The present disclosure is directed to systems and methods for strike through detection and, more particularly, to systems and methods for detecting a strike through in an address block of a mailpiece. The method is implemented in a computing device and includes: generating edges of lines within a text block identified through optical character recognition processes; locating text lines within the text block; characterizing the edges within the text lines and outside of the text lines; and grouping identified edges of the characterized edges outside of the text lines into co-linear groups.

The invention provides a method and device for acquiring clothing image attribute points, which can increase an efficiency of acquiring the clothing image attribute points. The method comprises: saving five attribute points manually calibrated and determining first to eleventh points in sequence by taking a horizontal direction as a horizontal coordinate and a vertical direction as a vertical coordinate, for a first side of two bilaterally symmetric sides of the clothing image. By means of a technical solution of an embodiment of the present invention, for the clothing image, five points are manually calibrated first; and then the other eleven attribute points can be determined by a computer, so that a manual workload is reduced, and the efficiency of acquiring the clothing image attribute points is increased.

A method of image processing in a structured light imaging system is provided that includes receiving a captured image of a scene, wherein the captured image is captured by a camera of a projector-camera pair, and wherein the captured image includes a binary pattern projected into the scene by the projector, applying a filter to the rectified captured image to generate a local threshold image, wherein the local threshold image includes a local threshold value for each pixel in the rectified captured image, and extracting a binary image from the rectified captured image wherein a value of each location in the binary image is determined based on a comparison of a value of a pixel in a corresponding location in the rectified captured image to a local threshold value in a corresponding location in the local threshold image.

The invention provides a method, a system and a computer-readable medium for calibrating a camera by using a multi-pattern calibration rig, each pattern being repetitive and comprising calibration shapes, wherein the following steps are carried out: capturing an image of the multi-pattern calibration rig with the camera, segmenting contours of the imaged calibration shapes, building groups of coherent contours based on the similarity of their shapes and their distances from each other, and identifying the positions of the patterns in the image based on the contour groups.

A system that compares the images submitted with a preprocessed database containing pictures, drawings, and patent drawings, among other media. The images are interrelated by comparing the content of the patent images, the narrative in the patents with the other visual media which may or may not be pre-tagged.

An identification method includes: sensing movement data; capturing multiple feature data from the movement data; cutting the first feature data into a plurality of first feature segments, dividing the first feature segments into a plurality of first feature groups, and calculating multiple first similarity parameters of the first feature groups respectively corresponding to a plurality of channels; making the first feature groups correspond to the channels according to the first similarity parameters; simplifying the first feature groups corresponding to the channels respectively by a convolution algorithm to obtain a plurality of first convolution results corresponding to the first feature groups; simplifying the first convolution results corresponding to the first feature groups respectively by a pooling algorithm to obtain multiple first pooling results corresponding to the first feature groups; and combining the first pooling results corresponding to the first feature groups to generate a first feature map.

A method for determining lane markers includes providing a camera at a vehicle so as to have a field of view exterior of the vehicle. An image processor processes a frame of image data captured by the camera to determine intensity gradient information of captured image data and to determine lane markers. Contrast values at both sides of a center region of the determined lane marker are determined via processing of the intensity gradient information. An angle of the determined lane marker relative to the direction of travel of the vehicle is determined responsive to the determined contrast values. Processing of a subsequent frame of captured image data is adjusted responsive to the determined angle of the determined lane marker relative to the direction of travel of the vehicle.

A method and system for scene parsing and model fusion in laparoscopic and endoscopic 2D/2.5D image data is disclosed. A current frame of an intra-operative image stream including a 2D image channel and a 2.5D depth channel is received. A 3D pre-operative model of a target organ segmented in pre-operative 3D medical image data is fused to the current frame of the intra-operative image stream. Semantic label information is propagated from the pre-operative 3D medical image data to each of a plurality of pixels in the current frame of the intra-operative image stream based on the fused pre-operative 3D model of the target organ, resulting in a rendered label map for the current frame of the intra-operative image stream. A semantic classifier is trained based on the rendered label map for the current frame of the intra-operative image stream.

Systems, methods, and computer-readable storage media are provided for identifying (recognizing) an object from its shape in a sequence of images utilizing sequence alignment matrices (SAMs). For a given image, an object is segmented and from the segmented object, a set of key points is extracted. From the extracted key points, a set of local feature descriptors, strictly related to the key points and uniquely ordered in sequence, are extracted. The feature sequence obtained from the segmented object is aligned with a counterpart or reference image (e.g., a model or another image) using a Sequence Alignment Matrix (SAM). A custom scoring technique for the alignment provides a quality index for the identification of the object.

Described herein is a method for adjusting one or more images of a sample to correct geometric distortions and/or to properly align the one or more images using a pattern of dots, e.g., a quasiperiodic grid.