ArticleFrontiers in neuroscience2015
On event-based optical flow detection.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Comparison of Anomaly Detection Methods on Event-Based Vision Sensor Data in a High Noise Environment.Sensors (Basel, Switzerland) · 2026Article
- Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: A Review.Sensors (Basel, Switzerland) · 2025Review
- TDE-3: an improved prior for optical flow computation in spiking neural networks.Frontiers in neuroscience · 2025Article
- High-efficiency sparse convolution operator for event-based cameras.Frontiers in neurorobotics · 2025Article
- Velocity and Color Estimation Using Event-Based Clustering.Sensors (Basel, Switzerland) · 2023Article
- Canonical circuit computations for computer vision.Biological cybernetics · 2023Review
- Event-Based Optical Flow Estimation with Spatio-Temporal Backpropagation Trained Spiking Neural Network.Micromachines · 2023Article
- Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird's-Eye View Transformation.Sensors (Basel, Switzerland) · 2022Article
- A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model.Computational intelligence and neuroscience · 2022Article
- Optical Flow Estimation by Matching Time Surface with Event-Based Cameras.Sensors (Basel, Switzerland) · 2021Article
- Event-Based Eccentric Motion Detection Exploiting Time Difference Encoding.Frontiers in neuroscience · 2020Article
- A Motion-Based Feature for Event-Based Pattern Recognition.Frontiers in neuroscience · 2016Article
- Evaluation of Event-Based Algorithms for Optical Flow with Ground-Truth from Inertial Measurement Sensor.Frontiers in neuroscience · 2016Article
- Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural Networks.PLoS computational biology · 2015Article
- Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System.PloS one · 2015Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations.
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