A method for training a convolutional neural network to reconstruct an image. The method includes forming a common loss function basing on the left and right images (I.sub.L, I.sub.R), reconstructed left and right images (I′.sub.L, I′.sub.R), disparity maps (d.sub.L, d.sub.R), reconstructed disparity maps (d′.sub.L, d′.sub.R) for the left and right images (I.sub.L, I.sub.R) and the auxiliary images (I″.sub.L, I″.sub.R) and training the neural network based on the formed loss function.

A method for training a convolutional neural network to reconstruct an image. The method includes forming a common loss function basing on the left and right images (I.sub.L, I.sub.R), reconstructed left and right images (I′.sub.L, I′.sub.R), disparity maps (d.sub.L, d.sub.R), reconstructed disparity maps (d′.sub.L, d′.sub.R) for the left and right images (I.sub.L, I.sub.R) and the auxiliary images (I″.sub.L, I″.sub.R) and training the neural network based on the formed loss function.

The image capture system of the present disclosure comprises an illuminator comprising at least one infrared light LED or laser and one visible light LED, an image sensor that is sensitive to infrared light and visible light, a memory configured to store instructions, and a processor configured to execute instructions to cause the image capture system to emit infrared light, receive image data comprising at least one infrared image, and determine depth maps, visible focus settings, or infrared exposure settings based on the infrared image data.

The image capture system of the present disclosure comprises an illuminator comprising at least one infrared light LED or laser and one visible light LED, an image sensor that is sensitive to infrared light and visible light, a memory configured to store instructions, and a processor configured to execute instructions to cause the image capture system to emit infrared light, receive image data comprising at least one infrared image, and determine depth maps, visible focus settings, or infrared exposure settings based on the infrared image data.

According to one embodiment, an electronic device includes one or more processors. The one or more processors obtain an image captured by a camera with a filter having a first area transmitting light of a first wavelength range and a second area transmitting light of a second wavelength range. The image includes a first color-component image based on the light of the first wavelength range and a second color-component image based on the light of the second wavelength range. The one or more processors notify a user of an effective area for calculation of depth information based on a bias of color information in the first color-component image and the second color-component image.

Matching timings of transmitting pieces of information for reproducing video and audio with presence and handling loss are provided. An information synchronization device is configured to synchronize, to a reference timestamp, data in which one or more labels for identifying respective one or more subjects on a video and one or more pieces of location information for identifying respective locations of the one or more subjects are respectively associated. The information synchronization device includes a reception unit for receiving the data at each time, a buffer for storing the received data, and a location information interpolation unit for generating, when pieces of the location information at times before and after the reference timestamp is stored into the buffer for any of the labels, location information of the label at the reference timestamp by interpolation using the pieces of the location information.

The distance measuring camera includes a first imaging system for obtaining a first image, a second imaging system for obtaining a second image and a size obtaining part 3 for measuring a distance between a plurality of feature points of a first subject in the first image to obtain a size of the first subject image and measuring a distance between a plurality of feature points of a second subject image in the second image to obtain a size of the second subject image. The size obtaining part 3 searches pixels on an epipolar line only in a search area of the second image in which a first imaging area corresponding to the first image can be overlapped with a second imaging area corresponding to the second image to detect the plurality of feature points of the second subject image.

A system and method for reflection removal of an image from a dual-pixel sensor. The image including a left view and a right view. The method including: determining a first gradient of the left view and a second gradient of the right view; determining disparity between the first gradient and the second gradient using a sum of squared differences (SSD); determining a confidence value at each pixel using the SSD; determining a weighted gradient map using the confidence values; minimizing a cost function to estimate the background layer, the cost function including the weighted gradient map, wherein the image includes the background layer added to a reflection layer; and outputting at least one of the background layer and the reflection layer.

A system and method for reflection removal of an image from a dual-pixel sensor. The image including a left view and a right view. The method including: determining a first gradient of the left view and a second gradient of the right view; determining disparity between the first gradient and the second gradient using a sum of squared differences (SSD); determining a confidence value at each pixel using the SSD; determining a weighted gradient map using the confidence values; minimizing a cost function to estimate the background layer, the cost function including the weighted gradient map, wherein the image includes the background layer added to a reflection layer; and outputting at least one of the background layer and the reflection layer.

This invention relates to a sensor and sensor platform, for an autonomous system. The sensor and its platform sense, perform signal or data processing, and make the decision locally at the point of sensing. More specifically, the sensor along with its platform simulates the human-like or human capacity to make decisions by combing the data from several sensors that detect different data sets, and combine them in a series of data processes that allows autonomous decisions to be made. Additionally, the sensor platform combines multiple sensors in one metasensor with the functionality of multiple sensors placed on a common carrier or platform.