W
Wen-Yuan Chen
Researcher at National Chin-Yi University of Technology
Publications - 18
Citations - 185
Wen-Yuan Chen is an academic researcher from National Chin-Yi University of Technology. The author has contributed to research in topics: Image processing & Color image. The author has an hindex of 6, co-authored 18 publications receiving 178 citations.
Papers
More filters
Journal ArticleDOI
Nested image steganography scheme using QR-barcode technique
Wen-Yuan Chen,Jing-Wein Wang +1 more
TL;DR: In this paper, QR bar code and image processing techniques are used to construct a nested steganography scheme that can conceal lossless and lossy secret data into a cover image simultaneously and is robust to JPEG attacks.
Proceedings ArticleDOI
Image Hidden Technique Using QR-Barcode
TL;DR: In this paper, QR(Quick Response) bar code and image processing techniques are used to construct a nested steganography scheme and it is evident that the scheme is robust to JPEG attacks.
Journal ArticleDOI
Transferring color to grayscale images using vector quantization of luminance mapping techniques
Chin-Ho Chung,Wen-Yuan Chen +1 more
TL;DR: An effective algorithm for colorizing a grayscale image using a reference color image, an RGB to color transform (=luminance, =chrominance), and a block-based vector quantization of luminance mapping (VQLM) technique is developed.
Journal ArticleDOI
Face Recognition Based on Projected Color Space With Lighting Compensation
TL;DR: A novel color space conversion method called adaptive projection color space (APCS), which includes two portions: adaptive singular value decomposition and an inner product conversion algorithm for color images.
Journal ArticleDOI
Singular value decomposition combined with wavelet transform for LCD defect detection
TL;DR: In this article, the mean value of the first and second singular value ratios of normal and defect LCD images was obtained by singular value decomposition, and then the third and fourth singular values matched with the standard deviation of first two singular value ratio were used to divide the defect images into two categories: coarse and fine.