Name
Title | ||
|---|---|---|
Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix |
Abstract | ||
|---|---|---|
In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, two techniques were employed. In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a Binary Pattern Run Length matrix was employed; this matrix is combination of Binary Pattern and a Run Length matrix. The binary pattern was calculated by randomly selected operator. In order to extract feature of training patch, we calculate statistical texture features from the Binary Pattern Run Length matrix. Moreover we perform some techniques to real-time operation, such as control the number of binary test. Experimental results show that our algorithm is efficient and robust against illumination change. |
Year | DOI | Keywords |
|---|---|---|
2014 | 10.1186/s13673-014-0009-7 | Head pose estimation, Random forests, Binary pattern, Run Length matrix, Illumination-invariant |
Field | DocType | Volume |
Data mining,Binary pattern,Matrix (mathematics),Computer science,Pose,Invariant (mathematics),Operator (computer programming),Random forest,Classifier (linguistics),Binary number | Journal | 4 |
Issue | ISSN | Citations |
1 | 2192-1962 | 9 |
PageRank | References | Authors |
0.52 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyunduk Kim | 1 | 49 | 10.91 |
| Sang-Heon Lee | 2 | 105 | 22.48 |
| Myoung-Kyu Sohn | 3 | 33 | 7.17 |
| Dong-Ju Kim | 4 | 65 | 11.80 |