This paper presents several improvements of [email protected], an image sharing and retrieval application for identifying plants [6]: (i) ported to most android platforms (ii) three times more data (iii) exploiting metadata as well as visual content in the identification process (iv) a new multi-plant-organ, multi-image and multi-feature merging strategy with separate indexes for each visual feature (v) integrating cross-languages functions. This paper also presents the new results achieved by our system in the ImageCLEF 2013 plant identification task and in real-world user trials.

[email protected] is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.

A non-intrusive real-time program detects the face, then eyes and nose of a moving workstation user at a rate of between 10 and 30 Hertz. Based on the head pose, it determines where on the display the subject is looking. The long term goal is to provide a system for controlling a computer using head movements and gaze direction. The current program creates a base facility for other capabilities such as detecting facial gestures, creating face models, and normalizing for face recognition. A skin color model is used along with geometric knowledge about the face and weak assumptions about the lighting. Good results are reported with various subjects and conditions, including facial hair; 3D motion, and use of eyeglasses.