Name
Abstract | ||
|---|---|---|
Visual IoT is a rapidly growing usage based on rich visual sensing, processing, and analytics. One approach for addressing visual IoT challenges is to move some computation closer to the edge device where data is captured. This article begins with a description of three key implications in ultra-low-power visual edge processing: the data footprint is constrained due to SRAM power, the available po... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MM.2017.4241343 | IEEE Micro |
Keywords | Field | DocType |
Visualization,Feature extraction,Pipelines,Character recognition,Random access memory,Image edge detection,Streaming media | Automatic summarization,Visual processing,Computer science,Visualization,Gesture,Real-time computing,Feature extraction,Edge device,Analytics,Computation | Journal |
Volume | Issue | ISSN |
37 | 6 | 0272-1732 |
Citations | PageRank | References |
4 | 0.53 | 8 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vui Seng Chua | 1 | 4 | 0.53 |
| Julio Zamora-Esquivel | 2 | 24 | 7.72 |
| Anindya S Paul | 3 | 4 | 0.87 |
| Thawee Techathamnukool | 4 | 4 | 0.53 |
| Carlos Flores Fajardo | 5 | 16 | 1.50 |
| Nilesh Jain | 6 | 4 | 0.87 |
| Tickoo, O. | 7 | 17 | 6.50 |
| Ravishankar K. Iyer | 8 | 1119 | 75.72 |