scholarly journals An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1427 ◽  
Author(s):  
E. Shamwell ◽  
William Nothwang ◽  
Donald Perlis
Keyword(s):  
Motion Estimation ◽  
Sensor Fusion ◽  
Visual Motion ◽  
Deep Networks ◽  
Fusion Approach

Optimal visual motion estimation: a note

1992 ◽  
Vol 14 (9) ◽  
pp. 959-964 ◽  
Author(s):  
M.E. Spetsakis ◽  
Y. Aloimonos
Keyword(s):  
Motion Estimation ◽  
Visual Motion

scholarly journals Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2847 ◽  
Author(s):  
Mengyun Liu ◽  
Ruizhi Chen ◽  
Deren Li ◽  
Yujin Chen ◽  
Guangyi Guo ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Indoor Localization ◽  
Scene Recognition ◽  
Fusion Approach

An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments

2018 ◽  
Author(s):  
Mohammad-Hashem Haghbayan ◽  
Fahimeh Farahnakian ◽  
Jonne Poikonen ◽  
Markus Laurinen ◽  
Paavo Nevalainen ◽  
...  
Keyword(s):  
Object Detection ◽  
Sensor Fusion ◽  
Fusion Approach

scholarly journals How multisensory neurons solve causal inference

2021 ◽  
Vol 118 (32) ◽  
pp. e2106235118
Author(s):  
Reuben Rideaux ◽  
Katherine R. Storrs ◽  
Guido Maiello ◽  
Andrew E. Welchman
Keyword(s):  
Motion Estimation ◽  
Causal Inference ◽  
Visual Motion ◽  
Temporal Area ◽  
Medial Superior Temporal ◽  
Electrophysiological Recordings ◽  
Visual Motion Cues ◽  
Integrated Or ◽  
Self Motion ◽  
The Brain

Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction (“congruent” neurons), while others prefer opposing directions (“opposite” neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference.

Sensor Fusion Approach for Dynamic Torque Estimation with Low Cost Sensors for Boosted 4-Cylinder Engine

2021 ◽  
Author(s):  
Cooper Heyne Minehart ◽  
Jeffrey Naber ◽  
Jason Blough ◽  
Xin Wang ◽  
Chris Glugla ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Low Cost ◽  
Torque Estimation ◽  
Fusion Approach
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