Synthetic Datasets for ICSC Flagship 2.6.1. "Extended Computer Vision at high rate" paper #1 "Datacube segmentation via Deep Spectral Clustering"

Synthetic Datasets for ICSC Flagship 2.6.1. "Fast Extended Computer Vision" paper #1 "Datacube segmentation via Deep Spectral Clustering" It is a preliminary paper for the ICSC Spoke 2 WP6 flagship 2.6.1 "Fast Extended Computer Vision". Code repository at: https://github.com/ICSC-Spoke2-repo/FastExtendedVision-DeepCluster Abstract: Extended Vision techniques are a ubiquitous in physics. However, the data cubes steaming from such analysis often pose a challenge in their interpretation, due to the intrinsic difficulty in discerning the relevant information from the spectra composing the data cube.<br> Furthermore, the huge dimensionality of data cube spectra poses a complex task in its statistical interpretation; nevertheless, this complexity contains a massive amount of statistical information that can be exploited in an unsupervised manner to outiline some essential properties of the case study at hand, e.g.~it is possible to obtain an image segmentation via (deep) clustering of data-cube's spectra, performed in a suitably defined low-dimensional embedding space.<br> To tackle this topic, we explore the possibility of applying unsupervised clustering methods in encoded space, i.e.~perform deep clustering on the spectral properties of datacube pixels. A statistical dimensional reduction is performed by an ad hoc trained AutoEncoder, in charge of mapping spectra into lower dimensional metric spaces, while the clustering process is performed by an iterative K-Means clustering algorithm.<br> We apply this technique on two different use cases, of different physical origin: a set of MA-XRF data on pictorial artworks, and a synthetic dataset of simualted astrophysical observations.ICSC旗舰项目2.6.1《快速扩展计算机视觉》第一篇论文《基于深度谱聚类的数据立方体分割》所用合成数据集 本论文为ICSC Spoke 2 WP6旗舰项目2.6.1《快速扩展计算机视觉》的预印研究论文。 代码仓库地址：https://github.com/ICSC-Spoke2-repo/FastExtendedVision-DeepCluster 摘要： 扩展视觉技术在物理学中应用广泛。然而，此类分析所生成的数据立方体往往难以解读，这是因为从构成数据立方体的光谱中甄别有效信息存在固有难度。 此外，数据立方体光谱的超高维度为其统计解读带来了复杂挑战；但这类高复杂度数据中蕴含着海量统计信息，可通过无监督方式加以利用，以勾勒当前研究案例的核心特征。例如，可在合理定义的低维嵌入空间中，通过对数据立方体光谱实施（深度）聚类来实现图像分割。 为解决这一问题，本文探索了在编码空间中应用无监督聚类方法的可行性，即针对数据立方体像素的光谱特征开展深度聚类。研究通过专门训练的自编码器（AutoEncoder）完成统计降维，将光谱映射至低维度量空间；聚类过程则采用迭代K-Means聚类算法实现。 本文将该技术应用于两个不同物理起源的场景：一组针对绘画艺术品的MA-XRF数据，以及一套模拟天体物理观测的合成数据集。