Dense optical flow for stitching

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Hi, I'm trying to stitch some pictures to create a cylindrical panorama with C# and EMGUCV, a wrapper for OpenCV. To do this I want to use a direct-tecnique because I have already tried with a feature-based tecnique (Harris corner detector + pyramid Lucas-Kanade) with good results. I follow these steps for the aligment:

1. Remap every image in new image by cylindrical equations (as shown in "Image Alignment and Stitching: A Tutorial" by Richard Szeliski).
2. Estimate optical flow by using a dense algorithm (OpenCV functions: cvCalcOpticalFlowLK or cvCalcOpticalFlowHS)
3. Using my function to estimate translation vectors based on RANSAC or using OpenCV function "findHomography".

I'm trying with images taken from images set:

After the 1) step I obtain this:

After the 3) I obtain this:

After optical flow estimation I convert the two returned maps (CvArr* velx, CvArr* vely) describing optical flow in the two directions to two arrays of points filtering flow vectors lower than a value (e.g. < 0.1 pixels). Finally I use findHomography with array of points to estimate the homography (translation). I put here my code to translate maps to array but it's in C# language.

        for (int j = 0; j < vely.Height; j += stepSize)
        {
            for (int i = 0; i < velx.Width; i += stepSize)
            {
                /* There's no need to calculate for every single point,
                if there's not much change, just ignore it */
                if (Math.Abs(velx.Data[j, i, 0]) < minDisplacement_x && Math.Abs(vely.Data[j, i, 0]) < minDisplacement_y)
                    continue;

                sourcePlane[num].X = i;
                sourcePlane[num].Y = j;
                destPlane[num].X = i + velx.Data[j, i, 0];
                destPlane[num].Y = j + vely.Data[j, i, 0];
                num++;
            }
        }
        // Resizes points found array
        System.Array.Resize(ref sourcePlane, num);
        System.Array.Resize(ref destPlane, num);

My problem is about the last step 3). findHomography give me bad results. For example, to estimate about a 100 pixels translation along horizontal direction, the findHomography give me a 2 or 3 pixels translation. I think that the problem is due to the outliers. In fact if I filter the arrays of points before findHomography to delete small vectors of translation by setting min value minDisplacement_x or minDisplacement_y to bigger than 0.1 (e.g. 50.0) the result is a little bit better but not enough. I know that feature-based tecniques are more robust than direct-tecnique but my results are very far from a good solution. Can someone help me? I don't want use feature-based tecnique (features or descriptors or blob) Thanks. Luca