The purpose of our demo is to show the application and performance of some low-complexity image descriptors in object recognition under realistic circumstances. We built a client-server system where several image retrieval methods and image segmentation approaches can be tested with the help of a network connected Android device (mobile phone, table or head mounted computer). A modified version of the CEDD (Color and Edge Directivity Descriptor) is proposed, as the most robust lightweight descriptor found in our tests, and manual or saliency based object selection are also included. The main purpose of the demo is to show the possibilities of lightweight object recognition with the modified descriptor and different object segmentation.

In this paper we deal with a special case of archive handwritten text recognition when word spotting can be used effectively. We analyze the use of local feature descriptors and show that the Scale Invariant Feature Transform can be used efficiently despite the large variety of word shape, and the effects of different noises. We evaluate the performance on a database of 1638 word records segmented from an archive book and show that the proposed feature processing method can achieve over 80 % hit rate. Different parameter settings and variations of the local feature descriptor are analyzed.

In modern societies new, lifestyle related chronic diseases are appearing, affecting more and more people. Besides decreasing the quality of life for these patients, their treatments require increasing financial and social support from governments (and in many cases, even from the patients themselves). Apart from socio-economic concerns, another serious problem is the increasing shortage of experts (e.g. doctors, dietitians, ergonomists) that could help people, as the need for them is growing faster than their numbers. In this paper, a framework for creating expert systems capable of containing and properly using the knowledge of such experts, for providing help to users in acquiring and maintaining a healthy lifestyle, is presented. By selecting two different areas, diet and physical oriented lifestyle management and workplace related ergonomics, the effectiveness of such systems is tested.

Rule-base formulation for clips-based work ergonomic assessment