We describe a computational technique for digitally authenticating works of art. This approach builds statistical models of an artist from a set of authenticated works. Additional works are then authenticated against this model. The statistical model consists of first- and higher-order wavelet statistics. We show preliminary results from our analysis of thirteen drawings by Pieter Bruegel the Elder. We also present preliminary results showing how these techniques may be applicable to determining how many hands contributed to a single painting
Image authorship attribution presents many challenges and difficulties which have increased with the...
Digital image forensics is a relatively new research field that aims to expose the origin and compos...
The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet person...
In this paper, we present a method of analyzing handwriting samples (distinguishing between authent...
Recently, statistical techniques have been used to assist art historians in the analysis of works of...
This paper examines whether machine learning and image analysis tools can be used to assist art expe...
Recent advances in digital image acquisition methods and the wide range of imaging modalities curren...
We describe a set of natural image statistics that are built upon two multi-scale image decompositio...
A digitally altered image, often leaving no visual clues of having been tampered with, can be indist...
This paper relates the style of 16th century Flemish paintings by Goossen van der Weyden (GvdW) to t...
This paper proposes a computational approach for analysis of strokes in line drawings by artists. W...
International audienceRecently, a growing interest has emerged for examining the potential of Image ...
This work investigates pattern recognition tech-niques that can be used to identify the author of fi...
Abstract- Today, with an increasing volume of images being captured across an ever expanding range o...
This paper investigates the application of a Convolutional Neural Network (CNN), AlexNet, on the aut...
Image authorship attribution presents many challenges and difficulties which have increased with the...
Digital image forensics is a relatively new research field that aims to expose the origin and compos...
The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet person...
In this paper, we present a method of analyzing handwriting samples (distinguishing between authent...
Recently, statistical techniques have been used to assist art historians in the analysis of works of...
This paper examines whether machine learning and image analysis tools can be used to assist art expe...
Recent advances in digital image acquisition methods and the wide range of imaging modalities curren...
We describe a set of natural image statistics that are built upon two multi-scale image decompositio...
A digitally altered image, often leaving no visual clues of having been tampered with, can be indist...
This paper relates the style of 16th century Flemish paintings by Goossen van der Weyden (GvdW) to t...
This paper proposes a computational approach for analysis of strokes in line drawings by artists. W...
International audienceRecently, a growing interest has emerged for examining the potential of Image ...
This work investigates pattern recognition tech-niques that can be used to identify the author of fi...
Abstract- Today, with an increasing volume of images being captured across an ever expanding range o...
This paper investigates the application of a Convolutional Neural Network (CNN), AlexNet, on the aut...
Image authorship attribution presents many challenges and difficulties which have increased with the...
Digital image forensics is a relatively new research field that aims to expose the origin and compos...
The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet person...