Keypoint Matching Technique and Its Applications

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<strong>Keypoint</strong> Detection • Harris-Laplacian corner [ICCV01] – Build Laplacian image pyramids in scale space – Compute Harris corners • Detect the local maxima ← Harris → ← Laplacian →
Rotation Invariants • To provide rotation invariant property, we compute the gradient orientation of . • Rotate the local region according to
Page 1 and 2: Keypoint Matching Technique and Its
Page 3 and 4: Invariant Local Features: SIFT •
Page 5: Interest Point Detection • Harris
Page 9 and 10: Contrast Context Histogram • The
Page 11 and 12: Experiments • Examples of images
Page 16 and 17: Experiments
Page 18 and 19: • COIL-100 Database Experiments
Page 21 and 22: Introduction • Shot change detect
Page 23 and 24: Introduction • What is a shot - A
Page 25 and 26: Matching Results
Page 27 and 28: 114 matches 106 matches 0 matches 8
Page 29 and 30: Intervals of Transitions • Find i
Page 31 and 32: Matching Nonadjacent Frames • Let
Page 33 and 34: • 7 videos Experimental Results
Page 35 and 36: Summary • A fast unified shot cha
Page 37 and 38: Application: Photo Content Analysis
Page 39 and 40: Photo Summarization
Page 41 and 42: Experimental Results
Page 43 and 44: Content-based Image Copy Detection
Page 45 and 46: Experiments
Page 47 and 48: Severity of the “Phishing” Prob
Page 49 and 50: Current Anti-phishing Solutions •
Page 51 and 52: Our Proposed Scheme Image-based Pag
Page 53 and 54: Lightweight CCH • Ignore scale an
Page 55 and 56: Page Matching with CCH • Pages of
Page 57 and 58:
Page Scoring • (Def) effective gr
Page 59 and 60:
Page Classification • Naïve Baye
Page 61 and 62:
Parameter Settings • CCH settings
Page 63 and 64:
References • Kuan-Ta Chen, Jau-Yu
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Keypoint Matching Technique and Its Applications

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