Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. Gestures can originate from any bodily motion or state, but commonly originate from the face or hand. Focuses in the field include emotion recognition from face and hand gesture recognition since they are all expressions. Users can make simple gestures to control or interact with devices without physically touching them. Many approaches have been made using cameras and computer vision algorithms to interpret sign language, however, the identification and recognition of posture, gait, proxemics, and human behaviors is also the subject of gesture recognition techniques. Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a better bridge between machines and humans than older text user interfaces or even GUIs (graphical user interfaces), which still limit the majority of input to keyboard and mouse and interact naturally without any mechanical devices.
The major application areas of gesture recognition in the current scenario are:
Automotive sector
Consumer electronics sector
Transit sector
Gaming sector
To unlock smartphones
Defense
Home automation
Automated sign language translation
Gesture recognition can be conducted with techniques from computer vision and .
The literature includes ongoing work in the computer vision field on capturing gestures or more general human pose and movements by cameras connected to a computer.
Gesture recognition and pen computing:
Pen computing reduces the hardware impact of a system and also increases the range of physical world objects usable for control beyond traditional digital objects like keyboards and mice. The term "gesture recognition" has been used to refer more narrowly to non-text-input handwriting symbols, such as inking on a graphics tablet, multi-touch gestures, and mouse gesture recognition.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. Gestures can originate from any bodily motion or state, but commonly originate from the face or hand. Focuses in the field include emotion recognition from face and hand gesture recognition since they are all expressions. Users can make simple gestures to control or interact with devices without physically touching them.
Human–computer interaction (HCI) is research in the design and the use of computer technology, which focuses on the interfaces between people (users) and computers. HCI researchers observe the ways humans interact with computers and design technologies that allow humans to interact with computers in novel ways. A device that allows interaction between human being and a computer is known as a "Human-computer Interface (HCI)".
A tablet computer, commonly shortened to tablet, is a mobile device, typically with a mobile operating system and touchscreen display processing circuitry, and a rechargeable battery in a single, thin and flat package. Tablets, being computers, have similar capabilities, but lack some input/output (I/O) abilities that others have. Modern tablets largely resemble modern smartphones, the only differences being that tablets are relatively larger than smartphones, with screens or larger, measured diagonally, and may not support access to a cellular network.
Explores the core factors contributing to the believability of virtual humans through animation, interaction, and hidden operators in real-time constraints.
Explains image recognition in LabVIEW, focusing on reading parameters and recognizing digits.
Explores advancements in user digitization through motion capture and activity recognition technologies.
The course will cover different aspects of multimodal processing (complementarity vs redundancy; alignment and synchrony; fusion), with an emphasis on the analysis of people, behaviors and interaction
Computer Vision aims at modeling the world from digital images acquired using video or infrared cameras, and other imaging sensors.We will focus on images acquired using digital cameras. We will int
The goal of VR is to embed the users in a potentially complex virtual environment while ensuring that they are able to react as if this environment were real. The course provides a human perception-ac
Cellulo, as tabletop tangible robots, are a great tool for interactive learning and upper limb rehabilita-tion. For the moment, the main way of interaction is to take them in hand and move them in the
Human detection and pose estimation are essential components for any artificial system responsive to the presence of humans and that react according to human-centered tasks. Robotic systems are typica
EPFL2021
,
HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensi