vault backup: 2025-01-14 01:48:39
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2 changed files with 16 additions and 15 deletions
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.obsidian/workspace.json
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"Biometric Systems/notes/11. Fingerprints.md",
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"Biometric Systems/slides/LEZIONE11_Fingerprints.pdf",
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"Biometric Systems/slides/LEZIONE12_MULBIOMETRIC.pdf",
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"Biometric Systems/frequently asked questions/BS_oral_questions_16022021.md",
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"Biometric Systems/frequently asked questions/BS_oral_questions_16022021.md",
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"Biometric Systems/slides/Riassunto_2021_2022.pdf",
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"Biometric Systems/slides/Biometric_System___Notes.pdf",
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"Biometric Systems/slides/LEZIONE11_Fingerprints.pdf",
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"Biometric Systems/notes/11. Fingerprints.md",
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"Biometric Systems/slides/LEZIONE12_MULBIOMETRIC.pdf",
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"Biometric Systems/notes/13. Multi biometric.md",
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"Biometric Systems/notes/13. Multi biometric.md",
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"Biometric Systems/notes/9. Ear recognition.md",
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"Biometric Systems/notes/9. Ear recognition.md",
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"Biometric Systems/slides/LEZIONE3_Affidabilita_del_riconoscimento.pdf",
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"Biometric Systems/slides/LEZIONE3_Affidabilita_del_riconoscimento.pdf",
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@ -245,8 +246,6 @@
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"Biometric Systems/notes/8 Face anti spoofing.md",
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"Biometric Systems/notes/8 Face anti spoofing.md",
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"Autonomous Networking/notes/q&a.md",
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"Autonomous Networking/notes/q&a.md",
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"Biometric Systems/slides/LEZIONE2_Indici_di_prestazione.pdf",
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"Biometric Systems/slides/LEZIONE2_Indici_di_prestazione.pdf",
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"Biometric Systems/slides/LEZIONE7_Face recognition3D.pdf",
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"Foundation of data science/slides/More on Neural Networks (1).pdf",
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"Foundation of data science/notes/9 XGBoost.md",
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"Foundation of data science/notes/9 XGBoost.md",
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"Foundation of data science/notes/Untitled.md",
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"Foundation of data science/notes/Untitled.md",
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"Foundation of data science/notes/9 Gradient Boosting.md",
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"Foundation of data science/notes/9 Gradient Boosting.md",
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@ -101,6 +101,8 @@ Once a fingerprint has been segmentated we can start extracting **macro-features
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##### Directional map
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##### Directional map
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~~The local orientation of the ridge line in the position [i, j] is defined as the angle $\theta(i,j)$ formed by considering the horizontal line at that point, and a point on the ridge line sufficiently closer to [i, j].~~
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~~The local orientation of the ridge line in the position [i, j] is defined as the angle $\theta(i,j)$ formed by considering the horizontal line at that point, and a point on the ridge line sufficiently closer to [i, j].~~
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Definizione: matrice in cui la cella [i, j] rappresenta l'angolo che la tangente alla ridge line forma con l'asse orizzontale.
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![[Pasted image 20241127224325.png]]
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![[Pasted image 20241127224325.png]]
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- most approaches use a grid measure (instead of measuring at each point)
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- most approaches use a grid measure (instead of measuring at each point)
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- the directional image D is a matrix in which each element denotes the average orientation of the ridge (of the tangent to the ridge) in a neighborhood of $[x_{i}, y_{i}]$
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- the directional image D is a matrix in which each element denotes the average orientation of the ridge (of the tangent to the ridge) in a neighborhood of $[x_{i}, y_{i}]$
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