All of this was done autonomously. Here we provide source code for GNU Octave 3. A movie that illustrates this visualisation can be seen below. Model visualisation – duration is 10 min [Movie 31 Mb]. The bottom left shows the probabilities of each of the 13 gestures this is the input to the model.

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# 87 Best Diku images | Background images, Frogs, Hair png

The top left image shows the binary image produced by the background subtraction. This method has the nice property that it is independent of the lighting on stage.

The nqme gets very scared but it manages to trick the lion into eating grass. Then it meets a nice and juicy pig and it sneaks up on it to make sure that the pig doesn’t hide. This allows us to compute a difference image, where the parts of the image that isn’t part of the stage geometry will stand out. By now the lion is furious but fortunately it meets a big elephant. To the right you can see one frame from the movie click to enlarge the image. The extension allows us to encode the order and expected duration of states in a simple yet efficient way.

To make this estimation as robust as possible we used statistics to model the process. Since the lion isn’t all that intelligent it tries to eat the elephant.

From this it is easy to compute the final binary image. The basic idea is to use the gesture recognition to execute the namme of the computer actors. The lion is looking for food to eat and it sees a giraffe.

In order for the computer actors to participate in the play autonomously it is necessary to know how far the play has progressed. Coming real soon — we promise: The model can be implemented using the optimal particle filter, which makes it extremely robust. The giraffe runs away and the lion is now even more angry.

This means that the behaviour of the human actor can be used to estimate ‘the current position’ in the manuscript. The bottom left shows the probabilities of each of the 13 gestures this is the input to the model.

For details on this quite general model see our paper see above. The final play – duration is 10 minutes [Movie mb].

Here we provide source code for GNU Octave 3. A movie that illustrates this visualisation namme be seen below. The MHI’s are computed from a sequence of binary images of the human actor. The following images illustrate the basic idea of the method.

We assume that the geometry of the stage is fixed. But the elephant just laughs and throws the lion up in the skies. Davis and Bobick suggested that Hu moments could be used, but we found that Zernike moments improved the recognition greatly.

An MHI is a summary of a sequence of images generated using a decay operator. Jane and Rasmus Sloth. To figure this out, we use a system for visual gesture recognition.

## Robotic Theater

The giraffe gets very scared but tricks the lion into eat leaves. Specifically the gestures of the human actor were used to control the computer actors. The bottom right image illustrates the model.

The corresponding MHI’s are: The story take place on the savanna where we meet the hungry and therefore very angry lion.