Abstract: A port departure service processing method, apparatus and system. The method includes: determining the current master control system for executing a port departure service; during the process of executing the port departure service, if an error is detected in operating data of the current master control system, switching the unfinished port departure service to another normally operating system so as to continue execution; if the execution of the port departure service is not completed and after it is detected that the operating data of the current master control system returns to normal, switching to the current master control system to continue to execute the remaining port departure service; and after switching from the current master control system to the other system or switching from the other system back to the current master control system, correcting inconsistent data before and after switching.

Abstract: Methods and systems for image alignment are provided. One method includes selecting three or more salient feature points for use in test image to reference image alignment by applying a selected salient feature point detection method to at least a reference image generated for the specimen. The method also includes detecting the three or more salient feature points in the test image and the reference image and aligning the detected three or more salient feature points in the test image to the detected three or more salient feature points in the reference image. The method further includes aligning remaining portions of the test image to remaining portions of the reference image based on results of the previous aligning step.

Abstract: The present specification discloses systems and methods for identifying and reporting contents of a tanker, container or vehicle. Programmatic tools are provided to assist an operator in analyzing contents of a tanker, container or vehicle. Manifest data is automatically imported into the system for each shipment, thereby helping security personnel to quickly determine container contents. In case of a mismatch between container contents shown by manifest data and the contents as ascertained from the scanning system, the container or vehicle may be withheld for further inspection.

Abstract: The present specification discloses systems and methods for identifying and reporting contents of a tanker, container or vehicle. Programmatic tools are provided to assist an operator in analyzing contents of a tanker, container or vehicle. Manifest data is automatically imported into the system for each shipment, thereby helping security personnel to quickly determine container contents. In case of a mismatch between container contents shown by manifest data and the contents as ascertained from the scanning system, the container or vehicle may be withheld for further inspection.

Abstract: The present specification discloses systems and methods for identifying and reporting contents of a tanker, container or vehicle. Programmatic tools are provided to assist an operator in analyzing contents of a tanker, container or vehicle. Manifest data is automatically imported into the system for each shipment, thereby helping security personnel to quickly determine container contents. In case of a mismatch between container contents shown by manifest data and the contents as ascertained from the scanning system, the container or vehicle may be withheld for further inspection.

Abstract: The present specification discloses systems and methods for identifying and reporting contents of a tanker, container or vehicle. Programmatic tools are provided to assist an operator in analyzing contents of a tanker, container or vehicle. Manifest data is automatically imported into the system for each shipment, thereby helping security personnel to quickly determine container contents. In case of a mismatch between container contents shown by manifest data and the contents as ascertained from the scanning system, the container or vehicle may be withheld for further inspection.

Abstract: The present specification discloses systems and methods for identifying and reporting contents of a tanker, container or vehicle. Programmatic tools are provided to assist an operator in analyzing contents of a tanker, container or vehicle. Manifest data is automatically imported into the system for each shipment, thereby helping security personnel to quickly determine container contents. In case of a mismatch between container contents shown by manifest data and the contents as ascertained from the scanning system, the container or vehicle may be withheld for further inspection.

Abstract: The present specification discloses systems and methods for identifying and reporting contents of a tanker, container or vehicle. Programmatic tools are provided to assist an operator in analyzing contents of a tanker, container or vehicle. Manifest data is automatically imported into the system for each shipment, thereby helping security personnel to quickly determine container contents. In case of a mismatch between container contents shown by manifest data and the contents as ascertained from the scanning system, the container or vehicle may be withheld for further inspection.

Abstract: A document authentication method determines the authenticity of a target hardcopy document, which purports to be a true copy of an original hardcopy document. The method compares a binarized image of the target document with a binarized image of the original document which has been stored in a storage device. The image of the original document is generated by binarizing a scanned grayscale image of the original document. Halftone and non-halftone text areas in the grayscale image area separated, and the two types of text are separately binarized. The non-halftone text areas are then down-sampled. During authenticating, a scanned grayscale image of the target document is binarized by separating halftone and non-halftone text areas and binarizing them separately, and then down-sampling the non-halftone text areas. The binarized images of the target document and the original document are compared to determine the authenticity of the target document.

Abstract: A document authentication method determines the authenticity of a target hardcopy document, which purports to be a true copy of an original hardcopy document. The method compares a binarized image of the target document with a binarized image of the original document which has been stored in a storage device. The image of the original document is generated by binarizing a scanned grayscale image of the original document. Halftone and non-halftone text areas in the grayscale image area separated, and the two types of text are separately binarized. The non-halftone text areas are then down-sampled. During authenticating, a scanned grayscale image of the target document is binarized by separating halftone and non-halftone text areas and binarizing them separately, and then down-sampling the non-halftone text areas. The binarized images of the target document and the original document are compared to determine the authenticity of the target document.

Abstract: A document authentication method determines the authenticity of a target hardcopy document, which purports to be a true copy of an original hardcopy document. The method compares a binarized image of the target document with a binarized image of the original document which has been stored in a storage device. The image of the original document is generated by binarizing a scanned grayscale image of the original document. Halftone and non-halftone text areas in the grayscale image area separated, and the two types of text are separately binarized. The non-halftone text areas are then down-sampled. During authenticating, a scanned grayscale image of the target document is binarized by separating halftone and non-halftone text areas and binarizing them separately, and then down-sampling the non-halftone text areas. The binarized images of the target document and the original document are compared to determine the authenticity of the target document.

Abstract: A method for binarizing a scanned document images containing gray or light colored text printed with halftone patterns. The document image is initially binarized and connected image components are extracted from the initial binary image as text characters. Each text character is classified as either a halftone text character or a non-halftone text character based on an analysis of its topology features. The topology features may be the Euler number of the text character; a text character with a Euler number below ?2 is classified as halftone text. The gray-scale document image is then divided into halftone text regions containing only halftone text characters and non-halftone text regions. Each region is binarized using its own pixel value statistics. This eliminates the influence of black text on the threshold values for binarizing halftone text. The binary maps of the regions are combined to generate the final binary map.

Abstract: A document authentication method determines the authenticity of a target hardcopy document, which purports to be a true copy of an original hardcopy document. The method compares a binarized image of the target document with a binarized image of the original document which has been stored in a storage device. The image of the original document is generated by binarizing a scanned grayscale image of the original document. Halftone and non-halftone text areas in the grayscale image area separated, and the two types of text are separately binarized. The non-halftone text areas are then down-sampled. During authenticating, a scanned grayscale image of the target document is binarized by separating halftone and non-halftone text areas and binarizing them separately, and then down-sampling the non-halftone text areas. The binarized images of the target document and the original document are compared to determine the authenticity of the target document.

Abstract: A document authentication method using block-by-block image comparison is disclosed. An image of an original document and an image of a target document are each segmented into multiple blocks corresponding to paragraphs of text. A first block in the original image is used to search the target image to find a corresponding first block using a cross-correlation method. The position mapping for the first block of the target image is calculated and alterations are detected. Then, for each subsequent block of the original image, a corresponding block of the target document is identified based on the position of the subsequent block of the original image relative to the first block of the original image and the position mapping for the first block of the target image. The corresponding subsequent blocks of the original and target images are compared to detect alterations using a method other than cross-correlation.

Abstract: A document alteration detection method compares a target image with an original image by comparing character shape features without actually recognizing the characters. Bounding boxes for the characters are generated for both images, each enclosing one or more connected groups of pixels of one character. The bounding boxes in the original and target images are matched into pairs. Addition and deletion of text is detected if a bounding box in one image does not have a matching one in the other image. Each pair of bounding boxes is processed to compare their shape features. The shape features include the Euler numbers of the characters, the aspect ratio of the bounding boxes, the pixel density of the bounding boxes, and the Hausdorff distance between the two characters. The two characters are determined to be the same or different based on the shape feature comparisons.

Abstract: Methods, systems, and computer-readable medium for detecting and removing unwanted information in a medical image. One method includes receiving, at a processor, an image, obtained using a shutter including at least a first blade and a second blade. The method also includes processing the image to identify a first plurality of candidate edges in the image for the first blade and a second plurality of candidate edges in the image for the second blade. The method further includes individually evaluating each of the first and second plurality of candidate edges to select one of the first plurality of candidate edges as a true edge for the first blade and one of the second plurality of candidate edges as a true edge for the second blade. In addition, the method includes removing the unwanted information from the image based on a location of each of the identified true edges.

Abstract: A document alteration detection method compares a target image with an original image by comparing character shape features without actually recognizing the characters. Bounding boxes for the characters are generated for both images, each enclosing one or more connected groups of pixels of one character. The bounding boxes in the original and target images are matched into pairs. Addition and deletion of text is detected if a bounding box in one image does not have a matching one in the other image. Each pair of bounding boxes is processed to compare their shape features. The shape features include the Euler numbers of the characters, the aspect ratio of the bounding boxes, the pixel density of the bounding boxes, and the Hausdorff distance between the two characters. The two characters are determined to be the same or different based on the shape feature comparisons.

Abstract: A method for removing shutter areas in an image, in particular an x-ray image is provided. Edges are examined in a multi-resolution image pyramid and evaluated to determine potential shutter blade candidates defining the shutter areas. Heuristic rules and/or an automatic classifier such as a Neuronal Network, are applied to distinguish true shutter blades from false positives. The rule set and the classifier are based on a set of features extracted from the potential shutter blade candidates as well as predetermined knowledge of the expected placement of the shutter, human anatomy. Up to four shutter blades are expected to be detected and based on these blades, the bright areas in the image that occur due to the shutters are removed.

Abstract: A method for binarizing a scanned document images containing gray or light colored text printed with halftone patterns. The document image is initially binarized and connected image components are extracted from the initial binary image as text characters. Each text character is classified as either a halftone text character or a non-halftone text character based on an analysis of its topology features. The topology features may be the Euler number of the text character; a text character with a Euler number below ?2 is classified as halftone text. The gray-scale document image is then divided into halftone text regions containing only halftone text characters and non-halftone text regions. Each region is binarized using its own pixel value statistics. This eliminates the influence of black text on the threshold values for binarizing halftone text. The binary maps of the regions are combined to generate the final binary map.

Abstract: A method for removing shutter areas in an image, in particular an x-ray image is provided. Edges are examined in a multi-resolution image pyramid and evaluated to determine potential shutter blade candidates defining the shutter areas. Heuristic rules and/or an automatic classifier such as a Neuronal Network, are applied to distinguish true shutter blades from false positives. The rule set and the classifier are based on a set of features extracted from the potential shutter blade candidates as well as predetermined knowledge of the expected placement of the shutter, human anatomy. Up to four shutter blades are expected to be detected and based on these blades, the bright areas in the image that occur due to the shutters are removed.