BRDF数据库的预处理与聚类方法

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Graphical Models Pub Date : 2022-01-01 DOI:10.1016/j.gmod.2021.101123

Mislene da Silva Nunes , Methanias Colaço Júnior , Gastão Florêncio Miranda Jr. , Beatriz Trinchão Andrade

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引用次数: 1

摘要

双向反射分布函数(BRDF)表示材料通过其表面的入射光。在这种情况下，材料聚类有助于选择具有代表性的brdf的基础、brdf的重建、材料外观的个性化以及基于图像的材料属性估计。目的提出了一种根据反射特征对BRDF数据库进行聚类的方法。方法首先将BRDF数据库映射到图像切片数据库进行预处理，然后通过检索低维数据库，通过实证分析找到LLE方法的最佳参数。我们使用k-means、k- medidoids和光谱聚类算法对低维数据库进行了对照实验。结论与其他聚类算法相比，k -means的总体效果最好。对于需要数据库中集群代表的应用程序，我们建议使用k-medoids，其结果接近k-means的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An Approach to Preprocess and Cluster a BRDF Database

Context

The Bidirectional Reflectance Distribution Function (BRDF) represents a material through the incoming light on its surface. In this context, material clustering contributes to selecting a basis of representative BRDFs, the reconstruction of BRDFs, the personalization of the appearance of materials, and image-based estimation of material properties.

Objective

This work presents an approach to cluster a BRDF database according to its reflectance features.

Method

We first preprocess a BRDF database by mapping it to an image slice database and then find the best parameters for the LLE method through an empirical analysis, retrieving lower-dimensional databases. We performed a controlled experiment using the k-means, k-medoids, and spectral clustering algorithms applied to the low-dimensional databases.

Conclusion

K-means presented the best overall result compared to the other clustering algorithms. For applications that require cluster representatives from the database, we suggest using k-medoids, which presented results close to those of the k-means.

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来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.