2nd Workshop on Neuromorphic Vision: Advantages and Applications of Event Cameras Call for Papers

NEVI 2025 Workshop @ International Conference on Computer Vision 2025

2nd Workshop on Neuromorphic Vision: Advantages and Applications of Event Cameras


Workshop Date: Oct 19-20 2025

Submission Deadline: July 03 2025

Honolulu, Hawaii, United States, 

https://sites.google.com/view/nevi-2025/home-page


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SCOPE AND MOTIVATION

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Neuromorphic sensors, also known as event cameras, are a class of
imaging devices mimicking biological visual systems. Unlike
traditional frame-based cameras, which capture images synchronously,
neuromorphic sensors continuously generate events capturing
asynchronous illumination changes.


Event cameras have initially gained interest in the field of robotics
due to their low power consumption, extremely low latency, high
dynamic range and absence of motion blur. Yet, this wide range of
intriguing properties has rapidly enabled new, cutting-edge
applications, especially for motion-centric tasks. The very fine
temporal granularity of event cameras allows to easily capture complex
temporal dynamics in a scene, so that the tackling of complex tasks
can abstract from the low-level processing, and focus directly on
higher-level cognition.


In the past few years, we have witnessed the development of new
astonishing technologies based on neuromorphic vision: low latency and
low power consumption have allowed drones to effectively avoid
fast-moving obstacles; high dynamic range and lack of motion blur
allowed self-driving cars to detect other vehicles and pedestrians in
adverse conditions such as low illumination; micro-second temporal
granularity has enhanced the analysis of human micro-expressions and
emotions. Many other groundbreaking applications are leveraging
neuromorphic sensors, from high-speed object counting and defect
detection to vibration measurement, fluid monitoring and
time-to-contact estimation for spacecraft landing. Event-based
processing has also been shown to provide an extra layer of privacy
preservation compared to standard cameras, an important addition
especially in light of the recent definition of the AI Act by the
European Commission to regulate the development of artificial
intelligence.


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TOPICS OF INTEREST

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This workshop aims to foster the growth of event-based research, by
gathering researchers in the field and improving the communication
between academia and industry, towards the discovery of new
bleeding-edge neuromorphic technologies. Following is a non-exhaustive
list of topics covered:


Event-based Vision


    Representations for event-based data

    Event camera simulators

    Event-based datasets

    Novel sensing techniques for event-based vision


Neuromorphic Event Data Processing


    Spiking neural networks

    Bio-inspired computational methods

    Event-based spatio-temporal feature extraction

    Learning methodologies with event data


Neuromorphic vision applications


    Event-based human analysis

    Driving monitoring systems

    Neuromorphic cameras for space

    High-speed counting

    Autonomous navigation


Hardware architectures for event-based vision


    ASIC and FPGA-based implementations

    Novel circuitry designs

    Benchmarking and characterization of event-based cameras


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 ORGANIZING COMMITTEE

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    Federico Becattini (University of Siena, Italy)

    Gaetano Di Caterina (University of Strathclyde, UK)

    Yulia Sandamirskaya (ZHAW Zurich University of Applied Sciences, Switzerland)

    Gregory Cohen (Western Sydney University, Australia)

    Luca Cultrera (University of Florence, Italy)

    Lorenzo Berlincioni (University of Florence, Italy)

    Suzanne Little (Dublin City University, Ireland)

    Joseph Lemley (University of Galway, Ireland)

    Gaurvi Goyal (Italian Institute of Technology, Italy)

    Arren Glover (Italian Institute of Technology, Italy)

    Axel von Arnim (Fortiss, Germany)



-- 
Università di Siena
Federico Becattini
Dipartimento di ingegneria dell'informazione e scienze matematiche
Tenure Track Assistant Professor (RTD-B)
T. +39 0577232867 [int.2867]
federico.becattini@unisi.it