Machine Vision systems represent an extensive area belonging to the digital signal processing with vast applications nowadays for industrial environment, robotics, vehicular/avionics/space technology, multimedia, entertainment, security, medicine, scientific and so on. Nevertheless, mostly these techniques cannot be achieved “off-line” due to the physical constraints of the complex environment and the context where they are applied. Additionally, as these systems are becoming complex, huge computational resources are demanding, mostly if hard real-time constraint is required.
The hardware implementation and the acceleration of these systems are gaining importance nowadays in order to accomplish with the requirements of the systems being usually needed to keep a scrupulous trade-off between accuracy, efficiency and power consumption.
The use of GPGPUs, Cells, FPGAs, DSPs as accelerators can achieves speedups of orders of magnitude vs. Any optimized CPU implementation. Each appropriate implementation solution is carried out depending on many final demands as power consumption, performance, accuracy, reliability, rapid prototyping, final cost, capability, reconfigurability among others.
The objective of this special issue is to bring together state-of-the-art research contributions as well as review articles that address recent developments in the hardware implementation of machine vision systems.
Original papers describing completed and unpublished work not currently under review by any other journal, magazine, or conference are solicited.

Potential topics include, but are not limited to:
- FPGAs Machine Vision Implementations
- GPUs Machine Vision Implementations
- Embedded Systems Machine Vision Implementations
- Cell/ASIC Machine Vision Implementations
- Multicore Machine Vision Implementations