Real-Time Image Processing Algorithms for Embedded Systems
It addresses the need for efficient and robust image processing in embedded vision systems for automotive, surveillance, and robotics sectors, though it appears incremental as it builds on existing techniques.
This research tackled the problem of real-time image processing on resource-constrained embedded systems by investigating optimized algorithms and hardware implementations, resulting in marked improvements in speed and energy efficiency compared to conventional methods.
Embedded vision systems need efficient and robust image processing algorithms to perform real-time, with resource-constrained hardware. This research investigates image processing algorithms, specifically edge detection, corner detection, and blob detection, that are implemented on embedded processors, including DSPs and FPGAs. To address latency, accuracy and power consumption noted in the image processing literature, optimized algorithm architectures and quantization techniques are employed. In addition, optimal techniques for inter-frame redundancy removal and adaptive frame averaging are used to improve throughput with reasonable image quality. Simulations and hardware trials of the proposed approaches show marked improvements in the speed and energy efficiency of processing as compared to conventional implementations. The advances of this research facilitate a path for scalable and inexpensive embedded imaging systems for the automotive, surveillance, and robotics sectors, and underscore the benefit of co-designing algorithms and hardware architectures for practical real-time embedded vision applications.