Disclosed here is a method of generating a dental recommendation based on image processing. Further, the method may include receiving at least one patient data comprising at least one image from at least one patient device. Further, the method may include retrieving at least one dental dataset. Further, the method may include analyzing the at least one patient data and the at least one dental dataset. Further, the method may include generating at least one landmark based on the analyzing. Further, the method may include retrieving at least one dental reference dataset. Further, the method may include processing the at least one landmark and the at least one dental reference dataset, determining at least one dental recommendation based on the processing, transmitting the at least one dental recommendation to at least one external device and storing the at least one dental recommendation.

A soldering quality detection platform comprises a 2D image acquisition device adapted to acquire a 2D image of a soldered region of a soldered product, a 3D image acquisition device adapted to acquire a 3D image of the soldered region of the soldered product, and a judgment device. The judgment device is adapted to determine whether a soldering quality is qualified based on the 2D image and the 3D image of the soldered region of the soldered product.

Introduced here is a machine learning related technique for supplying an observed model additional training data based upon previously received training data. To determine textual content of a character string based on a digital image that includes a handwritten version of the character string a substantial amount of training data is used. The character string can include one or more characters, and the characters can include any of letters, numerals, punctuation marks, symbols, spaces, etc. Disclosed herein is a technique to determine variations between different images of matching known character strings and substitute those variations into the images in order to create more images with the same known character string.

A method for processing sensor data in a number of controllers in a controller complex. The controllers are connected to at least one sensor via at least one communication bus, wherein the sensor data of the at least one sensor are processed by at least two different controllers in stages. At least one processing stage is concordant in the two controllers or is equivalent to the other stage at least in so far as the results of the processing are converted into one another by a conversion. Provision is made for a preprocessing unit to which the sensor data of the at least one sensor are supplied, wherein the processing of the sensor data in the at least one concordant processing stage is performed in the preprocessing unit, and the processed sensor data are forwarded to the at least two different controllers for individual further processing.

Dental images processed with artificial intelligence for e-commerce is described. In an example scenario, a processing device receives a dental image. The dental image is processed with a deep neural network and matched to anatomy and pathology datasets in real time. The matched and identified dental image is matched to a dental treatment recommendation dataset to further produce a real time dental product recommendation for a patient. One or more artificial intelligence entities may provide in real time dental treatment recommendations and/or real time dental product recommendations to a dental professional and/or an individual. The process may also produce a diagnostic treatment aid and/or a product recommendation such as an orthodontic aligner. Artificial intelligence dental datasets may be provided to dental professionals, health care professionals, individuals and e-commerce organizations that may buy, sell, exchange and/or transfer this information over a communication network such as the internet and/or a cell phone.

At least one example embodiment provides a method including obtaining a surface of a patient, obtaining first treatment information for the patient, the first treatment information associated with a treatment for the patient, the first treatment information corresponding to at least one of a treatment intent for the patient, a treatment plan for the patient or a structure of the patient, obtaining at least one model based on the first treatment information for the patient and determining a region of interest of the patient based on the surface of the patient and the at least one model.

Provided is an irradiation system for reliably detecting a desired detection target and precisely irradiating marking light on the detected detection target. The irradiation system includes: a server device detecting, by authentication processing, a detection target from image data capturing a monitored area, generating, from each piece of a plurality of pieces of image data captured in a time span including a capture time of image data in which the detection target is detected, lightweight data obtained by lightening an amount of data in the plurality of pieces of image data, and transmitting tracking data obtained by aggregating a plurality of pieces of generated lightweight data; and a terminal device capturing the monitored area and outputting image data, transmitting the captured image data to the server device and also receiving the tracking data from the server device, and irradiating light on the detection target, based on the received tracking data.

The present disclosure discloses a method and apparatus for analyzing medical image. A specific embodiment of the method includes: acquiring medical image data; generating multi-scale decision sample data based on the medical image data; inputting the multi-scale decision sample data into a deep neural network model to obtain an auxiliary diagnosis data of the medical image, the deep neural network model being trained according to a consistency principle between multi-scale training sample data and an output result of the deep neural network model. In the embodiment, a multi-scale training sample is used to accelerate the training process of the deep neural network model, thus the auxiliary diagnosis decision process can be accelerated, while the accuracy of the trained deep neural network model of the embodiment is improved according to a consistency principle of data between different scales and output results, thereby improving the accuracy of auxiliary diagnosis decision.

The present disclosure relates to an imaging device, a manufacturing method, a semiconductor device, and an electronic device that can further improve image quality. An imaging device includes a photoelectric conversion unit that receives and photoelectrically converts light, a floating diffusion layer that accumulates charge generated by the photoelectric conversion unit, and a diffusion layer that serves as a source or a drain of a transistor. Then, the floating diffusion layer is formed to have an impurity concentration lower than an impurity concentration of the diffusion layer. In addition, both a first photoelectric conversion unit that is able to accumulate the charge generated by the photoelectric conversion and a second photoelectric conversion unit from which the charge generated by the photoelectric conversion is sequentially taken out and accumulated in the floating diffusion layer are provided as the photoelectric conversion unit in one pixel, and the first photoelectric conversion unit and the second photoelectric conversion unit are arranged in a line in a longitudinal direction along a direction of illumination of light. The present technology can be applied to, for example, a back-illuminated CMOS image sensor.

According to one embodiment, a measurement method includes: acquiring a first picture including a background image and a substance, the substance allowing transmission of light from the background image; acquiring a second picture including the background image and the substance in a different positional relation with respect to the first picture; and calculating a first displacement amount representing a difference in position of the background image between the first picture and the second picture.