With the rapid development of Internet technology, the copyright protection of color images has become more and more important. In order to fulfill this purpose, this paper designs a blind color digital image watermarking method based on image correction and eigenvalue decomposition (EVD). Firstly, all the eigenvalues of the pixel block in the color host image are obtained by EVD. Then, the sum of the absolute value of the eigenvalues is quantified by the variable quantization steps to embed the color watermark image that encrypted by affine transform and encoded by the Hamming code. If the watermarked image is processed by geometric attack, then the attacked image can be corrected by using the geometric attributes. Finally, the inverse embedding process is performed to extract the color watermark. Moreover, the advantages of the proposed method are shown as follows: (1) all Peak Signal-to-noise Ratio (PSNR) values are greater than 42 dB; (2) the average Structural Similarity Index Metric (SSIM) values are greater than 0.97; (3) the maximum embedded capacity is 0.25bpp; (4) whole running-time is less than 20 s; (5) the key space is more than 2450; (6) most Normalized Cross-correlation (NC) values are more than 0.9. Compared with the related methods, the experimental results show that the proposed method not only has better watermark invisibility and larger watermark capacity, but also has higher security and stronger robustness against geometric attacks.

A blind color digital image watermarking method based on image correction and eigenvalue decomposition

A robust adaptive blind color image watermarking for resisting geometric attacks

Segmenting the foreground objects from an image is an essential low-level step for many expert and intelligent systems, and the success of this key process largely depends on the amount of available training data. However, the cost for obtaining annotations is still a bottleneck, requiring enormous human effort. Thus, in order to obtain higher segmentation accuracy without the need of annotations, computer vision researchers attempted to segment simultaneously the common regions from collections of images. Indeed, when images that share common foreground objects are available, we talk about cosegmentation. The task of cosegmentation can be considered as weakly supervised segmentation, seen that the input image set is sharing common foreground objects. In fact, several methods have been proposed while displaying different frameworks, models and strategies of work, for enhancing the cosegmentation accuracy. This work presents a comprehensive review of relevant cosegmentation methods, carried out extensively and in depth and completed by performance measurements. Indeed, the existing cosegmentation methods are roughly classified in this paper into the following categories: Markov random fields-based cosegmentation, Co-saliency-based cosegmentation, Image decomposition-based cosegmentation, Random Walker-based cosegmentation, Maps-based cosegmentation, Active contours-based cosegmentation, Clustering-based cosegmentation and Deep learning-based cosegmentation. For each category, we discuss the most relevant methods while presenting the proposed problem to deal, the degree of supervision, the adopted features, the number and the complexity of input images as well as the presented models and frameworks. Then, in order to exhibit and evaluate objectively and comprehensively the most relevant state-of-the-art methods, we provide a comparative study on various challenging datasets (iCoseg, MSRC, Internet, FlickrMFC and PASCAL-VOC datasets). The objective is to show the performances and the limitations of these methods, while using different metrics and providing the computation cost for many methods. In addition, we discuss various cosegmentation challenges, issues and some applications, what can be useful and helpful to understand the cosegmentation problem. Finally, we suggest some research directions for future research on image cosegmentation.

A comprehensive overview of relevant methods of image cosegmentation

Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation

Introduction: Hand washing is an activity of removing microorganism from hand using soap and flowing water. Video, image, and song methods are the ones that can be used for school-age children to provide health education about hand washing. The objective of research was to find out the effectiveness of video, image, and song methods on hand washing behavior. Method: This study was a research using Pre-experiment design with pre-post test design, the one using causal relationship without control group participation. The population of research consisting of 90 students. Technique of analyzing data used was to compare mean scores of video, image, and song methods. Result: The result of research showed that the mean score of hand washing ability in video group was 2.183 before intervention and 5.727 after intervention; there was an increase by 3.545 point in the mean score of hand washing ability. In Image group, the mean score of hand washing ability was 2.273 before intervention and 4.545 after intervention; there was increase by 2.273 in the mean score of hand washing ability. Meanwhile, in song group, the mean score of hand washing ability was 2.273 before intervention and 6.00 after intervention; there was increase by 3.727 in the mean score of hand washing ability. Analysis and Discussion: Respondents in song group had highest increase in the hand washing ability compared with those in video and image groups. Song method could be an alternative choice for hand washing practice.

http://ijnms.net/index.php/ijnms/article/download/133/62

The Effectiveness Of Method, Video, Image, And Song On Hand Washing Behavior In 1st-3rd Graders Of Sdit Permata Mulia Mojokerto

The processing on semantic and visual information has always been a challenging task and co-segmentation is one of them. Co-segmentation is a classic, demanding and most studied topic in literature of computer vision not only because of its challenging nature but also due to its countless applications. During the past 20 years, several general-purpose algorithms and techniques have been developed publicly available benchmarks, as well as the progress in the development of robust computer vision algorithms. In this paper, we review the current activities and recent advances in real-world image cosegmentation methodologies and techniques. These techniques are roughly categorized into two general schemes: Unsupervised and Interactive. Which are mainly based on energy optimization, clustering, random walk, heat diffusion based and others. This paper also explains the features, weaknesses and computational complexity of the different methods. Additionally, we also discuss some important issues, applications and future of the image co-segmentation.

Survey: Recent Trends and Techniques in Image Co-Segmentation Challenges, Issues and Its Applications

Medical image compression is in high demand in the medical field, since it reduces the required time to transmit an image in a bandwidth or store an image for medical purposes. Modern medical image diagnostics involves a large set of medical images that are often based on MRI, XRays or CT. However, the introduction of new technologies and the fast development of existing ones has resulted in a high demand for bandwidth. In this article, the proposed image compression method for medical images it uses the discrete wavelet transform as a compression tool, which provides a better compression ratio without losing valuable information and through the implementation of the proposed enhanced run length encoder, by the use of the elegant pairing function, we can achieve higher compression ratios and high visual quality. Despite this, the fidelity is subject to the capabilities and limitations of the Human Visual System.

A Akhtar, Mzu Baig, W haider

Papers

Survey: Recent Trends and Techniques in Image Co-Segmentation Challenges, Issues and Its Applications

An Enhanced Run Length Encoding using an Elegant Pairing Function for Medical Image Compression