Normalized measure from dynamic time warping
Sure.  There's a straightforward way to convert an unnormalized distance metric into a normalized similarity measure.  Basically, use

$$S(x,y) = \frac{M - D(x,y)}{M},$$

where $D(x,y)$ is the distance between $x$ and $y$, $S$ is the normalized similarity measure between $x$ and $y$, and $M$ is the maximum value that $D(x,y)$ could be.

In the case of dynamic time warping, given a template $x$, one can compute the maximum possible value of $D(x,y)$.  This will depend on the template, so $M$ becomes $M(x)$, and the formula becomes

$$S(x,y) = \frac{M(x) - D(x,y)}{M(x)}.$$

So how do you compute $M(x)$?  Basically, multiply the length of the template (the number of samples in the time series), times the maximum value of each sample.  If each sample is in the range $[0,1]$, then the maximum value of each sample is 1, so $M(x)$ becomes just the length of $x$.  This gives a simple upper bound on $D(x,y)$.  [Trivia: in fact one could compute a slightly tighter upper bound, but in practice that's probably not necessary.]

It is easy to verify that the similarity measure is always in the range $[0,1]$ and thus is normalized as you were hoping for.