@conference {458,
	title = {Adaptive HSL Filters and Inverse Perspective Transforms in Lane Detection for Autonomous Driving},
	booktitle = {CAT Vehicle REU},
	year = {2019},
	month = {03/2019},
	keywords = {autonomous vehicles, computer vision, image processing},
	doi = {10.13140/RG.2.2.18919.60327},
	url = {http://doi.org/10.13140/RG.2.2.18919.60327},
	author = {Hannah Mason and Landon Bentley and Joe MacInnes and Rahul Bhadani and Tamal Bose}
}
@conference {457,
	title = {A Pseudo-Derivative Method for Sliding Window Path Mapping in Robotics-Based Image Processing},
	year = {2019},
	publisher = {CAT Vehicle Research Experience for Undergraduates},
	organization = {CAT Vehicle Research Experience for Undergraduates},
	type = {Undergraduate Research},
	address = {Accepted at IEEE International Conference on Robotic Computing},
	abstract = {<p>A sliding window technique in robotics-based image processing applications is a common approach to path mapping from extracted features. Mapping a path inside an image requires finding a series of points representing the path. Previous approaches find these points by sliding a window along the path in fixed increments across one image dimension. After each slide, the center of the window in the other dimension is adjusted so that the window maximally covers the path in that area. This approach, however, fails to map paths that experience sharp curvature since the windows slide along only one dimension. The method proposed herein uses a pseudo-derivative approach to sliding windows that improves upon the traditional technique by dynamically adjusting the windows along both image dimensions during each slide. In this method, the directional components of a vector representing the previous slide are used as a naive estimation to perform the current slide. If this fails to map the path, the vector direction is used to enlarge the window dimensions. The method was tested in the domain of autonomous vehicles as an approach for detecting road lane markings. The algorithm proved more successful than previous sliding window approaches on perspective mapped lane images.</p>
},
	keywords = {autonomous vehicles, image processing},
	doi = {10.13140/RG.2.2.35628.10885},
	author = {Landon Bentley and Joe MacInnes and Rahul Bhadani and Tamal Bose}
}
@conference {468,
	title = {A Sliding Window for Path Mapping based on a Pseudo-Derivative Method in Autonomous Navigation},
	booktitle = {IEEE Vehicular Technology Conference, VTC-2019, Fall},
	year = {2019},
	month = {11/2019},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Honolulu, Hawaii},
	keywords = {autonomous vehicles},
	author = {Landon Bentley and Joe MacInnes and Hannah Mason and Rahul Bhadani and Tamal Bose}
}
