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 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.

Models of Statistical Visual Motion Estimation

1994 ◽  
Vol 60 (3) ◽  
pp. 300-312 ◽  
Author(s):  
M. Spetsakis
Keyword(s):  
Motion Estimation ◽  
Visual Motion

scholarly journals Neuromorphic Configurable Architecture for Robust Motion Estimation

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Guillermo Botella ◽  
Manuel Rodríguez ◽  
Antonio García ◽  
Eduardo Ros
Keyword(s):  
Visual System ◽  
Motion Estimation ◽  
Optical Flow ◽  
Visual Motion ◽  
Specific Model ◽  
Biologically Inspired ◽  
Visual Motion Perception ◽  
Illumination Changes ◽  
And Performance ◽  
Application Fields

The robustness of the human visual system recovering motion estimation in almost any visual situation is enviable, performing enormous calculation tasks continuously, robustly, efficiently, and effortlessly. There is obviously a great deal we can learn from our own visual system. Currently, there are several optical flow algorithms, although none of them deals efficiently with noise, illumination changes, second-order motion, occlusions, and so on. The main contribution of this work is the efficient implementation of a biologically inspired motion algorithm that borrows nature templates as inspiration in the design of architectures and makes use of a specific model of human visual motion perception: Multichannel Gradient Model (McGM). This novel customizable architecture of a neuromorphic robust optical flow can be constructed with FPGA or ASIC device using properties of the cortical motion pathway, constituting a useful framework for building future complex bioinspired systems running in real time with high computational complexity. This work includes the resource usage and performance data, and the comparison with actual systems. This hardware has many application fields like object recognition, navigation, or tracking in difficult environments due to its bioinspired and robustness properties.

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