Patents by Inventor TUNG-TSO TSAI

TUNG-TSO TSAI has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20220253997
    Abstract: An image defect detection method is used in an electronic device. The electronic device determines a training image feature set, and trains a Gaussian mixture model by using the feature set to obtain an image defect detection model and a reference error value. An image for analysis is input into the autoencoder to obtain a second implicit vector and a second reconstructed image, and to calculate a second reconstruction error. The electronic device obtains a test image feature of the image for analysis according to the second reconstruction error and the second implicit vector, and inputs the test image feature into the image defect detection model to obtain a prediction score. The image for analysis is determined to reveal a defect when the prediction score is less than or equal to the reference error value.
    Type: Application
    Filed: January 27, 2022
    Publication date: August 11, 2022
    Inventors: TUNG-TSO TSAI, CHIN-PIN KUO, TZU-CHEN LIN, SHIH-CHAO CHIEN
  • Publication number: 20220254148
    Abstract: A method for detecting product for defects implemented in an electronic device includes classifying a plurality of product images to be detected into linear images or non-linear images; performing dimension reduction processing on the product images after image classification according to a plurality of dimension reduction algorithms to obtain a plurality of dimension reduction data; determining an optimal dimension reduction data of the plurality of dimension reduction data; obtaining score data of the product image by inputting the optimal dimension reduction data into a Gaussian mixture model; comparing the score data with a threshold; determining whether the score data is less than the threshold; and determining that there is at least one defect in the product image in response that the score data is determined to be less than the threshold.
    Type: Application
    Filed: January 28, 2022
    Publication date: August 11, 2022
    Inventors: TZU-CHEN LIN, TUNG-TSO TSAI, CHIN-PIN KUO
  • Publication number: 20220254002
    Abstract: A method of detecting defects revealed in images of products obtains sample image training data. An underlying feature dimension of an autoencoder is selected and a score is obtained. By comparing the score with a standard score, an optimal underlying feature dimension is confirmed. A test image is inputted into the autoencoder with the optimal underlying feature dimension to obtain a reconstruction image. A reconstruction error between the test image and the reconstruction image is computed. By comparing the reconstruction error with the predefined threshold a result of analysis of the test image is outputted. An image defect detection apparatus and a computer readable storage medium applying the method are also provided.
    Type: Application
    Filed: January 26, 2022
    Publication date: August 11, 2022
    Inventors: CHIN-PIN KUO, TUNG-TSO TSAI, SHIH-CHAO CHIEN
  • Publication number: 20220253998
    Abstract: An image defect detection method used in an electronic device, calculates a Kullback-Leible divergence between a first probability distribution and a second probability distribution, and thereby obtains a total loss. Images of samples for testing are input into an autoencoder to calculate a second latent features of the testing sample images and the second reconstructed images. Second reconstruction errors are calculated by a preset error function, as is a third probability distribution of the second latent features, and a total error is calculated according to the third probability distribution and the second reconstruction errors. When the total error is greater than or equal to the threshold, determining that the images of samples for testing reveal defects, and when the total error is less than the threshold, determining that the images of samples for testing reveal no defects.
    Type: Application
    Filed: January 27, 2022
    Publication date: August 11, 2022
    Inventors: SHIH-CHAO CHIEN, CHIN-PIN KUO, TUNG-TSO TSAI
  • Publication number: 20220222800
    Abstract: A method for detecting image abnormities, applied in an electronic device, and stored in a storage medium are provided, obtains images for analysis. The images are divided into a plurality of first divided images and a plurality of second divided images by reference to image size. A first abnormity score is obtained by inputting the image into a first pre-trained abnormity detection model. A plurality of second abnormity scores are obtained by inputting the first divided images into a second pre-trained abnormity detection model. A plurality of third abnormity scores are obtained by inputting the second divided images into a third pre-trained abnormity detection model. An abnormal type of the image is determined according to a preset abnormity database in response to an abnormity detected in the image, the method improves accuracy of defect detection.
    Type: Application
    Filed: January 12, 2022
    Publication date: July 14, 2022
    Inventors: TZU-CHEN LIN, CHIN-PIN KUO, TUNG-TSO TSAI
  • Publication number: 20220222799
    Abstract: A method of detecting defects revealed in images of finished products includes importing flawless images into an autoencoder for model training and obtaining reconstructed images from them. The reconstructed images are compared with the flawless images to obtain groups of test errors. An error threshold is selected from the groups of the test errors according to preset rules. In practice, obtaining an image to be tested, and a reconstructed image to be tested, and an error to be tested; determining detection of the image to be tested according to the error and the error threshold; and importing the image into a classifier for defect classification if the image is found to reveal a defect. An electronic device and a non-volatile storage medium for performing the above-described method, are also disclosed.
    Type: Application
    Filed: January 11, 2022
    Publication date: July 14, 2022
    Inventors: I-HUA CHEN, TUNG-TSO TSAI, CHIN-PIN KUO, TZU-CHEN LIN
  • Publication number: 20220215247
    Abstract: A method for processing multiple modes of data with cross-learning and sharing to avoid duplicated learning generates a weighting when a neural network model is being trained with a plurality of multiple modes of training samples. The neural network model includes an input layer, a neural network backbone coupled to the input layer, and a plurality of different output layers coupled to the neural network backbone. Results of testing are output by inputting the obtained weighting into the neural network model and testing a multiple modes of test sample with the neural network model. The need for many neural network models is avoided. An electronic device and a non-transitory storage medium are also disclosed.
    Type: Application
    Filed: December 30, 2021
    Publication date: July 7, 2022
    Inventors: GUO-CHIN SUN, CHIN-PIN KUO, TUNG-TSO TSAI
  • Publication number: 20220207867
    Abstract: In a method for defecting surface defects, a trained weighting generated when defect-free training samples are used to train an autoencoder and pixel convolutional neural network is obtained. A test encoding feature is obtained by inputting the trained weighting into the autoencoder and pixel convolutional neural network and a weighted autoencoder of the weighted autoencoder and pixel convolutional neural network encoding a test sample. The test encoding feature is input into a weighted pixel convolution neural network of the weighted autoencoder and pixel convolutional neural network to output a result of test. The test result is either no defect in the test sample or at least one defect in the test sample. Inaccurate determinations as to defects are thereby avoided. An electronic device and a non-transitory storage medium are also disclosed.
    Type: Application
    Filed: December 29, 2021
    Publication date: June 30, 2022
    Inventors: TUNG-TSO TSAI, CHIN-PIN KUO, TZU-CHEN LIN, SHIH-CHAO CHIEN
  • Publication number: 20220207695
    Abstract: In a method for defecting surface defects, a weighting generated when defect-free training samples are used to train an autoencoder and pixel convolutional neural network is obtained. A test encoding feature is obtained by inputting the weighting into the autoencoder and pixel convolutional neural network and encoding a test sample with a weighted autoencoder of the weighted autoencoder and pixel convolutional neural network. The test encoding feature is divided into many sub-test encoding features. The sub-test encoding features are input into a weighted pixel convolution neural network of the weighted autoencoder and pixel convolutional neural network one by one to output a result of test, the test result being either no defect in the test sample or at least one defect in the test sample. Inaccurate defect determinations are avoided, and accurate determinations even of fine defects improved. An electronic device and a non-transitory storage medium are also disclosed.
    Type: Application
    Filed: December 29, 2021
    Publication date: June 30, 2022
    Inventors: TUNG-TSO TSAI, CHIN-PIN KUO, TZU-CHEN LIN, SHIH-CHAO CHIEN
  • Publication number: 20220207669
    Abstract: In an image correction method, a distorted image and a standard image corresponding to the distorted image are obtained. The distorted image is divided into first image regions, the standard image into second image regions. For each first image region and corresponding second image region, first and second points respectively are selected. A relationship between the first image region and the second image region is determined according to differences between the first points and the second points. The first image region is corrected according to the relationship between the first image region and the second image region and corrected first image regions are stitched to obtain a corrected image of the distorted image. The image correction method improves the accuracy of image correction.
    Type: Application
    Filed: December 24, 2021
    Publication date: June 30, 2022
    Inventors: CHIN-PIN KUO, TUNG-TSO TSAI, GUO-CHIN SUN, TZU-CHEN LIN
  • Publication number: 20220207687
    Abstract: A method applied in an electronic device for detecting and classifying apparent defects in images of products inputs an image to a trained autoencoder to obtain a reconstructed image, determines whether the image reveals defects based on a defect criterion for filtering out small noise reconstruction errors. If so revealed, the electronic device calculates a plurality of structural similarity values between the image and a plurality of template images with marked defect categories, determines a target defect category corresponding to the highest structural similarity value, and classifies the defect revealed in the image into the target defect category.
    Type: Application
    Filed: December 30, 2021
    Publication date: June 30, 2022
    Inventors: TUNG-TSO TSAI, TZU-CHEN LIN, CHIN-PIN KUO, SHIH-CHAO CHIEN
  • Publication number: 20220198228
    Abstract: A method for detecting defects in multi-scale images and a computing device applying the method acquires a to-be-detected image and converts the to-be-detected image into a plurality of target images of preset sizes. Feature extraction is performed on each target image by using a pre-trained encoder to obtain a latent vector, the latent vector of each target image is inputted into a decoder corresponding to the encoder to obtain a reconstructed image and then into a pre-trained Gaussian mixture model to obtain an estimated probability. Reconstruction error is calculated according to each target image and the corresponding reconstructed image. A total error is calculated according to the reconstruction error of each target image and the corresponding estimated probability, and a detection result is determined according to the total error of each target image and a corresponding preset threshold, thereby improving an accuracy of defect detection.
    Type: Application
    Filed: November 15, 2021
    Publication date: June 23, 2022
    Inventors: CHIN-PIN KUO, SHIH-CHAO CHIEN, TUNG-TSO TSAI
  • Publication number: 20220198633
    Abstract: A defect detection method based on an image of products and an electronic device can accurately determine the error threshold by determining the reconstruction error generated during image reconstruction and by determining the estimated probability generated by the Gaussian mixture model. The test error can then be compared with the error, since the test error and the error threshold are compared numerically, the existence of subtle defects are revealed in the product image, thereby improving the accuracy of defect detection.
    Type: Application
    Filed: November 15, 2021
    Publication date: June 23, 2022
    Inventors: CHIN-PIN KUO, TUNG-TSO TSAI, SHIH-CHAO CHIEN
  • Publication number: 20220198645
    Abstract: A model input size determination method, an electronic device and a storage medium are provided, the method includes acquiring a plurality of test images and a defect result; and encoding each test image to obtain an encoding vector. The encoding vector is decoded to obtain a reconstructed image, then a reconstruction error and a plurality of sub-vectors are calculated; the plurality of sub-vectors is inputted into a Gaussian mixture model, then a plurality of sub-probabilities, an estimated probability and a test error are determined; a detection result in the test image according to the test error and the corresponding error threshold are obtained; an accuracy according to the detection result and the defect result are determined, and an input size is selected from the plurality of preset sizes according to the accuracy. An accuracy of defect detection in manufacturing can be improved.
    Type: Application
    Filed: November 15, 2021
    Publication date: June 23, 2022
    Inventors: CHIN-PIN KUO, SHIH-CHAO CHIEN, TUNG-TSO TSAI
  • Publication number: 20220189008
    Abstract: A method for detecting data defects and a computing device applying the method obtains a test image for analysis. A field to which the test image relates is determined. Based on the field, a target convolutional layer is determined from a convolutional neural network. The target convolutional layer is used to extract features of the test image. A target score of the test image and a score threshold corresponding to the field are determined. If the target score is less than the score threshold, it is determined that the test image reveals defects, thereby improving an accuracy of defect detection.
    Type: Application
    Filed: December 15, 2021
    Publication date: June 16, 2022
    Inventors: TZU-CHEN LIN, TUNG-TSO TSAI, CHIN-PIN KUO
  • Patent number: 11354801
    Abstract: A method for detecting a tumor from images which are required to be shrunken in resolution obtains one or more first images. Then, the method segments or divides the detection images into a number of detection image blocks according to an input size of training data of a convolutional neural network architecture, before segmenting, each of the plurality of detection image blocks comprising coordinate values. The detection image blocks are input into a preset tumor detection model to generate image blocks of a result of the detection images. The method merges the image blocks into a single image according to the coordinate values of each detection image block. Colors of normal areas, abnormal areas, and overlapping areas of the abnormal areas are all different. The method generates a final detection according to color depths in the image. A tumor detection device and a non-transitory storage medium are provided.
    Type: Grant
    Filed: February 12, 2020
    Date of Patent: June 7, 2022
    Assignee: HON HAI PRECISION INDUSTRY CO., LTD.
    Inventors: Tzu-Chen Lin, Chin-Pin Kuo, Tung-Tso Tsai, Guo-Chin Sun, I-Hua Chen, Wan-Jhen Lee
  • Publication number: 20220058530
    Abstract: 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.
    Type: Application
    Filed: August 18, 2021
    Publication date: February 24, 2022
    Inventors: TZU-CHEN LIN, GUO-CHIN SUN, CHIH-TE LU, TUNG-TSO TSAI, JUNG-HAO YANG, CHUNG-YU WU, WAN-JHEN LEE
  • Publication number: 20220044061
    Abstract: A data labeling model training method, an electronic device employing the method, and a storage medium are provided. The method acquires medical image data. An improved quality of the medical image data to be used for training the data labeling model is obtained by filtering the medical data, so as to enable training with higher-quality training material. The data labeling model is used to label medical data with improved efficiency and accuracy.
    Type: Application
    Filed: August 4, 2021
    Publication date: February 10, 2022
    Inventors: Tung-Tso TSAI, Chin-Pin KUO, Wan-Jhen LEE, Guo-Chin SUN
  • Publication number: 20220036192
    Abstract: An image processing method and an electronic device are disclosed, the method acquires training data in response to receiving an image processing instruction and trains a deep learning model with the training data by using a preset deep learning framework to obtain an initial model. A data type of the initial model is converted to increase the data processing speed of the model. A correction layer is added to the converted initial model and the initial model is optimized by training weights of the correction layer to obtain an image processing model to further increase the data processing speed. After acquiring an image from the image processing instruction, the image processing model can be used to process the image and an image processing result can be outputted. The image is processed based on the optimized image processing model, and the data processing speed and accuracy are guaranteed.
    Type: Application
    Filed: July 27, 2021
    Publication date: February 3, 2022
    Inventors: I-HUA CHEN, TUNG-TSO TSAI, GUO-CHIN SUN, TZU-CHEN LIN, WAN-JHEN LEE, CHIN-PIN KUO
  • Publication number: 20220028055
    Abstract: A product defect detection method which includes acquiring a detection image of a product to be detected is provided. The method further includes dividing the detection image into a first preset number of detection blocks. Once a detection result of each detection block is obtained by inputting each detection block into a preset defect recognition model, according to a position of each detection block in the detection image, a detection result of the product is determined according to the detection result of each detection block.
    Type: Application
    Filed: July 22, 2021
    Publication date: January 27, 2022
    Inventors: WAN-JHEN LEE, TUNG-TSO TSAI, CHIN-PIN KUO, TZU-CHEN LIN, GUO-CHIN SUN