Shape Analysis in Archaeology

Shape Analysis in Archaeology

Juan A. Barceló (Universitat Autònoma de Barcelona, Spain)
Copyright: © 2009 |Pages: 20
DOI: 10.4018/978-1-59904-489-7.ch006
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Abstract

In order to be able to acquire visual information, our automated “observer” is equipped with range and intensity sensors. The former acquire range images, in which each pixel encodes the distance between the sensor and a point in the scene. The latter are the familiar TV cameras acquiring grey-level images. That is to say, what the automated archaeologist “sees” is just the pattern of structured light projected on the scene (Trucco, 1997). To understand such input data is the spatial pattern of visual bindings should be differentiated into sets of marks (points, lines, areas, volumes) that express the position and geometry of perceived boundaries, and retinal properties (color, shadow, texture) that carry additional information necessary for categorizing the constituents of perception.
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Why Archaeological Evidence Has “Shape”?

In order to be able to acquire visual information, our automated “observer” is equipped with range and intensity sensors. The former acquire range images, in which each pixel encodes the distance between the sensor and a point in the scene. The latter are the familiar TV cameras acquiring grey-level images. That is to say, what the automated archaeologist “sees” is just the pattern of structured light projected on the scene (Trucco, 1997). To understand such input data, the spatial pattern of visual bindings should be differentiated into sets of marks (points, lines, areas, volumes) that express the position and geometry of perceived boundaries, and retinal properties (color, shadow, texture) that carry additional information necessary for categorizing the constituents of perception.

Currently, recognition of archaeological artifacts is performed manually by an expert. Generally, the expert attempts to find already recognized artifacts that are perceptually similar to the unclassified artifact. In order to recognize such artifacts, the human expert usually searches through a reference collection. A reference collection is a collection of reference artifacts, which is usually published as a set of formalized descriptions together with line drawings of the artifacts. Manual comparison of excavated artifacts with artifacts from a reference collection is a highly intuitive and uncontrollable process. In order to overcome these drawbacks, an automated archaeologist will use a kind of content-based shape retrieval system to find geometrically similar artifacts. Here “shape” appears as the key aspect for the mechanization of visual perception.

The attempts at defining the term shape usually found in the related literature are often based on the concept of “object properties invariant to translation, rotation and scaling” (Dryden & Mardia, 1998; Palmer, 1999; Small, 1996). While such definitions manage to capture an important property of shapes as perceived by humans, namely what relates the different appearances of the same object seen from different perspectives, they do not clearly specify what a shape is. An alternative and less conventional definition of shape has been advanced by Costa and Cesar (2001, p. 266): a shape can be understood as any “single,” “distinct,” “whole” or “united” visual entity. Fortunately, these terms can be formalized using the mathematical concept of connectivity, which leads to the following definition:

SHAPE is any connected set of points.

Consequently, shape is not an intrinsic property of observed objects, but it arises in images in different contexts: linear separation between regions of relative light and dark within an image, discontinuity in the surface depth, discontinuity in surface orientation, markings on the surfaces, and so forth, usually called “interfacial boundaries:” surfaces and/or contours. In other words, “shape” is the characteristic that delimits distinct spatial areas which appear when visual appearances are “significantly different” from one area to the next.

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