Abstract: A system is disclosed.

Abstract: A system is disclosed. The system includes at least one physical memory device to store edge enhancement logic and one or more processors coupled with the at least one physical memory device to execute the edge enhancement logic to receive a plurality of pels in a continuous tone image (CTI), receive compensation data for pel forming elements associated with each of the plurality of pels, receive edge enhancement transfer functions, determine whether each of the plurality of pels is an edge pel, perform edge enhancement processing for each of the determined edge pels, including generating a final pel value for the pel based on the pel value for the pel, the edge enhancement transfer function associated with the pel, and the compensation data associated with the pel and perform compensation processing for each of the determined not edge pels, including generating a final pel value for the pel based on the pel value for the pel, and the compensation data associated with the pel.

Abstract: Computer systems and methods are provided for determining authenticity of a document. A computer system receives first image data from a remote third party. The first image data includes a first image of a document. The computer system generates an image hash based on at least a portion of the received image data. The computer system compares the generated image hash with a stored image hash to determine whether the generated image hash meets image matching criteria. The stored image hash is associated with a first predetermined authentication decision. In accordance with a determination that the generated image hash meets the image matching criteria, the computer system transmits the first predetermined authentication decision to the remote third party.

Abstract: Computer systems and methods are provided for receiving a first authentication request that includes an image of an identification document. A risk value is determined using one or more information factors that correspond to the authentication request. A validation user interface that includes the image of the identification document is displayed. A risk category that corresponds to the risk value is determined using at least a first risk threshold. In accordance with a determination that the risk value corresponds to a first risk category, a visual indication that corresponds to the first risk category is displayed. In accordance with a determination that the risk value corresponds to a second risk category, a visual indication that corresponds to the second risk category is displayed.

Abstract: A hand-held intraoral scanner apparatus includes a handle, a scanner shaft and a head unit, wherein the head unit is arranged at an end of the scanner shaft and the head unit includes at least one sensor. The hand-held intraoral scanner apparatus further includes a processing unit, a moving arrangement and at least one motor. The processing unit is electrically interconnected to the at least one motor, the moving arrangement and the at least one sensor, a first end of the moving arrangement is interconnected to the head unit, and a second end of the moving arrangement is interconnected to the at least one motor. The processing unit is configured to move the head unit via the at least one motor and the moving arrangement, wherein the head unit is configured to move around at least one of two rotation axes. The two rotation axes extend perpendicular to each other.

Abstract: Computer systems and methods are provided for using a machine learning system to analyze authentication information. First authentication information for a first transaction includes at least a first image that corresponds to a first identification document is received. First validation information that corresponds to a first validation fault is received from a validation system. Data storage of a machine learning system stores the first validation information. Second authentication information for a second transaction includes a second image that corresponds to a second image is received. The machine learning system determines a first validation value that corresponds to a probability that the second image includes the first validation fault. The first validation value is used to determine whether fault review criteria are met. In accordance with a determination that the fault review criteria are met, the second image is transmitted to the validation system.

Abstract: Computer systems and methods are provided for determining authenticity of a document. A computer system receives first image data from a remote third party. The first image data includes a first image of a document. The computer system generates an image hash based on at least a portion of the received image data. The computer system compares the generated image hash with a stored image hash to determine whether the generated image hash meets image matching criteria. The stored image hash is associated with a first predetermined authentication decision. In accordance with a determination that the generated image hash meets the image matching criteria, the computer system transmits the first predetermined authentication decision to the remote third party.

Abstract: Computer systems and methods are provided for receiving a first authentication request that includes an image of an identification document. A risk value is determined using one or more information factors that correspond to the authentication request. A validation user interface that includes the image of the identification document is displayed. A risk category that corresponds to the risk value is determined using at least a first risk threshold. In accordance with a determination that the risk value corresponds to a first risk category, a visual indication that corresponds to the first risk category is displayed. In accordance with a determination that the risk value corresponds to a second risk category, a visual indication that corresponds to the second risk category is displayed.

Abstract: Computer systems and methods are provided for using a machine learning system to analyze authentication information. First authentication information for a first transaction includes at least a first image that corresponds to a first identification document is received. First validation information that corresponds to a first validation fault is received from a validation system. Data storage of a machine learning system stores the first validation information. Second authentication information for a second transaction includes a second image that corresponds to a second image is received. The machine learning system determines a first validation value that corresponds to a probability that the second image includes the first validation fault. The first validation value is used to determine whether fault review criteria are met. In accordance with a determination that the fault review criteria are met, the second image is transmitted to the validation system.

Abstract: Computer systems and methods are provided for receiving a first authentication request that includes an image of an identification document. A risk value is determined using one or more information factors that correspond to the authentication request. A validation user interface that includes the image of the identification document is displayed. A risk category that corresponds to the risk value is determined using at least a first risk threshold. In accordance with a determination that the risk value corresponds to a first risk category, a visual indication that corresponds to the first risk category is displayed. In accordance with a determination that the risk value corresponds to a second risk category, a visual indication that corresponds to the second risk category is displayed.

Abstract: Computer systems and methods are provided for using a machine learning system to analyze authentication information. First authentication information for a first transaction includes at least a first image that corresponds to a first identification document is received. First validation information that corresponds to a first validation fault is received from a validation system. Data storage of a machine learning system stores the first validation information. Second authentication information for a second transaction includes a second image that corresponds to a second image is received. The machine learning system determines a first validation value that corresponds to a probability that the second image includes the first validation fault. The first validation value is used to determine whether fault review criteria are met. In accordance with a determination that the fault review criteria are met, the second image is transmitted to the validation system.

Abstract: Computer systems and methods are provided for receiving a first authentication request that includes an image of an identification document. A risk value is determined using one or more information factors that correspond to the authentication request. A validation user interface that includes the image of the identification document is displayed. A risk category that corresponds to the risk value is determined using at least a first risk threshold. In accordance with a determination that the risk value corresponds to a first risk category, a visual indication that corresponds to the first risk category is displayed. In accordance with a determination that the risk value corresponds to a second risk category, a visual indication that corresponds to the second risk category is displayed.

Abstract: A moveable console for holding an image acquisition or medical device, in particular for the purpose of brain surgical interventions. A method for the 3D scanning of, in particular, approached parts of the human body, and the electronic recording and reconstruction of information regarding the scanned object surface.