Novel algorithms for image classification and visualisation of multispectral datasets

用于图像分类和多光谱数据集可视化的新算法

基本信息

批准号：
516103-2017
负责人：
Braidy, Nadi
金额：
$ 1.82万
依托单位：
Université de Sherbrooke
依托单位国家：
加拿大
项目类别：
Engage Grants Program
财政年份：
2017
资助国家：
加拿大
起止时间：
2017-01-01 至 2018-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639630
关键词：
Novel algorithms image classification visualisation

项目摘要

The recent technological improvements in microscopy now allows the collection of extensive information ofsamples at the nanoscale. However, in many cases, the amount of data collected surpasses the currentvisualization capacity making their interpretation and analysis impractical. While two and three physicaldimensions visualization is quite intuitive, the interpretation of datasets with a higher number of dimensionssuch as with chemical tomography, is less intuitive. Nevertheless, the information provided by extradimensions is essential when attempting to uncover similarities or correlations essential for materialsdevelopment (e.g. distinguish phases in a material, identify and map impurities).The research group of Pr. Nadi Braidy have been successful in developing a classification algorithm to simplifythe interpretation of multi-spectral datasets. However, the efficiency of the algorithm and the code is notadapted to large datasets (i.e. several giga/terabytes). The goal of this project is to develop and implementscientific and software improvements to the existing algorithm to improve its computational efficiency. Therevamped algorithm will be implemented into Object Research System (ORS) Inc. visualization software,Dragonfly.In the framework of this Engage, the collaboration between Pr. Braidy and ORS will provide ORS with a toolto handle multi-modal datasets in its software while providing support in improving its efficiency.

现在，显微镜的技术改进现在允许在纳米级收集广泛的样本信息。但是，在许多情况下，收集的数据量超过了当前的视力，使其解释和分析不切实际。虽然两个和三个物理维度的可视化非常直观，但具有更高数量的尺寸的数据集（与化学断层扫描一样）的解释较不直观。然而，当试图发现材料开发必不可少的相似性或相关性时，引渡提供的信息至关重要（例如，区分材料中的相位，识别和映射杂质）。 Nadi Braidy已经成功地开发了一种分类算法，以简化对多光谱数据集的解释。但是，将算法和代码的效率记录到大型数据集（即几个GIGA/TERABYTES）。该项目的目的是开发和实现现有算法的科学和软件改进，以提高其计算效率。 vamped算法将被实施到对象研究系统（ORS）Inc.可视化软件，蜻蜓。 Braidy和ORS将为ORS提供工具，以处理其软件中的多模式数据集，同时为提高其效率提供支持。