Patents by Inventor WAN-JHEN LEE

WAN-JHEN LEE 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: 20220284720
    Abstract: A method of grouping certain cell densities to establish the number and volume of cells appearing in an image input the image into a self-encoder having a preset number of a density grouping models to obtain a preset number of reconstructed images. The image and each reconstructed image are input into a twin network model of the density grouping model corresponding to each reconstructed image, and a first error value is calculated between the image and each reconstructed image. A minimum first error value in the first error value set is determined, and a density range corresponding to the density grouping model corresponding to minimum first error value is taken as the density range. An electronic device and a non-volatile storage medium performing the above-described method are also disclosed.
    Type: Application
    Filed: March 2, 2022
    Publication date: September 8, 2022
    Inventors: WAN-JHEN LEE, CHIH-TE LU, CHIN-PIN KUO
  • Publication number: 20220245819
    Abstract: A method for processing images, an electronic device, and a storage medium are provided. A head portrait of a subject is obtained from a camera device. A hair region and a scalp region are identified from the head portrait. A proportion of the scalp region is calculated. The proportion of the scalp region is compared with a preset value, and baldness of the subject is determined accordingly. If found to be bald, complementary color processing is performed by processing the scalp region using a hair color of the hair region, and an updated head portrait is obtained after finishing the complementary color processing. The method automatically detects baldness and supplements the hair color in the scalp region.
    Type: Application
    Filed: January 27, 2022
    Publication date: August 4, 2022
    Inventors: I-HUA CHEN, WAN-JHEN LEE, TZU-CHEN LIN, CHIN-PIN KUO
  • Publication number: 20220215679
    Abstract: A method of determining a density of cells in a cell image, an electronic device and a storage medium are disclosed. The method acquires a cell image and extracts mapped features of the cell image by an autoencoder. The mapped features are inputted into a neural network classifier to obtain a feature category and a density range corresponding to the feature category is obtained. The density range is output. The present disclosure can improve n efficiency of obtaining a density of cells in a cell image.
    Type: Application
    Filed: December 8, 2021
    Publication date: July 7, 2022
    Inventors: WAN-JHEN LEE, CHIH-TE LU, CHIN-PIN KUO
  • Publication number: 20220207707
    Abstract: A method for detecting defects in products from images thereof and an electronic device applying the method inputs a defect image repair data set into an autoencoder to train the autoencoder, and generates a reconstructed image, calculates a reference error value between the sample image and the reconstructed image by a preset error function, and set a threshold value based on the reference error value. The electronic device inputs an image possibly revealing a defect into the autoencoder and generates the reconstructed image corresponding to the image to be detected, and uses the preset error function to calculate the reconstruction error between the image and the reconstructed image, thereby determining whether the image being analyzed does reveal defects. When the reconstruction error is greater than the threshold value, a determination is made that a defect is revealed.
    Type: Application
    Filed: December 30, 2021
    Publication date: June 30, 2022
    Inventors: JUNG-HAO YANG, CHIN-PIN KUO, CHIH-TE LU, TZU-CHEN LIN, WAN-JHEN LEE, WEI-CHUN WANG
  • Publication number: 20220207892
    Abstract: A method for classifying cells densities by cell images being input into artificial computer intelligence obtains positional information of all central points of all groups of first encoding features generated when training a model of convolutional neural network and ranges of densities of images of biological cells represented by different central points. The method inputs a test image of the biological cells into a trained model of the convolutional neural network to encode the test image, to obtain a second encoding feature. The method also determines a central point nearest to the second encoding feature according to the positional information. The method determines a range of densities of the test image according to the ranges of densities of the images represented by different central points and the central point nearest to the second encoding feature. An electronic device and a non-transitory storage medium are also disclosed.
    Type: Application
    Filed: December 29, 2021
    Publication date: June 30, 2022
    Inventors: WAN-JHEN LEE, CHIH-TE LU, CHIN-PIN KUO
  • Publication number: 20220207724
    Abstract: A method of determining a distribution of stein cells in a cell image, an electronic device and a storage medium are disclosed. The method acquires a cell image and segments the cell image and obtaining a plurality of sub-images. The plurality of sub-images is inputted into a stein cell detection model to detect to obtain a number of stein cells in each sub-image. A position of each sub-image in the cell image is determined. A distribution of the stein cells in the cell image is output, according to the number of stein cells in each sub-image and the position of each sub-image in the cell image. The present disclosure an accuracy of the distribution of stein cells in the cell image.
    Type: Application
    Filed: November 15, 2021
    Publication date: June 30, 2022
    Inventors: WAN-JHEN LEE, CHIN-PIN KUO, CHIH-TE LU
  • Publication number: 20220178814
    Abstract: A method for calculating a density of stem cells in a cell image and an electronic device are provided. A plurality of preset ratios and a plurality of density calculation models can be used to perform hierarchical density calculations on the cell image. Starting from the largest preset ratio (the first preset ratio) reduction of the cell image to no reduction, the density calculation is performed on the cell image using a model starting with a highest density calculation (the first density calculation model) to a model with the smallest density calculation (the third density calculation model), which can quickly detect densities of various stem cells. Using different preset ratios and corresponding density calculation models for calculation, it is not necessary to calculate the number of stem cells to obtain the density of stem cells, which improves a calculation efficiency of the density of stem cells.
    Type: Application
    Filed: November 11, 2021
    Publication date: June 9, 2022
    Inventors: WAN-JHEN LEE, CHIN-PIN KUO, CHIH-TE LU
  • 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: 20220165075
    Abstract: A method for classifying cells densities by cell images being input into artificial computer intelligence inputs an image of biological cells as a test image into one or more trained models of convolutional neural network until a reconstructed image of the biological cells generated by one trained model matches with the test image. Each of the trained models of the convolutional neural network corresponds to one certain density range in which cell densities of images of the biological cells are found. The method also determines that a cell density of the test image is within the density range corresponding to the trained model of the convolutional neural network for which the reconstructed image of the biological cells and the test image match. A related electronic device and a non-transitory storage medium are also disclosed.
    Type: Application
    Filed: November 23, 2021
    Publication date: May 26, 2022
    Inventors: Wan-Jhen Lee, Chin-Pin Kuo, Chih-Te Lu
  • 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
  • Publication number: 20210390336
    Abstract: A method for automatically selecting a suitable network model for machine deep learning includes identifying a category of an input content and selecting a training set based on the identified category. Several matched network models are selected based on the identified category. A test network model is selected based on performance data corresponding to the matched network models, and the selected model is trained based on the input content and the selected training set. When an output result in a display interface is not satisfactory, an optimization operation is executed for adjusting the structure of the test network model. An apparatus, an electronic device, and a storage medium applying the method are also disclosed.
    Type: Application
    Filed: June 1, 2021
    Publication date: December 16, 2021
    Inventors: Wan-Jhen Lee, Tung-Tso Tsai, Chin-Pin Kuo, Tzu-Chen Lin, Guo-Chin Sun
  • Publication number: 20210357806
    Abstract: A machine learning model training method includes receiving a problem type to be processed, determining a machine learning model corresponding to the problem type, receiving sample data to train the determined machine learning model, analyzing training results of the determined machine learning model obtained after training is completed and displaying the training results that meet preset conditions, and providing a machine learning model corresponding to the training result that meets the preset conditions.
    Type: Application
    Filed: September 18, 2020
    Publication date: November 18, 2021
    Inventors: GUO-CHIN SUN, TUNG-TSO TSAI, TZU-CHEN LIN, WAN-JHEN LEE, CHIN-PIN KUO
  • Publication number: 20210326641
    Abstract: A method for selecting a deep learning network which is optimal for solving an image processing task obtaining a type of the image processing task, selecting a data set according to the type of problem, and dividing selected data set into training data and test data. Similarities between different training data are calculated, and a batch size of the training data is adjusted according to the similarities of the training data. A plurality of deep learning networks is selected according to the type of problem, and the plurality of deep learning networks is trained through the training data to obtain network models. Each of the network models is tested through the test data, and the optimal deep learning network with the best test result is selected from the plurality of deep learning networks appropriate for image processing.
    Type: Application
    Filed: April 9, 2021
    Publication date: October 21, 2021
    Inventors: TUNG-TSO TSAI, CHIN-PIN KUO, GUO-CHIN SUN, TZU-CHEN LIN, WAN-JHEN LEE
  • Publication number: 20210089886
    Abstract: A method for processing data based on a neural network trained by different methods includes: dividing sample data into a training set and a test set; training a predetermined neural network to obtain a first detection model based on the training set; testing the first detection model based on the test set to count a first precision rate; cleaning the training set and the test set according to selected cleaning method; adjusting the first detection model by a predetermined rule and training adjusted first detection model based on cleaned training set to obtain a second detection model; testing the second detection model based on cleaned test set to count a second precision rate; selecting the first detection model or the second detection model as a final detection model based on a comparison between the first precision rate and the second precision rate.
    Type: Application
    Filed: March 23, 2020
    Publication date: March 25, 2021
    Inventors: TZU-CHEN LIN, TUNG-TSO TSAI, GUO-CHIN SUN, CHIN-PIN KUO, WAN-JHEN LEE
  • Publication number: 20210012485
    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: Application
    Filed: February 12, 2020
    Publication date: January 14, 2021
    Inventors: TZU-CHEN LIN, CHIN-PIN KUO, TUNG-TSO TSAI, GUO-CHIN SUN, I-HUA CHEN, WAN-JHEN LEE
  • Publication number: 20200410356
    Abstract: A method for optimizing a data model is used in a device. The device acquires data information and selecting at least two data models according to the data information, and utilizes the data information to train the at least two data models. The device acquires each accuracy of the at least two data models, determines a target data model which has greatest accuracy between the at least two data models, and optimizes the target data model.
    Type: Application
    Filed: March 6, 2020
    Publication date: December 31, 2020
    Inventors: CHIN-PIN KUO, TUNG-TSO TSAI, GUO-CHIN SUN, TZU-CHEN LIN, WAN-JHEN LEE