Abstract: In some implementations, a method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

Abstract: In some implementations, a method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

Abstract: Some implementations of the systems and techniques described include a tattoo recognition system that is capable of improving the accuracy and efficiency associated with matching edge curves extracted from tattoo images, which are typically performed during a tattoo matching operation. The system can perform a matching operation in two stages—a feature extraction stage and a matching stage. In the first stage, the system extracts one or more edge curves from the tattoo image. In the second stage, the system performs matching using a two-step comparison that compares features of the extracted edge curves.

Abstract: Some implementations of the systems and techniques described include a tattoo recognition system that is capable of improving the accuracy and efficiency associated with matching edge curves extracted from tattoo images, which are typically performed during a tattoo matching operation. The system can perform a matching operation in two stages—a feature extraction stage and a matching stage. In the first stage, the system extracts one or more edge curves from the tattoo image. In the second stage, the system performs matching using a two-step comparison that compares features of the extracted edge curves.

Abstract: In some implementations, a method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

Abstract: In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.

Abstract: In some implementations, a method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

Abstract: In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.

Abstract: In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.

Abstract: In some implementations, a method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

Abstract: Some implementations of the systems and techniques described include a tattoo recognition system that is capable of improving the accuracy and efficiency associated with matching edge curves extracted from tattoo images, which are typically performed during a tattoo matching operation. The system can perform a matching operation in two stages—a feature extraction stage and a matching stage. In the first stage, the system extracts one or more edge curves from the tattoo image. In the second stage, the system performs matching using a two-step comparison that compares features of the extracted edge curves.

Abstract: In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.

Abstract: In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.

Abstract: In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.

Abstract: In some implementations, a method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

Abstract: A method of classifying objects within a vehicle (12) includes the detection of an object and the generation of an object detection signal in response to the detection. A range signal is generated in response to the object detection signal. An image detection signal is generated having an image representation of the object. The object detection signal is projected on an image plane (122) in response to the image detection signal to generate a fused image reference (120). A search region (144) is generated on the image plane (122) in the vicinity of the fused image reference (120). A vector of feature values is calculated via generation of a sum-array (152) and in response to the range signal and the image detection signal. The search region (144) is classified in response to the vector of feature values.

Abstract: A method of performing object classification within a collision warning and countermeasure system (10) of a vehicle (12) includes the generation of an object detection signal in response to the detection of an object (52). An image detection signal is generated and includes an image representation of the object (52). The object detection signal is projected on an image plane (51) in response to the image detection signal to generate a fused image reference. A candidate template (55) is generated in response to the fused image reference. The candidate template (55) is validated. The object (52) is classified in response to the candidate template (55).

Abstract: A method of classifying objects within a vehicle (12) includes the detection of an object and the generation of an object detection signal in response to that detection. A range signal is generated in response to the object detection signal. An image detection signal is generated having an image representation of the object. The object detection signal is projected on an image plane (122) in response to the image detection signal to generate a fused image reference (120). A search region (144) is generated on the image plane (122) in the vicinity of fused image reference (120). A vector of feature values is calculated via generation of a sum-array (152) and in response to the range signal and the image detection signal. The search region (144) is classified in response to the vector of feature values.

Abstract: A method of performing object classification within a collision warning and countermeasure system (10) of a vehicle (12) includes the generation of an object detection signal in response to the detection of an object (52). An image detection signal is generated and includes an image representation of the object (52). The object detection signal is projected on an image plane (51) in response to the image detection signal to generate a fused image reference. A candidate template (55) is generated in response to the fused image reference. The candidate template (55) is validated. The object (52) is classified in response to the candidate template (55).