Pattern Recognition and Computer Vision: 7th Chinese Conference, PRCV 2024, Urumqi, China, October 1820, 2024, Proceedings, Part XV [Paperback]

This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 1820, 2024.

The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

Anchored Supervised Contrastive Learning for Long-Tailed Medical Image Regression.- Dynamic Feature Fusion Based on Consistency and Complementarity of Brain Atlases.- FUF-TransUNet: a transformer-based U-Net with fully utilize of features for liver and liver-tumor segmentation in CT images.- Dual-View Dual-Boundary Dual U-Nets for Multiscale Segmentation of Oral CBCT ImagesA Novel Diffusion Model with Wavelet Transform for Optic Disc and Cup Segmentation in Fundus Images.- STCTb: A Spatio-Temporal Collaborative Transformer Block for Brain Diseases Classification using fMRI Time Series.A Generalized Contrast-adjustment Guided Growth Method for Medical Image Segmentation.- MDNet: Morphology-Driven Weakly Supervised Polyp DetectionMMR-Sleep: A Multi-Channel and  Multi-Receptive Field Sleep Stage recognition  Model.- CPNet: Cross Prototype Network for Few-shot Medical Image Segmentation.- SBC-UNet: A Network Based on Improved Hourglass Attention Mechanism and U-Net for Medical Image Segmentation.- Bridge the gap of semantic context: A Boundary-guided Context Fusion UNet for Medical Image Segmentation.- Bilinear Fine-grained Classification of Ultrasound Images Integratl“-