Proceedings ArticleDOI
Multimodal Data Fusion Using Canonical Variates Analysis Confusion Matrix Fusion
Erik Blasch,Asad Vakil,Jia Li,Robert L. Ewing +3 more
- pp 1-10
TLDR
In this paper, the authors compare results from the fusion of histograms to that of fusion of confusion matrices developed from data of the same modality and that of a cross modality.Abstract:
Data fusion from a variety of sources requires alignment, association, and analysis. One method to determine the relationship between two variables measuring the same information is a correlation analysis. The canonical variates analysis (CVA) supports the assessments of two sets of data. In this paper, we compare results from the fusion of histograms to that of the fusion of confusion matrices developed from data of the same modality and that of a cross modality. We use the Confusion Matrix Fusion (CMF) approach in the analysis and compare the results for EO/RF fusion. In the analysis, the Experiments, Scenarios, Concept of Operations, and Prototype Engineering (ESCAPE) data set is used for comparison to previous aerospace results.read more
Citations
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Journal ArticleDOI
Multi-Modality Sensing and Data Fusion for Multi-Vehicle Detection
TL;DR: This work proposes several deep learning based frameworks for fusing different modalities through the exploitation of complementary latent embeddings, incorporating multiple state-of-the-art fusion strategies, and shows that fusion between image and non-image modalities improves vehicle tracking and detection under NLOS conditions.
Journal ArticleDOI
Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition
TL;DR: The proposed PRF-PIR system provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects.
Journal ArticleDOI
Multi-modality Sensing and Data Fusion for Multi-vehicle Detection
TL;DR: In this paper , the authors proposed different deep learning based frameworks for fusing different modalities (image, radar, acoustic, seismic) through the exploitation of complementary latent embeddings, and incorporating different state-of-the-art fusion strategies.
Journal ArticleDOI
Predicting the Quality of Pineapple Using the Naive Bayes Classifier Method
Tripathi Jaya,Machudor Yusman +1 more
TL;DR: The Naïve Bayes Classifier model is proposed as a classification method that is capable of producing high classification accuracy with low complexity with classification accuracy up to 75%, so this model can be efficient as an analytical tool.
Peer Review
Advances in Infrared Image Processing and Exploitation using Deep Learning
TL;DR: Recent advances of DL for Infrared (IR) applications are highlighted by conducting a literature review for IR only and IR plus another modality (e.g., Visual+IR), and there are emerging trends easily discernable on IR sensor analytics.
References
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Book
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Proceedings ArticleDOI
Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier
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Journal ArticleDOI
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