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Found 9 papers, 0 papers with code
Modelling Drosophila Motion Vision Pathways for Decoding the Direction of Translating Objects Against Cluttered Moving Backgrounds
The main contributions of this research are on two aspects: 1) the proposed model articulates the forming of both direction-selective (DS) and direction-opponent (DO) responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; 2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response.
Complementary Visual Neuronal Systems Model for Collision Sensing
Inspired by insects' visual brains, this paper presents original modelling of a complementary visual neuronal systems model for real-time and robust collision sensing.
Synthetic Neural Vision System Design for Motion Pattern Recognition in Dynamic Robot Scenes
The presented system is a synthetic neural network, which comprises two complementary sub-systems with four spiking neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for sensing looming and recession, and the direction selective neurons (DSN-R and DSN-L) in flies for translational motion extraction.
An LGMD Based Competitive Collision Avoidance Strategy for UAV
In this paper, we present an LGMD based competitive collision avoidance method for UAV indoor navigation.
Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review
A good number of motion detectors have been identified in the insects' visual pathways.
A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios
This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios.
Visual Cue Integration for Small Target Motion Detection in Natural Cluttered Backgrounds
To address this issue, we develop a new visual system model for small target motion detection against cluttered moving backgrounds.
A Directionally Selective Neural Network with Separated ON and OFF Pathways for Translational Motion Perception in a Visually Cluttered Environment
With respect to biological findings underlying fly's physiology in the past decade, we present a directionally selective neural network, with a feed-forward structure and entirely low-level visual processing, so as to implement direction selective neurons in the fly's visual system, which are mainly sensitive to wide-field translational movements in four cardinal directions.
Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts
The results demonstrated this framework is able to detect looming dark objects embedded in bright backgrounds selectively, which make it ideal for ground mobile platforms.
