Robust Painting Recognition and Registration for Mobile Augmented Reality

Martinel, Niki; Micheloni, Christian; Foresti, Gian Luca

doi:10.1109/lsp.2013.2279014

Cited by 19 publications

(7 citation statements)

References 16 publications

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“…This shows that the effects of localization error on standard feature-based homography estimation can be avoided effectively and the accuracy and robustness of homography estimation can be improved significantly by using the proposed method. Therefore, the performance of many applications such as image stitching [4], image mosaicking [29] and augmented reality [30] adopting homography estimation technique can be improved by using the proposed method.…”

Section: Resultsmentioning

confidence: 99%

Accurate and robust feature-based homography estimation using HALF-SIFT and feature localization error weighting

Zhao

2016

Journal of Visual Communication and Image Representation

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Section: Resultsmentioning

confidence: 99%

Accurate and robust feature-based homography estimation using HALF-SIFT and feature localization error weighting

Zhao

2016

Journal of Visual Communication and Image Representation

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“…Naive bayes and adaptive boosting have been used as classifiers to analyze the performance. There is also research on utilizing the painting recognition in the field of mobile augmented reality like [17].…”

Section: Related Workmentioning

confidence: 99%

Style classification and visualization of art painting’s genre using self-organizing maps

Lee

Cha

2016

Hum. Cent. Comput. Inf. Sci.

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With the spread of digitalization of art paintings, research on diverse scientific approaches on painted images has become active. In this paper, the method of classifying painting styles by extracting various features from paintings is suggested. Global features are extracted using the color-based statistical computation and composition-based local features of paintings are extracted through the segmentation of objects within the paintings to classify the styles of the paintings. Based on the extracted features, paintings are categorized by style using SOM, which are then analyzed through visualization using the map. We have proved the feasibility of the proposed method of categorizing paintings by style, and the objective features of paintings can contribute to the research on art history and aesthetics.

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“…To create the panoramic image view, the first step consists in detecting robust image features that can be used for alignment purposes. For this scope, as suggested in (Martinel et al, 2013), SURF features (Bay et al, 2008) have been adopted. Next, the features matching between two different images is performed by RANSAC.…”

Section: Panoramic Building Modulementioning

confidence: 99%

A Smart Visual Information Tool for Situational Awareness

Vernier

Farinosi

Foresti

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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In the last years, social media have grown in popularity with millions of users that everyday produce and share online digital content. This practice reveals to be particularly useful in extra-ordinary context, such as during a disaster, when the data posted by people can be integrated with traditional emergency management tools and used for event detection and hyperlocal situational awareness. In this contribution, we present SVISAT, an innovative visualization system for Twitter data mining, expressly conceived for signaling in real time a given event through the uploading and sharing of visual information (i.e., photos). Using geodata, it allows to display on a map the wide area where the event is happening, showing at the same time the most popular hashtags adopted by people to spread the tweets and the most relevant images/photos which describe the event itself. 238Vernier, M., Farinosi, M. and Foresti, G. A Smart Visual Information Tool for Situational Awareness.

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Robust Painting Recognition and Registration for Mobile Augmented Reality

Abstract: In this work we introduce a novel approach for painting recognition and registration for mobile

Cited by 19 publications

References 16 publications

Accurate and robust feature-based homography estimation using HALF-SIFT and feature localization error weighting

Accurate and robust feature-based homography estimation using HALF-SIFT and feature localization error weighting

Style classification and visualization of art painting’s genre using self-organizing maps

A Smart Visual Information Tool for Situational Awareness

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