Histogram Layer for Texture Classification Jun 2019 - Aug 2019
Paper written and presented as part of the 61st Annual UF SSTP; Assisted ML texture analysis research in Professor Alina Zare’s Machine Learning and Sensing Lab by implementing deep network models in PyTorch and conducted various experiments with different parameters
We propose a hybrid model that incorporates a stackable, localized histogram layer on convolutional neural network (CNN) for texture analysis applications.
Instead of using standard histogram operation, we used RBF (Radial Basis Function) to perform a localized binning operation without binning constraints.
- PaperHistogram Layer for Texture Classification
- Poster2019 SSTP Final Presentation
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Awards & Recognition
- SSTP 2019Best Paper Award