The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Visual hand-gesture recognition is being increasingly desired for human-computer interaction interfaces. In many applications, hands only occupy about 10% of the image, whereas the most of it contains background, human face, and human body. Spatial localization of the hands in such scenarios could be a challenging task and ground truth bounding boxes need to be provided for training, which is usually...
In this paper, a hand gesture-based recognition system is presented with the aim of recognizing finger-spelling using the American Sign Language. The solution makes use of the depth imagery acquired by the new Kinect 2 sensor that provides more depth resolution. The main novelty is the introduction of a Compressive Sensing step to reduce the dimension of a depth-based feature descriptor, called Depth...
A novel and robust vision-based human-machine interface system to naturally interact with computers/smart devices is proposed. The key contribution is the introduction of a Compressive Sensing technique to largely reduce the dimensionality of highly discriminative feature descriptors (computed from depth imagery), which originally have an excessive and inoperative high dimension to be applied to a...
A robust hand-gesture recognition system based on a novel descriptor called Volumetric Spatiogram of Local Binary Patterns is presented, which allows a more natural input interface for simulating a mouse. The recognition stage based on Support Vector Machines triggers different mouse functions depending on the recognized gesture.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.