In this paper, we present a parallel implementation of image mosaicing based on GPU using the Compute Unified. Device Architecture (CUDA). We obtain better ...
In this paper, the algorithm of image matching is parallelized based on Compute Unified Device Architecture (CUDA), which is a platform of GPGPU programming.
This chapter discusses implementation of a few de-mosaicing algorithms on GPUs using NVIDIA's CUDA GPU computing framework.
Nov 4, 2022 · This article is composed of three sections to explain how Taichi accelerates image processing in Python.
Mar 27, 2023 · ImageJ has several plugins for stitching, such as Grid/Collection Stitching or Pairwise Stitching, but they all use CPU and when working on lots of big files ...
Missing: mosaicing | Show results with:mosaicing
Oct 20, 2015 · The GIS software I'm aware of that makes the most of GPU horsepower is Manifold GIS. I believe it supports up to four GPUs using Nvidia CUDA cores.
We implemented Demons, a widely used deformable image registration algorithm, on. NVIDIA's Quadro FX 5600 GPU with the Compute Uni- fied Device Architecture ( ...
The experiments indicate that the mosaicking algorithm introduced in this paper enhances throughput and speedup by an average of 1.38 MB/S and 0.87 relative to ...
GPU implementation of video analytics algorithms for aerial imaging
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This work examines several algorithms that together make up parts of an image processing pipeline called Video Mosaicing and Summarization (VMZ).
GPU-FV is about 12 times faster than the CPU version, and 50\% faster than a non-optimized GPU implementation. For standard video input (320*240), GPU-FV ...