Cam-Unet: Print-Cam Image Correction for Zero-Bit Fourier Image Watermarking.

Image watermarking often involves the use of handheld devices under non-structured conditions for authentication purposes, particularly in the print-cam process where smartphone cameras are used to capture watermarked printed images. However, these images frequently suffer from perspective distortions, making them unsuitable for automated information detection. To address this issue, Cam-Unet, an end-to-end neural network architecture, is presented to predict the mapping from distorted images to rectified ones, specifically tailored for print-cam challenges applied to ID images. Given the limited availability of large-scale real datasets containing ground truth distortions, we created an extensive synthetic dataset by subjecting undistorted images to print-cam attacks. The proposed network is trained on this dataset, using various data augmentation techniques to improve its generalization capabilities. Accordingly, this paper presents an image watermarking system for the print-cam process. The approach combines Fourier transform-based watermarking with Cam-Unet as perspective distortion correction. Results show that the proposed method outperforms existing watermarking approaches typically employed to counter print-cam attacks and achieves an optimal balance between efficiency and cost-effectiveness.

Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet).

The use of thermography in the early diagnosis of Diabetic Foot (DF) has proven its effectiveness in identifying areas of the plantar foot that are susceptible to ulcer development. Segmentation of the foot sole is one of the most pertinent technical issues that must be performed with great precision. However, because of the inherent difficulties of foot thermal images, such as unclarity and the existence of ambiguities, segmentation approaches have not demonstrated sufficiently accurate and reliable results for clinical use. In this study, we aim to develop a fully automated, robust and accurate segmentation of the diabetic foot. To this end, we propose a deep neural network architecture adopting the encoder-decoder concept called Double Encoder-ResUnet (DE-ResUnet). This network combines the strengths of residual network and U-Net architecture. Moreover, it takes advantage of RGB (Red, Green, Blue) colour images and fuses thermal and colour information to improve segmentation accuracy. Our database consists of 398 pairs of thermal and RGB images. The population includes two groups. The first group of 54 healthy subjects. And a second group of 145 diabetic patients from the National Hospital Dos de Mayo in Peru. The dataset is splitted into 50% for training, 25% for validation and the last 25% is used for testing. This proposed model provided robust and accurate automatic segmentations of the DF and outperformed other state of the art methods with an average intersection over union (IoU) of 97%. In addition, it is able to accurately delineate the part of toes and heels which are high risk regions for ulceration.

Predicting learner's performance through video sequences viewing behavior analysis using educational data-mining.

This paper analyzes how learners interact with the pedagogical sequences of educational videos, and its effect on their performance. In this study, the suggested video courses are segmented on several pedagogical sequences. In fact, we're not focusing on the type of clicks made by learners, but we're concentrating on the pedagogical sequences in which those clicks were made. We focalize on the interpretation of the path followed by a learner watching an educational video, and the way they navigate the pedagogical sequences of that video, in order to predict whether a learner can pass or fail the video course. Learner's video clicks are collected and classified. We applied educational data mining technique using K-nearest Neighbours and Multilayer Perceptron algorithms to predict learner's performance. The classification results are acceptable, the kNN classifier achieves the best results with an average accuracy of 65.07%. The experimental result indicates that learners' performance could be predicted, we notice a correlation between video sequence viewing behavior and learning performances. This method may help instructors understand the way learners watch educational videos. It can be used for early detection of learners' video viewing behavior deviation and allow the instructor to provide well-timed, effective guidance.

Wound Size Imaging: Ready for Smart Assessment and Monitoring.

Significance: We introduce and evaluate emerging devices and modalities for wound size imaging and also promising image processing tools for smart wound assessment and monitoring. Recent Advances: Some commercial devices are available for optical wound assessment but with limited possibilities compared to the power of multimodal imaging. With new low-cost devices and machine learning, wound assessment has become more robust and accurate. Wound size imaging not only provides area and volume but also the proportion of each tissue on the wound bed. Near-infrared and thermal spectral bands also enhance the classical visual assessment. Critical Issues: The ability to embed advanced imaging technology in portable devices such as smartphones and tablets with tissue analysis software tools will significantly improve wound care. As wound care and measurement are performed by nurses, the equipment needs to remain user-friendly, enable quick measurements, provide advanced monitoring, and be connected to the patient data management system. Future Directions: Combining several image modalities and machine learning, optical wound assessment will be smart enough to enable real wound monitoring, to provide clinicians with relevant indications to adapt the treatments and to improve healing rates and speed. Sharing the wound care histories of a number of patients on databases and through telemedicine practice could induce a better knowledge of the healing process and thus a better efficiency when the recorded clinical experience has been converted into knowledge through deep learning.