One complete chapter of my thesis is devoted to explaining how the variational optical flow, disparity and level-set models can be solved. In this chapter I show, step by step, how these models can be solved using different kinds of solvers (Jacobi, Gauss Seidel, Alternating Line Relaxation and Additive Operator Splitting scheme). Related to this chapter, I release the optical flow and disparity codes on this page...these codes are quite similar to the ones that I have used for generating the videos on this site. However, they are not 100% the same...there have been some slight modifications. For anyone interested in the actual code, I suggest first to have a look at the thesis in order to better understand the code itself! The codes are released under LGPL license. If you use the codes, please reference my papers...in my papers, on the other hand, I reference those papers upon which my work is based on. Thanx!!


The optical flow codes are as follows:

  1. Late linerisation optical-flow for large displacements.
  2. Early linearisation optical-flow method for small displacements (basically Horn&Schunck type of formulation).
  3. Late linearisation method for disparity.

 

Pseudo Code

Below you can see pseudo codes for early- and late-linearization versions of the optical flow. I decided to include these here in order to give a better idea how the different codes actually work. However, all the details are explained in my phd thesis. Therefore, I recommend that you have a look at Chapter 5, Solving Equations.

Early Linearization

 

  • //----------------------------------------------------------------------------------------------------------------
  • //-COARSE-TO-FINE ALGORITHM FOR CALCULATING OPTICAL FLOW
  • //-Early linearisation (i.e. no warping)
  • //-Quadratic smoothness term (i.e. Tikhonov regulariser)
  • //-Inputs are \(I_0\) and \(I_1\), number of scales \(scl\), and scaling factor \(sclFactor\)
  • //----------------------------------------------------------------------------------------------------------------
  • \(\mathbf{INPUT:} \, I_0, \, I_1, \, scl, \, sclFactor \)
  • \(\mathbf{OUTPUT:} \, (u,\, v) \)
  • //Set \(u\) and \(v\) to zero
  • \(u=0\), \(v=0\)
  • //Create image pyramid
  • \([ Iscl_0\{\}, \, Iscl_1\{\} ] = pyramid(I_0, \, I_1, \, scl, \, sclFactor)\)
  • //This is the coarse-to-fine loop
  • WHILE( \(s=scl:-1:1\) )
    • \(I_0 = Iscl_0\{s\}, \, I_1 = Iscl_1\{s\} \)
    • Approximate derivatives for \(I_0\) and \(I_1\): \(\dfrac{\partial I_k}{\partial t}\), \(\dfrac{\partial I_{k,0}}{\partial x}\), \(\dfrac{\partial I_{k,0}}{\partial y} \)
    • //Solve for new \(u\) and \(v\)
    • \([u, \, v] = SOLVER ( u, \, v, \, nLoops, \, \dfrac{\partial I_k}{\partial t}, \, \dfrac{\partial I_{k,0}}{\partial x}, \, \dfrac{\partial I_{k,0}}{\partial y} )\)
    • //Interpolate (prolongate) solution
    • IF ( \(s-1>0\) )
      • \([u, \, v] = prolongate( u, \, v, \,sclFactor )\)
    • ENDIF
  • ENDWHILE

 

Late Linearization 

 

  • //----------------------------------------------------------------------------------------------------------------
  • //-COARSE-TO-FINE ALGORITHM FOR CALCULATING OPTICAL FLOW
  • //-Late linearisation (i.e. uses warping)
  • //-Robust error functions in both the data and the smoothness terms
  • //-Inputs are \(I_0\) and \(I_1\), number of scales \(scl\), and scaling factor \(sclFactor\)
  • //----------------------------------------------------------------------------------------------------------------
  • \(\mathbf{INPUT:} \, I_0, \, I_1, \, scl, \, sclFactor \)
  • \(\mathbf{OUTPUT:} \, (u,\, v) \)
  • //Set \(u \) and \(v \) to zero
  • \(u=0 \), \(v=0; \)
  • //Create image pyramid
  • \([ Iscl_0\{\} \, Iscl_1\{\} ] = pyramid(I_0, \, I_1, \, scl, \, sclFactor); \)
  • //Coarse-to-fine loop
  • WHILE( \(s=scl:-1:1 \) )
    • \(I_0 =Iscl_0\{s\} \), \(I_1 =Iscl_1\{s\}; \)
    • //Warping loop
    • WHILE( \( fstLoop \))
      • //Warp image as per \(u \) and \(v \)
      • \(I_{k,0}^w = warp( I_{k,0}, u, v ); \)
      • Approximate derivatives for \(I_0^w \) and \(I_1 \): \(\dfrac{\partial I_k}{\partial t} = I_{k,1} - I_{k,0}^w \), \(\dfrac{\partial I_{k,0}^w}{\partial x} \), \(\dfrac{\partial I_{k,0}^w}{\partial y} \)
      • //Reset \(du \) and \(dv \)
      • \(du=0 \), \(dv=0 \)
      • //Fixed-point loop due to the robust error functions
      • WHILE( \(sndLoop \))
        • //Calculate penalizer function values for data
        • \( \Psi{\prime} \Big( (E_k)_D \Big) \), where \(\left( E_k \right)_D = \left( \dfrac{ \partial I_k }{\partial t} - \dfrac{ \partial I_{k,0}^{w} }{\partial x}du - \dfrac{ \partial I_{k,0}^{w} }{\partial y}dv \right)^2 ; \)
        • //Calculate the diffusion weights
        • \( \Big[ \Psi{\prime} \big( E_R^{l,m} \big)_W \, \Psi{\prime} \big( E_R^{l,m} \big)_N \, \Psi{\prime} \big( E_R^{l,m} \big)_E \, \Psi{\prime} \big( E_R^{l,m} \big)_S \Big] = weights(u+du, v+dv); \)
        • //Solve for new \(du \) and \(dv \)
        • \( \begin{matrix} [du \, dv] = SOLVER ( & u, \, v, \, du, \, dv, \, nLoops, \, \dfrac{\partial I_k}{\partial t}, \, \dfrac{\partial I_{k,0}^w}{\partial x}, \, \dfrac{\partial I_{k,0}^w}{\partial y}, \, \Psi{\prime} \Big( (E_k)_D \Big), &\\ & \Psi{\prime} \big( E_R^{l,m} \big)_W, \, \Psi{\prime} \big( E_R^{l,m} \big)_N, &\\ & \Psi{\prime} \big( E_R^{l,m} \big)_S, \, \Psi{\prime} \big( E_R^{l,m} \big)_E & ); \end{matrix} \)
      • ENDWHILE
      • //Update \(u \) and \(v \)
      • \(u=u+du \), \(v=v+dv; \)
    • ENDWHILE
    • //Interpolate (prolongate) solution
    • IF( \(s-1>0 \) )
      • \([u \, v] = prolongate( u, \, v, \,sclFactor ); \)
    • ENDIF
  • ENDWHILE
Jarno Ralli
Author: Jarno Ralli
Jarno Ralli is a computer vision scientist and a programmer.

Let's get social!

FacebookTwitterGoogle BookmarksLinkedIn