Abstract. Classifying materials from their appearance is a challenging problem, especially if illumination and pose conditions are permitted to change: highlights and shadows caused by 3D structure can radically alter a sample's visual texture. Despite these difficulties, researchers have demonstrated impressive results on the CUReT database which contains many images of 61 materials under different conditions. A first contribution of this paper is to further advance the state-of-theart by applying Support Vector Machines to this problem. To our knowledge, we record the best results to date on the CUReT database. In our work we additionally investigate the effect of scale since robustness to viewing distance and zoom settings is crucial in many real-world situations. Indeed, a material's appearance can vary considerably as fine-level detail becomes visible or disappears as the camera moves towards or away from the subject. We handle scale-variations using a pure-learning approach, incorporating samples imaged at different distances into the training set. An empirical investigation is conducted to show how the classification accuracy decreases as less scale information is made available during training. Since the CUReT database contains little scale variation, we introduce a new database which images ten CUReT materials at different distances, while also maintaining some change in pose and illumination. The first aim of the database is thus to provide scale variations, but a second and equally important objective is to attempt to recognise different samples of the CUReT materials. For instance, does training on the CUReT database enable recognition of another piece of sandpaper? The results clearly demonstrate that it is not possible to do so with any acceptable degree of accuracy. Thus we conclude that impressive results even on a welldesigned database such as CUReT, does not imply that material classification is close to being a solved problem under real-world conditions.

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A linear self-calibration method is given for computing the calibration of a stationary but rotating camera. The internal parameters of the camera are allowed to vary from image to image, allowing for zooming (change of focal length) and possible variation of the principal point of the camera. In order for calibration to be possible some constraints must be placed on the calibration of each image. The method works under the minimal assumption of zeroskew (rectangular pixels), or the more restrictive but reasonable conditions of square pixels, known pixel aspect ratio, and known principal point. Being linea6 the algorithm is extremely rapid, and avoids the convergence problems characteristic of iterative algorithms.

Abstract. Classifying materials from their appearance is challenging. Impressive results have been obtained under varying illumination and pose conditions. Still, the effect of scale variations and the possibility to generalize across different material samples are still largely unexplored. This paper 1 addresses these issues, proposing a pure learning approach based on support vector machines. We study the effect of scale variations first on the artificially scaled CUReT database, showing how performance depends on the amount of scale information available during training. Since the CUReT database contains little scale variation and only one sample per material, we introduce a new database containing ten CUReT materials at different distances, pose and illumination. This database provides scale variations, while allowing to evaluate generalization capabilities: does training on the CUReT database enable recognition of another piece of sandpaper? Our results demonstrate that this is not yet possible, and that material classification is far from being solved in scenarios of practical interest.

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