6 papers
Comparative Assessment of Concrete Compressive Strength Prediction at Industry Scale Using Embedding-based Neural Networks, Transformers, and Traditional Machine Learning Approaches
Md Asiful Islam, Md Ahmed Al Muzaddid, Afia Jahin Prema +1
Concrete is the most widely used construction material worldwide; however, reliable prediction of compressive strength remains challenging due to material heterogeneity, variable m…
CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting
Md Ahmed Al Muzaddid, William J. Beksi
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined wi…
CropTrack: A Tracking with Re-Identification Framework for Precision Agriculture
Md Ahmed Al Muzaddid, Jordan A. James, William J. Beksi
Multiple-object tracking (MOT) in agricultural environments presents major challenges due to repetitive patterns, similar object appearances, sudden illumination changes, and frequ…
Person Re-Identification via Generalized Class Prototypes
Md Ahmed Al Muzaddid, William J. Beksi
Advanced feature extraction methods have significantly contributed to enhancing the task of person re-identification. In addition, modifications to objective functions have been de…
NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting
Md Ahmed Al Muzaddid, William J. Beksi
In agriculture, automating the accurate tracking of fruits, vegetables, and fiber is a very tough problem. The issue becomes extremely challenging in dynamic field environments. Ye…
Variable Rate Compression for Raw 3D Point Clouds
Md Ahmed Al Muzaddid, William J. Beksi
In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression metho…