An apparatus, method and computer program product are provided for determining a state of visibility of a road object, such as a road sign, using vehicle sensor data. For example, the apparatus determines whether one or more sensors of a first vehicle observes a road sign. If the road sign is not observed by the one or more sensors, the apparatus determines whether one or more second vehicles is obscuring the road sign. If the one or more second vehicles is not obscuring the road sign, the apparatus determines whether the road sign is obscured due to a weather condition. If the road sign is obscured due to the weather condition, the apparatus generates a signal indicating that the road sign was obscured due to the weather condition.

An authentication device includes a feature amount generation unit for generating a feature amount of an object in an image, a registered data acquisition unit for acquiring registered data where a feature amount of a predetermined object in an image is registered in advance as a registered feature amount, and NG information associated with the registered feature amount unsuitable for authentication is recorded, a similarity acquisition unit for acquiring a similarity between the predetermined feature amount and the registered feature amount acquired from the registered data acquisition unit, and a determination unit for performing authentication if the degree of similarity acquired by the degree of similarity acquisition unit satisfies a predetermined authentication condition, and even if the degree of similarity satisfies the predetermined authentication condition, if the NG information associated with the registered feature amount is acquired, the predetermined feature amount is not authenticated.

A surface inspection apparatus includes an imaging device configured to image a surface of an object to be inspected, and a processor configured to calculate a numerical value representing a quality of the surface by processing an image captured by the imaging device, and display, on a display device, the image including an index for specifying a position of a portion that has contributed to the calculation of the numerical value and the numerical value.

A control method for a computer to execute a process includes receiving a plurality of pieces of captured data of a person; generating weight information that indicates a weight applied to each of the plurality of pieces of captured data based on quality of each of the plurality of pieces of captured data and the number of the plurality of pieces of captured data; and applying, when representative data that represents the plurality of pieces of captured data is acquired from the plurality of pieces of captured data, an algorithm in which the smaller the weight indicated by the generated weight information, the smaller an influence of each of the plurality of pieces of captured data on a calculation result of the representative data.

A method for performing a crowdsourcing task is provided. The method executed on a computing device comprises generating a crowdsourcing task to take at least one image of an original face photo; sending the generated crowdsourcing task to a crowdsourcing server to publish it thereon, the crowdsourcing server communicating with at least one user device registered in the crowdsourcing server; receiving at least one confirmation from the crowdsourcing server, each confirmation corresponding to the crowdsourcing task accepted by a particular user with a corresponding one of the registered user devices; providing an original face photo for each user corresponding to one of the received confirmations; sending, through the crowdsourcing server, the original face photo to a particular user device corresponding to the user; and receiving at least one image from the user device, the received images being taken for the original face photo in accordance with the accepted crowdsourcing task.

Various systems and methods for clustering facial images in, for example, surveillance systems.

In some examples, a processor may receive images from a camera mounted on a vehicle. The processor may generate a disparity image based on features in at least one of the images. In addition, the processor may determine at least one region in a first image of the received images that has a brightness that exceeds a brightness threshold. Further, the processor may determine at least one region in the disparity image having a level of disparity information below a disparity information threshold. The processor may determine a region of light interference based on an overlap between at least one region in the first image and at least one region in the disparity image, and may perform at least one action based on the region of light interference.

For robust view classification and measurement estimation in sequential ultrasound imaging, the classification and/or measurements for a given image or sequence of images are gated. To prevent oscillation in results, the gating provides consistent output.

Systems and methods for detecting image anomalies include extracting one or more detected images from a submission file received from at least one computing device and generating an image identification (ID) for each of the one or more images. One or more image quality indices are determined for the submission file based on at least one of predetermined image features, an image type of the one or more images, and submission file attributes, and one or more image anomalies associated with the one or more images of the submission file are detected based on at least one of the image ID and the one or more image quality indices.

In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.