Youfu Li

TL;DR: A broad survey of developments in active vision in robotic applications over the last 15 years is provided, e.g. object recognition and modeling, site reconstruction and inspection, surveillance, tracking and search, as well as robotic manipulation and assembly, localization and mapping, navigation and exploration.

TL;DR: A novel multi-scale multi-path fusion network with cross-modal interactions (MMCI), in which the traditional two-stream fusion architecture with single fusion path is advanced by diversifying the fusion path to a global reasoning one and another local capturing one and meanwhile introducing cross- modal interactions in multiple layers.

TL;DR: A novel complementarity-aware fusion (CA-Fuse) module when adopting the Convolutional Neural Network (CNN) and the proposed RGB-D fusion network disambiguates both cross-modal and cross-level fusion processes and enables more sufficient fusion results.

TL;DR: In the proposed architecture, a cross-modal distillation stream, accompanying the RGB-specific and depth-specific streams, is introduced to extract new RGB-D features in each level in the bottom–up path, and the channel-wise attention mechanism is innovatively introduced to the cross- modal cross-level fusion problem to adaptively select complementary feature maps from each modality in eachlevel.

TL;DR: This paper summarizes the work on weld bead profile measurement, monitoring, and defect detection using a structured light-based vision inspection system and the image processing and extraction algorithms for laser profiles and feature points are presented.