Incremental engineering of computer vision systems

Abstract

Humans can effortlessly see and interpret the world around them. Yet, the development of computer vision systems capable of doing the same is an extremely challenging task. In developing vision systems, computer vision experts should explicitly express the knowledge required to guide the systems. The complex nature of vision systems however means that vision experts are only quasiexperts. They lack a strong understanding of the specific vision task or the ability to articulate their knowledge. Although some approaches automatically derive this knowledge using pattern recognition and machine learning methods, lack of sufficient labelled data makes these infeasible in some domains. The evolving nature of expertise, incrementally available data and shifting nature of the underlying vision tasks make it difficult for experts to express all knowledge a priori. This is especially true of domains such as medical imaging, where data tends to trickle in over time and expert's discover the precise knowledge only by applying different vision algorithms over time. Therefore, vision experts develop systems incrementally, by trial-and-error, and each ad-hoc revision to the system, although well intended, may in fact degrade the system performance.

This thesis proposes ways to mitigate the risks that ad-hoc incremental revisions pose to vision systems. It presents ProcessRDR and ProcessNet frameworks, which are an adaptation of the incremental validated change strategy employed by Ripple Down Rules for the vision domain. These frameworks assist the expert in systematically revising parts of a system, while maintaining the integrity of the whole. The thesis also establishes the role of quasi-expertise in vision domains, and studies its influences on the quality of the resulting vision systems. The studies suggest ways for experts to mitigate the influences of quasi-expertise and support the need for incremental development of vision systems.

Incremental development of computer vision systems give experts the capacity to adapt vision systems, as better expertise and more data becomes available. Although the frameworks do not eliminate quasiexpertise or lack of labelled data, they do support the expert in incrementally developing vision systems despite it. The systematic incremental revisions mean that the vision systems can continue to improve over time.