# genpark-image-connected-components-labeling-skill

> genpark-image-connected-components-labeling-skill — alphaparkinc-genpark-image-connected-components-labeling-skill. Use this tool when you need to extract distinct segments from binary masks in images, as it solves problems in image processing and object detection by taking binary image data as input and outputting labeled connected components. It utilizes a two-pass connected-component labeling approach with disjoint-set Union-Find for efficient segment extraction. This skill is particularly useful in applications requiring image segmentation, such as object recognition and scene understanding.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-image-connected-components-labeling-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-image-connected-components-labeling-skill

## Description

Two-pass connected-component labeling (CCL) with disjoint-set Union-Find for binary mask segment extraction.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/alphaparkinc/genpark-image-connected-components-labeling-skill)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "alphaparkinc-genpark-image-connected-components-labeling-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-image-connected-components-labeling-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-image-connected-components-labeling-skill/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt