Paolo Remagnino

TL;DR: The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures.

TL;DR: This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses to segmentation of blood vessels in retinal photographs.

TL;DR: This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.

TL;DR: This paper learns useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gains intuition of the chosen features based on a Deconvolutional Network (DN) approach, and gains insights into the design of new hybrid feature extraction models which are able to further improve the discriminative power of plant classification systems.

TL;DR: The main computational, morphometric and image processing methods that have been used in recent years to analyze images of plants are reviewed, introducing readers to relevant botanical concepts along the way.