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A Tutorial on the Implementations of Linear Image Filters in CPU and GPU
| Content Provider | Semantic Scholar |
|---|---|
| Author | Pardo, Alvaro |
| Copyright Year | 2017 |
| Abstract | This article presents an overview of the implementation of linear image filters in CPU and GPU. The main goal is to present a self contained discussion of different implementations and their background using tools from digital signal processing. First, using signal processing tools, we discuss different algorithms and estimate their computational cost. Then, we discuss the implementation of these filters in CPU and GPU. It is very common to find in the literature that GPUs can easily reduce computational times in many algorithms (straightforward implementations). In this work we show that GPU implementations not always reduce the computational time but also not all algorithms are suited for GPUs. We believe this is a review that can help researchers and students working in this area. Although the experimental results are not meant to show which is the best implementation (in terms of running time), the main results can be extrapolated to CPUs and GPUs of different capabilities. |
| Starting Page | 111 |
| Ending Page | 121 |
| Page Count | 11 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/978-3-319-75214-3_11 |
| Alternate Webpage(s) | http://sedici.unlp.edu.ar/bitstream/handle/10915/63666/Documento_completo.pdf?sequence=1 |
| Alternate Webpage(s) | http://sedici.unlp.edu.ar/bitstream/handle/10915/66260/A_Tutorial_on_the_Implementations_of_Linear_Image_Filters_in_CPU_and_GPU.pdf?isAllowed=y&sequence=3 |
| Alternate Webpage(s) | https://doi.org/10.1007/978-3-319-75214-3_11 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
