Electronics and Telecommunications Research Institute

Kye Kyung Kim

Jae Hong Kim

Jae Yeon Lee

Su Young Chi

Sang Seung Kang

Ho Sub Yoon

Jong Hyun Park

Yun Koo Chung

Do-Hyung Kim

Jong-Hyun Park

Joo Chan Sohn

Won Pil Yu

Woo Han Yun

Joong Bae Kim

Mun Sung Han

Suyoung Chi

Hyoung Gu Lee

Soo Hyun Cho

Hye Jin Kim

Young-Jo Cho

Chan Kyu Park

Dong-Jin Lee

Kyu-Dae Ban

Young Sup Hwang

Youngwoo Yoon

The present invention relates to an apparatus for recognizing intention of a horse-riding simulator user, and a method thereof, and the apparatus for recognizing intention of a horse-riding simulator user can provide a safe and realistic horse-riding simulation environment to a user by recognizing an aid signal and an intention signal of the user to sense a dangerous situation and accordingly coping with the situation. According to the present invention, it is possible to increase the sense of the real for the user by enabling the horse-riding simulator user to perform similar interaction to actual horse-riding, and to increase effects of horse-riding training using the horse-riding simulator. Particularly, there is an advantage that the dangerous situation is sensed for safe riding, and it is possible to contribute to formation of a related technology market by providing an effective method for recognition of the intention of the horse-riding simulator user.

Apparatus for recognizing intention of horse-riding simulator user and method thereof

The present invention relates to a device and a method of detecting arbitrarily piled objects, and a device and a method for picking a detected object. The present invention may provide a device and a method of detecting an object, which extract a unique local characterized part of the object by using a visual sensor, detect an object region, and estimate a posture from the detected object region. Also the present invention may provide an object picking system capable of being applied to an actual production process, such as assembling or packaging, in a cell producing method.

Object picking system, object detecting device, object detecting method

Disclosed is a working method using a sensor, which increases recognition of a component to increase mounting of a component and enhancing productivity. The working method includes: extracting an object to be picked from a pile of objects using the sensor; picking the extracted object to move the picked object to a predetermined place; and estimating an angle of the moved object in the current position using the sensor. Accordingly, the working method can perform precise component recognition and posture estimation by two steps: a component picking step and a component recognition step, and effectively apply to a manufacturing line, thereby improving mounting of a component and enhancing productivity of a product.

Working method using sensor and working system for performing same

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

An apparatus includes an image receiving module configured to collect a depth image provided from a camera, a human body detection module configured to detect a human body from the collected depth image, and an activity recognition module configured to recognize an action of the human body on the basis of a 3-dimensional action volume extracted from the human body and a previously learned action model.

Apparatus and method for providing activity recognition based application service

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

An apparatus for discriminating a disguised face includes a face area detector configured to detect a face area in an input image provided from an external source. The apparatus includes a skin color modeling module configured to separate a skin color area from the face area and a disguised face discriminator configured to determine whether signals in the skin color area have a pulse component to discriminate whether a face in the input image is the disguised face.

Apparatus and method for discriminating disguised face

Disclosed are a system and a method for recognizing a disguised face using a Gabor feature and a support vector machine (SVM) classifier according to the present invention. The system for recognizing a disguised face includes: a graph generation means to generate a single standard face graph from a plurality of facial image samples; a support vector machine (SVM) learning means to determine an optimal classification plane for discriminating a disguised face from the plurality of facial image samples and disguised facial image samples; and a facial recognition means to determine whether an input facial image is disguised using the standard face graph and the optimal classification plane when the facial image to be recognized is input.

System for recognizing disguised face using gabor feature and SVM classifier and method thereof

Disclosed is a method of segmenting handwritten touching numeral strings having a non-vertical segmentation line. The method comprises the steps of: receiving an image of the handwritten touching numeral strings having the non-vertical segmentation line extracted from a specially patterned document; smoothing an uneven shape of the image of handwritten touching numeral strings; searching a contour line for the image of smoothed handwritten touching numeral strings; searching a candidate segmentation region, in which the touching numeral strings may exist, from the searched contour line; searching a candidate segmentation point within the candidate segmentation region; searching a vertical segmentation line for segmenting the handwritten touching numeral strings from the candidate segmentation point; searching the non-vertical segmentation line for segmenting the handwritten touching numeral strings having any inclination from the candidate segmentation lines; and segmenting the handwritten touching numeral strings by use of the vertical and non-vertical segmentation lines.

Method of segmenting handwritten touching numeral strings having non-vertical segmentation line

Disclosed is a method of segmenting touching numeral strings contained in handwritten touching numeral strings, and recognizing the numeral strings by use of feature information and recognized results provided by inherent structure of digits. The method comprises the steps of: receiving a handwritten numeral string extracted from a pattern document; smoothing a curved numeral image of the handwritten numeral string, and searching connecting components in the numeral image; determining whether or not the numeral string is a touching numeral string; if it is determined that the numeral string is the touching numeral string, searching a contour of the touching numeral string image; searching candidate segmentation points in the contour, and segmenting sub-images; computing a segmentation confidence value on each segmented sub-image by use of a segmentation error function to select the sub-image with the highest segmentation confidence value as a segmented numeral image in the touching numeral string image; if it is determined in the step c that the numeral string is not the touching numeral string, extracting a feature to recognize the segmented numeral image; segmenting the numeral image selected from the touching numeral string in the highest segmenting confidence value; and obtaining remaining numeral string image.

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Daegu, South Korea

Kye Kyung Kim