Hon Hai Precision Industry Co., Ltd.

Tzu-Chen Lin

Chin-Pin Kuo

Tung-Tso Tsai

Chih-Te Lu

Wan-Jhen Lee

Guo-Chin Sun

Jung-Hao Yang

I-Hua Chen

Shih-chao Chien

Wei-chun Wang

Chung-Yu Wu

Jung-Yi Lin

Yueh Chang

Chung-yu Wu

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 method for training an autoencoder implemented in an electronic device includes obtaining a stereoscopic image as the vehicle is in motion, the stereoscopic image includes a left image and a right image; generating a stereo disparity map according to the left image; generating a predicted right image according to the left image and the stereo disparity map; and calculating a first mean square error between the predicted right image and the right image.

Method for training autoencoder, electronic device, and storage medium

A method for detecting cells in images using an autoencoder, a computer device, and a storage medium extracts a first feature vector from each of a plurality of sample medical images. The first feature vector is inputted into an autoencoder, and a first latent feature of each of the plurality of sample medical images is extracted. A first predicted value of a number of cells in each of the plurality of sample medical images is generated based on the first latent feature. The first latent feature is inputted into the autoencoder, and a plurality of reconstructed images are obtained. The autoencoder is optimized based on the plurality of reconstructed images and the first predicted value. This method can be run in the computer device to improve efficiency of detection from images.

Method for detecting cells in images using autoencoder, computer device, and storage medium

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.

Image correction method and computing device utilizing 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

In a product defect detection method, a detection image of a product is obtained. A first preset number of detection blocks are cut out from the detection image. The detection blocks are input into an auto-encoder to obtain reconstructed blocks and a mean square error between each detection block and the corresponding reconstructed block is calculated, an association between the mean square error and the detection block being established. Whether the product carries defective is determined according to the mean square error of each detection block. The method improves accuracy of detecting defects of the product.

Method for detecting product defect in detection image, computing device, and storage medium

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 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 method for recognizing different object-categories within images based on texture classification of the different categories, which is implemented in an electronic device, includes extracting texture features from block images segmented from original images according to at least one Gabor filter; determining a grayscale level co-occurrence matrix of each block image according to the texture features; calculating texture feature statistics of each block image according to the grayscale level co-occurrence matrix; training and generating an object recognition model using the texture features and the texture feature statistics; and recognizing and classifying at least one object in original image according to the object recognition model.

Electronic device and method for recognizing images based on texture classification

A method for detecting defects in images, is employed in a computer device, and stored in a storage medium. The method trains an autoencoder model using unblemished images, inputting an image to be detected into the autoencoder model, and obtaining a reconstructed image. An image error is calculated between the image to be detected and the reconstructed image, and the image error is inputted into a student's t-distribution and a calculation result is obtained. In response that the calculation result falls within a preset defect determination criterion range, the image to be detected is determined to be an unblemished image. In response that the calculation result does not fall within the preset defect determination criterion range, the image to be detected is determined to be a defective image. The method improves the efficiency and accuracy of defect detection.

Method for detecting defects in images, computer device, and storage medium

A method for measuring a growth height of a plant, an electronic device, and a storage medium are provided. The method controls a camera device to obtain a color image and a depth image of a plant to be detected. The color image is detected by a detection model which is pre-trained, and a plurality of detection boxes which includes a plurality of plants to be detected is obtained. The color image and the depth image are aligned to create an alignment image. A plurality of target boxes is acquired from the alignment image, and depth values of the plurality of target boxes are determined. The quantity of the target boxes and a height of one or more plants to be detected are determined, no manual operations are required.

Method for measuring growth height of plant, electronic device, and storage medium

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.

Method for detecting defects in divided and further divided images based on trained models, electronic device, and storage medium

A method for determining a growth height of a plant, an electronic device, and storage medium are provided. The method includes controlling a camera device to capture a plant to be detected, and obtaining a color image and a depth image of the plant to be detected. The color image and the depth image are aligned and an alignment image is obtained. The color image is detected using a pre-trained mobilenet-ssd network, and a detection box including the plant to be detected is obtained. A depth value of each of pixel points in the detection box is determined, and target depth values are obtained. A mean value and a standard deviation of the target depth values are determined, and a height of the plant to be detected is determined. According to the method, accuracy of the height of the plant can be improved.

Method for determining growth height of plant, electronic device, and medium

A method for detecting medical images implemented in an electronic device includes obtaining at least one image to be detected; obtaining a reconstructed image by inputting the at least one image to be detected as a target image into a pre-trained variational autoencoder model; determining a target area according to pixel values of pixels in the reconstructed image and the target image; obtaining a feature area and a lesion category of the feature area by inputting the target image into a pre-trained convolutional neural network model; when there is a feature area corresponding to the target area in the target image, determining a lesion area and a corresponding lesion category based on the target area and the feature area, and generating a detection result of the image to be detected.

Method for detecting medical images, electronic device, and storage medium

A method for evaluating environment and surroundings of a pedestrian passageway, used in an electronic device, obtains a position information of a target area, and obtains a streetscape image corresponding to the position information of the area. The method further inputs the streetscape image into a trained convolutional neural network, makes the trained convolutional neural network carry out a convolution calculation of the streetscape image to generate a feature vector for classifying a number of target objects in the streetscape image, and outputs the feature vector. The feature vector is input into a full convolution neural network to apply a certain color to a number of pixels belonging to a same target object, and outputs the streetscape image with colored target objects.

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Taipei, Taiwan

Tzu-Chen Lin