This paper deals with strategies for reliably obtaining the edges and the surface texture of metallic objects. Since illumination is a critical aspect regarding robustness and image quality, it is considered here as an active component of the image acquisition system. The performance of the methods presented is demonstrated -- among other examples -- with images of needles for blood sugar tests. Such objects show an optimized form consisting of several planar grinded surfaces delimited by sharp edges. To allow a reliable assessment of the quality of each surface, and a measurement of their edges, methods for fusing data obtained with different illumination constellations were developed. The fusion strategy is based on the minimization of suitable energy functions. First, an illumination-based segmentation of the object is performed. To obtain the boundaries of each surface, directional light-field illumination is used. By formulating suitable criteria, nearly binary images are selected by variation of the illumination direction. Hereafter, the surface edges are obtained by fusing the contours of the areas obtained before. Following, an optimally illuminated image is acquired for each surface of the object by varying the illumination direction. For this purpose, a criterion describing the quality of the surface texture has to be maximized. Finally, the images of all textured surfaces of the object are fused to an improved result, in which the whole object is contained with high contrast. Although the methods presented were designed for inspection of needles, they also perform robustly in other computer vision tasks where metallic objects have to be inspected.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.