2024/09/22 by Pritam Karmokar, Quan Nguyen, Karmokar, Pritam P. +3 · 1 citation
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Advanced Memory and Neural Computing #CCD and CMOS Imaging Sensors #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.14611
openalex publication_date 2024/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Event cameras capture the motion of intensity gradients (edges) in the image plane in the form of rapid asynchronous events. When accumulated in 2D histograms, these events depict overlays of the edges in motion, consequently obscuring the spatial structure of the generating edges. Contrast maximization (CM) is an optimization framework that can reverse this effect and produce sharp spatial structures that resemble the moving intensity gradients by estimating the motion trajectories of the events. Nonetheless, CM is still an underexplored area of research with avenues for improvement. In this paper, we propose a novel hybrid approach that extends CM from uni-modal (events only) to bi-modal (events and edges). We leverage the underpinning concept that, given a reference time, optimally warped events produce sharp gradients consistent with the moving edge at that time. Specifically, we formalize a correlation-based objective to aid CM and provide key insights into the incorporation of multiscale and multireference techniques. Moreover, our edge-informed CM method yields superior sharpness scores and establishes new state-of-the-art event optical flow benchmarks on the MVSEC, DSEC, and ECD datasets.