Muutke küpsiste eelistusi

Pattern Recognition: 46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 1013, 2024, Proceedings, Part II [Pehme köide]

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
  • Formaat: Paperback / softback, 367 pages, kõrgus x laius: 235x155 mm, 121 Illustrations, color; 36 Illustrations, black and white; XVII, 367 p. 157 illus., 121 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15298
  • Ilmumisaeg: 24-Apr-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031851862
  • ISBN-13: 9783031851865
Teised raamatud teemal:
  • Pehme köide
  • Hind: 63,52 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 74,73 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
Pattern Recognition: 46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 1013, 2024, Proceedings, Part IISuurem pilt
  • Formaat: Paperback / softback, 367 pages, kõrgus x laius: 235x155 mm, 121 Illustrations, color; 36 Illustrations, black and white; XVII, 367 p. 157 illus., 121 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15298
  • Ilmumisaeg: 24-Apr-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031851862
  • ISBN-13: 9783031851865
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031851865.html
Märksõnad:
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.
.- Modelling of Faces and Shapes.


.- 360° Volumetric Portrait Avatar.


.- How Do You Perceive My Face? Recognizing Facial Expressions in Multi-Modal
Context by Modeling Mental Representations.


.- A Latent Implicit 3D Shape Model for Multiple Levels of Detail.


.- Image Generation and Reconstruction.


.- Coloring the Past: Neural Historical Monuments Reconstruction from
Archival Photography.


.- Expanding the Image Embedding Space for Language-Free Text-to-Face Image
Generation.


.- Towards synthetic generation of realistic wooden logs.


.- 3D Analysis and Sythesis.


.- G3DST: Generalizing 3D Style Transfer with Neural Radiance Fields across
Scenes and Styles.


.- LiFCal: Online Light Field Camera Calibration via Bundle Adjustment.


.- CARLA Drone: Monocular 3D Object Detection from a Different Perspective.


.- Robust 3D Gaussian Splatting for Novel View Synthesis in Presence of
Distractors.


.- DynaPix SLAM: A Pixel-Based Dynamic Visual SLAM Approach.


.- Leveraging Image Matching Toward End-to-End Relative Camera Pose
Regression.


.- Erasing the Ephemeral: Joint Camera Refinement and Transient Object
Removal for Street View Synthesis.


.- Physically Plausible Object Pose Refinement in Cluttered Scenes.


.- Gaussian Splatting in Style.


.- Video Analysis.


.- Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection
Simplified.


.- STAR: Screen Time and Actor Recognition in Video Content.


.- Photogrammetry and Remote Sensing.


.- Exploring Seasonal Variability in the Context of Neural Radiance Fields
for 3D Reconstruction on Satellite Imagery.


.- Worldwide High-fidelity Road Extraction from Aerial and Satellite Imagery
enabled by Low-fidelity OpenStreetMap Labels.


.- SenPa-MAE: Sensor Parameter Aware Multi-Satellite Masked Autoencoder for
Multispectral Earth Observation Imagery.


.- PuzzleBoard: A new Camera Calibration Pattern with Position Encoding.


.- Efficient Multi-task Uncertainties for Joint Semantic Segmentation and
Monocular Depth Estimation.