Abstract: In this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network. Motivated by a popular dimensionality reduction approach, t-Distributed Stochastic Neighbor Embedding (t-SNE), our proposed method incorporates a classification loss computed on samples… Read More
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APPLICATION OF DIVISIVE CLUSTERING FOR REDUCING BIAS IN IMBALANCED DATA
Abstract: A lack of diversity and representativeness within training data causes bias in the machine learning pipeline by influencing the performance of many machine learning models to favor the majority of samples that are most similar. It is necessary to… Read More
EXPLAINABLE SYSTEMATIC ANALYSIS FOR SYNTHETIC APERTURE SONAR IMAGERY
Abstract: In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar… Read More
THE WEAKLY-LABELED RAND INDEX
Abstract: Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there… Read More
A REMOTE SENSING DERIVED DATA SET OF 100 MILLION INDIVIDUAL TREE CROWNS FOR THE NATIONAL ECOLOGICAL OBSERVATORY NETWORK
Abstract: Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote… Read More
EVALUATION OF POSTHARVEST SENESCENCE IN BROCCOLI VIA HYPERSPECTRAL IMAGING
Abstract: Fresh fruit and vegetables are invaluable for human health; however, their quality often deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. We currently lack any objective indices which indicate the freshness of fruit or vegetables… Read More
A BENCHMARK DATASET FOR INDIVIDUAL TREE CROWN DELINEATION IN CO-REGISTERED AIRBORNE RGB, LIDAR AND HYPERSPECTRAL IMAGERY FROM THE NATIONAL ECOLOGICAL OBSERVATION NETWORK
Abstract: Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been… Read More
NEW APPROACH FOR MEASURING INTERCONNECTIVITY OF FISSION GAS PORES IN NUCLEAR FUELS FROM 2D MICROGRAPHS
Abstract: In this work, we developed a simple and easily reproducible method to measure the interconnectivity of fission gas pore phases in irradiated nuclear fuels. The formation, growth and interconnection of fission gas pores contribute to the release of fission… Read More
MIL-CAM ACCEPTED TO ECCV 2020 WORKSHOP ON COMPUTER VISION PROBLEMS IN PLANT PHENOTYPING!
Congratulations to our labmates and collaborators: Guohao Yu, Alina Zare, Weihuang Xu, Roser Matamala, Joel Reyes-Cabrera, Felix B. Fritschi and Thomas E. Juenger! Their paper, “Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM” was recently accepted to the 16th European Conference… Read More
WEAKLY SUPERVISED MINIRHIZOTRON IMAGE SEGMENTATION WITH MIL-CAM
Abstract: We present a multiple instance learning class activation map (MIL-CAM) approach for pixel-level minirhizotron image segmentation given weak image-level labels. Minirhizotrons are used to image plant roots in situ. Minirhizotron imagery is often composed of soil containing a few… Read More