vault backup: 2025-01-12 21:43:10
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3 changed files with 63 additions and 24 deletions
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.obsidian/workspace.json
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@ -238,10 +280,6 @@
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"Foundation of data science/notes/3.2 LLM generated from notes.md",
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"Foundation of data science/notes/4 L1 and L2 normalization - Lasso and Ridge.md",
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"Foundation of data science/notes/3.1 Multi Class Logistic Regression.md",
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"Foundation of data science/notes/3 Logistic Regression.md",
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@ -113,7 +113,7 @@ The local orientation of the ridge line in the position [i, j] is defined as the
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##### Frequency map
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the frequency of the local ridge line $f_{xy}$ at the point $[x, y]$ is defined as the number of ridges per unit length along a hypothetical segment centered at $[x, y]$ and orthogonal to the orientation of the local ridge.
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- by estimating the frequency in discrete locations arranged in a grid, we can compute a frequency image F:![[Pasted image 20241127225853.png]]
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- a possible approach is to count the averaage number of pixels between consecutive peaks of gray levels along the direction orthogonal to the local orientation of the ridge line
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- a possible approach is to count the average number of pixels between consecutive peaks of gray levels along the direction orthogonal to the local orientation of the ridge line
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##### Singularities
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Most of approaches are based on directional map.
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@ -161,7 +161,7 @@ Hybrid method based on comparison of minutiae texture: combines the representati
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- rotation parameter is the average of rotation of all the individual pairs of corresponding minutiae
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- translation parameters are calculable using spatial coordinates of the pair of reference minutiae which produced the best alignment
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##### Masking and tesselation
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##### Masking and tessellation
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After masking the background, the images are normalized by building on them a grid that divides them into series of non-overlapping windows of the same size. Each window is normalized with reference to a constant mean and variance. Optimal size for 300 DPI is 30x30, as 30 pixel is the average distance inter-solco.
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![[Pasted image 20241128000431.png]]
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@ -96,7 +96,8 @@ si possono usare i landmark
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2. allineando facce minimizzando la distanza tra punti corrispondenti
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##### Fine
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Algoritmo ICP
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Algoritmo ICP (Iterative Closest Point)
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https://www.youtube.com/watch?v=QWDM4cFdKrE
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Date due superfici 3D
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1. trova un iniziale match tra le due (mapping di punti/superfici/linee/curve)
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2. calcola la distanza tra le superfici con il metodo least squares
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