  This paper introduces a fast Central Processing Unit (CPU) implementation of
geodesic morphological operations using stream processing. In contrast to the
current state-of-the-art, that focuses on achieving insensitivity to the filter
sizes with efficient data structures, the proposed approach achieves efficient
computation of long chains of elementary $3 \times 3$ filters using multicore
and Single Instruction Multiple Data (SIMD) processing. In comparison to the
related methods, up to $100$ times faster computation of common geodesic
operators is achieved in this way, allowing for real-time processing (with over
$30$ FPS) of up to $1500$ filters long chains, applied on $1024\times 1024$
images. In addition, the proposed approach outperformed GPGPU, and proved to be
more efficient than the comparable streaming method for the computation of
morphological erosions and dilations with window sizes up to $183\times 183$ in
the case of using char and $27\times27$ when using double data types.
