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Towards Automated Nanoenergetic Reaction Characterization with Computational Vision
Timothy B. Gaines;J. Alex Hurt;James M. Keller;Grant J. Scott;Camden I. Boyle;Matthew R. Maschmann;Stanton R. Price
2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Year: 2023 | Conference Paper |
In order to utilize aluminum-based nanoenergetic materials effectively, the mechanisms by which reactive aluminum fuel escapes a passivating aluminum shell must be better understood. These reactions can be classified based upon pre- and post-reaction imagery taken of the material. To aide in quantitatively understanding these reactions we investigate creating an improved reaction classifier throug...Show More
Multi-scale inverse design of optical metasurfaces using physics-informed computational intelligence
Marshall B. Lindsay;Andy G. Varner;Scott D. Kovaleski;Charlie T. Veal;Derek T. Anderson;Stanton R. Price;Steven R. Price
2023 IEEE Conference on Artificial Intelligence (CAI)
Year: 2023 | Conference Paper |
Interest in inverse design for the efficient and accurate design of optical devices has increased in recent years. In the case of complex optical problems which span several orders of magnitude, inverse design is an especially difficult problem. In this paper we propose a multi-scale inverse design process which leverages machine learning tools to encode the numerical simulation of optical wave pr...Show More
Evaluation of Road Segmentation Techniques on Visible and Infrared Low-Altitude UAS Imagery
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Year: 2022 | Conference Paper |
Road network understanding is an important component of determining where a vehicle can safely maneuver in an area of interest, especially in compromised environment scenarios such as disaster response. Low-altitude unmanned aircraft systems and semantic segmentation via deep neural networks provide an efficient solution to denoting the locations of roads in potentially large swaths of imagery. Un...Show More
Evaluating Visuospatial Features for Tracking Hazards in Overhead UAS Imagery
Trevor M. Bajkowski;J. Alex Hurt;Jeffrey Dale;David Huangal;James M. Keller;Grant J. Scott;Stanton R. Price
2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Tracking multiple objects as they enter and exit the field of view for a given image sequence has many applications in security, surveillance, traffic control, and additional important fields. To track objects of interest through videos a computer vision system needs two main components: an object detector, a model which can generate class predictions and bounding boxes for potential objects in a ...Show More
Neural Metric Learning using Contrastive Anchors to Enforce Feature Separability
Charlie Veal;Marshall Lindsay;Scott D. Kovaleski;Derek T. Anderson;Stanton R. Price
2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2021 | Conference Paper |
Deep metric learning is a paradigm to organize feature space such that dataset classes have small intra-class distance and large inter-class distance. While this domain has produced state-of-the-art results for challenging problems like facial recognition, one major limitation is the complexity of training these techniques. Methods like pairwise and triplet learning require heavy micromanagement w...Show More
A Comparison of Evolutionary and Neural Attention Modeling Relative to Adversarial Learning
Charlie Veal;Marshall Lindsay;Scott Kovaleski;Derek T. Anderson;Stanton R. Price
2021 IEEE Congress on Evolutionary Computation (CEC)
Year: 2021 | Conference Paper |
State-of-the-art machine learning, for computer vision applications, is based on data-driven feature learning. While these extraction paradigms often yield impressive results that outperform human hand-crafted solutions, they unfortunately suffer from a lack of explainability. In response to this, both the neural network and evolutionary communities have provided techniques tailored to visually ex...Show More
Aiding Material Design Through Machine Learning
Stanton R. Price;Christina H. Young;Matthew R. Maschmann;Steven R. Price
2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Machine learning is a field that has been around for decades whose impact and presence continues to increase across scientific and commercial communities. However, until recently, machine learning has not been thought of as a viable methodology that could significantly aid novel material discovery and design. That is, machine learning-aided material design and/or discovery is an emerging research ...Show More
Spatiotemporal Maneuverability Hazard Analytics from Low-Altitude UAS Sensors
Trevor M. Bajkowski;David Huangal;J. Alex Hurt;Jeffery Dale;James M. Keller;Grant J. Scott;Stanton R. Price
2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Low-altitude unmanned aerial systems (UAS) have a rapidly changing field of view for most sensor payloads, especially downward-looking high-resolution visual imagery. In this research, we explore visible-spectrum derived spatiotemporal awareness applied to companion sensor phenomenologies and computationally derived information, such as computer vision- based road extraction and maneuverability ha...Show More
Detection of unknown maneuverability hazards in low-altitude UAS color imagery using linear features
Jeffrey J. Dale;David Huangal;J. Alex Hurt;Trevor M. Bajkowski;James M. Keller;Grant J. Scott;Stanton R. Price
2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
Deep learning approaches have very quickly become the most popular framework for both semantic segmentation and object detection/recognition tasks. Especially in object detection, however, supervised models like deep neural networks are inherently prone to find only classes from the training data in the testing set. In domains where the safety and security of operators are entrusted to machine lea...Show More
Evolutionary Algorithm Driven Explainable Adversarial Artificial Intelligence
Charlie Veal;Marshall Lindsay;Scott D. Kovaleski;Derek T. Anderson;Stanton R. Price
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
It is well-known that machine learning algorithms can be susceptible to undesirable effects when exposed to conditions that are not expressed adequately in the training dataset. This leads to a growing interest throughout many communities; where do algorithms and trained models break? Recently, methods such as generative adversarial neural networks and variational autoencoders were proposed to cre...Show More
Introducing Fuzzy Layers for Deep Learning
2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2019 | Conference Paper |
Cited by: Papers (26)
Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and diffic...Show More
GOOFeD: Extracting Advanced Features for Image Classification via Improved Genetic Programming
2019 IEEE Congress on Evolutionary Computation (CEC)
Year: 2019 | Conference Paper |
Cited by: Papers (7)
Feature extraction is widely considered one of the most critical components to classification performance in computer vision. In the past, human-designed features, such as the histogram of oriented gradients, were used for extracting statistically rich features. Recently, there has been a movement away from human-designed features to machine-learned features. Herein, we propose a novel genetic pro...Show More
Genetic prOgramming for image feature descriptor learning
2017 IEEE Congress on Evolutionary Computation (CEC)
Year: 2017 | Conference Paper |
It is widely accepted that feature extraction is quite possibly the most critical step in computer vision. Typically, feature extraction is performed using a method such as the histogram of oriented gradients. In recent years, a shift has occurred from human to machine learned features, e.g., convolutional neural networks (CNNs) and Evolution-Constructed (ECO) features. An advantage of our improve...Show More
Soft segmentation weighted IECO descriptors for object recognition in satellite imagery
Stanton R. Price;Derek T. Anderson;Matthew R. England;Grant J. Scott
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Year: 2015 | Conference Paper |
Cited by: Papers (2)
Object recognition from remote sensing systems is a task of immense interest. With the vast deployment of aerial vehicles and space borne sensors for a wide variety of purposes, it is critical to have robust image processing techniques to analyze massive streams of collected data. Herein, we explore the utility of a feature descriptor learning framework, called improved Evolution-COnstructed (iECO...Show More
An improved evolution-constructed (iECO) features framework: Distribution statement A: Approved for public release; distribution is unlimited
2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)
Year: 2014 | Conference Paper |
Cited by: Papers (3)
In image processing and computer vision, significant progress has been made in feature learning for exploiting important cues in data that elude non-learned features. While the field of deep learning has demonstrated state-of-the-art performance, the Evolution-COnstructed (ECO) work of Lillywhite et. al has the advantage of interpretability, and it does not predispose the solution to one of convol...Show More
Regularization-based learning of the Choquet integral
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2014 | Conference Paper |
Cited by: Papers (33)
A number of data-driven fuzzy measure (FM) learning techniques have been put forth for the fuzzy integral (FI). Examples include quadratic programming, Gibbs sampling, gradient descent, reward and punishment and evolutionary optimization. However, most approaches focus solely on the minimization of the sum of squared error (SSE). Limited attention has been placed on characterizing and subsequently...Show More
Comparing Fuzzy, Probabilistic, and Possibilistic Partitions Using the Earth Mover’s Distance
A number of noteworthy techniques have been put forth recently in different research fields for comparing clusterings. Herein, we introduce a new method for comparing soft (fuzzy, probabilistic, and possibilistic) partitions based on the earth mover's distance (EMD) and the ordered weighted average (OWA). The proposed method is a metric, depending on the ground distance, for all but possibilistic ...Show More
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