The quantification of pebble shape has been of interest to geomorphologists for decades. Several authors developed parameters to describe pebble shapes from their images. The extraction of this information from images involves two steps: the segmentation of pebble contours and the application of a computational geometry algorithm to estimate shape parameters. When images are taken in the field, unavoidable shadows might hinder the possibility of using automatic segmentation methods. This paper introduces a new method for automatic segmentation of pebbles that improves segmentation accuracy in the presence of shadows. The method is based on the Canny edge detection algorithm which uses a double thresholding process to provide a classification of the strength of the detected edges. The proposed method applies this algorithm with an ensemble of thresholding values, estimating, for each pixel, the probability of being an edge. The resulting pebble contours were analysed using two computational geometry algorithms to obtain shape parameters. The algorithm was calibrated on a sample of five pebbles and then validated on a sample of 1696 pebbles. Its accuracy has been estimated by comparing the resulting shape parameters with those obtained using reference software, which was used as ground truth (GT). The proposed segmentation method was capable of accurately segmenting around 91% of the sample with a relative error for roundness of −1.7% and −0.4%; for elongation of −0.2% and −0.3% and for circularity of 0.2% and 0.1%, when shape parameters were computed using the algorithms of Zheng or Roussillon, respectively. The method could therefore be used to segment images of pebbles collected in the field with low contrast and shadowing, providing comparable accuracy with ‘manual’ segmentation, while removing operator bias.