Computer Vision – ECCV 2024 Workshops Milan, Italy, September 29–October 4, 2024, Proceedings, Part III /
The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29–October 4, 2024. These LNCS volumes contain 574 accepted...
I tiakina i:
| Kaituhi rangatōpū: | |
|---|---|
| Ētahi atu kaituhi: | , , , |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Putanga: | 1st ed. 2025. |
| Rangatū: | Lecture Notes in Computer Science,
15625 |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1007/978-3-031-91835-3 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
MARC
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| 245 | 1 | 0 | |a Computer Vision – ECCV 2024 Workshops |h [electronic resource] : |b Milan, Italy, September 29–October 4, 2024, Proceedings, Part III / |c edited by Alessio Del Bue, Cristian Canton, Jordi Pont-Tuset, Tatiana Tommasi. |
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| 490 | 1 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 15625 | |
| 505 | 0 | |a Wild Berry image dataset collected in Finnish forests and peatlands using drones -- Soybean pod and seed counting in both outdoor fields and indoor laboratories using unions of deep neural networks -- A Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning -- Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw) -- 3D Phenotyping of Canopy Occupation Volume as a Major Predictor for Canopy Photosynthesis in Rice (Oryza sativa L.) -- Retrieval of sun-induced plant fluorescence in the O2-A absorption band from DESIS imagery -- Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders -- KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation -- RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection -- Consolidation of symbolic instances using sensor data via tracklet merging for long-term monitoring of crops -- Automated Generation of Accurate, Compact and Focused Crop and Weed Segmentation Models -- Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection -- Towards Auto-Generated Ground Truth for Evaluation of Perception Systems in Agriculture -- AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models -- Deep Learning Based Growth Modeling of Plant Phenotypes -- A simple approach to pavement cell segmentation -- Enhancing weed detection performance by means of GenAI-based image augmentation -- SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture -- Robust UDA for Crop and Weed Segmentation: Multi-Scale Attention and Style-Adaptive Techniques -- Ordinal-Meta Learning for Fine-grained Fruit Quality Prediction -- Beyond Annotations: Efficient Wheat Head Segmentation Using L-Systems, Game Engines, and Student-Teacher Models -- Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves. | |
| 520 | |a The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29–October 4, 2024. These LNCS volumes contain 574 accepted papers from 53 of the 73 workshops. The list of workshops and distribution of the workshop papers in the LNCS volumes can be found in the preface that is freely accessible online. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Image processing. | |
| 650 | 0 | |a User interfaces (Computer systems). | |
| 650 | 0 | |a Human-computer interaction. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computers, Special purpose. | |
| 650 | 1 | 4 | |a Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 | 2 | 4 | |a Image Processing. |
| 650 | 2 | 4 | |a User Interfaces and Human Computer Interaction. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Special Purpose and Application-Based Systems. |
| 700 | 1 | |a Del Bue, Alessio. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Canton, Cristian. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Pont-Tuset, Jordi. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Tommasi, Tatiana. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 950 | |a Focus Bundle Computer Vision and Pattern Recognition (SpringerNature-85411) | ||