Efficient Method for Parallel Computation of Geodesic Transformation on CPU

This article 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
$27\times 27$
when using
double
data types.