A method displays information graphically on an image captured by an optical system used to simulate a rear-view mirror system of a vehicle. The method includes capturing the image and identifying an object in the image. The method the assigns a priority level to the object based on a predetermined criterion. The object is altered based on its priority level to create an altered object. An altered object may have a changed color, a colored halo surrounding it, or a colored object inside it. Other possible ways to alter the way the object looks are possible. The altered object is displayed in the image in place of the object to alert a vehicle operator of its presence and the priority level associated therewith.

Various embodiments of a facial recognition system are provided. In one embodiment, a processor determines a value for a lighting parameter associated with a captured facial image, determines whether any previously obtained images in a biometric database includes a similar value for the lighting parameter and, if not, stores the newly captured image in the database along with the lighting parameter value. In another embodiment, the processor calculates a score indicative of the likelihood that the face in the captured facial image is identical to the face of a previously obtained image in the database, determines whether the score exceeds a threshold value and, if so, generates a signal indicating a match. The processor adjusts the threshold based on one or more parameter values.

A processing device includes a processor including hardware. The processor is configured to implement an image acquisition process of acquiring a tissue image obtained by capturing an image of a tissue and a process of determining a property of the tissue image acquired, and setting a plurality of identification criteria for identifying a state of the tissue as a normal state or an abnormal state, based on the tissue image and the property of the tissue image.

The invention relates to an exterior mirror simulation with image data recording and a display of the recorded and improved data for the driver of a motor vehicle. The display on a display device shows the data in a way chosen by the driver or the vehicle manufacturer.

Provided is a value bill identifying method, which includes: step 1, collecting, by a color collection device including multiple color sensors, color data of a to-be-detected value bill and preprocessing the collected color data; step 2, extracting a feature from the preprocessed color data; step 3, matching the extracted feature with feature template sets corresponding to each type of value bills, to obtain matching scores, and regarding a feature template with the highest score as a matched template of the color data; and step 4, determining a type of the value bill based on a matching result.

A first electronic device controls a second electronic device to measure a position of the first electronic device. The first electronic device includes a motion sensor, a network interface circuit, a processor, and a memory. The motion sensor senses motion of the first electronic device. The network interface circuit communicates with the second electronic device. The memory stores program code that is executed by the processor to perform operations that include, responsive to determining that the first electronic device has a level of motion that satisfies a defined rule, transmitting a request for the second electronic device to measure a position of the first electronic device. The position of the first electronic device is sensed and then stored in the memory. An acknowledgement is received from the second electronic device indicating that it has stored sensor data that can be used to measure the position of the first electronic device.

In accordance with one embodiment of the present disclosure, a method includes receiving an input image having an object and a background, intrinsically decomposing the object and the background into an input image data having a set of features, augmenting the input image data with a 2.5D differentiable renderer for each feature of the set of features to create a set of augmented images, and compiling the input image and the set of augmented images into a training data set for training a downstream task network.

A method for detecting changes in a scene includes accessing a first set of images and corresponding pose data in a first coordinate system associated with a first user session of an augmented reality (AR) device and accessing a second set of images and corresponding pose data in a second coordinate system associated with a second user session. The method identifies the first set of images corresponding to a second image from the second set of images based on the pose data of the first set of images being determined spatially closest to the pose data of the second image after aligning the first coordinate system and the second coordinate system. A trained neural network generates a synthesized image from the first set of images. Features of the second image are subtracted from features of the synthesized image. Area of changes are identified based on the subtracted features.

A method for extracting a representation from an image includes inputting an image to a pre-trained neural network. The gradient of a loss function is computed with respect to parameters of the neural network, for the image. A gradient representation is extracted for the image based on the computed gradients, which can be used, for example, for classification or retrieval.

A monitoring system for determining component wear is provided. The monitoring system includes a memory device configured to store a reference model of a component and a component wear monitoring (CWM) device configured to receive a component image of a first component being inspected, detect a plurality of manmade structural features in the received component image, adjust the component image to mask out at least some of the plurality of manmade structural features from the received component image, compare the adjusted component image with the reference model to determine one or more potential defect areas in the first component, analyze each of the one or more defect areas to determine a state of the potential defect areas, and output the state of the one or more potential defect areas to a user.