In this paper, a contour-fitness improved adaptive snake, namely, edge-conducted rectification-adapted snake
(ECRA-snake) is proposed for segmenting complex-boundary objects in the noisy image. The ECRA-snake includes a
main ingredient called edge-conducted evolution (ECE), where the adaptations of model coefficients can accommodate
ECE itself to the characteristics of salient edges for better curve fitting in tracking. Following ECE, a direction-induced
rectification evolution (DIRE) will correct boundary-unmatched snake fragments by handling the initial direction and the
tensile-force weighting of unqualified snaxels in this snake re-evolution. Simulation results demonstrate that the
proposed ECRA-snake can obtain better object-boundary coincidence than the Gradient Vector Flow (GVF) model in
segmenting the complex-boundary object from noisy images.
KEYWORDS: Mobile robots, Control systems, Control systems design, Neural networks, Sensors, Machine learning, Gesture recognition, Data communications, Detection and tracking algorithms, Image processing
The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to
design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture
the human body skeleton with depth information, and a gesture training and identification method is designed using the
back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The
experimental results show that the designed mobile robots remote control system can achieve, on an average, more than
96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed
commands.
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