Hon Hai Precision Industry Co., Ltd.

Chung-yu Wu

Chin-Pin Kuo

Guo-Chin Sun

Chih-Te Lu

Tzu-Chen Lin

Tung-Tso Tsai

Wan-Jhen Lee

Jung-Hao Yang

Yueh Chang

A method for selecting a light source for illuminating defects, an electronic device, and a non-transitory storage medium are provided. The method includes acquire grayscale images of an object with a known defect and generates a pseudo-hyperspectral image cube based on the grayscale images, so that algorithms related to hyperspectral images can analyze the grayscale images collected under different light sources. A most effective or target light source can be automatically and quickly determined from the plurality of light sources, improving an efficiency of light source selection.

Method for selecting a light source for illuminating defects, electronic device, and non-transitory storage medium

A method for optimizing the conversion of a deep learning model to process other data, applied in a device, includes converting a first deep learning model to obtain a second deep learning model, obtaining a weighting arrangement of the two models according to their deep learning frameworks and performing a quantization on the two models. A similarity in weighting between the two models is analyzed to produce a weighting analysis based on the first and second weighting arrangement and the first and second model quantization result weighting. The two models are tested to establish a model performance analysis. One or more suggestions for optimization are obtained based on the weighting analysis and the model performance analysis, and are applied to optimize the second deep learning model, an optimized second deep learning model being employed to process the other data.

Method and device for optimizing deep learning model conversion, and storage medium

A method for detecting a product for defects implemented in an electronic device, the method obtains an image of a product to be detected, obtains a reconstructed image by inputting the image to be detected into a pre-trained autoencoder, generates a difference image from the image and the reconstructed image, obtains a number of feature absolute values by performing clustering processing on the difference image; generates a target image according to the number of feature absolute values, the difference image, and a preset value; and determines a defect detection result by detecting the target image for defects.

Method for detecting product for defects, electronic device, and storage medium

In an image comparison method, an original reference image and an original test image are obtained. The original reference image and the original test image are binarized to obtain a reference binary image and a test binary image. The reference binary image and the test binary image are detected edges to obtain a reference edge image and a test edge image. A morphological expansion is performed on the reference edge image to obtain an expanded reference edge image. An OR operation is performed on the extended reference edge image and the test edge image to obtain an extended test edge image. An XOR operation is performed on the expanded reference edge image and the expanded test edge image. The method improves the accuracy of image comparison.

Image comparison method and computing device utilizing method

A method of optimizing the detection of abnormalities in images of products generates a first image similar to training images and a second image similar to testing images with normal images and the images showing abnormalities inputted into a generative adversarial network (GAN). The GAN determines a first similarity ratio between the first image and the training image and generates a parameter based on the first similarity ratio for adjusting the GAN. A second similarity ratio between the second image and the testing image is determined. The testing image is deemed a normal image when the second similarity ratio is larger than the specified threshold value, and deemed to be an image revealing abnormalities when the second similarity ratio is less than or equal to the specified threshold value. A terminal device and a computer readable storage medium applying the method are also provided.

Method for optimizing detection of abnormalities in images, terminal device, and computer readable storage medium applying the method

In a method of distinguishing objects in images, a first image segmentation model is applied to segment a first segmented image including a first object from a test image. A second image segmentation model is applied to segment a second segmented image including a second object from the test image. A third segmented image marking the first object and the second object is obtained according to first coordinates of the first object in the first segmented image and/or second coordinates of the second object in the second segmented image. The method can segment different objects from an image quickly and accurately.

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Taipei, Taiwan

Chung-yu Wu