Designing human friendly human interaction proofs (HIPs)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Computer
Petroglyph digitization: enabling cultural heritage scholarship
Machine Vision and Applications
A Parallel Mechanism for Detecting Curves in Pictures
IEEE Transactions on Computers
Augmenting the generalized hough transform to enable the mining of petroglyphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
| Hi-index | 0.00 |
Rock art, human-made markings on stone, is an important cultural artifact and the earliest expression of abstract thinking. While there are tens of millions of photographs of rock art in existence, there have been no large-scale attempts to organize, classify or cluster them. This omission is not due to a lack of interest, but reflects the extraordinary difficultly of extracting useful data from an incredibly heterogeneous and noisy dataset. As we shall show, rock art is likely to resist efforts of automatic extraction from images for a long time. In this work we show that we can use CAPTCHAs, puzzles designed to tell humans and computers apart, to segment and index rock art. Unlike other CAPTCHAs which operate on inherently discrete data and expect discrete responses, our method considers inherently real-valued data and expects real-valued responses. This creates a challenge which we have overcome by using a recently introduced distance measure. We demonstrate our system is capable of acting as a secure CAPTCHA, while producing data that allows for indexing the rock art.