期刊文献+
共找到3篇文章
< 1 >
每页显示 20 50 100
Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching
1
作者 Khuram Nawaz Khayam Zahid Mehmood +4 位作者 Hassan Nazeer Chaudhry Muhammad Usman Ashraf Usman Tariq Mohammed Nawaf Altouri Khalid Alsubhi 《Computers, Materials & Continua》 SCIE EI 2022年第3期5039-5058,共20页
Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems... Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets. 展开更多
关键词 Face recognition local tetra patterns spatial pyramid matching robust kernel representation max-pooling
下载PDF
Scale invariant features extraction for stereo vision 被引量:4
2
作者 Liu Li Peng Fuyuan +1 位作者 Tian Yani Wan Yaping 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第1期50-54,共5页
Stable local feature detection is a fundamental component of many stereo vision problems such as 3-D reconstruction, object localization, and object tracking. A robust method for extracting scale-invariant feature poi... Stable local feature detection is a fundamental component of many stereo vision problems such as 3-D reconstruction, object localization, and object tracking. A robust method for extracting scale-invariant feature points is presented. First, the Harris corners in three-level pyramid are extracted. Then, the points detected at the highest level of the pyramid are correctly propagated to the lower level by pyramid based scale invariant (PBSI) method. The corners detected repeatedly in different levels are chosen as final feature points. Finally, the characteristic scale is obtained based on maximum entropy method. The experimental results show that the algorithm has low computation cost, strong antinoise capability, and excellent performance in the presence of significant scale changes. 展开更多
关键词 pyramid matching scale invariant Harris corners characteristics scale.
下载PDF
Robust Texture Classification via Group-Collaboratively Representation-Based Strategy 被引量:1
3
作者 Xiao-Ling Xia Hang-Hui Huang 《Journal of Electronic Science and Technology》 CAS 2013年第4期412-416,共5页
In this paper, we present a simple but powerful ensemble for robust texture classification. The proposed method uses a single type of feature descriptor, i.e. scale-invariant feature transform (SIFT), and inherits t... In this paper, we present a simple but powerful ensemble for robust texture classification. The proposed method uses a single type of feature descriptor, i.e. scale-invariant feature transform (SIFT), and inherits the spirit of the spatial pyramid matching model (SPM). In a flexible way of partitioning the original texture images, our approach can produce sufficient informative local features and thereby form a reliable feature pond or train a new class-specific dictionary. To take full advantage of this feature pond, we develop a group-collaboratively representation-based strategy (GCRS) for the final classification. It is solved by the well-known group lasso. But we go beyond of this and propose a locality-constraint method to speed up this, named local constraint-GCRS (LC-GCRS). Experimental results on three public texture datasets demonstrate the proposed approach achieves competitive outcomes and even outperforms the state-of-the-art methods. Particularly, most of methods cannot work well when only a few samples of each category are available for training, but our approach still achieves very high classification accuracy, e.g. an average accuracy of 92.1% for the Brodatz dataset when only one image is used for training, significantly higher than any other methods. 展开更多
关键词 Dictionary learning group lasso localconstraint spatial pyramid matching textureclassification.
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部