  Studying coronal mass ejections (CMEs) in coronagraph data can be challenging
due to their diffuse structure and transient nature, compounded by the
variations in their dynamics, morphology, and frequency of occurrence. The
large amounts of data available from missions like the Solar and Heliospheric
Observatory (SOHO) make manual cataloging of CMEs tedious and prone to human
error, and so a robust method of detection and analysis is required and often
preferred. A new coronal image processing catalog called CORIMP has been
developed in an effort to achieve this, through the implementation of a dynamic
background separation technique and multiscale edge detection. These algorithms
together isolate and characterise CME structure in the field-of-view of the
Large Angle Spectrometric Coronagraph (LASCO) onboard SOHO. CORIMP also applies
a Savitzky-Golay filter, along with quadratic and linear fits, to the
height-time measurements for better revealing the true CME speed and
acceleration profiles across the plane-of-sky. Here we present a sample of new
results from the CORIMP CME catalog, and directly compare them with the other
automated catalogs of Computer Aided CME Tracking (CACTus) and Solar Eruptive
Events Detection System (SEEDS), as well as the manual CME catalog at the
Coordinated Data Analysis Workshop (CDAW) Data Center and a previously
published study of the sample events. We further investigate a form of
unsupervised machine learning by using a k-means clustering algorithm to
distinguish detections of multiple CMEs that occur close together in space and
time. While challenges still exist, this investigation and comparison of
results demonstrates the reliability and robustness of the CORIMP catalog,
proving its effectiveness at detecting and tracking CMEs throughout the LASCO
dataset.
