Low-level computer vision algorithms have extreme computational requirements. In this
work, we compare two real-time architectures developed using FPGA and GPU devices for …

The proposed work presents a highly parallel architecture for motion estimation. Our system
implements the well-known Lucas and Kanade algorithm with the multi-scale extension for …

Recently, accelerator architectures of deep neural networks (DNNs) have been designed to
accelerate computer vision tasks, gaining the advantages of both accuracy and speed …

Optical flow is a highly efficient visual tracking algorithm, which is commonly used to
estimate pixel movement between two consecutive images in a video sequence. However …

Optical flow calculation algorithms are hard to implement on the hardware level in real-time,
due to their complexity and high computational load. Therefore, presented works in the …

This article presents an efficient hardware implementation of the Horn-Schunck algorithm
that can be used in an embedded optical flow sensor. An architecture is proposed, that …

This work proposes a highly tunable motion estimation architecture. We implement the Horn
and Schunck algorithm with the hierarchical extension for larger motion estimations in …

Low-level vision tasks pose an outstanding challenge in terms of computational effort: pixel-
wise operations require high-performance architectures to achieve real-time processing …

In this work, we present a real-time implementation of a stereo algorithm on field-
programmable gate array (FPGA). The approach is a phase-based model that allows …

Recently, accelerator architectures have been designed to use deep neural networks
(DNNs) to accelerate computer vision tasks, possessing the advantages of both accuracy …