Hon Hai Precision Industry Co., Ltd.

Wan-Jhen Lee

Chin-Pin Kuo

Tzu-Chen Lin

Chih-Te Lu

Tung-Tso Tsai

Guo-Chin Sun

I-Hua Chen

Jung-Hao Yang

Chung-yu Wu

Wei-chun Wang

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.

Image processing method, electronic device and storage medium

A multi-scale autoencoder generation method applied to an electronic device is provided. The method includes acquire product images and acquire an annotation of each product image. Latent spaces of a plurality of scales are constructed. Autoencoders are obtained according to the latent spaces and an image size of the product image. Learners are obtained by training each autoencoder based on non-defective images. Reconstructed images are obtained by inputting the product images into the learners. Detection results are obtained by detecting whether each product image has defects according to the reconstructed images. Similar images for each learner are determined based on a comparison result between each detection result and a corresponding annotation result. Once a correct rate of each learner is obtained according to the similar images, a learner from the plurality of learner is determined as a multi-scale autoencoder according to the correct rate of each learner.

Multi-scale autoencoder generation method, electronic device and readable storage medium

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.

Method for grouping cells according to density and electronic device employing method

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 defects in appearance of a product from images thereof, applied in an electronic device, obtains positive sample images, negative sample images, and product sample images, divides the product sample images into input image blocks, and inputs the input image blocks into a pre-trained autoencoder to obtain reconstructed image blocks. The electronic device determines corresponding pixel points in the input image blocks, and corresponding pixel difference values, and generates feature connection regions of each input image block according to the positive sample images and the pixel difference values. The electronic device generates a first threshold, selects target regions from the feature connection regions and the first threshold, and generates a second threshold. The electronic device further determines a detection result of a product sample in the product sample image according to an area of the target area and the second threshold.

Method for detecting appearance defects of a product and electronic device

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.

Image defect detection method, electronic device using the same

A method for preventing vehicle collision applied in an electronic device obtains a first image in the driving direction of a vehicle using the method (method vehicle), and detects a driving route of other vehicles in the first image, and takes one of the other vehicles in the first image as a target vehicle when it satisfies a first condition. The first condition comprises a vehicle being in the same lane as the method vehicle and having an opposing driving direction. The electronic device further detects whether a detectable distance between the method vehicle and the target vehicle is less than a preset distance, and generates a warning or a control command when the detectable distance is less than the preset distance.

Method for preventing vehicle collision and electronic device

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.

Defect detection method, computer device and storage medium

A defect detection method applied to an electronic device includes determining, pixel difference values based a test sample image and positive sample images. A color difference threshold is determined according to positive sample images. Feature connected regions of the test sample image are generated according to the color difference threshold and pixel difference values. A first threshold is generated according to image noises of positive sample images. A target region is determined from the feature connected regions according to a number of pixel points in each feature connected region and the first threshold. Once a second threshold is determined according to defective pixel points of negative sample images, a detection result of a test sample is determined according to an area of the target region and the second threshold.

Defect detection method, electronic device and readable storage medium

An image defect detection method applied to an electronic device is provided. The method includes dividing a detecting image into a plurality of detecting areas, generating a detection accuracy value for each detecting area based on a defective image and a non-defective image, and obtaining a plurality of detection accuracy values. An autoencoder is selected for each detection accuracy value. A model corresponding to each detecting area is obtained by training the autoencoder based on the non-defective image. A plurality of reconstructed image blocks is obtained by inputting each of a plurality of detecting blocks into the corresponding model, and a reconstruction error value between each reconstructed image block and the corresponding detecting block is obtained. A detection result of a product contained in the image to be detected is obtained based on the reconstruction error value corresponding to each detecting block.

Image defect detection method, electronic device and readable storage medium

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.

Method for calculating a density of stem cells in a cell image, electronic device, and storage medium

A method of determining a distribution of stem 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 stem cell detection model to detect to obtain a number of stem cells in each sub-image. A position of each sub-image in the cell image is determined. A distribution of the stem cells in the cell image is output, according to the number of stem 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 stem cells in the cell image.

Method of determining a distribution of stem cells in a cell image, electronic device, and storage medium

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.

Method for processing images, electronic device, and storage medium

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.

Machine learning model training method and machine learning model training device

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.

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Taipei, Taiwan

Wan-Jhen Lee