Most of the past document image watermarking schemes focus on providing same level of integrity and copyright protection for information present in the source document image. However, in a document image the information contents possess various levels of sensitivity. Each level of sensitivity needs different type of protection and this demands multiple watermarking techniques. In this paper, a novel intelligent multiple watermarking techniques are proposed. The sensitivity of the information content of a block is based on the homogeneity and relative energy contribution parameters. Appropriate watermarking scheme is applied based on sensitivity classification of the block. Experiments are conducted exhaustively on documents. Experimental results reveal the accurate identification of the sensitivity of information content in the block. The results reveal that multiple watermarking schemes has reduced the amount of data to be embedded and consequently improved perceptual quality of the watermarked image.

Computer Vision, Graphics, and Image Processing

Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective

Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression

The properties of fiber-reinforced composite materials greatly depend on the morphology of reinforcing fibers within the base materials, i.e., spatial uniformity, orientation, and length distribution. Accurately extracting this information in an automated manner from SEM images is essential for quality assessment, quality control, and process optimization. However, due to overlapping or cross-linking issue, morphological fiber extraction is very challenging and has not been well addressed in the existing literature. This paper takes into account this research gap and proposes five different methods, namely, the opening method, simple Hough transform (HT), partitioning HT, gradient-based HT, and break-merge method to automatically extract the straight fibers from SEM images to facilitate the morphological analysis. The robustness and accuracy of the proposed methods are evaluated and compared through both simulation and real case studies.

Automatic morphological extraction of fibers from SEM images for quality control of short fiber-reinforced composites manufacturing

http://manuscript.elsevier.com/S1047320322000657/pdf/S1047320322000657.pdf

Improving lung region segmentation accuracy in chest X-ray images using a two-model deep learning ensemble approach

The morphology of fibers (e.g., spatial uniformity and orientation) plays a decisive role in determining the material properties or fabrication quality of fiber-reinforced nanocomposites. The existing literature lacks a reliable and automatic fiber extraction method for morphology analysis based on the scanning electron microscope (SEM) images. This paper proposes four different methods, namely, the simple Hough Transform, opening method, partitioning Hough Transform and gradient based Hough Transform, to automatically identify the fibers from SEM images to expedite the morphology analysis. The performance of these methods are thoroughly evaluated and compared through simulation studies and real case studies.

Automated Fiber Extraction From SEM Images With Application to Quality Control of Fiber-Reinforced Composites Manufacturing

Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients. 

https://doi.org/10.1016/j.mlwa.2022.100365

Md. Fashiar Rahman

Papers

Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective

Automatic morphological extraction of fibers from SEM images for quality control of short fiber-reinforced composites manufacturing

Improving lung region segmentation accuracy in chest X-ray images using a two-model deep learning ensemble approach

Automated Fiber Extraction From SEM Images With Application to Quality Control of Fiber-Reinforced Composites Manufacturing

Time-to-event modeling for hospital length of stay prediction for COVID-19 patients