Quanta Computer Incorporated

The Procter Gamble Company

Jia-Chyi Wang

Kai-Ju Cheng

Shao-Ang Chen

Chin-Yuan Ting

Yu-Hsun Chen

Yu-Cheng Chien

Chih-Wei Sung

Faiz Feisal Sherman

Kuan-Chung Chen

Hsin-Lun Hsieh

Tsung-Hsin Lu

Hao-Ping Lee

Huan-Tang Wu

Huan-Pin Shen

Xinru Cui

Chung-Sheng Wu

Pei Li

Digital image stitching systems and methods are disclosed herein for generating one or more panoramic image views. A plurality of digital images depicting a target feature within an application area are captured by a camera of a scanner device. Motion data is captured, by a sensor coupled to the scanner device, as the scanner device moves relative to the target feature, where relative position data, corresponding to the plurality of digital images, is determined based on the motion data. An angle or position is generated for a first image relative to a second image of the plurality of images, and, based on image matching of the angle or the position of the second image with respect to the first image, a panoramic image view is generated depicting the target feature in a wider field of view of the application area than either the first image or the second image.

Digital image stitching systems and methods for generating one or more panoramic image views

A method for distinguishing plaque and calculus is provided. The method is used in a device and includes the following steps: emitting, by a blue light-emitting diode, blue light to illuminate teeth in an oral cavity, wherein the blue light is used to generate autofluorescence of plaque and calculus on the teeth; sensing, by an image sensor, the autofluorescence of plaque and calculus; and distinguishing, by a processor, a plaque area and a calculus area on the teeth based on the autofluorescence.

Method and device for distinguishing plaque and calculus

A tooth-position recognition system includes an electronic device and a calculation device. The electronic device includes a first camera. The first camera is configured to capture a plurality of tooth images. The calculation device includes a second camera and a processor. The second camera is configured to capture a user image. The processor is configured to receive the tooth images, compare the corresponding position of each pixel in each tooth image to generate a depth map, and input the tooth images, the depth map, and a plurality of first tooth-region identifiers into a tooth deep-learning model. The tooth deep-learning model outputs a plurality of deep-learning probability values that are the same in number as the first tooth-region identifiers. The processor inputs the user image and the plurality of second tooth-region identifiers into a user-image deep-learning model.

Tooth-position recognition system

Growing community of inventors

Taoyuan, Taiwan

Jia-Chyi Wang