![]() With this technique, you will spend 100 of your time looking at the object you’re drawing and 0 of your time looking at your paper. That means you can use this dataset for non-commerical purposes and your adapted work should be shared under similar conditions. Blind contour drawing is just that, a contour drawing that you make while blind or without looking at the paper. License: the dataset is licensed under CC BY-NC-SA (Attribution-NonCommercial-ShareAlike). Therefore, in addition to the 5,000 drawings accepted, we have 1,947 rejected submissions, which can be used in setting up an automatic quality guard. The quality control is realized through manual inspection by treating annotations of the following types as rejection candidates: (1) missing inner boundary, (2) missing important objects, (3) with large misalignment with original edges, (4) the content not recognizable, (5) drawing humans with stick figures, (6) shaded on empty areas. In order to obtain high-quality annotations, we design a labeling interface with a detailed instruction page including many positive and negative examples. The Turkers are asked to trace over a fainted background image. The dataset is collected with Amazon Mechanical Turk. The drawings have strokes roughly aligned for image boundaries, making it easier to correspond human strokes with image edges. You can create contour line drawings with pencil and paper or brainstorm some more unconventional tools to use. If your students have sketchbooks, try a timed contour line drawing to start class. They are low pressure, quick, and easy to explain. In this dataset, there are 1,000 outdoor images and each is paired with 5 human drawings (5,000 drawings in total). Blind contours are a perfect warm-up drawing. We present a new dataset of paired images and contour drawings for the study of visual understanding and sketch generation. Surprisingly, when our model fine-tunes on BSDS500, we achieve the state-of-the-art performance in salient boundary detection, suggesting contour drawing might be a scalable alternative to boundary annotation, which at the same time is easier and more interesting for annotators to draw. Our method surpasses previous methods quantitatively and qualitatively. We address these issues by collecting a new dataset of contour drawings and proposing a learning-based method that resolves diversity in the annotation and, unlike boundary detectors, can work with imperfect alignment of the annotation and the actual ground truth. However, the set of visual cues presented in the boundary detection output are different from the ones in contour drawings, and also the artistic style is ignored. ![]() Prior art often cast this problem as boundary detection. In this paper, we aim to generate contour drawings, boundary-like drawings that capture the outline of the visual scene. On one hand, they are the 2D elements that convey 3D shapes, on the other hand, they are indicative of occlusion events and thus separation of objects or semantic concepts. Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.
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