Provided is an image sensor evaluation method using a computing device including a processor, the method including receiving, by the processor, image data obtained by capturing a plurality of neighboring lines by an image sensor, performing, by the processor, a spatial domain analysis on the image data to generate a first quality score of the image sensor, performing, by the processor, a frequency domain analysis on the image data to generate a second quality score of the image sensor, and generating, by the processor, a final quality score of the image sensor based on the first quality score and the second quality score.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive, at a neural network, frequency filtered spatial domain data, compare an output generated by the neural network to a loss function including a frequency-based feature consistency constraint, and update at least one weight of the neural network according to the loss function.

The inventive method involves receiving as input a representation of an ordered set of two dimensional images. The ordered set of two dimensional images is analyzed to determine at least one first view of an object in at least two dimensions and at least one motion vector. The next step is analyzing the combination of the first view of the object in at least two dimensions, the motion vector, and the ordered set of two dimensional images to determine at least a second view of the object; generating a three dimensional representation of the ordered set of two dimensional images on the basis of at least the first view of the object and the second view of the object. Finally, the method involves providing indicia of the three dimensional representation as an output.

A detection system (100) is disclosed herein. The system includes a sensor system (120) positioned to obtain image sensor data at different times of a live cannabis plant and a data storage system (130) configured to store the image sensor data. The system further includes a processor (140) coupled to the data storage system to receive the image sensor data. The processor includes a target region selection module (160) configured to determine a region of the live cannabis plant that contains a flower and generate a feature indicative of a characteristic of the flower. The processor further includes a grade estimation module (170) configured to estimate a qualitative assessment for the flower based on the feature and a temporal aggregation module (540) configured to combine the estimated qualitative assessments to output a final aggregated assessment.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

An example method includes receiving, from one or more sensors associated with an autonomous vehicle, sensor data associated with a target object in an environment of the vehicle during a first environmental condition, where at least one sensor of the sensor(s) is configurable to be associated with one of a plurality of operating field of view volumes. The method also includes based on the sensor data, determining at least one parameter associated with the target object. The method also includes determining a degradation in the parameter(s) between the sensor data and past sensor data, where the past sensor data is associated with the target object in the environment during a second environmental condition different from the first and, based on the degradation, adjusting the operating field of view volume of the at least one sensor to a different one of the operating field of view volumes.

A product defect detection method, device and system are disclosed. The method comprises: constructing a defect detection framework including segmentation networks, a concatenating network and a classification network, and setting a quantity of the segmentation network according to product defect types, wherein each segmentation network corresponds to a defect type; concatenating the sample image with the mask image output by each segmentation network by using the concatenating network to obtain a concatenated image; training the classification network by using the concatenated images to obtain a classification network capable of correctly identifying a product defect and a defect type; and when performing product defect detection, inputting a product image acquired into the defect detection framework, and detecting a product defect and a defect type existing in the product by using the segmentation networks, the concatenating network and the classification network.

An electronic device is provided. The electronic device includes a transparent member comprising a transparent material, a display panel disposed under the transparent member and including a plurality of pixels, a biometric sensor disposed under the display panel, and a filter disposed between the display panel and the biometric sensor and covering the biometric sensor.

An example method includes receiving, from one or more sensors associated with an autonomous vehicle, sensor data associated with a target object in an environment of the vehicle during a first environmental condition, where at least one sensor of the sensor(s) is configurable to be associated with one of a plurality of operating field of view volumes. The method also includes based on the sensor data, determining at least one parameter associated with the target object. The method also includes determining a degradation in the parameter(s) between the sensor data and past sensor data, where the past sensor data is associated with the target object in the environment during a second environmental condition different from the first and, based on the degradation, adjusting the operating field of view volume of the at least one sensor to a different one of the operating field of view volumes.

Techniques for generating magnetic resonance (MR) images from MR data obtained by a magnetic resonance imaging (MRI) system comprising a plurality of RF coils configured to detect RF signals. The techniques include: obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils; generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique; estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils; generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and outputting the generated MR image.