Category: Publication

Abstract: In my dissertation, we present multiple instance learning U-net (MILUnet) algorithm and multiple instance learning class activation map (MILCAM) algorithm for weakly supervised semantic segmentation. Both the MILUnet and MILCAM algorithms requires only training images paired with image-level label to classify pixels into one or other classes into images. Compared with supervised semantic segmentation […]

Abstract: Automated individual tree crown (ITC) delineation plays an important role in forest remote sensing. Accurate ITC delineation benefits biomass estimation, allometry estimation, and species classification among other forest-related tasks, all of which are used to monitor forest health and make important decisions in forest management. In this article, we introduce neuro-symbolic DeepForest, a convolutional […]

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) , local binary patterns (LBP), and edge histogram descriptors (EHD). Features such as pixel gradient directions and magnitudes for HOG, encoded pixel differences for LBP, […]

Abstract: The domain of multi-look scene understanding problems includes scenarios where multiple passes over the same area have occurred and combining information from them is desired. For example, in remotely sensed SAS surveys, the same location on the seafloor is captured from multiple views where the UTM coordinates may not fully overlap. Additionally, error in […]

Abstract: Heterogeneous data fusion can enhance the robustness and accuracy of an algorithm on a given task. However, due to the difference in various modalities, aligning the sensors and embedding their information into discriminative and compact representations is challenging. In this paper, we propose a Contrastive learning based MultiModal Alignment Network (CoMMANet) to align data […]

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 […]