杨世强,弓逯琦.Log Gaussian Cox场手部指节的图像偏移特征学习与识别[J].农业机械学报,2017,48(1):353-360.
YANG Shiqiang,GONG Luqi.Excursion Characteristic Learning and Recognition for Hand Image Knuckles Based on Log Gaussian Cox Field[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(1):353-360.
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Log Gaussian Cox场手部指节的图像偏移特征学习与识别   [下载全文]
Excursion Characteristic Learning and Recognition for Hand Image Knuckles Based on Log Gaussian Cox Field   [Download Pdf][in English]
投稿时间:2016-08-09  
DOI:10.6041/j.issn.1000-1298.2017.01.047
中文关键词:  Log Gaussian Cox随机场  偏移特征学习  手部指节识别
基金项目:国家自然科学基金项目(51475365)、陕西省教育厅省级重点实验室科学研究计划项目(12JS071)和陕西省教育厅科学研究计划项目(2013JK1000)
作者单位
杨世强 西安理工大学 
弓逯琦 西安理工大学 
中文摘要:针对手部指节图像结构特征模糊与建模困难的问题,以Log Gaussian Cox随机场为图像建模基础,给出了随机图像上偏移特征的抽取与学习方法,实现了手部图像中指节的识别。在缺乏Cox过程图像模型先验假设的条件下,结合随机图像的水平集分解,得到了图像偏移表示的逼近结果。在图像灰度分布非参数密度核估计基础上,利用非线性各向异性滤波对偏移特征进行增强,建立了偏移测度特征的Bayesian估计。提出了不同偏移参数下偏移特征的模型学习与融合算法,获得了指节图像特征的融合表示,并在手部指节图像数据库中比较了不同分层偏移模型下的识别结果,给出了批量识别ROC曲线统计规律。结果表明,识别方法具有较为稳定的正确分类能力,具有可行性。
YANG Shiqiang  GONG Luqi
Xi’an University of Technology and Xi’an University of Technology
Key Words:Log Gaussian Cox random field  excursion characteristic leaning  hand knuckles recognition
Abstract:The effective description method for hand gesture is the most important in intelligent coordination assembly process based on human computer interaction. And effective hand finger knuckle detection is beneficial to the description of hand gesture.The structure characteristics of hand knuckles image are fuzzy and it is difficult to feature modeling. The extraction and learning method of excursion characteristic for hand knuckles image was presented and the hand knuckle was recognized by hand image based on Log Gaussian Cox random image model theory. The approximations of image excursion representation were given combined with level set decomposition of random image when the priori hypothesis was absented in Cox process image model. On the basis of nonparametric kernel estimation of image gray distribution, excursion characteristic was enhanced by nonlinear anisotropic filtering. And the Bayesian form of excursion measurement was established. The model learning and feature fusion algorithm on excursion characteristics with different excursion parameters was presented. And the features fusion representation of hand knuckle image was acquired. The hand knuckles image recognition results with many different hierarchical excursion data models were compared. The knuckle detection algorithm on hand image was presented. The ROC curves statisical law of hand knuckles detection with defferent models showed that the classification ablility of this method was correct and stable.The results also showed that the knuckle recognition ability of the model had some difference for different knuckle categories, and there were some differences in the deep distribution of image data between far knuckles and mid-knuckles. And the method was feasible.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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