Congratulations to our labmates, Akshatha Mohan and Joshua Peeples, for the paper acceptance to the IEEE/CVF Computer Vision and Pattern Recognition Workshop on Vision for Agriculture (V4A)! In this work, we present a lacunarity pooling layer for plant image classification. The paper will be presented on during the V4A poster session. Check out the code and paper here!
Publications
Histogram Layer Time Delay Neural Networks for Passive Sonar Classification Accepted to IEEE WASPAA 2023!
Congratulations to our lab and collaborators, Jarin Ritu, Ethan Barnes, Riley Martell, Alexandra Van Dine and Joshua Peeples, for the paper acceptance to the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) 2023! In this work, we present a new time delay artificial neural network integrated with histogram layer(s) for improved target recognition. The paper will be presented on Monday, October 23rd in the Music and Audio Signal Processing Poster Session I. Check out the paper here!
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classification Accepted to IEEE IGARSS 2023!
Congratulations to our labmates, Akshatha Mohan and Joshua Peeples, for the paper acceptance to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2023! In this work, we present a comprehensive analysis of quantitatively evaluating explainable artificial intelligence (XAI) techniques for remote sensing image classification. The paper will be presented on Wednesday, July 12th in the Community Contributed Session Opening the Black Box: Explainable AI/ML in Remote Sensing Analysis I. Check out the paper here!
Spatial and Texture Analysis of Root System distribution with Earth mover’s Distance (STARSEED) Accepted to Plant Methods!
Congratulations to our lab and collaborators (Joshua Peeples, Weihuang Xu, Romain Gloaguen, Diane Rowland, Alina Zare, and Zachary Brym) for the paper acceptance to the Plant Methods journal! In this work, a novel method leveraging Earth Mover’s Distance was developed to compare root system distributions for agronomic discovery. Check out the paper here!
Histogram Layers for Synthetic Aperture Sonar Imagery Accepted to IEEE ICMLA 2022!
Congratulations to our lab and collaborators (Joshua Peeples, Alina Zare, Jeffery Dale, and James Keller) for the paper acceptance to the 2022 IEEE International Conference on Machine Learning and Applications (ICMLA)! In this work, a novel application of histogram layer models is investigated for synthetic aperture sonar (SAS) imagery. The paper will be presented on Monday, December 12th in the Image Processing Session II. Check out the paper here!