  Observations of small-scale brightenings in the low solar atmosphere can
provide valuable constraints on possible heating/heat-transport mechanisms. We
present a method for the detection and analysis of brightenings and demonstrate
its application to IRIS EUV time-series imagery. The method uses band-pass
filtering, adaptive thresholding and centroid tracking, and records an event's
position, duration, and total/maximum brightness. Area, brightness, and
position are also recorded as functions of time throughout their lifetime.
Detected brightenings can fragment or merge over time, thus the number of
distinct regions constituting a brightening event is recorded over time and the
maximum number of regions are recorded as a simple measure of an event's
coherence/complexity. A test is made on a synthetic datacube composed of a
static background based on IRIS data, Poisson noise and $\approx10^4$
randomly-distributed, moving, small-scale Gaussian brightenings. Maximum
brightness, total brightness, area, and duration follow power-law distributions
and the results show the range over which the method can extract information.
The recorded maximum brightness is a reliable measure for the brightest and
most accurately detected events with an error of 6%. Area, duration, and speed
are generally underestimated by 15% with an uncertainty of 20-30%. Total
brightness is underestimated by 30% with an uncertainty of 30%. Applying this
method to real IRIS QS data spanning 19 minutes over a 54.40"$\times$55.23" FOV
yields 2997 detections. 1340 of these either remain un-fragmented or fragment
to two distinct regions at least once during their lifetime equating to an
event density of $3.96\times10^{-4}$arcsec$^{-2}$s$^{-1}$. The method will be
used for a future large-scale statistical analysis of several QS data sets from
IRIS, other EUV imagers, as well as H-$\alpha$ and visible photospheric
imagery.
