A computer implemented method for recognizing facial expressions by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and recognizing a facial expression in the face image in accordance to the hybrid decision.

A vision based light detection and ranging (LIDAR) system captures images including a targeted object and identifies the targeted object using an object recognition model. To identify the targeted object, the vision based LIDAR system determines a type of object and pixel locations or a boundary box associated with the targeted object. Based on the identification, the vision based LIDAR system directs a tracking beam onto one or more spots on the targeted object and detects distances to the one or more spots. The vision based LIDAR system updates the identification of the targeted object based on the one or more determined distances.

A valuation platform may receive, from a user device, video data associated with a vehicle, and may receive a vehicle history report of the vehicle based on a vehicle identification number of the vehicle. The valuation platform may receive, from the user device, feature data associated with the vehicle, and may process the video data, the vehicle history report, and the feature data, with a machine learning model, to determine one or more values for the vehicle. The valuation platform may determine a valuation for the vehicle based on the determined one or more values for the vehicle. The valuation platform may create a vehicle profile for the vehicle based on the video data, the vehicle history report, the feature data, the determined one or more values for the vehicle, and the valuation for the vehicle, and may perform one or more actions based on the vehicle profile.

The present application discloses a method, device, and system for processing a medical image. The method includes obtaining a source spinal image, identifying one or more vertebral bodies and one or more intervertebral discs comprised in the source spinal image, determining the vertebral body recognition results corresponding to the one or more vertebral bodies and the intervertebral disc recognition results corresponding to the one or more intervertebral discs, and determining target recognition results corresponding to the source spinal image based at least in part one on one or more of the vertebral body recognition results and the intervertebral disc recognition results.

A system receives an observation probability distribution function associated with a target object that was detected by sensors of an autonomous vehicle. The system identifies a target attribute of the target object, and detects a target attribute value associated with the target object. The system determines a first probability distribution function representing a probability of the autonomous vehicle detecting an object having an object label, determines a second probability distribution function defining a probability of the autonomous vehicle detecting the target attribute, determines a third probability distribution function defining a probability of the target attribute being present for the target object based on the target attribute value, and determines an attribute probability distribution function defining a probability that the target attribute is actually present for the target object. The system executes vehicle control instructions that cause the autonomous vehicle to adjust driving operations based on the attribute probability distribution function.

A vehicle includes one or more sensors configured to obtain raw data related to a scene, one or more processors, and machine readable instructions stored in one or more memory modules. The one machine readable instructions, when executed by the one or more processors, cause the vehicle to: process the raw data with a first neural network stored in the one or more memory modules to obtain a first prediction about the scene, transmit the raw data to a computing device external to the vehicle, receive a second prediction about the scene from the computing device in response to transmitting the raw data to the computing device, and determine an updated prediction about the scene based on a combination of the first prediction and the second prediction.

A quantum computing based video alert system converts captured video and audio signals, in real time, into a sequence of video qubits and a sequence of audio qubits. An entanglement score is generated based on a comparison of the video qubits to historical video qubits that are verified to show malicious activity. A second entanglement score is generated based on a comparison of the audio qubits to historical audio qubits that are verified to show malicious activity. A probability score is generated for each segment of the video qubit sequence and for each segment of the audio qubit sequence. If the probability score for the video qubit sequence, the audio qubit sequence, or a combination of probability scores for both the video qubit sequence and the audio qubit sequence meet a threshold, then an alert is generated to identify possible malicious activity at the location of a CCTV camera capturing the real-time data.

Systems and methods for controlling the operation of an autonomous vehicle are disclosed herein. One embodiment performs traffic light detection at an intersection using a sensor-based traffic light detector to produce a sensor-based detection output, the sensor-based detection output having an associated first confidence level; performs traffic light detection at the intersection using a vehicle-to-infrastructure-based (V2I-based) traffic light detector to produce a V2I-based detection output, the V2I-based detection output having an associated second confidence level; performs one of (1) selecting as a final traffic-light-detection output whichever of the sensor-based detection output and the V2I-based detection output has a higher associated confidence level and (2) generating the final traffic-light-detection output by fusing the sensor-based detection output and the V2I-based detection output using a first learning-based classifier; and controls the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output.

The present disclosure relates to a category labelling method and apparatus, an electronic device, a storage medium, and a computer program. The method includes: detecting a video stream acquired by an image acquisition device to determine a detection result of a target video frame in the video stream, wherein the detection result includes a detected category, and the detected category includes at least one of: an object category of an object in the target video frame, and a scene category corresponding to the target video frame; and determining a category labelling result corresponding to the image acquisition device according to detection results of a plurality of target video frames.

This application provides an image recognition method, a storage medium, and a computer device. The method includes: obtaining a to-be-recognized image; preprocessing the to-be-recognized image, to obtain a preprocessed image; obtaining, through a first submodel in a machine learning model, a first image feature corresponding to the to-be-recognized image, and obtaining, through a second submodel in the machine learning model, a second image feature corresponding to the preprocessed image; and determining, according to the first image feature and the second image feature, a first probability that the to-be-recognized image belongs to a classification category corresponding to the machine learning model. It may be seen that, the solutions provided by this application can improve recognition efficiency and accuracy.