期刊
MULTIMEDIA TOOLS AND APPLICATIONS
卷 80, 期 4, 页码 5997-6017出版社
SPRINGER
DOI: 10.1007/s11042-020-09859-6
关键词
Deep features correlation analysis; Robot vision; Dimension reduction; Transfer learning
类别
资金
- National Natural Science Foundation of China [61972183, 61672268]
- National Engineering Laboratory Director Foundation of Big Data Application for Social Security Risk Perception and Prevention
In the era of Industry 4.0, the paper introduces a new model called Deep Three Dimensional Spearman Correlation Analysis (D3D-SCA) to tackle nonlinear dimensionality reduction in robot vision for three-dimensional data. This model utilizes deep learning for feature mapping, spearman correlation analysis for deep feature comparison, and a customized classifier for industrial robot vision applications, showing effectiveness in experimental datasets.
During the era of Industry 4.0, the industrial robot monitoring process is getting success and popularity day by day. It also plays a vital role in the enhancement of robot vision algorithms. This paper proposed a model Deep Three Dimensional Spearman Correlation Analysis (D3D-SCA) to address nonlinear dimensionality reduction in robot vision for three-dimensional data. Dealing with three-dimensional multimedia datasets using traditional algorithms, to date, researchers have been facing limitations and challenges because mostly sub-space learning algorithms and their developments cannot perform satisfactorily in most of the time with linear and non-linear data dependency. The proposed model directly finds the relations between two sets of three-dimensional data without reshaping the data into 2D-matrices or vectors and dramatically reduces the dimensional reduction and computational algorithm complexity. The proposed model extracts deep information and translates it into a decision. To do so, three components are employed in the proposed model: customized deep learning model Inception_V3 for deep feature mapping, three-dimensional spearman correlation analysis for comparing pairwise deep features without a singular matrix and spatial dilemma problem, and the customized Xception classifier with automatic online updating ability and adjustable neural architecture for low latency models. The motivation of the proposed model is to advance the scalability of existing industrial robot vision applications which based on recognition, detection and re-identification approaches. Extensive findings on industrial datasets named 3D Objects on turntable and Caltech 101 demonstrate the effectiveness of the proposed model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据