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Pattern Recognition

46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 10-13, 2024, Proceedings, Part I

Medium: Buch
ISBN: 978-3-031-85180-3
Verlag: Springer
Erscheinungstermin: 23.04.2025
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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.

Produkteigenschaften

  • Artikelnummer: 9783031851803
  • Medium: Buch
  • ISBN: 978-3-031-85180-3
  • Verlag: Springer
  • Erscheinungstermin: 23.04.2025
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2025
  • Serie: Lecture Notes in Computer Science
  • Produktform: Kartoniert, Paperback
  • Gewicht: 581 g
  • Seiten: 365
  • Format (B x H x T): 155 x 235 x 21 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

.- 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.