In some embodiments, a method includes receiving an image of produce and an image of marking. The image of produce has a set of pixels, each associated with a position and a color value. The method further includes generating a grayscale image from the image of produce. The method further includes cropping out a portion from the grayscale image. The method further includes locating a marking position pixel on the image of produce by: (a) producing a list of pixels that are part of the cropped portion, (b) selecting, from the list of pixels, a subset of pixels having grayscale pixel values above a threshold, and (c) randomly selecting the marking position pixel from the subset of pixels. The method further includes overlaying the image of marking on the image of produce by coinciding a pixel of the image of marking with the marker position pixel.

Vision-assist devices and methods are disclosed. In one embodiment, a vision-assist device includes an image sensor for generating image data corresponding to a scene, a processor, and an inertia measuring unit. The inertia measuring unit is configured to measure forces acting on the image sensor and the orientation of the image sensor. The processor is configured to receive the image data from the image sensor and process the force and orientation of the image sensor so as to determine a tilt of the image sensor. The processor is further configured to process the image data based on at least the tilt of the at least one image sensor so as to generate a corrected image data, wherein the corrected image data does not include the tilt.

A liveness detection method includes: obtaining multiple first feature points and multiple second feature points according to a first image, a second image, and a feature point process, wherein the first feature points include two first fixed feature points and at least one first variable feature point, and the second feature points include two second fixed feature points and at least one second variable point; obtaining a first transform function according to the first fixed feature points and the second fixed feature points; obtaining at least one check feature point according to the first transform function and the first variable feature point(s); and determining whether the second image is a spoofing image according to the check feature point(s) and the second variable point(s).

According to the present disclosure, edges of a document image contained in a read image read by an image-reading apparatus including a paper path are detected, a candidate edge-skew occurrence position is detected, a candidate content-skew occurrence position is detected, an accumulated-skew occurrence position is detected based on the candidate edge-skew occurrence position and the candidate content-skew occurrence position, skew of a document rear-part image rearward from the accumulated-skew occurrence position in the conveyance direction is corrected, and image data corrected for accumulated skew is acquired.

An electronic device is provided. The electronic device includes a display, an input sensing circuit capable of acquiring a handwriting input inputted through the display, and at least one processor electrically connected to the display and the input sensing circuit, wherein the at least one processor uses the input sensing circuit to recognize written characters generated by means of the handwriting input inputted through the display, determines the slope of the recognized written characters, horizontally aligns the recognized written characters on the basis of the determined slope, and controls the display so that the recognized written characters and the horizontally aligned written characters are overlapped and displayed.

A device may receive document image data that includes an image of a document to be digitized. The device may detect, from the document image data, a table of information that is depicted in the image. The device may determine a data extraction score associated with a table image, wherein the data extraction score is associated with using a data conversion technique to convert the table image to digitized table data. The device may perform, based on the data extraction score not satisfying a threshold, a morphological operation on the table image to generate an enhanced table image that corresponds to an enhanced table of information associated with the table of information. The device may process, using the data conversion technique, the enhanced table image to extract the information from the enhanced table. The device may perform an action associated with the extracted information.

Deskew for image review, such as SEM review, aligns inspection and review coordinate systems. Deskew can be automated using design files or inspection images. A controller that communicates with a review tool can align a file of the wafer, such as a design file or an inspection image, to an image of the wafer from the review tool; compare alignment sites of the file to alignment sites of the image from the review tool; and generate a deskew transform of coordinates of the alignment sites of the file and coordinates of alignment sites of the image from the review tool. The image of the wafer may not contain defects.

Generating a table with at least one skewed row, skewed column, shifted row, or shifted column is described. A table generation system generates a table that includes cells arranged in a grid comprising rows and columns, and defines each cell using a grid address, a grid span, a grid angle, a string skew value, a string shift value, and a shift indicator for the cell. The table generation system may receive input modifying a grid angle for at least one row or column and generate a modified table by skewing cells included in the at least one row or column by the grid angle. The table generation system may additionally or alternatively receive input shifting at least one row or column by a string shift value and modify the display of the table by shifting the at least one row or column according to the string shift value.

A method for reconstructing a three-dimensional space scene based on photographing is disclosed, comprising the following steps: S1, importing photos of all spaces, and making the photos correspond to a three-dimensional space according to directions and viewing angles during capture, so that a viewing direction of each pixel, when viewed from the camera position of the three-dimensional space, is in line with that during capture; S2, regarding a room as a set of multiple planes, determining a first plane, and then determining all the planes one by one according to relationships and intersections between the planes; S3, marking a spatial structure of the room by a marking system and obtaining dimension information; and S4, establishing a three-dimensional space model of the room by point coordinate information collected in the step S3. In the present disclosure, the three-dimensional space model of a scene including dimensions and texture can be restored with no details lost; meanwhile, the three-dimensional space scene can be edited and modified quickly and conveniently, and a two-dimensional floor plan with dimensions and adequate accuracy can also be generated.

The disclosed system and method relate to automatically detecting empty spaces on retail store shelves, identifying the missing product(s) and causing the space to be replenished or restocked. For example, stores may use shelf-mounted imaging devices to capture images of shelves across the aisle from the imaging devices. The images captured by the imaging devices may be pre-processed to de-warp, de-skew images and stitch together multiple images in order to retrieve an image that captures a full width of a shelf. The pre-processed images can then be used to detect products on the shelf, identify the detected products. For example, the captured image may be compared against a reference background image using a background modeling algorithm to identify empty spaces and mis-shelved items within the shelf.