20 research outputs found

    Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models

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    Hierarchical layout-aware graph convolutional network for unified aesthetics assessment

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    Learning computational models of image aesthetics can have a substantial impact on visual art and graphic design. Although automatic image aesthetics assessment is a challenging topic by its subjective nature, psychological studies have confirmed a strong correlation between image layouts and perceived image quality. While previous state-of-the-art methods attempt to learn holistic information using deep Convolutional Neural Networks (CNNs), our approach is motivated by the fact that Graph Convolutional Network (GCN) architecture is conceivably more suited for modeling complex relations among image regions than vanilla convolutional layers. Specifically, we present a Hierarchical Layout-Aware Graph Convolutional Network (HLA-GCN) to capture layout information. It is a dedicated double-subnet neural network consisting of two LA-GCN modules. The first LA-GCN module constructs an aesthetics-related graph in the coordinate space and performs reasoning over spatial nodes. The second LA-GCN module performs graph reasoning after aggregating significant regions in a latent space. The model output is a hierarchical representation with layout-aware features from both spatial and aggregated nodes for unified aesthetics assessment. Extensive evaluations show that our proposed model outperforms the state-of-the-art on the AVA and AADB datasets across three different tasks. The code is available at http://github.com/days1011/HLAGCN

    Irish Machine Vision and Image Processing Conference Proceedings 2017

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    Entropy in Image Analysis II

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    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

    Deep Learning in Medical Image Analysis

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    The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

    Higher level techniques for the artistic rendering of images and video

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    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementaci贸n sistem谩tica de la telemedicina dentro de un gran centro de evaluaci贸n de COVID-19 en el 谩rea de Baja California, M茅xico. Nuestro modelo se basa en factores de dise帽o centrados en el ser humano y colaboraciones interdisciplinarias para la habilitaci贸n escalable basada en datos de tecnolog铆as de teleconsulta de tel茅fonos inteligentes, celulares y video para vincular hospitales, cl铆nicas y servicios m茅dicos de emergencia para evaluaciones de COVID en el punto de atenci贸n. pruebas, y para el tratamiento posterior y decisiones de cuarentena. R谩pidamente se cre贸 un equipo multidisciplinario, en cooperaci贸n con diferentes instituciones, entre ellas: la Universidad Aut贸noma de Baja California, la Secretar铆a de Salud, el Centro de Comando, Comunicaciones y Control Inform谩tico. de la Secretar铆a del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psic贸logos. Nuestro objetivo es proporcionar informaci贸n al p煤blico y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignaci贸n de recursos con la anticipaci贸n de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
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