{"version":"1.0","type":"rich","title":"Gist 1ec7d82931c03d4134419af0ccfdd985","description":"This visualization demonstrates a synthetic image-segmentation experiment using TensorFlow and Keras, where a binary mask of a filled circle is generated with the skimage.draw.circle function and displayed via matplotlib. The notebook focuses on computing the Hausdorff distance metric between predicted and ground-truth segmentation masks, using Keras backend operations to implement the distance calculation. The visualization shows the input circle image as a matplotlib axes image, illustrating how the metric is applied to evaluate segmentation quality. The code imports TensorFlow, NumPy, Keras backend, and matplotlib inline, establishing a workflow for comparing segmentation outputs against reference shapes.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-1ec7d82931c03d4134419af0ccfdd985?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}