基于稀疏表达的森林火焰及烟雾多层次奇异性特征识别机制研究

项目名称： 基于稀疏表达的森林火焰及烟雾多层次奇异性特征识别机制研究

项目编号： No.61262031

项目类型： 地区科学基金项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 蒋先刚

作者单位： 华东交通大学

项目金额： 40万元

中文摘要： 针对复杂环境下运动云台摄制的森林图片中的火焰及烟雾的各特征数据的界限模糊，使得在现有彩色空间和分类方法下仍不能精确快速地对其进行定位和识别现象，引起人们对其特征空间定义、变换、融合和用多层次稀疏整体表达和分类机制产生研究兴趣。本研究围绕火焰及烟雾在新的彩色空间的自适应正交变换方法展开，探索基于颜色、边缘和纹理等特征的多层稀疏表示的图像识别算法，进行火焰及烟雾区域定位和识别方法的比较性研究，为复杂环境下森林火灾识别系统的设计提供方法和指导。重点研究：（1）对火焰及烟雾的在彩色空间的自适应变换方法和颜色奇异性特征描述的研究；（2）探索火焰及烟雾的多层次稀疏表达和分类机制和方法；（3）探索基于颜色、边缘和纹理等的多层稀疏表示的图像识别算法，（4）完成与稀疏表达相关的图像特征和分类的各种方法比较研究；（5）建立火焰及烟雾区域定位和识别方法的评价体系，为新型森林火灾快速识别系统的设计和集成奠定基础。

中文关键词： 彩色空间转换；稀疏表达；特征词袋；火焰探测；特征融合

英文摘要： Due to the obscurity boundary of features of flame and smog in a forest image captured by a moving tripod head in complex natural environment, the region rapidly orientated and accurately recognized are difficult in currently used color spaces and available classification methods. The feature space's orithognal transfermation making them with less ralativity is reasonable and consistent with image sparse representaion. This project aims at revealing the multi-level sparse representation and classification mechanism of flame and smog, probes image recognition algorithm based on multi-level sparse representation, and finally provides proposals methodolog for the design of forest fire detecting system in complicated conditions. The project is going to be undertaken in 3 aspects as followings: (1) Do research into the color transformation and the textures correlation of flame and smog, and compare the properties between each channel in available color spaces. The definition and transformation of each channel of flame and smog and the selection and fusion of their features depend on the method used for processing data in higher-dimension space in theory. The orthogonalization of the color and feature space makes the boundary of each variable demarcated through using principal component analysis with minimum correlat

英文关键词： Color Space Transformation；Sparse Representation；Feature Word Bag；Fire Detection；Feature Fusion