertian surfaces to a certain extent.
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