Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

© 2018 Awan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053. The content of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University

Artificial Intelligence Tools for Facial Expression Analysis.

Inner emotions show visibly upon the human face and are understood as a basic guide to an individual’s inner world. It is, therefore, possible to determine a person’s attitudes and the effects of others’ behaviour on their deeper feelings through examining facial expressions. In real world applications, machines that interact with people need strong facial expression recognition. This recognition is seen to hold advantages for varied applications in affective computing, advanced human-computer interaction, security, stress and depression analysis, robotic systems, and machine learning. This thesis starts by proposing a benchmark of dynamic versus static methods for facial Action Unit (AU) detection. AU activation is a set of local individual facial muscle parts that occur in unison constituting a natural facial expression event. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. For this research, AU occurrence activation detection was conducted by extracting features (static and dynamic) of both nominal hand-crafted and deep learning representation from each static image of a video. This confirmed the superior ability of a pretrained model that leaps in performance. Next, temporal modelling was investigated to detect the underlying temporal variation phases using supervised and unsupervised methods from dynamic sequences. During these processes, the importance of stacking dynamic on top of static was discovered in encoding deep features for learning temporal information when combining the spatial and temporal schemes simultaneously. Also, this study found that fusing both temporal and temporal features will give more long term temporal pattern information. Moreover, we hypothesised that using an unsupervised method would enable the leaching of invariant information from dynamic textures. Recently, fresh cutting-edge developments have been created by approaches based on Generative Adversarial Networks (GANs). In the second section of this thesis, we propose a model based on the adoption of an unsupervised DCGAN for the facial features’ extraction and classification to achieve the following: the creation of facial expression images under different arbitrary poses (frontal, multi-view, and in the wild), and the recognition of emotion categories and AUs, in an attempt to resolve the problem of recognising the static seven classes of emotion in the wild. Thorough experimentation with the proposed cross-database performance demonstrates that this approach can improve the generalization results. Additionally, we showed that the features learnt by the DCGAN process are poorly suited to encoding facial expressions when observed under multiple views, or when trained from a limited number of positive examples. Finally, this research focuses on disentangling identity from expression for facial expression recognition. A novel technique was implemented for emotion recognition from a single monocular image. A large-scale dataset (Face vid) was created from facial image videos which were rich in variations and distribution of facial dynamics, appearance, identities, expressions, and 3D poses. This dataset was used to train a DCNN (ResNet) to regress the expression parameters from a 3D Morphable Model jointly with a back-end classifier

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area