I am a computer scientist with a key interest in machine learning techniques for image manipulation and analysis. I investigate innovative deep-learning algorithms to identify and categorize images taken into the wild with mobile phones. Together with my collaborators, I work on Modern Approaches to the Monitoring of BiOdiversity (MAMBO) project that applies such techniques in Europe.
Biodiversity monitoring, deep learning, computer vision, recognition, nature awareness
Computer vision and biodiversity monitoring are increasingly related due to the overflow of data collected with devices. Over the past few decades, biologists have collected more than billions of images in the wild with drones, camera traps, and mobile phones. This is happening faster with the advent of innovative technologies. How to deal with such an amount of data? How can we have a fast taxonomical classification of the images to start studying biodiversity? Time is ticking in nature we can no anymore relate only to humans' lifetime.
Visual information and object representation are some major sources of information for biodiversity monitoring and a fervent field of research in computer science. Deep learning is one of the most prolific fields in computer vision providing competitive algorithms to analyse and classify such data trying to emulate the human ability of observation. We study these algorithms in the realm of natural images from mobiles where the main providers of such data are citizen science. We will investigate modern algorithms and propose new ones to improve automatic animal monitoring and contribute to the study of nature.
MAMBODNN for Species Classification
MAMBO (Modern Approaches to the Monitoring of BiOdiversity) is a research and innovation project of Horizon Europe, funded by the European Commission for 5 million euros. The project is a collaboration between ten partners. Scientists Koos Biesmeijer and Vincent Kalkman lead two work packages, one of which is aimed at properly aligning newly developed techniques with existing data and ICT infrastructure. The other work package focuses on the application of image and sound recognition for biomonitoring. For the latter work package, work is being done, among other things, on the further development of image and sound recognition for mobile telephones with the aim of making this available for citizen scientists throughout Europe for virtually all policy-relevant species.
Rita Pucci, Christian Micheloni, Niki Martinel. Pro-CCaps: Progressively Teaching Colourisation to Capsules. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 2271-2279
Rita Pucci, Christian Micheloni, Niki Martinel. CVGAN: Image Generation with Capsule Vector-VAE. International Conference on IMAGE ANALYSIS AND PROCESSING (ICIAP), 2022, pp 536–547
Rita Pucci, Christian Micheloni, Niki Martinel. Self-Attention Agreement Among Capsules, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 272-280
Rita Pucci, Christian Micheloni, Niki Martinel. Collaborative Image and Object Level Features for Image Colourisation Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) workshops, 2021, pp. 2160-2169
TeachingCapsule network introduction
Machine learning laboratory at INTERNATIONAL SUMMER SCHOOL ON ARTIFICIAL INTELLIGENCE - AI-DLDA where I talk about a Gentle Introduction to Capsule Networks and Automatic Old Image Colourisation