Keypoint Matching Technique and Its Applications

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<strong>Matching</strong> criterion • <strong>Matching</strong> criterion – Find the nearest neighbor with the minimum Euclidean distance d1 – Find the second nearest neighbor with Euclidean distance d2 – If d1 < 0.6d2, matched
Experiments • Examples of images used for the evaluation The data set is available at http://www.robots.ox.ac.uk/~vgg/research/affine
Page 1 and 2: Keypoint Matching Technique and Its
Page 3 and 4: Invariant Local Features: SIFT •
Page 5 and 6: Interest Point Detection • Harris
Page 7 and 8: Rotation Invariants • To provide
Page 9: Contrast Context Histogram • The
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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