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Dimitris Zermas
Researcher at University of Minnesota
Publications - 14
Citations - 443
Dimitris Zermas is an academic researcher from University of Minnesota. The author has contributed to research in topics: Object detection & Object (computer science). The author has an hindex of 8, co-authored 14 publications receiving 263 citations. Previous affiliations of Dimitris Zermas include Delphi Automotive.
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Proceedings ArticleDOI
Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications
TL;DR: The proposed algorithm first extracts the ground surface in an iterative fashion using deterministically assigned seed points, and then clusters the remaining non-ground points taking advantage of the structure of the LiDAR point cloud.
Journal ArticleDOI
3D model processing for high throughput phenotype extraction – the case of corn
TL;DR: The experiments conclude that phenotypic characteristics of individual plants can be extracted automatically with high accuracy based on a 3D model.
Journal ArticleDOI
Covariance based point cloud descriptors for object detection and recognition
TL;DR: The aim of this paper is to introduce techniques from other fields, such as image processing, into 3D point cloud processing in order to improve rendering, classification, and recognition.
Proceedings ArticleDOI
Automation solutions for the evaluation of plant health in corn fields
Dimitris Zermas,Da Teng,Panagiotis Stanitsas,Michael E. Bazakos,Daniel E. Kaiser,Vassilios Morellas,David J. Mulla,Nikolaos Papanikolopoulos +7 more
TL;DR: The proposed methodology promotes the use of small-scale Unmanned Aerial Vehicles (UAVs) and Computer Vision algorithms that operate with information in the visual (RGB) spectrum for identifying Nitrogen (N) deficiencies in corn fields.
Proceedings ArticleDOI
RGB-D object classification using covariance descriptors
TL;DR: This work introduces a new covariance based feature descriptor to be used on “colored” point clouds gathered by a mobile robot equipped with an RGB-D camera, and presents the notion of a covariance onRGB-D data.