67 citations · 113 across the 11 of their papers we have counts for
12 papers
Spatial Transformer Network YOLO Model for Agricultural Object Detection
Yash Zambre, Ekdev Rajkitkul, Akshatha Mohan +1
Object detection plays a crucial role in the field of computer vision by autonomously locating and identifying objects of interest. The You Only Look Once (YOLO) model is an effect…
Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis
Akshatha Mohan, Joshua Peeples
Pooling layers (e.g., max and average) may overlook important information encoded in the spatial arrangement of pixel intensity and/or feature values. We propose a novel lacunarity…
Histogram Layers for Neural Engineered Features
Joshua Peeples, Salim Al Kharsa, Luke Saleh +1
In the computer vision literature, many effective histogram-based features have been developed. These engineered features include local binary patterns and edge histogram descripto…
Histogram Layer Time Delay Neural Networks for Passive Sonar Classification
Jarin Ritu, Ethan Barnes, Riley Martell +2
Underwater acoustic target detection in remote marine sensing operations is challenging due to complex sound wave propagation. Despite the availability of reliable sonar systems, t…
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classification
Akshatha Mohan, Joshua Peeples
We present a comprehensive analysis of quantitatively evaluating explainable artificial intelligence (XAI) techniques for remote sensing image classification. Our approach leverage…
Histogram Layers for Synthetic Aperture Sonar Imagery
Joshua Peeples, Alina Zare, Jeffrey Dale +1
Synthetic aperture sonar (SAS) imagery is crucial for several applications, including target recognition and environmental segmentation. Deep learning models have led to much succe…
Learnable Adaptive Cosine Estimator (LACE) for Image Classification
Joshua Peeples, Connor McCurley, Sarah Walker +2
In this work, we propose a new loss to improve feature discriminability and classification performance. Motivated by the adaptive cosine/coherence estimator (ACE), our proposed met…
Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation
Joshua Peeples, Daniel Suen, Alina Zare +1
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 t…
Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery
Sarah Walker, Joshua Peeples, Jeff Dale +2
In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) (arXiv:1602.04938) and divergence measures t…
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification
Joshua Peeples, Sarah Walker, Connor McCurley +3
Feature representation is an important aspect of remote-sensing based image classification. While deep convolutional neural networks are able to effectively amalgamate information,…
Histogram Layers for Texture Analysis
Joshua Peeples, Weihuang Xu, Alina Zare
An essential aspect of texture analysis is the extraction of features that describe the distribution of values in local, spatial regions. We present a localized histogram layer for…
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
Joshua Peeples, Matthew Cook, Daniel Suen +2
Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloo…