Nvidia Corporation

Apple Inc.

Semiconductor Components Industries, LLC

Robin Brian Jenkin

Sean Midthun Pieper

Paul James Kane

Hongqin Zhang

Eugene Lam

In one embodiment, a system receives pixel data from a pair of regions of an image generated by an imaging device, the pair of regions includes a first region and a second region, where the first region includes a first plurality of pixels and the second region includes a second plurality of pixels. The system determines a plurality of pixel pairs, where a pixel pair includes a first pixel from the first plurality of pixels and a second pixel from the second plurality of pixels. The system calculates a plurality of contrasts based on the plurality of pixel pairs. The system determines a contrast distribution based on the plurality of contrasts. The system calculates a value representative of a capability of the imaging device to detect contrast based on the contrast distribution. The system determines a reduction in contrast detectability of the imaging device based on the value.

Applications for detection capabilities of cameras

Apparatuses, systems, and techniques to receive, at one or more processors associated with an image signal processing (ISP) pipeline for a camera, an image generated using an image sensor of the camera, wherein the image comprises a plurality of channels associated with color information of the image; process, by the one or more processors, the plurality of channels of the image to generate a plurality of luminance and/or radiance values; generate, by the one or more processors, an updated version of the image using the plurality of luminance and/or radiance values; and output the updated version of the image.

Determination of luminance values using image signal processing pipeline

Apparatuses, systems, and techniques to process luminance and/or radiance values of one or more images from one or more cameras using one or more neural networks to perform a machine vision task. In at least one embodiment, one or more neural networks determine detection difficulty levels of objects within the one or more images and performs a machine vision task based on the determined detection difficulty levels of objects within images associated with that ask.

Training a neural network using luminance

In one embodiment, a system determines pixel data from a pair of regions of an image generated by an imaging device, the pair of regions includes a first region and a second region, where the first region includes a first plurality of pixels and the second region includes a second plurality of pixels. The system determines a plurality of pixel pairs of the image, where a pixel pair includes a first pixel from the first plurality of pixels and a second pixel from the second plurality of pixels. The system calculates a plurality of contrasts based on the plurality of pixel pairs, where a contrast is calculated between the first pixel and the second pixel. The system determines a contrast distribution based on the plurality of contrasts. The system calculates a value representative of a capability of the imaging device to detect contrast based on the contrast distribution.

Evaluating detection capabilities of cameras

An imaging device may have a monochrome image sensor and a bi-chromatic image sensor. A beam splitter may split incident light between the two image sensors. The monochrome image sensor may have an array of broadband image sensor pixels that generate broadband image signals. The bi-chromatic image sensor may have an array of red and blue image pixels that generate red and blue image signals. The image sensors may be coupled to processing circuitry that performs processing operations on only the broadband image signals to produce monochrome images, or on the red, blue, and broadband image signals to produce color images. Processing operations used to produce color images may include chroma-demosaicking and/or point filter operations.

Imaging systems having broadband monochromatic and chromatic image sensors

An in-focus lens position may be determined by computing a focus metric value, by counting the number of transition pixels for images of a target captured at different lens positions. Using as little as two frames to compute two focus metric values, a reasonable approximation of the in-focus lens position may be obtained. The approximation of the in-focus lens position may then be used as a starting point for a fine focus search process, to determine an in-focus lens position. An advantage here is that the focus metric values relate to the number of transition pixels and are easy to compute, and yield a reasonable approximation of the in-focus position with just a few frames. Other embodiments are also described.

Determining an in-focus position of a lens

The embodiments of the invention here are automated systems and processes for testing a camera device. The processes can detect dark signal non-uniformity (DSNU) within a digital image that has been captured by a solid-state imaging sensor of the camera device under test. Techniques that may be able to detecting localized and global color drifts of various shapes have been described and claimed.

Growing community of inventors

Morgan Hill, CA, United States of America

Robin Brian Jenkin