39 citations · 191 across the 29 of their papers we have counts for
30 papers
Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data
Aditya Dutt, Alina Zare, Paul Gader
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 em…
Histogram Layers for Synthetic Aperture Sonar Imagery
Joshua Peeples, Alina Zare, Jeffrey Dale +1
Synthetic aperture sonar (SAS) imagery is crucial for several applications, including target recognition and environmental segmentation. Deep learning models have led to much succe…
Image-to-Height Domain Translation for Synthetic Aperture Sonar
Dylan Stewart, Shawn Johnson, Alina Zare
Observations of seabed texture with synthetic aperture sonar are dependent upon several factors. In this work, we focus on collection geometry with respect to isotropic and anisotr…
Learnable Adaptive Cosine Estimator (LACE) for Image Classification
Joshua Peeples, Connor McCurley, Sarah Walker +2
In this work, we propose a new loss to improve feature discriminability and classification performance. Motivated by the adaptive cosine/coherence estimator (ACE), our proposed met…
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security
Allen Starke, Keerthiraj Nagaraj, Cody Ruben +5
Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environm…
Robust Semi-Supervised Classification using GANs with Self-Organizing Maps
Ronald Fick, Paul Gader, Alina Zare
Generative adversarial networks (GANs) have shown tremendous promise in learning to generate data and effective at aiding semi-supervised classification. However, to this point, se…
Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation
Joshua Peeples, Daniel Suen, Alina Zare +1
The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS) imagery into distinct regions of t…
RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation
Dylan Stewart, Alina Zare, Sergio Marconi +5
Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when tar…
Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator
James Bocinsky, Connor McCurley, Daniel Shats +1
Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EM…
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
Joshua Peeples, Matthew Cook, Daniel Suen +2
Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloo…
Comparison of Hand-held WEMI Target Detection Algorithms
Connor H. McCurley, James Bocinsky, Alina Zare
Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at lo…
Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach
Wenshuai Chen, Shuiping Gou, Xinlin Wang +3
Existing polarimetric synthetic aperture radar (PolSAR) image classification methods cannot achieve satisfactory performance on complex scenes characterized by several types of lan…
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
Xiaoxiao Du, Alina Zare
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 sup…
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
Xiaoxiao Du, Alina Zare
In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance. Standard supervised fusion algorithms often require accur…
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
Changzhe Jiao, Chao Chen, Ronald G. McGarvey +3
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection p…
Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation
Alina Zare, Nicholas Young, Daniel Suen +3
Side-look synthetic aperture sonar (SAS) can produce very high quality images of the sea-floor. When viewing this imagery, a human observer can often easily identify various sea-fl…
Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms
Changzhe Jiao, Bo-Yu Su, Princess Lyons +3
A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to…
Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation
Sheng Zou, Hao Sun, Alina Zare
A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability…
Multiple Instance Hybrid Estimator for Learning Target Signatures
Changzhe Jiao, Alina Zare
Signature-based detectors for hyperspectral target detection rely on knowing the specific target signature in advance. However, target signature are often difficult or impossible t…
Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps
Hao Sun, Alina Zare
A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC su…
Partial Membership Latent Dirichlet Allocation
Chao Chen, Alina Zare, Huy Trinh +3
Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image p…
Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation
Sheng Zou, Alina Zare
The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a h…
Multiple Instance Hyperspectral Target Characterization
Alina Zare, Changzhe Jiao, Taylor Glenn
In this paper, two methods for multiple instance target characterization, MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target signature from imprec…
Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances
Changzhe Jiao, Princess Lyons, Alina Zare +2
A multiple instance learning (MIL) method, extended Function of Multiple Instances (FUMI), is applied to ballistocardiogram (BCG) signals produced by a hydraulic bed sensor. The…