Electronics and Telecommunications Research Institute

Mun Sung Han

Jae Hong Kim

Hye Jin Kim

Joo Chan Sohn

Jae Yeon Lee

Su Young Chi

Yun Koo Chung

Kye Kyung Kim

Woo Han Yun

Young Giu Jung

Hyun Kyu Kim

Jong-Hyun Park

Ho Sub Yoon

Jun Seok Park

Jae Il Cho

Hee Sook Shin

Sang Seung Kang

Dong Won Han

Dae Ha Lee

Young Woo Yoon

Kyu Dae Ban

Suyoung Chi

Dong-Jin Lee

Kwan Hyun Cho

An apparatus for recognizing a user's posture in a horse-riding simulator, the apparatus comprising: a standard posture model generation module configured to find out a standard posture model by selecting feature points from an expert database, and generate the standard posture model; and a posture recognizing module configured to obtain a user's posture from the horse-riding simulator, recognize a user's horse-riding posture by matching the obtained user's posture with the standard posture model generated in the standard posture model generation module, and suggest a standard posture model appropriate for a user's level.

Apparatus and method for recognizing user's posture in horse-riding simulator

Disclosed are a component recognizing apparatus and a component recognizing method. The component recognizing apparatus includes: an image preprocessing unit configured to extract component edges from an input component image by using a plurality of edge detecting techniques, and detect a component region by using the extracted component edges; a feature extracting unit configured to extract a component feature from the detected component region, and create a feature vector by using the component feature; and a component recognizing unit configured to input the created feature vector to an artificial neural network which has learned in advance to recognize a component category through a plurality of component image samples, and recognize the component category according to a result.

Component recognizing apparatus and component recognizing method

Disclosed is a system for integrating gestures and sounds including: a gesture recognition unit that extracts gesture feature information corresponding to user commands from image information and acquires gesture recognition information from the gesture feature information; a background recognition unit acquiring background sound information using the predetermined background sound model from the sound information; a sound recognition unit that extracts the sound feature information corresponding to user commands from the sound information and extracts the sound feature information based on the background sound information and acquires the sound recognition information from the sound feature information; and an integration unit that generates integration information by integrating the gesture recognition information and the sound recognition information.

System and method for integrating gesture and sound for controlling device

Disclosed is a method of detecting an upper body. The method includes detecting an omega candidate area including a shape formed of a face and a shoulder line of a human from a target image, cutting the target image into the upper body candidate area including the omega candidate area, detecting a human face from the upper body candidate area, and judging whether the upper body of the human is included in the target image according to the result of detecting the human face.

Apparatus and method for detecting upper body

Provided is a method and an apparatus for vocal-cord signal recognition. A signal processing unit receives and digitalizes a vocal cord signal, and a noise removing unit which channel noise included in the vocal cord signal. A feature extracting unit extracts a feature vector from the vocal cord signal, which has the channel noise removed therefrom, and a recognizing unit calculates a similarity between the vocal cord signal and the learned model parameter. Consequently, the apparatus is robust in a noisy environment.

Method and apparatus for vocal-cord signal recognition

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Daejeon, South Korea

Mun Sung Han