  Bias triangles represent features in stability diagrams of Quantum Dot (QD)
devices, whose occurrence and property analysis are crucial indicators for spin
physics. Nevertheless, challenges associated with quality and availability of
data as well as the subtlety of physical phenomena of interest have hindered an
automatic and bespoke analysis framework, often still relying (in part) on
human labelling and verification. We introduce a feature extraction framework
for bias triangles, built from unsupervised, segmentation-based computer vision
methods, which facilitates the direct identification and quantification of
physical properties of the former. Thereby, the need for human input or large
training datasets to inform supervised learning approaches is circumvented,
while additionally enabling the automation of pixelwise shape and feature
labeling. In particular, we demonstrate that Pauli Spin Blockade (PSB)
detection can be conducted effectively, efficiently and without any training
data as a direct result of this approach.
