Task: Exploratory Data Analysis on Image Data

SUMMARY

In textile manufacturing, analyzing fabric and garment inspection images is crucial for ensuring product quality. But how can we identify potential issues such as fabric defects, stitching irregularities, or color inconsistencies before moving on to complex models?

In this hands-on lesson, you’ll learn how to perform exploratory data analysis (EDA) on textile industry image data. You’ll explore common problems in fabric and garment images, such as misalignment, low resolution, lighting variations, and distortions, and apply the appropriate EDA techniques to detect these issues.

You’ll also gain experience with Python, where you will implement code to visualize and summarize textile image datasets. This will help you assess image quality and prepare your data for more advanced analysis or machine learning models. By the end of the lesson, you will be able to efficiently analyze textile industry image data and ensure that it is ready for accurate quality assessment and anomaly detection.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using Internet of Things (IoT) and Machine Learning (ML) techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.