A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
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One of the main challenges when creating an undergraduate introduction to robotics course is connecting the theory taught in the lectures with the current practices of research. The primary cause of this difficulty is an inability to find a hardware solution that is powerful enough to run complex cutting-edge algorithms yet inexpensive enough to be purchased by an undergraduate class budget. An ideal system needs to have a gentle learning curve to allow students with minimal background in the field to get a robot up and running. Lastly, a fleet of classroom robots needs to be easy to administrate and maintain given the limited time of a Teaching Assistant. Our approach is to implement a centralized server system. In this system individual robots are inexpensive yet capable of establishing a WiFi link to a main server so that all the compilation and system administration, as well as much of the computationally intensive processing, are done on that server. We find that this solution saves both time and money and provides an effective teaching tool. This paper describes the hardware and software architecture of the system, and example applications implemented by undergraduate students.