vault backup: 2024-10-02 10:05:56
This commit is contained in:
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0c6db8f9f4
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9 changed files with 16442 additions and 57 deletions
3
.obsidian/community-plugins.json
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3
.obsidian/community-plugins.json
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@ -1,5 +1,6 @@
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[
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"obsidian-ocr",
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"pdf-plus",
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"obsidian-git"
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"obsidian-git",
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"mathlive-in-editor-mode"
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]
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9
.obsidian/plugins/mathlive-in-editor-mode/data.json
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9
.obsidian/plugins/mathlive-in-editor-mode/data.json
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@ -0,0 +1,9 @@
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{
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16116
.obsidian/plugins/mathlive-in-editor-mode/main.js
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16116
.obsidian/plugins/mathlive-in-editor-mode/main.js
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10
.obsidian/plugins/mathlive-in-editor-mode/manifest.json
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10
.obsidian/plugins/mathlive-in-editor-mode/manifest.json
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{
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"description": "MathLive input in editor mode",
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"author": "MizarZh",
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"authorUrl": "https://github.com/MizarZh",
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"isDesktopOnly": false
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203
.obsidian/plugins/mathlive-in-editor-mode/styles.css
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203
.obsidian/plugins/mathlive-in-editor-mode/styles.css
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99
.obsidian/workspace.json
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99
.obsidian/workspace.json
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@ -1,40 +1,21 @@
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"mode": "source",
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@ -44,15 +25,15 @@
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"direction": "vertical"
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@ -62,7 +43,7 @@
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@ -77,7 +58,7 @@
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@ -91,19 +72,20 @@
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@ -115,18 +97,19 @@
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@ -137,15 +120,17 @@
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@ -157,40 +142,42 @@
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BIN
Foundation of data science/slides/FDS_intro_new.pdf
Normal file
BIN
Foundation of data science/slides/FDS_intro_new.pdf
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Binary file not shown.
12
Foundation of data science/slides/Untitled.md
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12
Foundation of data science/slides/Untitled.md
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$$f[[m,n]+[m^{\prime},n^{\prime}]]=f\left\lbrack m+m^{\prime},n+n^{\prime}\right\rbrack=f\left\lbrack m,n\right\rbrack+f\left\lbrack m^{\prime},n^{\prime}\right\rbrack
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$$
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$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[m-k,n-l]g[k,l]}+\sum_{k,l}{I[m'-k,n'-l]g[k,l]}$$
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$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[m-k,n-l]g[k,l] + I[m'-k,n'-l]g[k,l]}$$
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$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{(I[m-k,n-l] + I[m'-k,n'-l])g[k,l]}$$
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$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}$$
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47
Foundation of data science/slides/notes 2.md
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47
Foundation of data science/slides/notes 2.md
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#### Object recognition
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Different types of recognition
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- object identification
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- object classification
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##### Which level is right for Object Classes?
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- Basic-Level Categories
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###### Challenges
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- multi-view: different view points
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- multi-class: different types of the same object (different car models)
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- varying illumination
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- ecc
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### Filtering basics
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- Linear filtering
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- Gaussian filtering
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- Multi scale image representation
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- gaussian pyramid
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- edge detection
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- recognition using line drawings
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- image derivatives (1st and 2nd order)
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- object instance identification using color histograms
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- performing evaluation
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probabilità dadi
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$Px(5) = 1/6$
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$Py(5) = 1/6$
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$Px+y(5) = ?$
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We can count the possible cases
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total cases: $6*6=36$
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| 6 | 7 | 8 | 9 | 10 | 11 | 12 |
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| --- | --- | --- | --- | --- | --- | --- |
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| 5 | 6 | 7 | 8 | 9 | 10 | 11 |
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| 4 | 5 | 6 | 7 | 8 | 9 | 10 |
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| 3 | 4 | 5 | 6 | 7 | 8 | 9 |
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| 2 | 3 | 4 | 5 | 6 | 7 | 8 |
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| 1 | 2 | 3 | 4 | 5 | 6 | 7 |
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| | 1 | 2 | 3 | 4 | 5 | 6 |
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possible cases: $P(3)P(1)+P(2)P(2)+P(1)P(3)$
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$P[x*y](S) = $
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