Looking for Spelke's Object with Computer Vision: First Try

As I read in the SimpleCV textbook, I found that there were functions to find 'interesting parts' in motion pictures.  So I tested one of them, hoping that it could be used to detect Spelke's objects (ref. my current research plan).
For video input, I used a scene from the V-Rep simulator where a camera moves along a quasi-elliptical path tracking a cube inside the orbit.
I fed it to the RunningSegmentation function of SimpleCV and the result was something I had expected: parts of the floor was 'as interesting as' the cube for the algorithm, so that the cube alone was not chosen as the interesting object (the Fig. below).

Fig.  The part enclosed with the red lines 'interested' the algorithm.
(It changes dynamically over time.)

In my blog post mentioned above, I wrote that I would try figure-ground separation algorithms and optical flow detectors to find Spelke's objects.  I tried a very rudimentary border ownership detection algorithm (for figure-ground separation) and an optical flow type algorithm (this time), and do not feel either approach alone is promising.  Regular figure-ground separation does not take motion into account and regular optical flow approaches would not leave out spurious flow caused by agent motion.
As the cognitive mechanisms of infants for animate objects and still-life objects may differ, I might start again from static figure-ground separation for still-life objects and try to figure out how to develop the concept of 3D objects via kinesthetic interaction between the agent and objects...