# Patch and Shuffle: A Preprocessing Technique for Texture Classification in Autonomous Cementitious Fabrication

arXiv: https://arxiv.org/abs/2504.10353 · HTML: https://mlsanity.com/paper/2504.10353/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Open Source Computer Vision-based Layer-wise 3D Printing Analysis](https://mlsanity.com/paper/2003.05660/) (2020)

## Abstract (arXiv)

Autonomous fabrication systems are transforming construction and manufacturing, yet they remain vulnerable to print errors. Texture classification is a key component of computer vision systems that enable real-time monitoring and adjustment during cementitious fabrication. Traditional classification methods often rely on global image features, which can bias the model toward semantic content rather than low-level textures. In this paper, we introduce a novel preprocessing technique called "patch and shuffle," which segments input images into smaller patches, shuffles them, and reconstructs a jumbled image before classification. This transformation removes semantic context, forcing the classifier to rely on local texture features. We evaluate this approach on a dataset of extruded cement images, using a ResNet-18-based architecture. Our experiments compare the patch and shuffle method to a standard pipeline, holding all other factors constant. Results show a significant improvement in accuracy: the patch and shuffle model achieved 90.64% test accuracy versus 72.46% for the baseline. These findings suggest that disrupting global structure enhances performance in texture-based classification tasks. This method has implications for broader vision tasks where low-level features matter more than high-level semantics. The technique may improve classification in applications ranging from fabrication monitoring to medical imaging.