Signal Processing for Art Investigation [From the Guest Editors]

While early research in applying signal processing to art investigation emphasized image acquisition and image manipulation, various manifestations of image feature mining have achieved more prominence in the past decade as evidenced by the preceding citations providing successive indicators of the directions being taken in this growing field. The 11 articles in this special issue of SPM include studies of photographic paper classification, ancient coin classification, Mayan epigraphy analysis, 3-D color print graph signal processing, laid paper chain-line pattern matching, content-based image indexing, canvas weave analysis, crack detection for simulated in-painting, painting style characterization, and face recognition in portraits. All of these articles engage, to a substantial degree, in image feature mining. There is an enormous need in art history and conservation to locate, classify, identify, and measure features in multispectral and multidirectional images of artworks. One barrier of entry into this field is access to images of sufficient quality and quantity of artworks. Therefore, for this special issue, the authors were requested to make the images they processed accessible to other researchers (when possible) to stimulate the exploration of new and improved solutions, and most have complied. With that added bonus, we welcome you to the modest beginnings of an emerging field that promises to offer many satisfying challenges and will require specialization of and advances in the tools and techniques of signal processing.