refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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id: wiki-2026-0508-shape-feature-extraction
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title: Shape Feature Extraction
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Shape Descriptors, HOG, SIFT, Contour Features]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [computer-vision, feature-extraction, image-processing]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: OpenCV / scikit-image / PyTorch
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---
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# Shape Feature Extraction
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## 매 한 줄
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> **"매 image / object 에서 numerical descriptor 뽑기 — boundary, region, gradient"**. 매 classical (HOG, SIFT, Hu moments, Fourier descriptors) 부터 매 deep features (CNN backbone, DINOv2/v3, SAM2 mask embedding) 까지의 spectrum. 매 2026 default: deep features for recognition, classical for low-data / explainable / edge.
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## 매 핵심
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### 매 분류
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- **Boundary-based**: contour chain code, Fourier descriptors, polygon approx.
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- **Region-based**: area, perimeter, eccentricity, Hu moments (rotation/scale invariant).
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- **Gradient-based**: HOG (Dalal 2005), SIFT (Lowe 2004), SURF, ORB.
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- **Texture+shape**: LBP, GLCM.
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- **Deep**: CNN penultimate layer, ViT [CLS] token, DINOv3 patch features.
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### 매 Invariance 요구
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- Translation: 매 거의 모든 method.
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- Rotation: Hu moments, SIFT, RIFT.
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- Scale: SIFT, multi-scale CNN.
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- Illumination: HOG (gradient), normalized embeddings.
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- Affine: ASIFT.
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### 매 응용
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1. Object recognition (legacy + edge).
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2. Image retrieval / re-id (deep embeddings).
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3. OCR pre-processing (contour).
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4. Medical imaging (lesion shape descriptors).
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5. Industrial defect inspection.
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6. Robot grasp planning (object silhouette).
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## 💻 패턴
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### Contour features (OpenCV)
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```python
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import cv2, numpy as np
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gray = cv2.imread("obj.png", 0)
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_, bw = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
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contours, _ = cv2.findContours(bw, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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c = max(contours, key=cv2.contourArea)
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area = cv2.contourArea(c)
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peri = cv2.arcLength(c, True)
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circ = 4 * np.pi * area / (peri ** 2)
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hu = cv2.HuMoments(cv2.moments(c)).flatten()
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```
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### HOG
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```python
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from skimage.feature import hog
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feat, vis = hog(gray, orientations=9, pixels_per_cell=(8,8),
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cells_per_block=(2,2), visualize=True)
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```
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### SIFT (OpenCV)
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```python
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sift = cv2.SIFT_create()
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kp, desc = sift.detectAndCompute(gray, None) # desc: (N, 128)
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```
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### Fourier descriptors
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```python
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def fourier_descriptors(contour, k=20):
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pts = contour[:, 0, 0] + 1j * contour[:, 0, 1]
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fd = np.fft.fft(pts)
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fd[0] = 0 # translation invariant
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fd /= np.abs(fd[1]) # scale invariant
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return np.abs(fd[1:k+1]) # rotation invariant (magnitude)
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```
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### Deep feature (DINOv3)
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```python
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import torch
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from transformers import AutoModel, AutoImageProcessor
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proc = AutoImageProcessor.from_pretrained("facebook/dinov3-base")
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model = AutoModel.from_pretrained("facebook/dinov3-base").eval().cuda()
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inp = proc(img, return_tensors="pt").to("cuda")
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with torch.no_grad():
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feats = model(**inp).last_hidden_state # (1, N+1, D)
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cls_emb = feats[:, 0] # global shape/appearance
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```
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### SAM2 mask + descriptor pipeline
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```python
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from sam2.build_sam import build_sam2
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from sam2.sam2_image_predictor import SAM2ImagePredictor
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sam = build_sam2("sam2_hiera_l.yaml", "sam2_l.pt")
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pred = SAM2ImagePredictor(sam)
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pred.set_image(img)
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masks, _, _ = pred.predict(point_coords=pts, point_labels=lbl)
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# 매 mask 내부 영역만 dino feature 뽑기 → object-centric descriptor
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```
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### Image retrieval pipeline
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```python
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emb = []
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for p in paths:
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e = dino_embed(load(p))
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emb.append(e / e.norm())
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emb = torch.stack(emb)
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# query
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q = dino_embed(load(query))
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q /= q.norm()
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sims = (emb @ q.T).flatten()
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topk = sims.topk(10).indices
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Modern recognition / retrieval | DINOv3 / CLIP embedding |
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| Explainable / regulatory | Hu moments, contour |
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| Real-time embedded | ORB or tiny CNN |
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| Robust to occlusion | local features (SIFT/SuperPoint) |
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| Mask 필요 + descriptor | SAM2 + DINO |
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**기본값**: DINOv3 embedding for general purpose.
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## 🔗 Graph
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- 부모: [[Computer Vision|Computer-Vision]] · [[Feature-Extraction]]
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- 변형: [[HOG]] · [[SIFT]]
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- 응용: [[OCR]]
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- Adjacent: [[Image-Segmentation]] · [[CLIP]]
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## 🤖 LLM 활용
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**언제**: dataset 작거나 explainability 요구 → classical. Otherwise deep.
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**언제 X**: 매 generic image classification — end-to-end deep model 가 매 simpler.
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## ❌ 안티패턴
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- **HOG + SVM in 2026**: deep baseline 보다 명확히 약함 unless tiny data.
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- **Hand-crafted features then deep classifier**: 매 mismatch — pick one paradigm.
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- **No normalization**: scale/illumination drift → 매 retrieval 실패.
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- **SIFT 특허 우려**: 2020+ 매 expired, 그래도 license 확인.
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## 🧪 검증 / 중복
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- Verified (Lowe 2004 SIFT, Dalal 2005 HOG, OpenCV docs, DINOv3 paper).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — classical + DINOv3/SAM2 2026 |
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