Technologies are generally described for methods and systems effective to identify a photographer of an image based on the image. The image may correspond to image data generated by a device. In an example, a processor may identify feature data in the image data. The feature data may correspond to a static feature in real space. The processor may also determine a position of the photographer based on the identified feature data. The processor may also retrieve video data from a memory. The video data may include the feature data. The processor may also determine a time that may correspond to the generation of the image data. The determination of the time may be based on the video data. The processor may also identify the photographer based on the position and based on the time.

Computer vision systems and methods for localizing image forgery are provided. The system generates a constrained convolution via a plurality of learned rich filters. The system trains a convolutional neural network with the constrained convolution and a plurality of images of a dataset to learn a low level representation of each image among the plurality of images. The low level representation is indicative of a statistical signature of at least one source camera model of each image. The system can determine a splicing manipulation localization by the trained convolutional neural network.

An image processing apparatus that selects images for digest reproduction from a plurality of images, comprises: an information acquisition unit configured to acquire, for every image, shooting information generated at a time of shooting; an image evaluation unit configured to derive evaluation values for images based on the shooting information and an evaluation criterion; and an image selection unit configured to select images for digest reproduction by ranking images based on the evaluation values, wherein the image evaluation unit changes the evaluation criterion based on information on a lens used in shooting the images.

Methods and apparatuses for authenticating communication devices and securely transmitting and/or receiving encrypted voice and data information. A biometric scanner, for example a fingerprint scanner, is utilized for authenticating the communication device and for generating the encryption key. The fingerprint scanner can be an area or swipe type of scanner is registered to a particular user and has unique intrinsic characteristics (the scanner pattern) that are permanent over time and can identify the scanner even among scanners of the same manufacturer and model. The unique scanner pattern of the scanner generates a unique encryption key that cannot be reproduced using another fingerprint scanner.

Generating fingerprint templates, including receiving a fingerprint image of a user that is obtained at a first fingerprint sensor; identifying a plurality of fingerprint template creation models, each fingerprint template creation model associated with a respective other fingerprint sensor, each other fingerprint sensor differing from one another and differing from the first fingerprint sensor; applying each of the fingerprint template creation models to the fingerprint image to generate respective fingerprint templates; associating, for each generated fingerprint template, i) an identification (ID) of the other fingerprint sensor that corresponds to the fingerprint model that generated the fingerprint template and ii) a user identification (ID) of the user associated with the fingerprint image obtained at the first fingerprint sensor; and storing each of the generated fingerprint templates in a database.

An example operation may include one or more of scanning, by a mobile node, a physical object to generate a scan data, extracting, by the mobile node, a set of features from the scan data, generating, by the mobile node, a feature vector based on the set of the features, applying, by the mobile node, a cryptographic hash function to the feature vector to produce a hash value, encrypting, by the mobile node, the set of the features with the hash value, and executing a smart contract to store the encrypted set of the features on a blockchain.

An autonomous cargo handling system having a sensor self-calibration system may comprise a sensing agent configured to monitor a sensing zone, and a system controller in electronic communication with the first sensing agent. The system controller may be configured to receive structural cargo deck data from the first sensing agent, generate a real-time cargo deck model, identify a cargo deck component in the real-time cargo deck model, and determine a position of the sensing agent relative to the cargo deck component.

Techniques are disclosed for generating a two-dimensional (2D) map of signal-to-noise ratio (SNR) values for sensor-acquired images. The techniques leverage the use of lookup tables (LUTs) to generate a transformation LUT that functions to map pixel values to SNR values. The transformation LUT may be generated by first generating an intermediate LUT that uses the operating parameters identified with the sensor to map pixel values to light level values. The light level values are then used together with an SNR model that outputs a prediction of electrons identified with a signal portion and a noise portion of images acquired by the sensor to thus map the pixel values to SNR values. The 2D map may be used to improve upon the accuracy of the classification of objects and/or scene characteristics for various applications.

A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.

A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.