In recent years, there is growing interest in the study of percussion scars and breakage patterns on hammerstones, cores and tools from Oldowan African and Eurasian lithic assemblages. Oldowan stone toolkits generally contain abundant small-sized flakes and their corresponding cores, and are characterized by their structural dichotomy of heavy- and light-duty tools. This paper explores the significance of the lesser known heavy-duty tool component, providing data from the late Lower Pleistocene sites of Barranco León and Fuente Nueva 3 (Orce, Spain), dated 1.4–1.2 Myr. Using quantitative and qualitative data from the large-sized limestone industries from these two major sites, we present a new methodology highlighting their morpho-technological features. In the light of the results, we discuss the shortfalls of extant classificatory methods for interpreting the role of percussive technology in early toolkits. This work is rooted in an experimental program designed to reproduce the wide range of percussion marks observed on the limestone artefacts from these two sites. A visual and descriptive reference is provided as an interpretative aid for future comparative research. Further experiments using a variety of materials and gestures are still needed before the elusive traces yield the secrets of the kinds of percussive activities carried out by hominins at these, and other, Oldowan sites.

In this paper we will discuss the use of some graph-based representations and techniques for image processing and analysis. Instead of making an extensive review of the graph techniques in this field, we will explain how we are using these techniques in an active vision system for an autonomous mobile robot developed in the Institut de Robòtica i Informàtica Industrial within the project "Active Vision System with Automatic Learning Capacity for Industrial Applications (CICYT TAP98-0473)". Specifically we will discuss the use of graph-based representations and techniques for image segmentation, image perceptual grouping and object recognition. We first present a generalisation of a graph partitioning greedy algorithm for colour image segmentation. Next we describe a novel fusion of colour-based segmentation and depth from stereo that yields a graph representing every object in the scene. Finally we describe a new representation of a set of attributed graphs (AGs), denominated Function Described Graphs (FDGs), a distance measure for matching AGs with FDGs and some applications for robot vision.

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