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Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised.

This work updates the recent progress made toward fabricating a real-time, quantitative, and biocompatible glucose sensor based on surface-enhanced Raman scattering (SERS). The sensor design relies on an alkanethiolate tri(ethylene glycol) monolayer that acts as a partition layer, preconcentrating glucose near a SERS-active surface. Chemometric analysis of the captured SERS spectra demonstrates that glucose is quantitatively detected in the physiological concentration range (0-450 mg/dL, 0-25 mM). In fact, 94% of the predicted glucose concentrations fall within regions A and B of the Clarke error grid, making acceptable predictions in a clinically relevant range. The data presented herein also demonstrate that the glucose sensor provides stable SERS spectra for at least 3 days, making the SERS substrate a candidate for implantable sensing. Glucose sensor reversibility and reusability is evaluated as the sensor is alternately exposed to glucose and saline solutions; after each cycle, difference spectra reveal that the partitioning process is largely reversible. Finally, the SERS glucose sensor successfully partitions glucose even when challenged with bovine serum albumin, a serum protein mimic.