Pattern Recognition and Computer Vision : 7th Chinese Conference, PRCV 2024, Urumqi, China, October 18–20, 2024, Proceedings, Part XIV / edited by Zhouchen Lin, Ming-Ming Cheng, Ran He, Kurban Ubul, Wushouer Silamu, Hongbin Zha, Jie Zhou, Cheng-Lin Liu.

Format:

Book

Author/Creator:

Lin, Zhouchen.

Contributor:

Cheng, Ming-Ming.

He, Ran.

Ubul, Kurban.

Silamu, Wushouer.

Zha, Hongbin.

Zhou, Jie.

Liu, Cheng-Lin.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 15044

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Artificial intelligence.

Application software.

Computer networks.

Computer systems.

Machine learning.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computer and Information Systems Applications.

Computer Communication Networks.

Computer System Implementation.

Machine Learning.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computer and Information Systems Applications.

Computer Communication Networks.

Computer System Implementation.

Machine Learning.

Physical Description:

1 online resource (586 pages)

Edition:

1st ed. 2025.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2025.

Summary:

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 18–20, 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.

Contents:

A Fine-grained Recurrent Network for Image Segmentation via Vector Field Guided Refinement

Semi-supervised Medical Image Segmentation with Strong/Weak Task-aware Consistency

Steerable Pyramid Transform Enables Robust Left Ventricle Quantification

Semantics Guided Disentangled GAN for Chest X-ray Image Rib Segmentation

MedPrompt: Cross-Modal Prompting for Multi-Task Medical Image Translation

Enhancing Hippocampus Segmentation: Swin

UNETR Model Optimization with CPS

Uncertainty-inspired Credible Pseudo-Labeling in Semi-Supervised Medical Image Segmentation

MFPNet: Mixed Feature Perception Network for Automated Skin Lesion Segmentation

LD-BSAM:Combined Latent Diffusion with Bounding SAM for HIFU target region segmentation

Hierarchical Decoder with Parallel Transformer and CNN for Medical Image Segmentation. -CLASS-AWARE CROSS PSEUDO SUPERVISION FRAMEWORK FOR SEMI-SUPERVISED MULTI-ORGAN SEGMENTATION IN ABDOMINAL CT

SCANSAPAN: Anti-curriculum Pseudo-labelling and Adversarial Noises Training for Semi-supervised Medical Image Classification

Multi-Modal Learning for Predicting the Progression of Transarterial Chemoembolization Therapy in Hepatocellular Carcinoma

Growing with the help of multiple teachers: lightweight and noise-resistant student model for medical image classification

DRA-CN: A novel Dual-Resolution Attention Capsule Network for Histopathology Image Classification

A Mask Guided Network for Self-Supervised Low-Dose CT ImagingDental Diagnosis from X-Ray Panoramic Radiography Images: A Dataset and A Hybrid Framework

Edge-Guided Bidirectional-Attention Residual Network for Polyp SegmentationFrom Coarse to Fine: A Novel Colon Polyp Segmentation Method Like Human Observation

Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification

Multi-Perspective Text-Guided Multimodal Fusion Network for Brain Tumor Segmentation

Continual Learning for Fundus Image Segmentation

Embedded Deep Learning Based CT Images for Rifampicin Resistant Tuberculosis Diagnosis

Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Segmentation

Swin-HAUnet: A Swin-Hierarchical Attention Unet For Enhanced Medical Image Segmentation

ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation

Two-Stage Multi-Scale Feature Fusion for Small Medical Object Segmentation

A Two-Stage Automatic Collateral Scoring Framework Based on Brain Vessel Segmentation

SPARK: Cross-Guided Knowledge Distillation with Spatial Position Augmentation for Medical Image Segmentation

VATBoost-Net: Integrating Enhanced Feature Perturbation and Detail Enhancement for Medical Image Segmentation

DTIL-Net: Dual-Task Interactive Learning Network for Automated Grading of Diabetic Retinopathy and Macular Edema

DeformSegNet: Segmentation Network Fused with Deformation Field for Pancreatic CT Scans

InsSegLN: A Novel 3D Instance Segmentation Method for Mediastinal Lymph NodeRRANet: A Reverse Region-Aware Network with Edge Difference for Accurate Breast Tumor Segmentation in Ultrasound ImagesLearning Frequency and Structure in UDA for Medical Object Detection

Skin Lesion Segmentation Method Based On Global Pixel Weighted Focal Loss

Competing Dual-Network with Pseudo-Supervision Rectification for Semi-Supervised Medical Image Segmentation

Dual-Branch Perturbation and Conflict-Based Scribble-Supervised Meibomian Gland Segmentation.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9784-96-4

OCLC:

1467875827