In Situ Growth of Nitrogen-Doped Fluorescent Carbon Dots on Sisal Fibers: Investigating Their Selective and Enhanced Adsorption Capabilities for Methyl Blue Dye.

Preparing a biomass adsorbent material with high-absorption performance but low cost plays a vital role in wastewater treatment. In this study, a novel nitrogen-doped sisal fiber-based carbon dots (SF-N-CDs) composite was prepared by directly growing carbon dots (CDs) on sisal fiber (SF) using a microwave method with polyethyleneimine (PEI) as a raw material. The prepared SF-N-CDs were characterized using FTIR, XRD, Contact angle(CA), TGA, XPS, and SEM.

ULMR: An Unsupervised Learning Framework for Mismatch Removal.

Due to radiometric and geometric distortions between images, mismatches are inevitable. Thus, a mismatch removal process is required for improving matching accuracy. Although deep learning methods have been proved to outperform handcraft methods in specific scenarios, including image identification and point cloud classification, most learning methods are supervised and are susceptible to incorrect labeling, and labeling data is a time-consuming task.

Image Stitching Based on Nonrigid Warping for Urban Scene.

Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in this paper.

Detecting Matching Blunders of Multi-Source Remote Sensing Images via Graph Theory.

Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunder detection methods cannot work effectively. To address this problem, we propose two matching blunder detection methods based on graph theory.