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ORCID18th ECCV Workshops 2024: Milan, Italy - Part III
- Alessio Del Bue, Cristian Canton, Jordi Pont-Tuset, Tatiana Tommasi:
Computer Vision - ECCV 2024 Workshops - Milan, Italy, September 29-October 4, 2024, Proceedings, Part III. Lecture Notes in Computer Science 15625, Springer 2025, ISBN 978-3-031-91834-6 - Luigi Riz
, Sergio Povoli, Andrea Caraffa, Davide Boscaini, Mohamed Lamine Mekhalfi, Paul Chippendale, Marjut Turtiainen, Birgitta Partanen, Laura Smith Ballester, Juan Fco. Blanes Noguera, Alessio Franchi, Elisa Castelli, Giacomo Piccinini, Luca Marchesotti, Micael Santos Couceiro, Fabio Poiesi:
[inline-graphic not available: see fulltext] rry Image Dataset Collected in Finnish Forests and Peatlands Using Drones. 1-16 - Tianyou Jiang, Mingshun Shao, Tianyi Zhang, Xiaoyu Liu, Qun Yu:
Soybean Pod and Seed Counting in Both Outdoor Fields and Indoor Laboratories Using Unions of Deep Neural Networks. 17-30 - Ahmed Emam, Mohamed M. Farag, Jana Kierdorf, Lasse Klingbeil, Uwe Rascher, Ribana Roscher:
A Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning. 31-45 - Katherine Margaret Frances James, Karoline Heiwolt, Daniel James Sargent, Grzegorz Cielniak:
Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw). 46-63 - Jiaren Zhou, Man Zhang, Mengqi Zhang, Minjuan Wang:
3D Phenotyping of Canopy Occupation Volume as a Major Predictor for Canopy Photosynthesis in Rice (Oryza sativa L.). 64-80 - Jim Buffat, Miguel Pato, Kevin Alonso, Stefan Auer, Emiliano Carmona, Stefan W. Maier, Rupert Müller, Patrick Rademske, Uwe Rascher, Hanno Scharr:
Retrieval of Sun-Induced Plant Fluorescence in the O2-A Absorption Band from DESIS Imagery. 81-100 - Mahmoud Abdulsalam, Usman A. Zahidi, Bradley Hurst, Simon Pearson, Grzegorz Cielniak, James Brown:
Unsupervised Tomato Split Anomaly Detection Using Hyperspectral Imaging and Variational Autoencoders. 101-114 - Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor, Tania Cerquitelli, Elena Baralis, Paolo Garza:
KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation. 115-131 - Pasquale De Marinis, Gennaro Vessio, Giovanna Castellano:
RoWeeder: Unsupervised Weed Mapping Through Crop-Row Detection. 132-145 - Mark Niemeyer, Joachim Hertzberg, Grzegorz Cielniak:
Consolidation of Symbolic Instances Using Sensor Data via Tracklet Merging for Long-Term Monitoring of Crops. 146-159 - Soma Dasgupta, Swarnava Dey:
Automated Generation of Accurate, Compact and Focused Crop and Weed Segmentation Models. 160-176 - Ahmet Oguz Saltik, Alicia Allmendinger, Anthony Stein:
Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection. 177-193 - Jan Christoph Krause, Mark Niemeyer, Janosch Bajorath, Naeem Iqbal, Joachim Hertzberg:
Towards Auto-generated Ground Truth for Evaluation of Perception Systems in Agriculture. 194-206 - Yutong Zhou, Masahiro Ryo:
AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models. 207-223 - Renke Hohl, Moritz Schauer, Seyed Eghbal Ghobadi:
Deep Learning Based Growth Modeling of Plant Phenotypes. 224-239 - Rostislav Shepel, Andrew Romanowski, Mario Valerio Giuffrida:
A Simple Approach to Pavement Cell Segmentation. 240-251 - Sourav Modak, Anthony Stein:
Enhancing Weed Detection Performance by Means of GenAI-Based Image Augmentation. 252-266 - Andrew Heschl, Mauricio Murillo, Keyhan Najafian, Farhad Maleki:
SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture. 267-283 - Numair Nadeem, Muhammad Hamza Asad, Abdul Bais:
Robust UDA for Crop and Weed Segmentation: Multi-scale Attention and Style-Adaptive Techniques. 284-302 - Aayush Mishra, Manasi Patwardhan, Parijat Deshpande, Beena Rai:
Ordinal-Meta Learning for Fine-Grained Fruit Quality Prediction. 303-318 - Hosein Beheshtifard, Elijah Mickelson, Keyhan Najafian, Farhad Maleki:
Beyond Annotations: Efficient Wheat Head Segmentation Using L-Systems, Game Engines, and Student-Teacher Models. 319-334 - Madeleine Darbyshire, Elizabeth Sklar, Simon Parsons:
Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves. 335-349
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