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    "# Module 2: Computer Vision"
   ]
  },
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   "metadata": {},
   "source": [
    "## Unit 1: A Survey of Computer Vision\n",
    "\n",
    "__Goal__: Understand the motivation and princples of utilizing computer vision as a means of perception for autonomous systems. Survey several common applications and current research trends in the field of computer vision from a robotics perspective. "
   ]
  },
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   "source": [
    "## Unit 2: Kernels, Edges, Thresholds\n",
    "\n",
    "__Goal:__ This unit introduces the fundamentals of image analysis for machine vision. We will treat images and videos as two-dimensional signals, allowing us to apply signal processing techniques to detect and interpret features in a camera stream on an embedded platform in real-time, using [the OpenCV library](https://opencv.org/).  \n",
    "\n",
    "__Practicals:__\n",
    "\n",
    " 1. [Derivatives](https://github.com/BWSI-UAV/laboratory/blob/master/computer_vision/derivatives_and_windows.ipynb)\n",
    " 2. [Convolution](https://github.com/BWSI-UAV/laboratory/blob/master/computer_vision/convolutions.ipynb)\n",
    " 3. [OpenCV](https://github.com/BWSI-UAV/laboratory/blob/master/computer_vision/OpenCV.ipynb)"
   ]
  },
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   "metadata": {},
   "source": [
    "## Unit 3: Regression, Line Parameterization\n",
    "\n",
    "__Goal:__ Learn how to detect an LED rope present in a camera image by applying linear regression on thresholded pixel values, as the first step in enabling the drone to follow an LED track.\n",
    "\n",
    "__Practicals:__\n",
    "\n",
    " 1. [Linear Regression](https://github.com/BWSI-UAV/laboratory/blob/master/computer_vision/LinearRegression.ipynb)\n",
    " 2. [Detecting the LED line](https://github.com/BWSI-UAV/laboratory/blob/master/computer_vision/Downward.ipynb)"
   ]
  },
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   "metadata": {},
   "source": [
    "## Unit 4: Color Segmentation\n",
    "\n",
    "__Goal:__ Understand how to detect and segment a specific, narrow range of colors within an image.\n",
    "\n",
    "__Practicals:__\n",
    "\n",
    " 1. [Color Segmentation and Bounding Boxes](https://github.com/BWSI-UAV/laboratory/blob/master/computer_vision/ColorSegmentation.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unit 5: Integration with Quadrotor\n",
    "\n",
    "__Goal:__ In order to utilize computer vision techniques for UAV control, we need to be able to pass information between our vision processes (i.e. ROS nodes) and our command/control processes in a parameterized format. The goal of this unit is to develop such ROS messages.\n",
    "\n",
    "__Practicals__\n",
    "\n",
    " 1. [Drone-based ROS Bag Collection](downward_cam_bag.html)\n",
    " 2. [Line Param ROS Message](detector.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unit 6: Advanced Topics\n",
    "\n",
    "__Goal:__ Understand the theory behind some of the \"black box\" algorithms we have used thus far as well as the frontiers of computer vision research\n",
    "\n",
    "__Lectures:__\n",
    "\n",
    "1. [AR Tag Theory]\n",
    "2. [Optical Flow Theory]\n",
    "3. [Machine Learning]\n"
   ]
  }
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