Form-factor and light efficiency are important issues Head-Mounted Displays face, since they both restrict their usage. Improving the form-factor means that for a defined visual stimulus, the system is smaller in volume. The light efficiency issue is linked to power consumption and time of use as well as the device’s ability to deliver, within a specific environment, enough luminance for the virtual image to be seen. This trade-off can also be found in imaging systems and Christophe Gaschet previously explored the optical design of onaxis imaging systems using curved sensors and particularly diopters number reduction thanks to Petzval shaped image plane. However, the behavior of an optical system changes dramatically when the design is off-axis. This paper focuses on demonstrating how using a curved microdisplay helps to improve the form-factor of a HMD system optimized using freeform optical design on a practical example. Curvature can also plays a great role in reducing the losses of light, but this imposes more constraints on the shapes to be given to the microdisplay. We discuss the trade-offs between these two advantages given by curved microdisplays. The mechanical feasibility of curved micro-displays will also be discussed, as well as the process to make a curved microdisplay, which is compatible with current mass-production CMOS displays. For OLED technology, the main resistance to curvature is the silicon substrate. The case for GaN technologies shows other mechanical limitations. We can predict the highest reachable curvature values, depending on microdisplay size and technology.
With advances in technology, a large part of our cultural heritage is becoming digitally available. In particular, in the field of historical document image analysis, there is now a growing need for indexing and data mining tools, thus allowing us to spot and retrieve the occurrences of an object of interest, called a pattern, in a large database of document images. Patterns may present some variability in terms of color, shape, or context, making the spotting of patterns a challenging task. Pattern spotting is a relatively new field of research, still hampered by the lack of available annotated resources. We present a new publicly available dataset named DocExplore dedicated to spotting patterns in historical document images. The dataset contains 1500 images and 1464 queries, and allows the evaluation of two tasks: image retrieval and pattern localization. A standardized benchmark protocol along with ad hoc metrics is provided for a fair comparison of the submitted approaches. We also provide some first results obtained with our baseline system on this new dataset, which show that there is room for improvement and that should encourage researchers of the document image analysis community to design new systems and submit improved results.