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HOG Descriptor Implementation

Mar 23, 2016
by SubjuGator
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HOG Descriptors can be uesd to identify an object with a sliding window. It is very slow, so the goal is to train an SVM on the data to speed up the process. This method is much more reliable then the color segmentation we had previously relied on. Below is a snapshot of the selection window in action:
HOG
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About Us

The Machine Intelligence Laboratory (MIL) provides a synergistic environment dedicated to the study and development of intelligent, autonomous robots. The faculty and students associated with the laboratory conduct research in the theory and realization of machine intelligence covering topics such as machine learning, real-time computer vision, statistical modeling, robot kinematics, autonomous vehicles, teleoperation and human interfaces, robot and nonlinear control, computational intelligence, neural networks, and general robotics. Applications of MIL research include autonomous underwater vehicles (AUVs), autonomous water surface vehicles (ASVs), autonomous land vehicles, autonomous air vehicles (AAVs including quadcopters and micro air vehicles, MAVs) , swarm robots, humanoid robots, and autonomous household robots.

MIL’s SubjuGator is the three time champion autonomous submarine of the RoboSub AUVSI/ONR underwater competition (2005-2007), and placed in the top 3 in eleven of the 21 years of the competition (including second place in 2012, 2013 and 2014). MIL’s NaviGator AMS, is the defending champion in the Maritime RobotX Challenge (from our victory in our only entry in this biennial competition in 2016). In 2013, MIL participated for the first time in the RoboBoat AUVSI/ONR water surface vechicle competition with our PropaGator robot boat; we won! In 2014, we earned second place in the RoboBoat competition. We also won the static division of the 2011 ION Robot Lawnmower competition with MIL’s InstiGator robot lawnmower.

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