  The construction of a spatio-temporal wavelet and its tuning to speed was
first realized in the 90s on the Morlet wavelet by M. Duval-Destin
\cite{Duval-Destin91a,Duval-Destin92}. This enabled to demonstrate the
capacities of the speed-tuned Morlet for psychovisual analysis. This
construction was also used very efficiently in a powerful aerial target
tracking algorithm by Mujica et al.\cite{Mujica99,Mujica2000}. In the last
decade, this tool was proposed as an elegant and efficient alternative
framework to the Optical Flow (OF), the Block Matching (BM) or the phase
difference, for the study of motion estimation in image sequences.
Nevertheless, the aperture selectivity of the 2D+T Morlet wavelet presents some
difficulties. Here we propose to replace the 2D Morlet wavelet by a
Gaussian-Conical (GC) wavelet for the spatial part of the spatio-temporal
wavelet, since the GC wavelet has a better aperture selectivity and allows a
very simple adjustment of the aperture. Therefore we build a new, highly
directional, speed-tuned wavelet called Gaussian-Conical-Morlet (GCM) wavelet.
Like the speed-tuned 2D+T Morlet, the new wavelet presents very good
characteristics in motion estimation and tracking, namely long temporal
dependence, robustness to noise and to occlusions, and supersedes the OF
(Optical Flow) and BM (Block Matching) techniques. However, for aperture
selectivity, directional speed-capture and spectral recognition and tracking,
GCM easily outperforms Morlet. This paper describes the GCM construction,
utilization and aperture performances.
