Towards robust identification and tracking of nevi in sparse photographic time series

We introduce a precise deterministic approach for pixelwise change detection in images taken of a scene of interest over time. Our motivation is for applications such as artefact condition monitoring and structural inspection, where a common problem is the need to efficiently and accurately identify subtle signs of damage and deterioration. The approach we describe is designed to compensate for the three most common sources of nuisance variation encountered when tackling the problem of change detection, namely: viewpoint variation due to camera motion between images, photometric variation due to lighting differences, and changes in image resolution/focal settings.To tackle viewpoint variation, particularly in areas of low texture, we propose the use of the generalised PatchMatch (PM) correspondence algorithm to compute a dense flow field. The flow field is regularized using a Thin Plate Spline (TPS) model which assumes a smooth underlying geometry and allows registration to be interpolated precisely through areas of low texture or uncertain flow. To compensate for low-frequency lighting variation, we fit a second TPS model to the photometric differences between registered images. Finally, to account for changes in focal settings, we estimate and apply a blurring kernel via optimisation over image differences.We provide a thorough evaluation of the performance of our method on an illustrative toy dataset and on two recent, real-world inspection datasets. Our approach performs favourably versus state-of-the-art baselines in both cases, while remaining relatively transparent to understand and simple to compute.

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“…A further possible application is the monitoring of skin lesions to provide early warning or tracking of melanocytic skin cancers [14].…”

Section: Introductionmentioning

confidence: 99%