John Harer

Conference IEEE Aerospace Conference Proceedings · March 1, 2019 In this work, we address fusion of heterogeneous sensor data using wavelet-based summaries of fused self-similarity information from each sensor. The technique we develop is quite general, does not require domain specific knowledge or physical models, and ... Full text Cite

Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration

Conference IEEE Aerospace Conference Proceedings · June 25, 2018 This paper presents a processing pipeline for fusing 'raw' and / or feature-level multi-sensor data - upstream fusion - and initial results from this pipeline using imagery, radar, and radio frequency (RF) signals data to determine which tracked object, am ... Full text Cite

Geometric Cross-Modal Comparison of Heterogeneous Sensor Data

Conference Proceedings of the 39th IEEE Aerospace Conference · March 2018 In this work, we address the problem of cross-modal comparison of aerial data streams. A variety of simulated automobile trajectories are sensed using two different modalities: full-motion video, and radio-frequency (RF) signals received by detectors at va ... Open Access Link to item Cite

Geometric models for musical audio data

Conference Leibniz International Proceedings in Informatics Lipics · June 1, 2016 We study the geometry of sliding window embeddings of audio features that summarize perceptual information about audio, including its pitch and timbre. These embeddings can be viewed as point clouds in high dimensions, and we add structure to the point clo ... Full text Open Access Cite

Geometric Models for Musical Audio Data

Conference Proceedings of the 32st International Symposium on Computational Geometry (SOCG) · June 2016 Link to item Cite

Multi-scale local shape analysis and feature selection in machine learning applications

Conference Proceedings of the International Joint Conference on Neural Networks · September 28, 2015 We introduce a method called multi-scale local shape analysis for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse ... Full text Open Access Cite

Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers

Conference Proceedings of SPIE the International Society for Optical Engineering · January 1, 2015 This paper introduces a method to integrate target behavior into the multiple hypothesis tracker (MHT) likelihood ratio. In particular, a periodic track appraisal based on behavior is introduced that uses elementary topological data analysis coupled with b ... Full text Cite

John Harer

Scholarly Works - Conferences

Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion

Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration

Geometric Cross-Modal Comparison of Heterogeneous Sensor Data

Geometric models for musical audio data

Geometric Models for Musical Audio Data

Multi-scale local shape analysis and feature selection in machine learning applications

Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers

Persistent homology - a survey

Testing Expansion in Bounded-Degree Graphs

Inferring Local Homology from Sampled Stratified Spaces