A method, computer system, and a computer program product for identifying sections in a document based on a plurality of visual features is provided. The present invention may include receiving a plurality of documents. The present invention may also include extracting a plurality of content blocks. The present invention may further include determining the plurality of visual features. The present invention may then include grouping the extracted plurality of content blocks into a plurality of categories. The present invention may also include generating a plurality of closeness scores for the plurality of categories by utilizing a Visual Similarity Measure. The present invention may further include generating a plurality of Association Matrices on the plurality of categories for each of the received plurality of documents based on the Visual Similarity Measure. The present invention may further include merging the plurality of categories into a plurality of clusters.

A method of label detection includes: obtaining, by an imaging controller, an image depicting a shelf; increasing an intensity of a foreground subset of image pixels exceeding an upper intensity threshold, and decreasing an intensity of a background subset of pixels below a lower intensity threshold; responsive to the increasing and the decreasing, (i) determining gradients for each of the pixels and (ii) selecting a candidate set of the pixels based on the gradients; overlaying a plurality of shelf candidate lines on the image derived from the candidate set of pixels; identifying a pair of the shelf candidate lines satisfying a predetermined sequence of intensity transitions; and generating and storing a shelf edge bounding box corresponding to the pair of shelf candidate lines.

Provided is a segment-block-based handwritten signature authentication system and a method thereof, and more particularly, to a handwritten signature authentication system and a method thereof that enrolls a handwritten signature including handwritten signature characteristics information based on segment blocks disjointed by a user, acquires segment-block-based handwritten signature characteristics information from the handwritten signature upon request for handwritten signature authentication, and performs handwritten signature authentication by comparing the pre-enrolled handwritten signature characteristics information based on segments and the acquired handwritten signature characteristics information.

An image comparison method constituted of: receiving images; point matching the images to identify tentatively corresponding points; responsive to the identified points, defining a plurality of first image tiles within the first image; defining a plurality of second image tiles within the second image, each corresponding to a respective first image tile; adjusting the intensity of a set of pixels responsive to the cumulative relative frequency of the respective pixel intensity within the respective tile; for each tile, applying a plurality of non-linear functions; for each function, separately determining a moment of the outcome of the respective non-linear function for each axis of the respective tile; for each first image tile and corresponding second image tile, determining a distance between the subspaces spanned by moment vectors; and responsive to determined distances, determining whether respective first and second image tiles comprise observations of the same portion.

An image matching technique locates feature points in a template image such as a logo and then does the same in a test image. Feature points from the template image are then matched to the feature points in the test image. An additional matching technique boosts the number of points that match each other. The additional points improve the match quality and help discriminate true from false positive matches.

An image similarity determination apparatus includes a memory and a processor configured to acquire a first image and a second image, perform selection of a first group and a second group from a plurality of feature points included in the first image and perform selection of a third group and a fourth group from a plurality of feature points included in the second image, calculate feature quantity for each feature point included in the first group and the third group on the basis of luminance and calculate feature quantity for each feature point included in the second group and the fourth group on the basis of hue, and determine similarity between the first image and the second image on the basis of both first comparison of the first group with the third group and second comparison of the second group with the fourth group.

The disclosed technology generally relates to detecting and identifying objects in digital images, and more particularly to detecting, identifying and/or tracking moving objects in video images using an artificial intelligence neural network configured for deep learning. In one aspect, a method comprises capturing a video input from a scene comprising one or more candidate moving objects using a video image-capturing device, where the video input comprises at least two temporally spaced image frames captured from the scene. The method additionally includes transforming the video input into one or more image pattern layers, where each of the image pattern layers comprises a pattern representing one of the candidate moving objects. The method additionally includes determining a probability of match between each of the image pattern layers and a stored image in a big data library. The method additionally includes adding one or more image pattern layers having the probability of match that exceeds a predetermined level to the big data library automatically, and outputting the probability of match to a user.

A hardness tester includes a memory associating and storing a parts program having defined measurement conditions with respect to a sample, including a test position, and an image file acquired by capturing an image of the shape of the sample; an image acquirer acquiring image data of the sample to be measured; a pattern matcher performing a pattern matching process on the image data of the sample using the image file associated with the parts program; a determiner determining whether an image file exists which has a shape related to the image data of the sample; a retriever retrieving the parts program associated with the image file having a related shape; and a measurer measuring hardness of the sample based on the retrieved parts program.

A system includes a memory and at least one processor to acquire a hyperspectral image of a food object by an imaging device, the hyperspectral image of the food object comprising a three-dimensional set of images of the food object, each image in the set of images representing the food object in a wavelength range of the electromagnetic spectrum, normalize the hyperspectral image of the food object, select a region of interest in the hyperspectral image, the region of interest comprising a subset of at least one image in the set of images, extract spectral features from the region of interest in the hyperspectral image, and compare the spectral features from the region of interest with a plurality of images in a training set to determine particular characteristics of the food object and determine that the hyperspectral image indicates a foreign object.

Disclosed herein is a computing system for performing medical image analysis. A computing system for performing medical image analysis according to an embodiment of the present invention includes at least one processor. The at least one processor performs image processing on a first medical image, and segments at least one anatomical region in the first medical image. The at least one processor generates a first quantitative parameter for the at least one anatomical region based on quantitative measurement conditions that can be measured in the first medical image, and stores the first quantitative parameter in a database in association with the first medical image and the at least one anatomical region.