Abstract
Image sensors used in current machine vision systems suffer from low dynamic range and poor colour constancy and are brittle and unmalleable, limiting their use in applications for which there will be considerable demand in the future. Most approaches aiming to resolve these inadequacies focus on developing improvements in the lighting, software (processing algorithms) or hardware surrounding the photosensor such as the filters. Other strategies involve changing the architecture of the image sensor and the photosensing material; both have experienced recent success. Although they are yet to break fully into the market, image sensors developed from alternative solution-processed materials such as organic semiconductors and organohalide perovskites have immense potential to address the above issues and to ‘disrupt’ machine vision technology.
Highlights
1.1.1 Image Sensing in Machine Vision SystemsDigital cameras offer many advantages over conventional photo technologies, including the elimination of film processing, the ease of editing and affordability
Current machine vision systems (MVS) have been recognized as having severe limitations when it comes to the demands of modern-day applications involving machine vision and robotics
These include weak light absorption over the visible range, low dynamic range, existence of crosstalk, an inability to cope with illuminant variation and incompatibility with complicated processing and fabrication on flexible, miniaturized devices
Summary
Digital cameras offer many advantages over conventional photo technologies, including the elimination of film processing, the ease of editing and affordability Evidence for their increasing popularity worldwide can be seen in the resultant consumer success. The market for image sensors has experienced major growth over recent years with the value predicted to reach USD 23.97 billion by 2023 [1, 2] This increase is largely due to digital still and video cameras, and includes the expansion of digital imaging to cellular phones, laptop and personal computers (e.g. Internet-based video conferencing), security and surveillance, and the automotive, medical and entertainment industries. Digital cameras are used extensively for image capture in machine vision systems (MVS), which rely upon object recognition and image analysis/indexing to extract data which is used to control a process or activity. A key objective of this chapter is to flesh out two proposed alternative approaches, namely changes that can be made to the architecture of the image sensor and the photosensor material within the image sensor
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