Category: Publication

Abstract: Synthetic aperture sonar (SAS) intensity statistics are dependent upon the sensing geometry at the time of capture. Estimating bathymetry from acoustic surveys is challenging. While several methods have been proposed to estimate seabed relief via intensity, we develop the first large-scale study that relies on deep learning models. In this work, we pose bathymetric […]

Abstract: Objective: We quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation. Impact Statement: To our knowledge, this study is the first investigation to apply convolutional neural network (CNN) models to identify and segment adipose tissue in histological images from silk fibroin biomaterial implants. Introduction: When designing biomaterials for the treatment […]

Abstract: Generative adversarial networks (GANs) have shown tremendous promise in learning to generate data and effective at aiding semi-supervised classification. However, to this point, semi-supervised GAN methods make the assumption that the unlabeled data set contains only samples of the joint distribution of the classes of interest, referred to as inliers. Consequently, when presented with […]

Abstract: In this work, we propose a new loss to improve feature discriminability and classification performance. Motivated by the adaptive cosine/coherence estimator [42] (ACE), our proposed method incorporates angular information that is inherently learned by artificial neural networks. Our learnable ACE (LACE) transforms the data into a new “whitened” space that improves the inter-class separability […]

Abstract: Purpose: Root system architectures are complex, multidimensional, and challenging to characterize effectively for agronomic and ecological discovery. Methods: We propose a new method, Spatial and Texture Analysis of Root System architecture with Earth mover’s Distance (STARSEED), for comparing root architectures that incorporate spatial information through a novel application of the Earth Mover’s Distance (EMD). […]

Abstract: Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a […]

Abstract: Precision dairy farming, specifically the design of management strategies according to the animal’s needs, may soon become the norm since automated technologies that generate large amounts of data for each individual are becoming more affordable. Our objectives were to determine whether the use of behavioral changes could improve the accuracy of prediction of the […]

Abstract: We present a histogram layer for artificial neural networks (ANNs). An essential aspect of texture analysis is the extraction of features that describe the distribution of values in local spatial regions. The proposed histogram layer directly computes the spatial distribution of features for texture analysis and parameters for the layer are estimated during backpropagation. […]

Congratulations to our labmates and collaborators: Ben Weinstein, Sarah Graves, Sergio Marconi, Aditya Singh, Alina Zare, Dylan Stewart, Stephanie Bohlman and Ethan P. White!  Their paper, “A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network”, was recently accepted to PLOS Computational Biology.  […]

Abstract: Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when targets are of irregular shape or difficult to distinguish from background or neighboring objects. Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management. However, tree crowns […]