Chi-Fang Lin

Bio: Chi-Fang Lin is an academic researcher from Yuan Ze University. The author has contributed to research in topics: Panchromatic film & Distortion. The author has an hindex of 5, co-authored 7 publications receiving 857 citations.

Hiding data in images by optimal moderately-significant-bit replacement

Abstract: A data hiding technique for the storage and transmission of important data is proposed. It embeds the important data in the moderately-significant-bit of an image, and applies a global substitution step and a local pixel adjustment process to reduce any image degradation. Experimental results show that the visual quality of the resulting image is acceptable.

Approach to maximize increased details and minimize color distortion for IKONOS and QuickBird image fusion

Abstract: Both IKONOS and QuickBird offer 11-bit panchromatic and multispectral data in which more details can be extracted from scenes that are very dark under shadows or very washed out due to excessive sun reflectance. In this work, a discrete cosine transform (DCT)-based single-scale Retinex (SSR) technique is presented to extract the highest possible spatial details from the panchromatic image, and a minimum/maximum percent cutoff approach is used to preserve the most spectral information in the multispectral imagery. By fusing those two images, the typical bright target recovery (BTR) response can be avoided. Furthermore, to address the color distortion problem by the fusion process, a fast intensity hue saturation (IHS) image fusion technique with direct saturation stretching in RGB space is also proposed. To verify the efficacy of the proposed techniques, experiments are conducted using real IKONOS and QuickBird imagery covering different areas. Experimental results have shown that the perfect performance, maximizing increased details and minimizing color distortion, is achieved.

Target-cluster fusion approach for classifying high resolution IKONOS imagery

Abstract: To extract GIS features from high spatial resolution imagery is an important task in remote sensing applications. However, traditional pixel-based classification methods, which were developed in the era of 10–100 m ground pixel size imagery, cannot exploit the advantages of new images provided by IKONOS and QuickBird. This is due to the increase of the within-class variability inherent from more detailed and higher spatial resolution data. To successfully extract various land covers from high resolution imagery, a target-clustering fusion (TCF) system is presented in the paper. Compared to the conventional classification methods that typically produce more salt-and-pepper-like results, the proposed TCF system can preserve detailed spatial information on each classified target related to its neighbours. To evaluate the efficacy of TCF, experiments are conducted using real IKONOS images.

Automatic Signature Verification: The State of the Art

Abstract: In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

Image steganographic scheme based on pixel-value differencing and LSB replacement methods

Abstract: In order to improve the capacity of the hidden secret data and to provide an imperceptible stego-image quality, a novel steganographic method based on least-significant-bit (LSB) replacement and pixel-value differencing (PVD) method is presented. First, a different value from two consecutive pixels by utilising the PVD method is obtained. A small difference value can be located on a smooth area and the large one is located on an edged area. In the smooth areas, the secret data is hidden into the cover image by LSB method while using the PVD method in the edged areas. Because the range width is variable, and the area in which the secret data is concealed by LSB or PVD method are hard to guess, the security level is the same as that of a single using the PVD method of the proposed method. From the experimental results, compared with the PVD method being used alone, the proposed method can hide a much larger information and maintains a good visual quality of stego-image.

Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems

Abstract: This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.