0000000000414892

AUTHOR

Hosameldin Ahmed

showing 2 related works from this author

A Cost-Effective 3D Acquisition and Visualization Framework for Cultural Heritage

2020

Museums and cultural institutions, in general, are in a constant challenge of adding more value to their collections. The attractiveness of assets is practically tightly related to their value obeying the offer and demand law. New digital visualization technologies are found to give more excitements, especially to the younger generation as it is proven by multiple studies. Nowadays, museums around the world are currently trying to promote their collections through new multimedia and digital technologies such as 3D modeling, virtual reality (VR), augmented reality (AR), and serious games. However, the difficulty and the resources required to implement such technologies present a real challen…

Value (ethics)Artificial intelligence3D interaction3D interactionComputer science02 engineering and technologyVirtual realityConstant (computer programming)11. Sustainability0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]CEPROQHA projectComputingMilieux_MISCELLANEOUSMotion controllerbusiness.industryDeep learningDeep learning020207 software engineeringData science3D modellingVisualizationCultural heritageCultural heritage020201 artificial intelligence & image processingAugmented realityArtificial intelligencebusiness
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Study and Evaluation of Pre-trained CNN Networks for Cultural Heritage Image Classification

2021

The classification of digital images is an essential task during the restoration and preservation of cultural heritage (CH). In computer vision, cultural heritage classification relies on the classification of asset images regarding a certain task such as type, artist, genre, style identification, etc. CH classification is challenging as various CH asset images have similar colors, textures, and shapes. In this chapter, the aim is to study and evaluate the use of pre-trained deep convolutional neural networks such as VGG16, VGG-19, ResNet50, and Inception-V3 for cultural heritage images classification using transfer learning techniques. The main idea is to start with CNN models previously t…

Cultural heritageIdentification (information)Digital imageContextual image classificationComputer sciencebusiness.industryDeep learningPattern recognitionArtificial intelligenceTransfer of learningbusinessConvolutional neural networkTask (project management)
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