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1   Link   "Augmenting the Generalized Hough Transform to Enable the Mining of Petroglyphs", by Qiang Zhu, Xiaoyue Wang, Eamonn Keogh and Sang-Hee Lee

Rock art is an archaeological term for human-made markings on stone, including carved markings, known as petroglyphs, and painted markings, known as pictographs. It is believed that there are millions of petroglyphs in North America alone, and the study of this valued cultural resource has implications even beyond anthropology and history. Surprisingly, although image processing, information retrieval and data mining have had large impacts on many human endeavors, they have had essentially zero impact on the study of rock art. In this work we identify the reasons for this, and introduce a novel distance measure and algorithms which allow efficient and effective data mining of large collections of rock art.
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2   Link   "Automatic Coin Classification by Image Matching" by Sebastian Zambanini and Martin Kampel

This paper presents an automatic image-based ancient coin classification method that adopts the recently proposed SIFT flow method in order to assess the similarity of coin images. Our system does not rely on pattern classification as discriminative feature extraction and classification becomes very difficult for large coin databases. This is mainly caused by the specific challenges that ancient coins pose to a classification method based on 2D images.
In this paper we highlight these challenges and argue to use SIFT flow image matching. Our classification system is applied to an image database containing 24 classes of early Roman Republican coinage and achieves a classification rate of 74% on the coins’ reverse side. This is a significant improvement over an earlier proposed coin matching method based on interest point matching which only achieves 33% on the same dataset.
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3   Link   "Using CAPTCHAs to index cultural artifacts" by Q. Zhu and E. Keogh

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.
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