Pattern Recognition 46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 10–13, 2024, Proceedings, Part I /
This 2-volume set LNCS 15297-15298 constitutes the refereed proceedings of the 46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024, held in Munich, Germany, during September 10-13, 2024. The 44 full papers included in these proceedings were carefully reviewed and...
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Springer Nature Switzerland : Imprint: Springer,
2025.
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| Edisyon: | 1st ed. 2025. |
| Seri Bilgileri: | Lecture Notes in Computer Science,
15297 |
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| Online Erişim: | https://doi.org/10.1007/978-3-031-85181-0 |
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| 245 | 1 | 0 | |a Pattern Recognition |h [electronic resource] : |b 46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 10–13, 2024, Proceedings, Part I / |c edited by Daniel Cremers, Zorah Lähner, Michael Moeller, Matthias Nießner, Björn Ommer, Rudolph Triebel. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a XVII, 365 p. 113 illus., 103 illus. in color. |b online resource. | ||
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| 490 | 1 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 15297 | |
| 505 | 0 | |a -- Clustering and Segmentation. -- PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks. -- A State-of-the-Art Cutting Plane Algorithm for Clique Partitioning. -- Self-Supervised Semantic Segmentation from Audio-Visual Data. -- BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation. -- Learning Techniques. -- FullCert: Deterministic End-to-End Certification for Training and Inference of Neural Networks. -- Self-Masking Networks for Unsupervised Adaptation. -- A Theoretical Formulation on the Use of Multiple Positive Views in Contrastive Learning -- Decoupling of neural network calibration measures. -- Examining Common Paradigms in Multi-Task Learning. -- DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning. -- Efficient and Discriminative Image Feature Extraction for Universal Image Retrieval .. -- Anomaly Detection with Conditioned Denoising Diffusion Models. -- Medical and Biological Applications. -- SurgeoNet: Realtime 3D Pose Estimation of Articulated Surgical Instruments from Stereo Images using a Synthetically-trained Network. -- Foundation Models Permit Retinal Layer Segmentation Across OCT Devices. -- Correlation Clustering of Organoid Images. -- Animal Identification with Independent Foreground and Background Modeling. -- Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks. -- Bigger Isn’t Always Better: Towards a General Prior for Medical Image Reconstruction. -- Uncertainty and Explainability. -- Latent Diffusion Counterfactual Explanations. -- Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations. -- Uncertainty Voting Ensemble for Imbalanced Deep Regression. -- Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning. | |
| 520 | |a This 2-volume set LNCS 15297-15298 constitutes the refereed proceedings of the 46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024, held in Munich, Germany, during September 10-13, 2024. The 44 full papers included in these proceedings were carefully reviewed and selected from 81 submissions. They are organized in these topical sections: Part I: Clustering and Segmentation; Learning Techniques; Medical and Biological Applications; Uncertainty and Explainability. Part II: Modelling of Faces and Shapes; Image Generation and Reconstruction; 3D Analysis and Sythesis; Video Analysis; Photogrammetry and Remote Sensing. | ||
| 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 | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer systems. | |
| 650 | 0 | |a Education |x Data processing. | |
| 650 | 0 | |a Application software. | |
| 650 | 1 | 4 | |a Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computer System Implementation. |
| 650 | 2 | 4 | |a Computers and Education. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 700 | 1 | |a Cremers, Daniel. |e editor. |0 (orcid)0000-0002-3079-7984 |1 https://orcid.org/0000-0002-3079-7984 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Lähner, Zorah. |e editor. |0 (orcid)0000-0003-0599-094X |1 https://orcid.org/0000-0003-0599-094X |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Moeller, Michael. |e editor. |0 (orcid)0000-0002-0492-6527 |1 https://orcid.org/0000-0002-0492-6527 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Nießner, Matthias. |e editor. |0 (orcid)0000-0001-6093-5199 |1 https://orcid.org/0000-0001-6093-5199 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 700 | 1 | |a Triebel, Rudolph. |e editor. |0 (orcid)0000-0002-7975-036X |1 https://orcid.org/0000-0002-7975-036X |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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