Simple library coded in TI Basic to help student with the course of Digital Image Processing.
- Conv2d(m, k)
- conv1d(f, h)
- bilinear(x,y,m,v)
- nearestneighbor(x,y,m,v)
- covariance_vec(x,y,b,v)
- covariancemat(x,b,v)
- erode(m,k)
- dilation(m,k)
- open(m,k)
- close(m,k)
- hitormiss(m,b)
- white_top_hat(m,b,v)
- black_top_hat(m,b,v)
- dft(x)
- idft(x)
- dft_2d(m)
- idft_2d(m)
- bayes_osc(x1,x2,a)
- morpho_grad_in(m,b)
- morpho_grad_out(m,b)
- morpho_grad(m,b)
- TI-nspire CX II-T CAS
- Conv2D(m,k)
- m: matrix
- k: kernel
- Conv1D(f,h)
- f: vector 1
- k: vector 2
- bilinear(x,y,m,v)
- x: x coordinate
- y: y coordinate
- m: matrix
- v: verbose mode
- nearestneighbor(x,y,m)
- x: x coordinate
- y: y coordinate
- m: matrix
- covariance_vec(x,y,b,v)
- x: vector x
- y: vector y
- b: bias (0 = no bias / N-1 | 1 = bias / N)
- v: verbose mode
- covariancemat(x,b,v)
- x: matrix
- b: bias (0 = no bias / N-1 | 1 = bias / N)
- v: verbose mode
- Morphological Transformation
- erode(m,k)
- dilation(m,k)
- open(m,k)
- close(m,k)
- white_top_hat(m,b,v)
- black_top_hat(m,b,v)
- morpho_grad_in(m,b)
- morpho_grad_out(m,b)
- morpho_grad(m,b)
- m: matrix
- k-b: structucual matrix (put 1 where you need)
- v: verbose mode
- hitormiss(m,b,v)
- m: matrix
- b: structural element (must be 0,1, or infinite for dont care)
- v: verbose mode
- Fourier
- dft(x)
- idft(x)
- dft_2d(m)
- idft_2d(m)
- x: vector line
- m: matrix
- Pattern Classification
- bayes_osc(x1,x2,a,b) -> bayes Optimal statistical classifier
- x1: classe1
- x2: classe2
- a: formula to use, page 926
- b: bias
- bayes_osc(x1,x2,a,b) -> bayes Optimal statistical classifier
in TI BASIC the index of the matrix start from 1, not from 0.