- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Fusion of United Sparse Principal Component Analysis Dictionary Based on Linear Unmixing Image Technique
摘要： Based on the linear unmixing images of different surface objects, online dictionary learning algorithm was utilized to compute the sparse dictionaries for multispectral linear unmixing images and panchromatic images. Principal component analysis (PCA) was then utilized to generate united sparse PCA dictionaries through the extraction of the first principal components of panchromatic images and unmixing image dictionaries. The number of dictionaries is determined to be 480 after taking into consideration of the limitation in computing power and root-mean-square error of restructured images. Based on these dictionaries, orthogonal matching pursuit method was utilized to calculate the sparse coefficients of multispectral and panchromatic images, separately, while nonnegative matrix factorization fusion algorithm was utilized to calculate multispectral and panchromatic sparse coefficients to obtain sparse coefficient of the fusional image on all bands, with the resulted matrix having a size of 480 × 255 025. These united sparse PCA dictionaries and fusion sparse coefficients were then used to reconstruct the fusional image. Through the analysis of five quantitative indices of fusion assessment, the proposed fusion algorithm has retained the multispectral information of images and enhanced the detailed information in image texture.
关键词： nonnegative matrix factorization (NMF) fusion,principal component analysis (PCA) dictionary,Linear unmixing,orthogonal matching pursuit (OMP) algorithm,online dictionary learning (ODL) algorithm
更新于2025-09-04 15:30:14
-
[Studies in Computational Intelligence] Recent Advances in Computer Vision Volume 804 (Theories and Applications) || Face Recognition Using Exact Gaussian-Hermit Moments
摘要： Face recognition systems have gained more attention during the last decades. Accurate features are the corner stones in these systems where the performance of recognition and classification processes mainly depends on these features. In this chapter, a new method is proposed for a highly accurate face recognition system. Exact Gaussian-Hermit moments (EGHMs) are used to extract the features of face images where the higher order EGHMs are able to capture the higher-order nonlinear features of these images. The rotation, scaling and translation invariants of EGHMs are used to overcome the geometric distortions. The non-negative matrix factorization (NMF) is a popular image representation method that is able to avoid the drawbacks of principle component analysis (PCA) and independent component analysis (ICA) methods and is able to maintain the image variations. The NMF is used to classify the extracted features. The proposed method is assessed using three face datasets, the ORL, Ncku and UMIST which have different characteristics. The experimental results illustrate the high accuracy of the proposed method against other methods.
关键词： Feature extraction,Face recognition,Exact Gaussian-Hermit moments,Non-negative matrix factorization,Classification
更新于2025-09-04 15:30:14