Industrial Technology Research Institute

Wei-Yao Chiu

Keng-Hao Chang

Yu-Ting Lin

Ya-Hui Tsai

Chin-Kuei Chang

Jwu-Sheng Hu

Kuo-Feng Hung

Bor-Tung Jiang

Shang-Chieh Lu

An automated optical inspection (AOI) image classification method includes sending a plurality of NG information of a plurality of samples from an AOI device into an Artificial Intelligence (AI) module; performing discrete output calculation on the NG information of the samples by the AI module to obtain a plurality of classification information of the samples; performing kernel function calculation on the classification information of the samples by the AI module to calculate respective similarity distances of the samples and performing weighting analysis; based on weighting analysis results of the samples, judging classification results of the samples; and based on the classification results of the samples, performing classification of the samples.

Automated optical inspection (AOI) image classification method, system and computer-readable media

The present disclosure provides an image merging method. The image merging method includes the following step. First, the calibration unit is provided, wherein a calibration device of the calibration unit includes a plurality of known characteristic information. The calibration device is captured. A conversion relationship is created. A relationship of positions of the images is analysis according to the conversion relationship. The images are formed. In additional, an image merging device is provided.

Method and device for merging images of calibration devices

A detecting method for a workpiece surface includes the following steps. Firstly, a workpiece is provided with a first environment, wherein the first environment has a first environmental temperature higher than a first saturation temperature corresponding to an environmental-relative humidity. Then, the workpiece is provided with a second environment, wherein the second environment has a second environmental temperature lower than the first environmental temperature, such that a itself-temperature of the workpiece reduces to a mist temperature, wherein the mist temperature is substantially equal to or higher than the second environmental temperature. Then, the workpiece is provided with a mist environment, wherein the mist environment has a mist-saturation temperature corresponding to a mist-environmental relative humidity is equal to or higher than the mist temperature for misting a surface of the workpiece. Then, the surface of the misted workpiece is detected.

Workpiece surface detection method and system using the same

The present disclosure discloses a method for suturing 3D coordinate information. The method includes disposing a correction block on a test platform; capturing first 3D coordinate information represented by a first viewing angle and second 3D coordinate information represented by a second viewing angle from the correction block; determining a first center coordinate of the first 3D coordinate information and a second center coordinate of the second 3D coordinate information; superimposing the first 3D coordinate information to the second 3D coordinate information to form first overlap 3D coordinate information; suturing the first 3D coordinate information into the second 3D coordinate information to form a first 3D coordinate suturing result according to an iterative closet point algorithm; and determining a first transformation relation of the first viewing angle versus the second viewing angle according to the first 3D coordinate information and the first 3D coordinate suturing result.

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Chiayi, Taiwan

Wei-Yao Chiu