The Gauss map projects surface normals to a unit sphere, providing a powerful visualization of the geometry of a graphical object. It can be used to predict visual events caused by changes in lighting, shading, and camera control. We present an interactive technique for portraying the Gauss map of polygonal models, mapping surface normals and the magnitudes of surface curvature using a spherical projection. Unlike other visualizations of surface curvature, we create our Gauss map directly from polygonal meshes without requiring any complex intermediate calculations of differential geometry. For anything other than simple shapes, surface information is densely mapped into the Gaussian normal image, inviting the use of visualization techniques to amplify and emphasize details hidden within the wealth of data. We present the use of interactive visualization tools such as brushing and linking to explore the surface properties of solid shapes. The Gauss map is shown to be simple to compute, easy to view dynamically, and effective at portraying important features of polygonal models.

This paper describes two methods for constructing a depth map from images. Each methods has two stages. First, one or more needle maps are determined using a pair of images. This process employs either the Marr-Poggio-Grinson stereo and shape-from-shading, or, instead, photometric stereo. Secondly, a depth map is constructed from the needle map or needle maps computed by the first stage. Both methods make use of an iterative relaxation method to obtain the final depth map.

This thesis presents new techniques which enable the automatic recognition of everyday objects like chairs and ladders in images of highly cluttered scenes. Given an image, we extract information about the shape and texture properties present in small patches of the image and use that information to identify parts of the objects we are interested in. We then assemble those parts into overall hypotheses about what objects are present in the image, and where they are. Solving this problem in a general setting is one of the central problems in computer vision, as doing so would have an immediate impact on a far-reaching set of applications in medicine, surveillance, manufacturing, robotics, and other areas.

The Gauss map projects surface normals to a unit sphere, providing a powerful visualization of the geometry of a graphical object. It can be used to predict visual events caused by changes in lighting, shading, and camera control. We present an interactive technique for portraying the Gauss map of polygonal models, mapping surface normals and the magnitudes of surface curvature using a spherical projection. Unlike other visualizations of surface curvature, we create our Gauss map directly from polygonal meshes without requiring any complex intermediate calculations of differential geometry. For anything other than simple shapes, surface information is densely mapped into the Gaussian normal image, inviting the use of visualization techniques to amplify and emphasize details hidden within the wealth of data. We present the use of interactive visualization tools such as brushing and linking to explore the surface properties of solid shapes. The Gauss map is shown to be simple to compute, easy to view dynamically, and effective at portraying important features of polygonal models.