Adaptive Image Contrast Enhancement by Computing Distances into a 4-Dimensional Fuzzy Unit Hypercube

A new fuzzy procedure for adaptive gray-level image contrast enhancement (CE) is presented in this paper. Starting from the pixels belonging to a normalized gray-level image, an appropriate smooth S-shaped fuzzy membership function (MF) is considered for gray-scale transformation and is adaptively developed through noise reduction and information loss minimization. Then, a set of fuzzy patches is extracted from the MF, and for each support of each patch, we compute four ascending-order statistics that become points inside a 4-D fuzzy unit hypercube after a suitable fuzzification step. CE is performed by computing the distances among the above points and the points of maximum darkness and maximum brightness (special vertexes in the hypercube), and by determining the rotation of the tangent line to the MF around a crucial point where fuzzy patches and the MF coexist. The proposed procedure enables high CE in all the treated images with performance that is fully comparable with that obtained by three more sophisticated fuzzy techniques and by standard histogram equalization

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression

Recent technological advancements in Artificial Intelligence make it easy to create deepfakes and hyper-realistic videos, in which images and video clips are processed to create fake videos that appear authentic. Many of them are based on swapping faces without the consent of the person whose appearance and voice are used. As emotions are inherent in human communication, studying how deepfakes transfer emotional expressions from original to fakes is relevant. In this work, we conduct an in-depth study on facial emotional expression in deepfakes using a well-known face swap-based deepfake database. Firstly, we extracted the photograms from their videos. Then, we analyzed the emotional expression in the original and faked versions of video recordings for all performers in the database. Results show that emotional expressions are not adequately transferred between original recordings and the deepfakes created from them. High variability in emotions and performers detected between original and fake recordings indicates that performer emotion expressiveness should be considered for better deepfake generation or detection. Dades primàries associades a l'article https://doi.org/10.34810/data262This work was supported by the Ministry for Science and Innovation through the State Research Agency (MCIN/AEI/10.13039/501100011033) under grant number (PID2020-117912RB-C22)

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Impact of Nanoparticles and Fillers in Polymer Nanocomposites and Additive Manufacturing

The dissertation presents work that improves our understanding of the impact of soft nanoparticles on the dynamics of linear polymer in all-polymer nanocomposites and the impact of graphene on the thermal and mechanical properties of PLA in fused deposition modeling. Polymer nanocomposites in which soft, polymer-based nanoparticles are dispersed in the polymer matrix have received great interest lately due to their potential use in a range of applications, including drug delivery and self healing materials. However, the impact of this new class of nanoparticles on the dynamics of a linear polymer matrix in an all-polymer nanocomposite is still largely unknown. In the first chapter, we determine the impact of 10 nm radius polystyrene soft nanoparticles on the diffusion of high molecular weight linear PS chains as a function of nanoparticle loading. Our results show that at loadings below 1% of the nanoparticle, the diffusion of the linear matrix increases by a factor of two presumably via a constraint release mechanism, while at loadings above 1% the increase in diffusion is mitigated by confinement effects of the nanoparticles. The transition appears to happen when the distance between nanoparticles is similar to the size of the polymer chain of the matrix (ID/2Rg ~ 1). The next project presents a protocol for determining tracer diffusion coefficients of soft nanoparticles and correlate its topology to observed dynamics. The results suggest that the nanoparticle softness and deformability dictate its motion. Increasing the crosslinking density of the nanoparticle increases its hardness and suppresses its motion in the linear matrix. Additionally, the nanoparticle molecular weight dependence deviates from the exponential dependence for star polymer suggesting that these nanoparticles resemble fractal microgels that benefit from the cooperative motion of the matrix to open pathways for the nanoparticle. The next project, examines the effect of graphene on thermal transport and inter-filament bonding in 3D printing of PLA. The incorporation of graphene at law loadings appears to enhance thermal conductivity and lead to more uniform thermal gradients. Additionally, at low graphene loading, high bed temperatures can be utilized to enhance thermal transfer in the z direction and improve mechanical strength. At higher loadings the improvement in heat transfer is undermined by the slow diffusion of polymer chains due to confinement. Finally, the last project evaluates the impact of graphene on irreversible thermal strains of PLA in FDM. The results demonstrate the potential to mitigating warping through graphene incorporation and control of thermal evolution throughout the printing process

Brain Tumor Segmentation by Generative Adversarial Network (GAN)

The concept of a brain tumor is one of the most significant health issues in terms of both economic and social stability. This disease is extensive growth of abnormal cells in the brain and any growth inside can lead to any serious problem. The cost of a patient’s life is a primary concern, so multiple monitoring and treatment systems are still improving to build up the long-term life expectancy of the better life of those patients who have severe brain tumor problems. However, there exists a lack of data available associated with medical diagnosis and images in which intensive diagnostic analytics (DA) techniques are demanded today. In these cases, accurate performance improvement is a major factor of positive enhancement in treatment and diagnostics by the fact that a lack of medical images has constant distribution compared with real image distributions. Therefore, deep learning of structural variability of brain tumors substantially offers contrast-enhanced images to eliminate attainable data gaps and lacks in image distribution

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas