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Updated: May 28, 2025

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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SuroTex: Surrounding texture dataset.

Muhammad Ardi Putra1, Wahyono1, Agus Harjoko1

  • 1Department of Computer Science and Electronics, Universitas Gadjah Mada, Indonesia.

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Summary

A new dataset, SuroTex, addresses limitations in existing texture classification datasets. It offers 5000 images across 50 classes with uniform sizes, improving texture analysis reliability.

Keywords:
Computer VisionFeature extractionImage classificationImage processingMaterial classification

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Existing texture datasets have limitations, including a restricted number of classes and non-uniform image sizes.
  • These limitations impact the reliability of texture classification algorithms and analyses.
  • This necessitates the development of more comprehensive and standardized texture datasets.

Purpose of the Study:

  • Introduce the SuroTex (Surrounding Texture) Dataset to overcome the shortcomings of current texture datasets.
  • Provide a benchmark dataset for general and specific texture classification tasks.
  • Facilitate the development of more robust texture analysis models.

Main Methods:

  • Collected 5000 RGB image textures in Ulsan, South Korea, in November 2023.
  • Organized the dataset into 50 distinct texture classes, with 100 samples per class.
  • Ensured all images are uniformly sized at 400 × 400 pixels.

Main Results:

  • The SuroTex dataset comprises 5000 high-resolution (400 × 400 pixels) texture images.
  • The dataset is divided into 50 classes, offering a broader range for classification tasks.
  • The uniform image size enhances consistency in texture analysis.

Conclusions:

  • SuroTex provides a valuable resource for advancing texture classification and general image classification research.
  • The dataset can serve as a benchmark for evaluating new algorithms and developing pre-trained models.
  • Models trained on SuroTex can be fine-tuned for specialized applications like material classification.