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The ubiquity and miniaturization of mobile sensing devices as well as the increase of computational power of mobile and handheld devices has led to a large increase in research, algorithms and applications in the area. In the near future mobile imaging technology will become ubiquitous such as in camera phones, vision enhanced handhelds, and wearable cameras. Beyond passive data display and transmission, future information technologies will provide smart mobile services being capable of real-time analysis, purposeful selection and interpretation of enormous quantities of sensed and retrieved data. Image understanding for mobile multimodal interfaces would make new approaches possible in object recognition, context awareness, and augmented reality, aiming towards application scenarios of personal assistance, mobile work, and assistive services. The research challenges are not only efficiency, speed and low-complexity algorithms, but also additional demands on robustness of interpretation because of the mobility of the devices as well as the impact of dynamically changing and noisy conditions within urban environments. Since multi-sensor information analysis affords cueing and indexing into databases with nowadays huge information spaces, the sensed data have to be processed in an intelligent way to provide "in time delivery" of the requested relevant information. Knowledge has to be applied in an intelligent way about what needs to be attended to, and when, and what to do in a meaningful sequence, in correspondence with multi-sensor feedback.
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