## ALGORITHMS |

Dynamic image

Dynamic mouse

Behavioral

Neural networks

In principle it is from a polygonal line

Applied once on the polygonal line

Recursively applying this method on polylines

Generation of polygonal approximations

And generation of polygonal approximations

The vertices

----------------------------------

```
typedef struct {
```

unsigned char r, g, b, a;

} Pixel;

----------------------------------

Where

Define a dynamic pixel structure by:

----------------------------------

```
typedef struct {
```

unsigned char r, g, b, a;

float m, s, v;

} Pixel;

----------------------------------

Where

1) an array of nx by ny pixels dynamic image at time

2) the same table, but with (r, g, b,a) in floats (to avoid rounding errors in the calculations of the dynamic).

3) a memory table of static pixels (r, g, b, a) when it was created (at time 0).

4) a memory speeds of static pixels.

The springs are stretched between the image at time

Control commands allow to restrict their color, their velocities and their accelerations have been implemented.

A simplified version is obtained by giving all pixels the same mass, the same stiffness and the same viscosity. The tables have more then 4 components (r, g, b, a) in place of 7 (r, g, b, a, m, r, w).

A linearization method simplifies the calculations, allowing the real-time images of average size (about 300 pixels per side).

A linear approximation is carried out of the analysis of that signal in a time window in small amplitude

curvilinear velocity

the tangent vector

whose components are:

Components of the unit tangent vector are:

if

so:

The radius of curvature

Practically just hold buffers storing the last n vectors

mouse dynamic smooth(d) and mouse dynamic adjust(1) used to correct for variations in noise or too irregular for the mouse.

1) The source code of the function.

2) Compiled code for execution.

3) A pointer (initially zero) front one linked list of objects of type

Such a function is dupplication a particular function and can be found in several distinct objects but can be ordered differently (since compilations are distinct) and thus giving these objects of identical (in design) but different (in updating them). Such functions can communicate with each other (at the same object) or local functions of other objects.

This method is used (especially with neural networks) to determine which actors simply learn a certain behavior (eg walking) so that it knows to behave properly in situatiuons varied (eg walking or flat climbing stairs) without it being necessary for any remodel each of these situations. Maybe one day film producers synthesis types realize what they would realize savings by buying such an actor instead of paying armies of graphic designers to work purely repetitive ...).