Proactive 3D Robot Mapping in Environments with Sparse Features

2014 ◽  
pp. 773-782 ◽  
Author(s):  
Jianhao Du ◽  
Weihua Sheng ◽  
Qi Cheng ◽  
Meiqin Liu
Keyword(s):  
Sparse Features

Block kernel nonnegative matrix factorization for face recognition

2019 ◽  
Vol 17 (01) ◽  
pp. 1850059
Author(s):  
Wen-Sheng Chen ◽  
Jingmin Liu ◽  
Binbin Pan ◽  
Yugao Li
Keyword(s):  
Face Recognition ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Superior Performance ◽  
Nonlinear Features ◽  
Data Points ◽  
Class Information ◽  
Sparse Features ◽  
Facial Images

Nonnegative matrix factorization (NMF) is a linear approach for extracting localized feature of facial image. However, NMF may fail to process the data points that are nonlinearly separable. The kernel extension of NMF, named kernel NMF (KNMF), can model the nonlinear relationship among data points and extract nonlinear features of facial images. KNMF is an unsupervised method, thus it does not utilize the supervision information. Moreover, the extracted features by KNMF are not sparse enough. To overcome these limitations, this paper proposes a supervised KNMF called block kernel NMF (BKNMF). A novel objective function is established by incorporating the intra-class information. The algorithm is derived by making use of the block strategy and kernel theory. Our BKNMF has some merits for face recognition, such as highly sparse features and orthogonal features from different classes. We theoretically analyze the convergence of the proposed BKNMF. Compared with some state-of-the-art methods, our BKNMF achieves superior performance in face recognition.

Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

2019 ◽  
Author(s):  
Laisen Nie ◽  
Huizhi Wang ◽  
Shimin Gong ◽  
Zhaolong Ning ◽  
Mohammad S. Obaidat ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Spatio Temporal ◽  
Sparse Features

ICA-based sparse features recovery from fMRI datasets

2010 ◽  
Author(s):  
Gael Varoquaux ◽  
Merlin Keller ◽  
Jean-Baptiste Poline ◽  
Philippe Ciuciu ◽  
Bertrand Thirion
Keyword(s):  
Sparse Features

A robust self-driven surface crack detection algorithm using local features

2020 ◽  
Vol 62 (5) ◽  
pp. 269-276 ◽  
Author(s):  
Aixi Zhu ◽  
Yiming Zhu ◽  
Nizhuan Wang ◽  
Yingying Chen
Keyword(s):  
Surface Crack ◽  
Crack Detection ◽  
Building Materials ◽  
Detection Method ◽  
Detection Algorithm ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Local Texture ◽  
Original Photograph ◽  
Sparse Features

This paper presents an effective image analysis method for visual surface crack detection, called a robust self-driven crack detection algorithm (RSCDA). Firstly, a local texture anisotropy (LTA) is estimated based on self-driven local feature statistics from the original photograph. Secondly, the LTA is used to detect candidate crack pixels. Finally, the actual crack pixels are accurately identified using two effective measurements for connected domains based on discriminative direction and relative sparse features. The results demonstrate that the RSCDA is an effective and robust surface crack detection method for building materials or textiles.

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