Category: Publication

Abstract: Advances in remote sensing imagery and machine learning applications unlock the potential for developing algorithms for species classification at the level of individual tree crowns at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. Little is known about how well these approaches generalize across […]

Abstract: Feature engineering often plays a vital role in the fields of computer vision and machine learning. A few common examples of engineered features include histogram of oriented gradients (HOG) (Dalal and Triggs, 2005), local binary patterns (LBP) (Ojala et al., 1994), and edge histogram descriptors (EHD) (Frigui and Gader, 2008). Features such as pixel […]

Abstract: Aided target detection in infrared data has proven an important area of investigation for both military and civilian applications. While target detection at the object or pixel-level has been explored extensively, existing approaches require precisely-annotated data which is often expensive or difficult to obtain. Leveraging advancements in weakly supervised semantic segmentation, this paper explores […]

Abstract: Openly available community science digital vouchers provide a wealth of data to study phenotypic change across space and time. However, extracting phenotypic data from these resources requires significant human effort. Here, we demonstrate a workflow and computer vision model for automatically categorizing species color pattern from community science images. Our work is focused on […]

Abstract: Understanding a plant’s root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying […]

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). […]