Quanta Computer Incorporated

Chang Gung Memorial Hospital, Linkou

Yi-Ling Chen

Kuan-Chung Chen

Yu-Cheng Chien

Chi-Hung Lin

Chih-Wei Sung

Chang-Fu Kuo

Meng-Jiun Chiou

Kai-Ju Cheng

The speech correction system includes a storage device, an audio receiver and a processing device. The processing device includes a speech recognition engine and a determination module. The storage device is configured to store a database. The audio receiver is configured to receive an audio signal. The speech recognition engine is configured to identify a key speech pattern in the audio signal and generate a candidate vocabulary list and a transcode corresponding to the key speech pattern; wherein the candidate vocabulary list includes a candidate vocabulary corresponding to the key speech pattern and a vocabulary score corresponding to the candidate vocabulary. The determination module is configured to determine whether the vocabulary score is greater than a score threshold. If the vocabulary score is greater than the score threshold, the determination module stores the candidate vocabulary corresponding to the vocabulary score in the database.

Speech correction system and speech correction method

A pattern recognition method of the immunofluorescence images of autoantibody identification is disclosed. The method includes the following steps: inputting a plurality of original cell immunofluorescence images; conducting an operation of a plurality of convolutional neural networks by a processor, the plurality of convolutional neural networks include a convolution layer, a pooling layer and an inception layer for capturing the plurality of convolution features; conducting a judgment process to obtain the proportions of the antinuclear antibodies morphological patterns; and outputting the recognition results.

Pattern recognition method of autoantibody immunofluorescence image

A classification system and a classification method of the autoantibody immunofluorescence image are disclosed. The system includes an input device, a processor and an output device. The plurality of cell immunofluorescence images and the corresponded extractable nuclear antigen results are input through the input device. The processor conducts a plurality of convolution neural network calculation and output the classification based on the extractable nuclear antigen results, so as to obtain an extractable nuclear antigen classification model. When the autoantibody immunofluorescence image is input by the input device, the corresponded classification of the extractable nuclear antigen can be predicted. The classification result is output through the output device.

Classification system and classification method of autoantibody immunofluorescence image

The speech correction system includes a storage device and a processing device. The storage device stores a first database. The processing device includes an audio receiver, a speech recognition engine, a calculation module, and a determination module. The audio receiver receives multiple voice inputs. The speech recognition engine recognizes the voice inputs, generates multiple candidate vocabularies corresponding to each of the voice inputs, and generates a vocabulary probability corresponding to each of the candidate vocabularies. The calculation module performs a specific operation on the vocabulary probabilities corresponding to the same candidate vocabulary, to generate a plurality of corresponding operation results. The determination module determines whether each of the operation results is greater than a score threshold, and stores at least one output result that is greater than the score threshold to the first database.

A method for improving the efficiency of reconstructing a three-dimensional model is provided. The method includes: dividing a series of different Gray code binary illumination patterns into a plurality of groups; converting binary values of Gray code binary illumination patterns in each group to a plurality of sets of two specific values to generate decimal illumination patterns corresponding to the specific values; overlapping the decimal illumination patterns in each group to a grayscale illumination pattern; using a projector to project each grayscale illumination pattern onto an object from a projection direction; using a camera to capture one or more object images of the object; reverting the object images to non-overlapping Gray code binary images corresponding to the object images; and reconstructing the depth of the object according to the non-overlapping Gray code binary images.

Growing community of inventors

Taoyuan, Taiwan

Yi-Ling Chen