红外技术2025，Vol.47Issue(5)：611-618,8.
基于稀疏形状先验与变分正则的典型红外目标分割
Typical Infrared Object Segmentation Based on Sparse Shape Prior and Variational Regularization
摘要
Abstract
The infrared images captured by the uncooled detector often exhibit interference issues,such as blurred edge details and uneven grayscale distribution,which can significantly impact the accuracy of object segmentation.To address this,we propose an enhanced implicit shape representation framework based on a sparse representation model.This framework guides the evolution of implicit shapes using sparse linear combinations of probabilistic shapes drawn from a predefined dictionary.First,representative shape components are selected from the dictionary to form sparse combinations that effectively model the target shape.The object contour prior is implicitly incorporated into the sparse representation,facilitating more accurate contour alignment.A new energy function is then constructed,integrating region-based segmentation with sparse representation.The optimal level-set function is obtained through iterative optimization,ultimately yielding precise object segmentation results.Experimental evaluations demonstrate that the proposed model delivers robust segmentation performance,especially for typical objects in complex backgrounds.关键词
红外图像/目标分割/形状先验/水平集/稀疏表示/压缩感知Key words
infrared image/object segmentation/shape prior/level set/sparse representation/compression sensing分类
计算机与自动化引用本文复制引用
曹敏,王尧..基于稀疏形状先验与变分正则的典型红外目标分割[J].红外技术,2025,47(5):611-618,8.基金项目
教育部产学合作协同育人项目(231105522220441) （231105522220441）
福建省中青年教师教育科研项目资助(JAT220238) （JAT220238）
福建省社会科学研究基地重大项目(FJ2021MJDZ042). （FJ2021MJDZ042）
