Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

An image forensics system estimates a camera response function (CRF) associated with a digital image, and compares the estimated CRF to a set of rules and compares the estimated CRF to a known CRF. The known CRF is associated with a make and a model of an image sensing device. The system applies a fusion analysis to results obtained from comparing the estimated CRF to a set of rules and from comparing the estimated CRF to the known CRF, and assesses the integrity of the digital image as a function of the fusion analysis.

An object is recognized from image data as a target object and linked to a user based on an interaction by the user, information about the target object is obtained and a purchase of the target object is initiated.

In example implementations, a method is provided. The method includes printing a three-dimensional (3D) object that includes a secondary structure. The secondary structure is removed. A representation of a surface of the 3D object where the secondary structure was removed is captured. The 3D object is authenticated based on the representation of the surface.

In example implementations, a method is provided. The method includes printing a three-dimensional (3D) object that includes a secondary structure. The secondary structure is removed. A representation of a surface of the 3D object where the secondary structure was removed is captured. The 3D object is authenticated based on the representation of the surface.

Systems for item validation and image evaluation are provided. In some examples, a system may receive an instrument and associated data. The instrument may be received and at least one of a bill pay profile and a user profile may be retrieved. The bill pay profile and user profile may each include a plurality of previously processed instruments that have been determined to be valid and/or authentic. The instrument may be compared to the plurality of previously processed instruments to determine whether one or more elements of the instrument being evaluated match one or more corresponding elements of the plurality of previously processed instruments. Matching or non-matching elements may be identified. In some examples, one or more user interfaces may be generated displaying the instruments and including any highlighting or enhancements identifying matching or non-matching elements.

A management system is configured to include a first data acquisition unit, a second data acquisition unit, and a comparison unit. The first data acquisition unit acquires first image data obtained by capturing an image of a first object, and identification information about the owner of the first object. The second data acquisition unit acquires second image data obtained by capturing an image of a second object. The comparison unit 3 identifies the identification information about the owner of the first object by comparing the characteristics of the surface pattern of the first object as represented by the first image data with the characteristics of the surface pattern of the second object as represented by the second image data.

Exemplary embodiments relate to a method for unlocking a mobile device using authentication based on ear recognition including obtaining an image of a target showing at least part of the target's body in a lock state, extracting a set of ear features of the target from the image of the target, when the image of the target includes at least part of the target's ear, and determining if the extracted set of ear features of the target satisfies a preset condition, and a mobile device performing the same.

A computer-implemented method for surface modeling includes: receiving one or more polarization raw frames of a surface of a physical object, the polarization raw frames being captured with a polarizing filter at different linear polarization angles; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and detecting a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces.