Industrial Technology Research Institute

Novatek Microelectronics Corp.

Meng-Ju Han

Kai-Tai Song

Jwu-Sheng Hu

Shih-Chieh Wang

Cheng-Hua Wu

Chia-Ho Lin

Wei-Han Wang

Ming-Feng Chiang

Ching-Yi Kuo

Fuh-Yu Chang

Kuan-Chun Sun

Jing-Huai Hsu

Jung-Wei Hong

Chi-Yi Lin

An automatic guided vehicle includes a vehicle body and a positioning identification module which being furnished in the vehicle body further includes a three-axis magnetic signal sensing unit and a logic operation processing unit. The logic operation processing unit is connected to the three-axis magnetic signal sensing unit by signal transmitted therefrom. A magnetic pointer unit is furnished adjacent to the marching route of the automatic guided vehicle. The three-axis magnetic signal sensing unit senses the magnetic field of magnetic pointer unit and generates a magnetic field information that transmits to the logic operation processing unit.

Automatic guided vehicle and method for controlling the same

A distance measurement apparatus and a distance measurement method are provided. The apparatus includes a line-shaped laser transmitter, an image sensing device and a processing unit. The line-shaped laser transmitter transmits a line-shaped laser, and the image sensing device senses the line-shaped laser to output a line-shaped laser image. The processing unit receives the line-shaped laser image, and segments the line-shaped laser image into several sub-line-shaped laser images. The processing unit further calculates a vertical position for a laser line in each sub-line-shaped laser image, and outputs each distance information according to the corresponding sub-line-shaped laser image and a transformation relation.

Distance measurement apparatus and method

A method is disclosed in the present disclosure for recognizing emotion by setting different weights to at least of two kinds of unknown information, such as image and audio information, based on their recognition reliability respectively. The weights are determined by the distance between test data and hyperplane and the standard deviation of training data and normalized by the mean distance between training data and hyperplane, representing the classification reliability of different information. The method recognizes the emotion according to the unidentified information having higher weights while the at least two kinds of unidentified information have different result classified by the hyperplane and correcting wrong classification result of the other unidentified information so as to raise the accuracy while emotion recognition. Meanwhile, the present disclosure also provides a learning step with a characteristic of higher learning speed through an algorithm of iteration.

Bimodal emotion recognition method and system utilizing a support vector machine

A face detection apparatus and a face detection method thereof are provided. The face detection apparatus includes a rectangle integral image unit, a feature mapping unit and a cascade and score unit. The rectangle integral image unit provides a rectangle integral image according to an original image. The feature mapping unit determines a face candidate region according to rectangular face feature templates, and calculates feature values of the rectangular face feature templates according to the rectangle integral image. The cascade and score unit judges whether the face candidate region conforms to cascade conditions or not, and gives the face candidate region a score according to the feature values when the face candidate region conforms to the cascade conditions. The face candidate region is a non-face region if the score of the face candidate region is lower than a threshold value.

Face detection apparatus and face detection method

A facial expression recognition apparatus and a facial expression recognition method thereof are provided. The facial expression recognition apparatus comprises a gray image generating unit, a face edge detection unit, a motion skin extraction unit, a face contour generating unit and a facial expression recognition unit. The gray image generating unit generates a gray image according to an original image. The face edge detection unit outputs a face edge detection result according to the gray image. The motion skin extraction unit generates a motion skin extraction result according to the original image, and generates a face and background division result according to the motion skin extraction result. The face contour generating unit outputs a face contour according to the gray image, the face edge detection result and the face and background division result. The facial expression recognition unit outputs a facial expression recognition result according to the face contour.

Facial expression recognition apparatus and facial expression recognition method thereof

Growing community of inventors

Taipei County, Taiwan

Meng-Ju Han