In recent years, various image recognition tasks have been used in many real-world applications thanks to the development and open sources of computer vision technologies. However, the expertise of users is often required for selecting appropriate recognition engines for the analysis of given images. This limits the use of beginners who wanted to apply image recognitions for their real-world demands. To make such a selection process easier, we propose a consulting system in this paper that can automatically suggest appropriate recognition engines for given images or videos. In addition, the system can provide alternative editing operations, such as enlarging or shrinking, when the size or quality of an image is inappropriate for any recognitions. The effectiveness, easy-useness, and user-friendliness is demonstrated by the proposed consulting system.

This paper introduces an action detection system based on pose information. The system utilizes view-invariant pose feature not relying on machine learning techniques, and it can detect human actions regardless of camera settings thus it is easy to apply for any target actions. System users only need to register sample images including target actions beforehand. In detection phase, the system receives an image from live camera at short intervals and computes the similarity between captured image and each pre-registered sample image. If the similarity is higher than the specified threshold, the system judges the target action is detected. We evaluated the detection performance of a common action "phone-call" using cellphone and confirmed its effectiveness.