Zhen Ding Technology Co., Ltd.

Foxconn Advanced Technology Inc.

Htc Corporation

Fukui Precision Component (shenzhen) Co., Ltd.

Hon Hai Precision Industry Co., Ltd.

Tsinghua University

Chunghwa Telecom Co., Ltd.

Cheng-Hsien Lin

Wen-Chin Lee

Hung-Yi Yang

Yao-Wen Bai

Hung-Yi Chang

Po-Chuan Cho

Chia-Cheng Chen

Chih-Yi Tu

Chung-Jen Tsai

Ming Wang

Tung-Ting Yang

Kai-Li Jiang

Chen Feng

Rui Zhang

Rui Guo Zhang

Chun-Li Wang

Ying Su

I-Hsien Chiang

Chih-Kang Yang

Dong-Qing He

Tung-Yao Kuo

Hsin-Ti Chueh

Jing-Lung Wu

Lin Ren

Yu-Cheng Huang

Shing-Tza Liou

Hsiao-Chun Huang

Tso-Hung Yeh

Pol-Lin Tai

Meng-Hung Wu

Ching-Hung Pi

Cheng-Ta Tu

Ching-Fu Lin

Tao-Ming Liao

Hu-Hai Zhang

Gwo-Hwa Ju

Wen-Tsun Chen

Chao-Ching Wang

Shin-Chih Liaw

Chia-Hung Shen

Chih-Lung Hsiao

Meng-Chieh Hsu

Pai-Hung Huang

Xiao-Hong Zhang

Ya-Ling Fan

Chiang-Hua Huang

Yong-Hui Feng

Jian-Hueng Chen

Yen-Jung Lee

Shun-Fang Yang

Jung-Kuei Chen

Shih-Hao Wang

Chung-Chieh Yang

San-Wei Sun

Hsin-Ching Sun

A model constructing method for a neural network model applicable for image recognition processing is disclosed. The model constructing method includes the following operation: updating, by a processor, a plurality of connection variables between a plurality of layers of the neural network model, according to a plurality of inputs and a plurality of outputs of the neural network model. The plurality of outputs represent a plurality of image recognition results. The plurality of connection variables represent a plurality of connection intensities between each two of the plurality of layers.

Model constructing method of neural network model, model constructing system, and non-transitory computer readable storage medium

An image processing method includes the following steps: dividing an object block into a two-dimensional image; identifying at least one view hotspot in a viewing field corresponding to pupil gaze direction; receiving the view hotspot and an indicator signal; wherein the indicator signal is used to remark the object block; and generating a mask block that corresponds to the object block according to the view hotspot; wherein the indicator signal determines the label of the mask block.

Image processing system and image processing method

An image segmentation method is providing that includes the steps outlined below. A first image corresponding to a first time spot and a second image corresponding to a second time spot are received from a video stream, wherein the second time spot is behind the first time spot. Segmentation is performed on the second image by a segmentation neural network to generate a label probability set. Similarity determination is performed on the first image and the second image by a similarity calculation neural network to generate a similarity probability set. The label probability set and the similarity probability set are concatenated by a concatenating unit to generate a concatenated result. Further inference is performed on the concatenated result by a strategic neural network to generate a label mask.

Image segmentation method, apparatus and non-transitory computer readable medium of the same

A scene reconstructing system, scene reconstructing method and non-transitory computer-readable medium are provided in this disclosure. The scene reconstructing system includes a first electronic device and a second electronic device. A first electronic device includes a first camera unit, a first processor, and a first communication unit. The first processor is configured for recognizing at least a first object from a first image to construct a first map. The second electronic device includes a second camera unit, a second processor, and a second communication unit. The second processor is configured for recognizing at least a second object from a second image to construct a second map; calculating a plurality of confidence values corresponding to the second map. The second communication unit is configured for transmitting a location information to the first communication unit according to the plurality of confidence values.

Scene reconstructing system, scene reconstructing method and non-transitory computer-readable medium

A facial expression modeling method used in a facial expression modeling apparatus is provided that includes the steps outlined below. Two two-dimensional images of a facial expression retrieved by two image retrieving modules respectively are received. A deep learning process is performed on the two two-dimensional images to generate a disparity map. The two two-dimensional images and the disparity map are concatenated to generate a three-channel feature map. The three-channel feature map is processed by a weighting calculation neural network to generate a plurality of blend-shape weightings. A three-dimensional facial expression is modeled according to the blend-shape weightings.

Facial expression modeling method, apparatus and non-transitory computer readable medium of the same

An image segmentation method for performing image segmentation through a neural network implemented by an image segmentation apparatus is provided. The image segmentation method includes the steps outlined below. Previous CNN weight data is received by a current convolution neural network unit of the neural network, wherein the previous CNN weight data is generated by a previous convolution neural network unit of the neural network based on a previous image of video data corresponding to a previous time spot. A current image of the video data corresponding to a current time spot next to the previous time spot is received by the current convolution neural network unit. Convolution is performed according to the previous CNN weight data and the current image to generate a current image segmentation result by the current convolution neural network unit.

Image segmentation method and apparatus

An image correspondence determining method is provided that includes the steps outlined below. A first image and a second image are concatenated to generate a concatenated image having global information. Features are extracted from the concatenated image to generate a plurality of feature maps and the feature maps are divided into first feature maps and second feature maps. First image patches are extracted from the first feature maps corresponding to a first region and second image patches are extracted from the second feature maps corresponding to a second region. The first and the second image patches are concatenated to generate concatenated image patches. A similarity metric is calculated according to the concatenated image patches to determine a similarity between the first region and the second region.

Image correspondence determining method and apparatus

An image segmentation method for performing image segmentation through a neural network implemented by an image segmentation apparatus is provided. The image segmentation method includes the steps outlined below. An input image is down-sampled to generate down-sampled images. Previous convolution neural network (CNN) data having a first resolution is received to up-sample the previous CNN result to generate an up-sampled previous CNN data having a second resolution. A current down-sampled image of the down-sampled images having the second resolution and the up-sampled previous CNN data are received. Convolution is performed according to the up-sampled previous CNN data and the current down-sampled image to generate a current image segmentation result.

An image enhancement device that includes a down-sampling module, correction modules and an up-sampling module is provided. The down-sampling module down-samples an input image to generate down-sampled images having different down-sampled resolutions. Each of the correction modules performs correction on one of the down-sampled images according to a correction model based on at least one correction parameter to generate one of corrected images. The up-sampling module up-samples the corrected images to generate up-sampled images, wherein each of the up-sampled images is of a same up-sampled resolution. The concatenating module concatenates the up-sampled images into an output image.

Image enhancement device and method for convolutional network apparatus

An electromagnetic shielding composite includes a polymer and a carbon nanotube film structure. The carbon nanotube structure includes a number of carbon nanotubes disposed in the polymer. The number of carbon nanotubes are parallel with each other.

Electromagnetic shielding composite

A method for making conductive wires is provided. Firstly, an ink having carbon nanotubes is provided. Secondly, a baseline is formed using the ink on a substrate. Thirdly, the baseline is electroless plated.

Method for making conductive wires

An electronic device and methods for image stabilization for use in an electronic device are provided. The method includes the steps of: receiving a plurality of motion data, at least a portion of the plurality of motion data corresponding to a first image frame, determining a motion value corresponding to the first image frame according to the portion of motion data, determining a display offset between the first image frame to a reference image frame according to the motion value and providing a portion of the first image frame for display according to the display offset. The reference image frame is displayed prior to the first image frame and is stored in a storage unit.

Electronic device and method for image stabilization

A method for forming electrical traces on a substrate includes the steps of: providing a substrate; printing an ink pattern using an ink on the substrate, the ink including a aqueous medium containing silver ions and a heat sensitive reducing agent; heating the ink pattern to reduce silver ions into silver particles thereby forming an semi-finished traces; and forming a metal overcoat on the semi-finished traces by electroless plating thereby obtaining patterned electrical traces.

Method for forming electrical traces on substrate

A pre-plating solution for making a printed circuit board includes carbon nanotubes of 0.01-3 wt %, a surfactant of 0.01-4 wt %, an alkaline substance of 0.01-1 wt % and a solvent. A method for preparing a pre-plating solution comprising the steps of: providing a plurality of carbon nanotubes; purifying the carbon nanotubes; treating the purified carbon nanotubes with an acid; mixing the treated carbon nanotubes, an alkaline substance and a solvent to form suspension; and adding surfactant into suspension.

Pre-plating solutions for making printed circuit boards and methods for preparing the same

The present invention relates to an apparatus for spraying an etchant and a method for manufacturing a printed circuit board. In one exemplary embodiment the apparatus includes a manifold, a plurality of feed pipes in fluid communication with the manifold, each of the feed pipes having a plurality of spray nozzles mounted thereon, the feed pipes cooperatively constitute a spray region, and a pressure-boosting device configured for increasing a spray pressure of the spray nozzles which are located at a central area of the spray region. The apparatus can overcome 'the puddle effect' on an upper surface of the printed circuit board.

Apparatus for spraying etchant onto printed circuit board

Growing community of inventors

Taoyuan, Taiwan

Cheng-Hsien Lin