Special Issue "Convolutional Neural Network Design and Hardware Implementation for Real-Time Vision Applications"
Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 27426
Interests: object recognition; hardware friendly computer vision algorithms; real-time robotic vision applications
Special Issues, Collections and Topics in MDPI journals
Processing speed is critical for many visual computing tasks. Many computer vision algorithms generate accurate results, but run too slowly to produce results in real time. On the other hand, some algorithms process at camera frame rates, but with reduced accuracy, a more useful combination for real-time applications. Moreover, FPGAs are increasing in capacity and decreasing in power consumption, making them more attractive for embedded applications, such as onboard vision and control for unmanned vehicles. Convolutional neural networks (CNNs) offer state-of-the-art accuracy for many computer vision tasks. Their capabilities are generalizable to many different real-world applications. Real-world applications often require real-time responsiveness from the vision system. This Special Issue focuses on CNNs and their application to real-time computer vision tasks.
General topics covered in this Special Issue include, but are not limited to:
- FPGA-based hardware acceleration of vision algorithms;
- GPU-based acceleration of vision algorithms;
- Embedded vision sensors for applications that require real-time performance;
- CNN architecture optimizations for real-time performance;
- CNN acceleration through approximate computing;
- GPU-based implementations for real-time CNN performance;
- FPGA-based implementations for real-time CNN performance;
- Real-time CNN performance on resource limited systems;
- CNN applications that require real-time performance;
- Tradeoff analysis between speed and accuracy in CNNs.
Prof. Dr. Dah-Jye Lee
Manuscript Submission Information
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