Congratulations to our labmates and collaborators: Connor H. McCurley, Daniel Rodriguez, Chandler Trousdale, Arielle Stevens, Anthony Baldino, Eugene Li, Isabella Perlmutter, and Alina Zare. Their paper, “Bag-level classification network for infrared target detection”, was recently accepted to Proc. SPIE 12096, Automatic… Read More
Tag: feature selection
Bag-level Classification Network for Infrared Target Detection
Abstract: Aided target detection in infrared data has proven an important area of investigation for both military and civilian applications. While target detection at the object or pixel-level has been explored extensively, existing approaches require precisely-annotated data which is often… Read More
Bidirectional LSTM accepted to IEEE EMB!
Congratulations to Gatorsense alumni, Changzhe Jiao and Chao Chen, as well as Alina Zare and collaborators Shuipong Guo, Dong Hai, Bo-Yu Su, Marjorie Skubic , Licheng Jiao and Domonic Ho! Their paper, “Non-Invasive Heart Rate Estimation from Ballistocardiograms using Bidirectional… Read More
Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression
Abstract: Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The… Read More
Evaluation of image features for discriminating targets from false positives in synthetic aperture sonar imagery
Abstract: With the increasing popularity of using autonomous underwater vehicles (AUVs) to gather large quantities of Synthetic Aperture Sonar (SAS) seafloor imagery, the burden on human operators to identify targets in these seafloor images has increased significantly. Existing methods of… Read More
Possibilistic fuzzy local information C-means with automated feature selection for seafloor segmentation
Abstract: The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS) imagery into distinct regions of the sea-floor. In this work, we investigate and present the results of an automated… Read More