We propose a semantic segmentation method for converting line drawings in raster format into vector format and verify the accuracy of the extraction of basic ...

In this paper, we propose a learnable histogram layer, which learns histogram features within deep neural networks in end-to-end training. Such a layer is able ...

Tensorflow implementation of Semantic Segmentation for Line Drawing Vectorization Using Neural Networks.

Once the segmentation is computed, we use existing vectorization approaches to vectorize each path, and then combine all paths into the final output vector ...

Sep 20, 2024 · In this work, we present a method to vectorize raster images of line art. Inverting the rasterization procedure is inherently ...

In this paper, we present a fast raster-to-vector method for line art images based on a convolutional neural network (CNN). State-of-the-art approaches for ...

May 22, 2018 · Once the segmentation is computed, we use existing vectorization approaches to vectorize each path, and then combine all paths into the final ...

Missing: Deep | Show results with:Deep

We present a simple and efficient method based on deep Convolutional Neural Networks. (CNNs) for semantic sketch segmentation and labeling. As illustrated in ...

We propose a two-phase method to vectorize raster line drawing via line subdivision and topology reconstruction. • Our vectorization method is data-driven by ...