computer vision
The Digital Humanities Unveiled
On 21, Dec 2015 | In Picturebooks | By Chris Vitale
Spratt, Emily L. “The Digital Humanities Unveiled: Perceptions Held by Art Historians and Computer Scientists about Computer Vision Technology” (Self Published).
Referrer: Scott Dexter
Categories: digital humanities, distant reading, data mining, computer vision, art, art history, computer science, methodology
Annotation:
This paper outlines a survey completed by both art historians and computer scientists in relation to a computers ability to interpret aesthetic and beauty. The value of this work lies in the responses of this survey. Computer vision is rapidly becoming a more accepted and accessible method of examining art. For art historians and computer scientists, the implications are obvious. This digital humanities project used, “twenty-one questions for art historians and sixteen for computer scientists that were intended to shed light on field members’ knowledge of the capabilities and applications of computer vision technology, attitudes and perceptions about the use of it, and reactions to the meaning of this type of digitization in the humanities.” Spratt discusses the positive and negative reactions to computer vision’s ability to detect and automatically recognize aesthetic experiences of beauty. Channeling philosophy, Spratt defines what these variables mean for her survey.
Toward Automated Discovery of Artistic Influence
On 21, Dec 2015 | In Picturebooks | By Chris Vitale
Saleh, Babak, Kanako Abe, Ravneet Singh Arora, and Ahmed Elgammal. “Toward Automated Discovery of Artistic Influence.” Multimed Tools Appl Multimedia Tools and Applications (2014). Web.
Referrer: Scott Dexter
Categories: digital humanities, distant reading, data mining, computer vision, art, art history, computer science
Annotation:
A team from the Department of Computer Science at Rutgers experimented with art and computer vision in 2014. Using advanced computer driven recognition of images, the team was able to explore the influence of other artists on particular pieces of art. The study was focused on two types of computational inquiry: “discriminative vs. generative models” as well as feature extraction and comparison. The importance of this work is the argument that computers have a, to a certain degree, level of ability in recognizing the influence of other artists in multiple works of art. The work of a Art Historian is arguably completed by an automated process that involves computer vision and classification. The high level computer algorithms are paired with art history style analysis of the paintings similarity to compare the traditional and novel methodologies. This paper is a valuable source of tools and methods for computer vision in illustrations.
