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Found 22 papers, 2 papers with code
OppLoD: the Opponency based Looming Detector, Model Extension of Looming Sensitivity from LGMD to LPLC2
As a vital capability evolutionary survival, it has been extensively studied in neuroscience and is attracting increasing research interest in robotics due to its close relationship with collision detection and navigation.
Spatio-Temporal Feedback Control of Small Target Motion Detection Visual System
The first subnetwork is designed to extract spatial and temporal motion patterns of cluttered backgrounds by neuronal ensemble coding.
DVM-CAR: A large-scale automotive dataset for visual marketing research and applications
There is a growing interest in product aesthetics analytics and design.
Attention and Prediction Guided Motion Detection for Low-Contrast Small Moving Targets
The developed visual system comprises three main subsystems, namely, an attention module, an STMD-based neural network, and a prediction module.
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.
Enhancing LGMD's Looming Selectivity for UAV with Spatial-temporal Distributed Presynaptic Connections
The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably, and has potential to be implemented on embedded collision detection systems for small or micro UAVs.
A Time-Delay Feedback Neural Network for Discriminating Small, Fast-Moving Targets in Complex Dynamic Environments
Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro robots that are generally limited in computational power.
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.
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.
A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds
The directional contrast and the extracted motion information by the motion pathway are integrated in the mushroom body for small target discrimination.
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.
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.
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.
A Feedback Neural Network for Small Target Motion Detection in Cluttered Backgrounds
However, if there exists a feedback loop in the STMD visual pathway or if a feedback loop could significantly improve the detection performance of STMD neurons, is unclear.
2D Reconstruction of Small Intestine's Interior Wall
The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration.
An Improved LPTC Neural Model for Background Motion Direction Estimation
A class of specialized neurons, called lobula plate tangential cells (LPTCs) has been shown to respond strongly to wide-field motion.
A Directionally Selective Small Target Motion Detecting Visual Neural Network in Cluttered Backgrounds
Discriminating targets moving against a cluttered background is a huge challenge, let alone detecting a target as small as one or a few pixels and tracking it in flight.
A Bio-inspired Collision Detecotr for Small Quadcopter
The observed results from the experiments demonstrated that the LGMD collision detector is feasible to work as a vision module for the quadcopter's collision avoidance task.
A Resilient Image Matching Method with an Affine Invariant Feature Detector and Descriptor
In this paper we propose a novel image feature matching algorithm, integrating our previous proposed Affine Invariant Feature Detector (AIFD) and new proposed Affine Invariant Feature Descriptor (AIFDd).
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.
