T
Tsampikos Kounalakis
Researcher at Aalborg University – Copenhagen
Publications - 18
Citations - 152
Tsampikos Kounalakis is an academic researcher from Aalborg University – Copenhagen. The author has contributed to research in topics: Feature extraction & Deep learning. The author has an hindex of 6, co-authored 18 publications receiving 87 citations. Previous affiliations of Tsampikos Kounalakis include Brunel University London & Aalborg University.
Papers
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Journal ArticleDOI
Deep learning-based visual recognition of rumex for robotic precision farming
TL;DR: The evaluation of the proposed algorithm shows that it outperforms competing weed/plant recognition methods in recognition accuracy, while producing low false-positive rates under real-world operation conditions.
Proceedings ArticleDOI
Weed recognition framework for robotic precision farming
TL;DR: A novel framework which applies known image features combined with advanced linear image representations for weed recognition resulting in a novel and generic weed control approach that in this knowledge is unique among weed recognition methods and systems is introduced.
Journal ArticleDOI
Image-based recognition framework for robotic weed control systems
TL;DR: A novel and efficient image-based weed recognition system that uses an image input resolution of 200 ×150, SURF features over dense feature extraction, an optimized Gaussian Mixture Model based codebook combined with Fisher encoding, using a two level image representation.
Proceedings ArticleDOI
A Robotic System Employing Deep Learning for Visual Recognition and Detection of Weeds in Grasslands
Tsampikos Kounalakis,Michal Jerzy Malinowski,Leandro Chelini,Georgios Triantafyllidis,Lazaros Nalpantidis +4 more
TL;DR: The vision system of a robot prototype that operates in dairy farm grasslands and detects the presence of the harmful Broad-leaved dock and yields state-of-the-art detection and recognition performance, while being able to keep low false-positive rates under challenging operation conditions.
Journal ArticleDOI
Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching
TL;DR: This paper presents a complete system for multiple object detection and classification in a 3D scene using an RGB-D sensor such as the Microsoft Kinect sensor in order to design a complete image processing algorithm for efficient object detection of multiple individual objects in a single scene, even in complex scenes with many objects.