Abstract: Described is an invention that provides methods to identify key features from magnetic image captures of complex magnetic fields of randomly positioned and oriented magnetized particles in an object. The key features are described in a compact form to generate a unique identifier that services as a “fingerprint” of a chip.

Abstract: A mobile device with camera automatically captures an image of a check from a video sequence. A computing application assesses quality metrics of a frame of the video and, if acceptable, initiates capture of the check in that frame without user selection. Metrics include an aspect ratio of the check, image quality of the routing transit symbols that delineate a routing transit number on a MICR line of the check, distances between the routing transit symbols and to an edge of the check, recognition of digits of the routing transit number, checksum of the routing transit numbers, and image sharpness. Other embodiments note cropping of the check from the background of the image, properly orienting the check for viewing, and providing color coded visual feedback to users about the quality of the image frame about the check, to name a few.

Abstract: A mobile device with camera automatically captures an image of a check from a video sequence. A computing application assesses quality metrics of a frame of the video and, if acceptable, initiates capture of the check in that frame without user selection. Metrics include an aspect ratio of the check, image quality of the routing transit symbols that delineate a routing transit number on a MICR line of the check, distances between the routing transit symbols and to an edge of the check, recognition of digits of the routing transit number, checksum of the routing transit numbers, and image sharpness. Other embodiments note cropping of the check from the background of the image, properly orienting the check for viewing, and providing color coded visual feedback to users about the quality of the image frame about the check, to name a few.

Abstract: Systems and methods search pixels of source images and compare them to pixels of templates. Best matches correlate to objects in the image. That environmental conditions impact the appearance of objects, best matching scores suffer under poor lighting and other adverse conditions. Improving scores includes augmenting traditional correlation techniques with object features extracted from the source image and matching them to templates corresponding to the features. Certain embodiments contemplate corrupting pixels of image templates corresponding to objects with pixels extracted from the source image corresponding to features. Representative features include corners, edges, ridges, points/regions of interest, etc. Other embodiments note augmented correlation as a computing application and computing devices therefore, including cameras for capturing images and displaying results to users.

Abstract: A mobile device with camera automatically captures an image of a check from a video sequence. A computing application assesses quality metrics of a frame of the video and, if acceptable, initiates capture of the check in that frame without user selection. Metrics include an aspect ratio of the check, image quality of the routing transit symbols that delineate a routing transit number on a MICR line of the check, distances between the routing transit symbols and to an edge of the check, recognition of digits of the routing transit number, checksum of the routing transit numbers, and image sharpness. Other embodiments note cropping of the check from the background of the image, properly orienting the check for viewing, and providing color coded visual feedback to users about the quality of the image frame about the check, to name a few.

Abstract: A mobile device with camera automatically captures an image of a check from a video sequence. A computing application assesses quality metrics of a frame of the video and, if acceptable, initiates capture of the check in that frame without user selection. Metrics include an aspect ratio of the check, image quality of the routing transit symbols that delineate a routing transit number on a MICR line of the check, distances between the routing transit symbols and to an edge of the check, recognition of digits of the routing transit number, checksum of the routing transit numbers, and image sharpness. Other embodiments note cropping of the check from the background of the image, properly orienting the check for viewing, and providing color coded visual feedback to users about the quality of the image frame about the check, to name a few.

Abstract: Systems and methods search pixels of source images and compare them to pixels of templates. Best matches correlate to objects in the image. That environmental conditions impact the appearance of objects, best matching scores suffer under poor lighting and other adverse conditions. Improving scores includes augmenting traditional correlation techniques with object features extracted from the source image and matching them to templates corresponding to the features. Certain embodiments contemplate corrupting pixels of image templates corresponding to objects with pixels extracted from the source image corresponding to features. Representative features include corners, edges, ridges, points/regions of interest, etc. Other embodiments note augmented correlation as a computing application and computing devices therefore, including cameras for capturing images and displaying results to users.

Abstract: A method of identifying one or more image features of an image based on content of the image according to one example embodiment includes receiving an image, correcting a distortion and a tone of the image, segmenting the corrected image and extracting the one or more image features, generating an input image descriptor based on the one or more extracted image features, matching the input image descriptor with a reference image descriptor, and performing an action based on the matched reference image descriptor.

Abstract: A method of cropping an image based on a content of the image according to one example embodiment includes identifying image features in the content of the image, determining horizontal histogram frequencies and vertical histogram frequencies of the identified image features, determining borders of a document bounding box based on the determined horizontal histogram frequencies and vertical histogram frequencies, and cropping the image according to the document bounding box.

Abstract: A method of identifying one or more image features of an image based on content of the image according to one example embodiment includes receiving an image, correcting a distortion and a tone of the image, segmenting the corrected image and extracting the one or more image features, generating an input image descriptor based on the one or more extracted image features, matching the input image descriptor with a reference image descriptor, and performing an action based on the matched reference image descriptor.

Abstract: A method of cropping an image based on a content of the image according to one example embodiment includes identifying image features in the content of the image, determining horizontal histogram frequencies and vertical histogram frequencies of the identified image features, determining borders of a document bounding box based on the determined horizontal histogram frequencies and vertical histogram frequencies, and cropping the image according to the document bounding box.

Abstract: In an array of pixels, a method for segmenting a selected pixel of the array between at least two layers including identifying an N-by-N window centered upon the selected pixel, evaluating at least one pixel in the N-by-N window to determine whether the selected pixel is a potential text element, identifying an M-by-M window centered upon the selected pixel when the evaluation determines that the selected pixel is a potential text element, wherein the M-by-M window is smaller than the N-by-N window, and determining whether the potential text element includes text by comparing at least two pixels within the M-by-M window.

Abstract: A color gamut in a first color space is expanded to utilize a larger gamut in a second color space. The gamut in the first color space is first converted by black generation or geometric distortion to a gamut in a second color space. The gamut in the second color space is converted by forward mapping to a gamut in a third color. The gamut in the third color space is expanded by linear rescaling. Finally, the linearly rescaled gamut is mapped to a gamut in the second color space through inverse modeling to form a final color gamut. The final color gamut utilizes the gamut available in the second color space, which is larger than the gamut available in the first color space.

Abstract: The method includes identifying a set of points defining a gamut for a color image or image forming device, selecting a parameter &agr;, and computing the gamut boundary of the color image from the set of points and &agr; using Delaunay triangulation. The system includes a processing unit, a memory, and software operative on the processing unit. The software is capable of identifying a gamut boundary for the image, identifying a gamut boundary for the image forming unit, and calculating the percentage of colors in the image capable of being rendered by the image forming unit from the gamut boundary for the image and the image forming unit. The software is also capable of identifying an alpha shape associated with a color gamut and calculating a color gamut volume. The alpha shape includes a plurality of tetrahedra and each of the plurality of tetrahedra has a volume. The plurality of volumes are summed to generate the color gamut volume.