Interactive segmentation

User Manual To initiate the program, read the readme.md and follow these steps: 1. Create a folder named "**Workspace_data**". 2. Place your input file named "**input.npy**" inside this folder. 3. Create an empty folder named "**Raw_Figs**". 4. Run "**main.py**". **Object Creation** The "**Object_creation**" file contains functions dedicated to generating an object set. Initially, the target objects are identified, cropped, and organized into an object set. This set includes various shape features extracted for each individual object. Additionally, you may need to develop your own function for detecting objects tailored to your specific dataset. **Feature Space Clustering** Within the "**feature_space_clustering**" file, functions are designed to cluster objects based on their shapes into different groups within the feature space. Notably, the number of clusters is automatically determined during the clustering process. **Segmentation** The "**Segmentation.py**" file includes all functions used for segmentation purposes. If desired, additional segmentation methods can be easily incorporated to evaluate their effectiveness on the objects under study.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-30
DOI
https://doi.org/10.5281/zenodo.5886947
Primary Topic
Advanced Clustering Algorithms Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Interactive segmentation

Zenodo (CERN European Organization for Nuclear Research)
Advanced Clustering Algorithms Research
article

Interactive segmentation

article en

Abstract

User Manual To initiate the program, read the readme.md and follow these steps: 1. Create a folder named "**Workspace_data**". 2. Place your input file named "**input.npy**" inside this folder. 3. Create an empty folder named "**Raw_Figs**". 4. Run "**main.py**". **Object Creation** The "**Object_creation**" file contains functions dedicated to generating an object set. Initially, the target objects are identified, cropped, and organized into an object set. This set includes various shape features extracted for each individual object. Additionally, you may need to develop your own function for detecting objects tailored to your specific dataset. **Feature Space Clustering** Within the "**feature_space_clustering**" file, functions are designed to cluster objects based on their shapes into different groups within the feature space. Notably, the number of clusters is automatically determined during the clustering process. **Segmentation** The "**Segmentation.py**" file includes all functions used for segmentation purposes. If desired, additional segmentation methods can be easily incorporated to evaluate their effectiveness on the objects under study.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 61%
Advanced Clustering Algorithms Research
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Interactive segmentation · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS