Konica Minolta Laboratory U.s.a., Inc.

Rapiscan Systems, Inc.

Kla Corporation

Songyang Yu

Wei Ming

Rupa Chittineni

Mark J McCarthy

Xiaonong Zhan

Premchandra M Shankar

Chaohong Wu

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.

Salient feature point based image alignment

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.

Systems and methods for detecting threats and contraband in cargo

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.

Method and apparatus for authenticating printed documents that contains both dark and halftone text

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 &#x2212;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.

Method for binarizing scanned document images containing gray or light colored text printed with halftone pattern

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.

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Milpitas, CA, United States of America

Songyang Yu