RIFT2: Speeding-up RIFT with A New Rotation-Invariance Technique

1 code implementation • 1 Mar 2023 • Jiayuan Li, Pengcheng Shi, Qingwu Hu, Yongjun Zhang

Multimodal image matching is an important prerequisite for multisource image information fusion.

W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data

no code implementations • 27 Aug 2020 • Gautam Rajendrakumar Gare, Jiayuan Li, Rohan Joshi, Mrunal Prashant Vaze, Rishikesh Magar, Michael Yousefpour, Ricardo Luis Rodriguez, John Micheal Galeotti

To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyses ultrasound raw RF data along with the gray image.

Semantic Segmentation

RIFT: Multi-modal Image Matching Based on Radiation-invariant Feature Transform

no code implementations • 25 Apr 2018 • Jiayuan Li, Qingwu Hu, Mingyao Ai

Traditional feature matching methods such as scale-invariant feature transform (SIFT) usually use image intensity or gradient information to detect and describe feature points; however, both intensity and gradient are sensitive to nonlinear radiation distortions (NRD).