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Engineers have utilised spectral information and have steadily improved its applications in imaging systems for more than a century. The course of technological developments in colour imaging has been dictated by system improvements measured by their efficacy for direct human consumption. It seems reasonable to us to try to emulate nature and boost capabilities of machine vision systems by optimising the way in which they exploit spectral information. This is a two-step process: first step involves using a few spectrally broad detectors to compress the information content of the scene and the second step constructs spectral discriminants for image segmentation based on a small number of spectrally generated features assigned to each pixel. In animals the discriminant value is attributed to the object as what is called colour. Previous papers have concentrated on the final segmentation step. Here we show a straightforward way to design application-specific spectral sensitivity functions to improve image segmentation. The resulting functions can be used for reliable recognition of objects in a hyperspectral image in real time. These functions can also be used to design task-specific specialised cameras that can outperform current hyperspectral systems in terms of sensitivity, size, power consumption, robustness, price and complexity.