Congratulations to our labmate, Akira Sewnath, for winning the 2018 ECE Undergraduate Excellence Award for her research. Akira is working on a study where she used possibilistic K-NN on Peanut Mesocarp Classification Application.
Author: weihuang.xu@ufl.edu
A Target Classification Algorithm for Underwater Synthetic Aperture Sonar Imagery
Abstract: The ability to discern the characteristics of the seafloor has many applications. Due to minimal visibility, Synthetic Aperture Sonar Imagery (SAS) uses sonar to produce a texture map of the seabed below. In this paper, we discuss an approach… Read More
Fractal Analysis of Seafloor Textures for Target Detection in Synthetic Aperture Sonar Imagery
Abstract: Fractal analysis of an image is a mathematical approach to generate surface related features from an image or image tile that can be applied to image segmentation and to object recognition. In undersea target countermeasures, the targets of interest… Read More
Sample spacing variations on the feature performance for subsurface object detection using handheld ground penetrating radar
Abstract: The use of handheld ground penetrating radar (GPR) for subsurface object detection often faces challenges coming from the human operator effect, antenna height variation and uneven data sample spacing. This paper investigates the artifact of uneven sample spacing on… Read More
A novel multi-perspective imaging platform (M-PIP) for phenotyping soybean root crowns in the field increases throughput and separation ability of genotype root properties
Abstract: Background: Root crown phenotyping has linked root properties to shoot mass, nutrient uptake, and yield in the field, which increases the understanding of soil resource acquisition and presents opportunities for breeding. The original methods using manual measurements have been… Read More
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
Abstract: In remote sensing, each sensor can provide complementary or reinforcing information. It is valuable to fuse outputs from multiple sensors to boost overall performance. Previous supervised fusion methods often require accurate labels for each pixel in the training data.… Read More
Sheng Zou Recently Presented His Work at AAG 2018 in New Orleans!
Our labmate Sheng Zou recently presented his work “Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation” at AAG 2018 conference in New Orleans. Good job!
Quantitative Evaluation Metrics for Superpixel Segmentation
Abstract: Superpixel segmentation methods have been found to be increasingly valuable in image processing and analysis. Superpixel segmentation approaches have been used as a preprocessing step for a wide variety of image analysis tasks such as full scene segmentation, automated… Read More
Congratulations to our labmate Joshua Peeples for Winning an NSF Graduate Research Fellowship!
Congratulations to our labmate Joshua Peeples for winning a National Science Foundation Graduate Research Fellowship for his research entitled “Getting Active: AUVs for Automated Sea Understanding” ! Josh proposed a study into novel active learning techniques for automated undersea scene… Read More
Comparison of Prescreening Algorithms for Target Detection in Synthetic Aperture Sonar Imagery
Abstract: Automated anomaly and target detection are commonly used as a prescreening step within a larger target detection and target classification framework to find regions of interest for further analysis. A number of anomaly and target detection algorithms have been… Read More