1,812 research outputs found

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    International consensus statement on allergy and rhinology: Allergic rhinitis – 2023

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    Background In the 5 years that have passed since the publication of the 2018 International Consensus Statement on Allergy and Rhinology: Allergic Rhinitis (ICAR-Allergic Rhinitis 2018), the literature has expanded substantially. The ICAR-Allergic Rhinitis 2023 update presents 144 individual topics on allergic rhinitis (AR), expanded by over 40 topics from the 2018 document. Originally presented topics from 2018 have also been reviewed and updated. The executive summary highlights key evidence-based findings and recommendation from the full document. Methods ICAR-Allergic Rhinitis 2023 employed established evidence-based review with recommendation (EBRR) methodology to individually evaluate each topic. Stepwise iterative peer review and consensus was performed for each topic. The final document was then collated and includes the results of this work. Results ICAR-Allergic Rhinitis 2023 includes 10 major content areas and 144 individual topics related to AR. For a substantial proportion of topics included, an aggregate grade of evidence is presented, which is determined by collating the levels of evidence for each available study identified in the literature. For topics in which a diagnostic or therapeutic intervention is considered, a recommendation summary is presented, which considers the aggregate grade of evidence, benefit, harm, and cost. Conclusion The ICAR-Allergic Rhinitis 2023 update provides a comprehensive evaluation of AR and the currently available evidence. It is this evidence that contributes to our current knowledge base and recommendations for patient evaluation and treatment

    Spatial, temporal, and circuit-specific activation patterns of basolateral amygdala projection neurons during stress

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    In humans and rodents, the amygdala is rapidly activated by stress and hyperactivated in conditions of pathological stress or trauma. However, there is a striking lack of information of the anatomical specificity of amygdala subregions and circuits explicitly activated by stress, and of its role in governing typical responses to stress such as hypothalamic-pituitary-adrenal (HPA) axis activation. The overarching aim of this thesis was to conduct a systematic investigation of the spatial, temporal, and circuit-specific activation patterns of basolateral amygdala (BLA) projection neurons during exposure to acute stress. Additionally, we explicitly tested the role of the BLA in activation of the HPA axis, as this remains a poorly understood process. Chapter 1 describes how the BLA is anatomically well-situated for cognitive evaluation of emotional stimuli and describes the role of the BLA in diverse behavioural and physiological processes via efferent projections to many different brain structures. Chapter 2 identifies a common BLA subregion that is responsive to stressful stimuli, albeit with distinct temporal activation patterns, and which bidirectionally influences HPA axis activity. Chapter 3 maps the topographical distribution of six different populations of projection neurons throughout the BLA, and demonstrates that, although widely activated by stress exposure, inhibition of isolated populations does not influence HPA axis activity. Chapter 4 investigates the topographical distribution and stress-induced activation of BLA neurons expressing corticotropin-releasing hormone receptor type I (CRHR1), which, just like discrete circuits, does not influence HPA axis activity on its own. Together, this emphasizes the heterogeneity of BLA projection populations, while providing evidence that a large, diverse population of BLA projection neurons are activated by exposure to acute psychological stress

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    The role of facial movements in emotion recognition

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    Most past research on emotion recognition has used photographs of posed expressions intended to depict the apex of the emotional display. Although these studies have provided important insights into how emotions are perceived in the face, they necessarily leave out any role of dynamic information. In this Review, we synthesize evidence from vision science, affective science and neuroscience to ask when, how and why dynamic information contributes to emotion recognition, beyond the information conveyed in static images. Dynamic displays offer distinctive temporal information such as the direction, quality and speed of movement, which recruit higher-level cognitive processes and support social and emotional inferences that enhance judgements of facial affect. The positive influence of dynamic information on emotion recognition is most evident in suboptimal conditions when observers are impaired and/or facial expressions are degraded or subtle. Dynamic displays further recruit early attentional and motivational resources in the perceiver, facilitating the prompt detection and prediction of others’ emotional states, with benefits for social interaction. Finally, because emotions can be expressed in various modalities, we examine the multimodal integration of dynamic and static cues across different channels, and conclude with suggestions for future research

    Face Image and Video Analysis in Biometrics and Health Applications

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    Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis

    Orvosképzés 2023

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    2023 Summer Experience Program Abstracts

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    https://openworks.mdanderson.org/sumexp23/1130/thumbnail.jp

    Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

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    In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options
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