Visual Odometry Based on K-Nearest Neighbor Matching and Robust Motion Estimation

Pudchuen, Noppadol; Deelertpaiboon, Chirdpong

doi:10.1109/ieecon.2018.8712323

Cited by 3 publications

(1 citation statement)

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“…Feature matching mainly uses the K-NN (K nearest neighbor) algorithm with KD-tree as the key points index to accelerate the matching process [20]. Figure 1 presents the proposed system architecture.…”

Section: Matchermentioning

confidence: 99%

Intelligent Visual Similarity-Based Phishing Websites Detection

Chen

Huang

2020

Symmetry

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This work proposes an intelligent visual technique for detecting phishing websites. The phishing websites are classified into three categories: very similar, local similar, and non-imitating. For cases of ‘very similar’, this study uses the wavelet Hashing (wHash) mechanism with a color histogram to evaluate the similarity. In cases of ‘local similarity’, this study uses the Scale-Invariant Feature Transform (SIFT) technique to evaluate the similarity. This work concerns ‘very similar’ and ‘local similar’ cases to detect phishing websites. The results of the experiments reveal that the wHash mechanism with a color histogram is more accurate than the currently used perceptual Hashing (pHash) mechanism. The accuracies of SIFT technique are 97.93%, 98.61%, and 99.95% related to Microsoft, Dropbox, and Bank of America data, respectively.

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Section: Matchermentioning

confidence: 99%