Category: Publication

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 in a low-dimensional embedding space. We compare the learned sample embeddings against coordinates found by […]

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 have diverse and representative training data, especially for application domains in which people of varying […]

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 (SAS) data. We examine the sensitivity to factors in the fine tuning process such as […]

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 are many approaches to quantify performance in standard crisp segmentation schemes, drawing hard boundaries in […]

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 sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in […]

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 resulting in limited capacity to improve product quality eventually leading to food loss and waste. […]

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 proposed, their performance is typically not compared based on standard data or evaluation metrics, making […]

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 gases from the fuel meat to the fuel cladding resulting in swelling, delamination, pillowing and […]

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 on Computer Vision (ECCV) Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP 2020).   Their […]

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 long and thin root objects of small diameter. The roots prove to be challenging for […]