Much work in computer vision in the 70’s and 80’s aimed at the development of high-level vision, whereby the numerical processes feed a symbolic level of knowledge with which an agent is capable of interpreting the world. These early attempts were frustrated by the non-existence at the time of efficient algorithms for dealing with uncertainty, of tractable knowledge representation formalisms and also by the rudimentary stage of image-processing algorithms. Since then, important advances in Artificial Intelligence (AI) suggest that we may be at the stage of bridging the gap between AI and Computer Vision. One possible way of bridging this gap is the development of Qualitative Spatial Reasoning (QSR) methods based on sensor data. This idea is intrinsically connected to the tradition on logic-based image interpretation. This paper presents a brief introduction to these fields and discusses a possible research agenda for the future.