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Found 9 papers, 3 papers with code
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Long-Lived Accurate Keypoints in Event Streams
Since this integration is required, we claim it is better to predict the keypoints' trajectories for the time period rather than single locations, as done in previous approaches.
Learning to Detect Objects with a 1 Megapixel Event Camera
However, due to the novelty of the field, the performance of event-based systems on many vision tasks is still lower compared to conventional frame-based solutions.
A Large Scale Event-based Detection Dataset for Automotive
We introduce the first very large detection dataset for event cameras.
Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras
We first introduce an efficient way to compute a time surface that is invariant to the speed of the objects.
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification
Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation.
Ranked #10 on
Robust classification
on N-ImageNet
Projection Onto the Manifold of Elongated Structures for Accurate Extraction
Detection of elongated structures in 2D images and 3D image stacks is a critical prerequisite in many applications and Machine Learning-based approaches have recently been shown to deliver superior performance.
Multiscale Centerline Detection by Learning a Scale-Space Distance Transform
We propose a robust and accurate method to extract the centerlines and scale of tubular structures in 2D images and 3D volumes.
Learning Separable Filters
Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes.
