Hon Hai Precision Industry Co., Ltd.

I-Hua Chen

Chin-Pin Kuo

Tzu-Chen Lin

Tung-Tso Tsai

Wan-Jhen Lee

Jung-Yi Lin

Guo-Chin Sun

Wei-chun Wang

Yu-Cheng Chen

Tsung-Yuan Tu

Shuo-Yen Chung

Chien-Tsung Lee

Chun-Chang Chang

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 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 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.

Method for detecting defect in products and electronic device using method

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 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.

Method for detecting tumor by image analysis, device using method, and non-transitory storage medium

A cloud device, a terminal device, and an image classification system and method includes the terminal reading an image to be classified; transmitting an instruction of image classification to the cloud device; determining whether the instruction of image classification comprises an intermediate operation result of the first partial operation; performing the second partial operation to obtain an image classification result, when the instruction of image classification comprises intermediate operation result of the first partial operation; and transmitting the image classification result to the terminal device; performing the first partial operation to obtain the intermediate operation result, when the intermediate operation result is not contained in the instruction of image classification; transmitting the intermediate operation result to the terminal device; and performing the second partial operation to obtain an image classification result.

Cloud device, terminal device, and method for classifying images

A speech recognition device includes a speech acquiring unit, a speech outputting unit, a camera unit, and a processor. The processor obtains speech input acquired by the speech acquiring unit, obtains images acquired by the camera unit, chronologically links the obtained speech and the obtained images together, compares the obtained speech to a speech database to confirm matching speech and a confidence level of the matching speech, determines whether the confidence level of the matching speech exceeds a predetermined confidence level, and outputs the matching speech when the confidence level of the matching speech exceeds the predetermined confidence level.

Speech recognition device and method of identifying speech

A magnetic scanning method includes acquiring an image from a storage device of a magnetic scanning device, controlling a magnetic read-write head of the magnetic scanning device to touch a reference point on a plane, generating an electric signal which reflects relevant information of the acquired image, inputting the electric signal reflecting the relevant information of the acquired image to the magnetic read-write head, when the electric signal reflecting the relevant information of the acquired image flows through the magnetic read-write head, controlling the magnetic read-write head to generate a magnetic field corresponding to the electric signal, controlling the magnetic read-write head to move and scan the plane from the reference point, to magnetize the magnetic powder on the plane by the magnetic field, and driving the magnetic powder to move relatively to generate an image similar to the acquired image.

Growing community of inventors

Taipei, Taiwan

I-Hua Chen