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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
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Spatial and Temporal Downsampling in Event-Based Visual Classification.

Gregory Cohen, Saeed Afshar, Garrick Orchard

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    Downsampling event-based vision data improves classification accuracy and reduces data rates. This preprocessing technique is crucial for power- and bandwidth-constrained mobile and aerial applications.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Sensor Technology

    Background:

    • Event-based vision sensors are gaining traction for mobile and aerial applications.
    • High-speed, robust algorithms are needed for processing event-based data.
    • Event rate and network structure impact system power consumption.

    Purpose of the Study:

    • To investigate the impact of spatial and temporal downsampling on event-based vision data.
    • To analyze the efficiency of event-based encoding for power-constrained systems.
    • To characterize downsampling operations using diverse datasets.

    Main Methods:

    • The study focused exclusively on spatial and temporal downsampling effects on event-based vision data.
    • Various datasets were utilized to thoroughly explore and characterize downsampling.
    • Classification accuracy and data rates were measured before and after downsampling.

    Main Results:

    • Both spatial and temporal downsampling enhanced classification accuracy on N-MNIST and N-Caltech101 datasets.
    • Downsampling significantly reduced overall data rates.
    • For N-MNIST, downsampled systems achieved 89.38% accuracy versus 81.03% without downsampling.
    • For N-Caltech101, downsampled systems achieved 18.25% accuracy versus 7.43% without downsampling.

    Conclusions:

    • Downsampling is a vital preprocessing step for event-based visual processing.
    • This technique is particularly beneficial for applications sensitive to power consumption and transmission bandwidth.
    • Optimizing event-based encoding through downsampling enhances system efficiency.