vault backup: 2024-10-02 10:05:56

Foundation of data science/slides/Untitled.md

@@ -0,0 +1,12 @@
+$$f[[m,n]+[m^{\prime},n^{\prime}]]=f\left\lbrack m+m^{\prime},n+n^{\prime}\right\rbrack=f\left\lbrack m,n\right\rbrack+f\left\lbrack m^{\prime},n^{\prime}\right\rbrack
+
+
+$$
+$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[m-k,n-l]g[k,l]}+\sum_{k,l}{I[m'-k,n'-l]g[k,l]}$$
+
+$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[m-k,n-l]g[k,l] + I[m'-k,n'-l]g[k,l]}$$
+
+$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{(I[m-k,n-l] + I[m'-k,n'-l])g[k,l]}$$
+
+$$\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}=\sum_{k,l}{I[(m+m')-k,(n+n')-l]g[k,l]}$$
+

Foundation of data science/slides/notes 2.md

@@ -0,0 +1,47 @@
+#### Object recognition
+Different types of recognition
+- object identification
+- object classification
+
+##### Which level is right for Object Classes?
+- Basic-Level Categories
+
+###### Challenges
+- multi-view: different view points
+- multi-class: different types of the same object (different car models)
+- varying illumination
+- ecc
+
+### Filtering basics
+- Linear filtering
+	- Gaussian filtering
+- Multi scale image representation
+	- gaussian pyramid
+- edge detection
+	- recognition using line drawings
+	- image derivatives (1st and 2nd order)
+- object instance identification using color histograms
+- performing evaluation
+
+probabilità dadi
+$Px(5) = 1/6$
+$Py(5) = 1/6$
+$Px+y(5) = ?$
+
+We can count the possible cases
+total cases: $6*6=36$
+
+
+| 6   | 7   | 8   | 9   | 10  | 11  | 12  |
+| --- | --- | --- | --- | --- | --- | --- |
+| 5   | 6   | 7   | 8   | 9   | 10  | 11  |
+| 4   | 5   | 6   | 7   | 8   | 9   | 10  |
+| 3   | 4   | 5   | 6   | 7   | 8   | 9   |
+| 2   | 3   | 4   | 5   | 6   | 7   | 8   |
+| 1   | 2   | 3   | 4   | 5   | 6   | 7   |
+|     | 1   | 2   | 3   | 4   | 5   | 6   |
+
+possible cases: $P(3)P(1)+P(2)P(2)+P(1)P(3)$
+
+
+$P[x*y](S) = $