Extensions of principle component analysis with applications on vision based computing

Abstract

This paper mainly focuses on the principle component analysis (PCA) and its applications on vision based computing. The underlying mechanism of PCA given and several significant factors, involved with subspace training are discussed theoretically in detail including principle components energy, residuals assessment, and decomposition computation. The typical extensions, including probabilistic PCA (PPCA), kernel PCA (KPCA), multi-dimensional PCA and robust PCA (RPCA), have been presented with critical analysis on both mechanisms and computations. Combining with the studies on, such as, image compression, visual tracking, image recognition and super-resolution image reconstruction, PCA and its extensions applied to computer vision are critically reviewed and evaluated on the targeted issues in each application as well as the role they played at specific tasks to the characteristics, cost and suitable situations of each PCA based vision processing technique.

Pengenal Wajah Real Time Berbiaya Rendah Menggunakan Fpga Digilent Pynq-Z1 Dengan Metode Pca

“…Dalam literatur, dapat ditemukan berbagai pendekatan berbasis perangkat lunak untuk pengenalan wajah. Dalam [2] aplikasi PCA dibahas dalam komputasi berbasis visi. Hasil penelitian menunjukkan bahwa pengurangan dimensi berbasis PCA dan klasifikasi gambar memiliki potensi untuk industrialisasi, tetapi membutuhkan jumlah komputasi yang besar.…”

Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis

“…e most well known is the principal component analysis (PCA) [13], which aims to preserve the main energy of the original feature set and maximize the second-order statistics of original features [3]. For its superior capability of maintaining global data structure, the PCA is widely used as a data preprocessing and feature extraction technique for face recognition, computer vision, and fault detection [14][15][16][17]. Despite some advantages, PCA is not suitable for manifoldstructure data because it is a linear method that only considers the global Euclidean structure of samples.…”

FPGA-based low-cost real-time face recognition

“…In the literature various software-based approaches can be found for face recognition. In [2] the applications of PCA are discussed in vision based computing. The results showed, that PCA based dimension reduction and image classification has a potential for industrialization, but it requires large amount of computation.…”