Robust feature point detection for freehand strokes with deep learning approach

Abstract

A robust feature point detection (FPD) tool is critical for sketch-based engineering modelling. We propose an FPD algorithm for freehand strokes based on a deep learning approach, denoted as FPD-DL. First, a point-wise neural network is trained to learn local and global features from points' multi-scale context images to classify each point of a stroke into a line-point or a curve-point. Then, a segment merging procedure is designed to extract primitives from the identified segments. It explores the distribution patterns of neighbouring segments. Finally, the tangent points and lost corners are added among consecutive primitives. The algorithm robustness and accuracy are experimented on a dataset with 1500 strokes of 20 shapes, which are grouped into two categories: set-by-person and set-by-shape. In the set-by-person category, the AON (all-or-nothing) accuracy of FPD-DL is 97%, compared to 94.3% of the state-of-the-art algorithm. In the set-by-shape category, the robustness to stroke shape is tested, which is similar to the practical usage scenario. The AON accuracy of FPD-DL is 95%, without too much fluctuation. Thus, the new proposed approach is robust to both stroke shape and stroke style.