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"bookmarks", | ||
"outline", | ||
"word-count", | ||
"slides", | ||
"file-recovery" | ||
] |
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], | ||
"key": "T" | ||
} | ||
], | ||
"slides:start": [ | ||
{ | ||
"modifiers": [ | ||
"Alt" | ||
], | ||
"key": "F" | ||
} | ||
] | ||
} |
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--- | ||
title: 型態學影像處理介紹PPT | ||
draft: false | ||
tags: | ||
date: 2024-09-03 | ||
--- | ||
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## 型態學影像處理 | ||
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深綠色投影片作者: 台科大黃正民教授 | ||
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--- | ||
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### 型態學影像處理的基本概念 | ||
型態學的處理可以用於 | ||
簡化影像資料 | ||
保留重要的形狀特性 | ||
消除雜訊(不相關的部分) | ||
灰階影像 --> 二值化影像 --> 型態學 --> 連通元件 | ||
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![[Pasted image 20240828154900.png]] | ||
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### 膨脹處理 | ||
![[Pasted image 20240828155904.png]] | ||
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## 膨脹處理 | ||
![[Pasted image 20240828160030.png]] | ||
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## 膨脹處理 | ||
![[Pasted image 20240828160329.png]] | ||
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## 膨脹處理 | ||
滿足交換律 | ||
A ⊕ B == B ⊕ A | ||
滿足結合律 | ||
(A ⊕ B ) ⊕ C == A ⊕ (B ⊕ C) | ||
位移不變性 | ||
A ⊕ B_z == (A ⊕ B )_z | ||
應用 | ||
文字辨識 | ||
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## 侵蝕處理 | ||
![[Pasted image 20240828162410.png]] | ||
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## 侵蝕處理 | ||
![[Pasted image 20240828162531.png]] | ||
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## 斷開與閉合 | ||
斷開: 先侵蝕再膨脹 | ||
閉合: 先膨脹再侵蝕 | ||
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## 斷開與閉合 | ||
![[Pasted image 20240828164133.png]] | ||
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## 斷開與閉合 | ||
![[Pasted image 20240828164241.png]] | ||
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## 斷開與閉合 ![[Pasted image 20240828164412.png]] | ||
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## 斷開與閉合 ![[Pasted image 20240828164502.png]] | ||
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## H-M轉換 | ||
![[Pasted image 20240828165258.png]] | ||
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## H-M轉換 | ||
![[Pasted image 20240828165340.png]] | ||
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## 區塊填補 | ||
![[Pasted image 20240828190852.png]] | ||
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## 連通元件 | ||
![[Pasted image 20240828191012.png]] | ||
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## 凸殼 | ||
![[Pasted image 20240828191135.png]] | ||
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## 細線化 | ||
![[Pasted image 20240828191252.png]] | ||
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## 增厚 | ||
![[Pasted image 20240828191350.png]] | ||
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## 骨架萃取 | ||
![[Pasted image 20240828191545.png]] | ||
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## 剪除 | ||
![[Pasted image 20240828191617.png]] | ||
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## 輪胎胎紋影像檢測 | ||
![[Pasted image 20240828191911.png]] | ||
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![[Pasted image 20240828192004.png]] | ||
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![[Pasted image 20240828192049.png]] | ||
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### 車牌影像偵測 | ||
在固定環境下,車牌辨識的 成果已經非常完善 | ||
在環境較多干擾的情況·車 牌辨識的成功率會大幅降低 | ||
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![[Pasted image 20240828192339.png]] | ||
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![[Pasted image 20240828192419.png]] | ||
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![[Pasted image 20240828192503.png]] | ||
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### 車道標線影像偵測 | ||
為了彌補路面或車道標線反光、 陰影遮蔽等造成偵測的圖像破碎, 使部分車道線無法成功找到的問題 | ||
使用形態學去除雜訊干擾、填補車道線破碎的部分 | ||
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![[Pasted image 20240828192844.png]] | ||
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![[Pasted image 20240828192906.png]] | ||
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![[Pasted image 20240828193332.png]] | ||
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## **Thanks** |
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- 圖樣與圖樣類別 | ||
- 基於決策方法的辨識 | ||
- 匹配 | ||
- 最小距離分類器 | ||
- 最小距離分類器 | ||
- 最佳統計分類器 | ||
- 神經網路 |
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- 形態學平滑 | ||
- 形態學梯度 | ||
- top hat transformation | ||
- 粗糙度 | ||
- 粗糙度 | ||
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- 書本提供細節,但如果只想要快速了解方法以及流程,參考[[單元五 型態學影像處理_YT線上課程]] |
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title: | ||
draft: false | ||
tags: | ||
date: 2024-08-30 | ||
date: 2024-09-03 | ||
--- | ||
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