Provided is an apparatus for recognizing user postures. The apparatus recognizes detailed postures such as a stand posture, a bending forward posture, a bending knees posture, a tilt right posture, and a tilt left posture, based on a variation between body information at a previous time and body information at a current time.

Disclosed is a method for image registration performed by a computing device including at least one processor according to some exemplary embodiments of the present disclosure. The method for image registration may include: determining whether to perform preprocessing on a first image and a second image, based on at least one of the number of first pixels of the first image or the number of second pixels of the second image; when performing the preprocessing, generating a first divided image and a second divided image from each of the first image and the second image through a preprocessing process; and registering the first image and the second image, based on the first divided image and the second divided image.

A dimensioning system can include stored data indicative of coordinate locations of each reference element in a reference image containing a pseudorandom pattern of elements. Data indicative of the coordinates of elements appearing in an acquired image of a three-dimensional space including an object can be compared to the stored data indicative of coordinate locations of each reference element. After the elements in the acquired image corresponding to the reference elements in the reference image are identified, a spatial correlation between the acquired image and the reference image can be determined. Such a numerical comparison of coordinate data reduces the computing resource requirements of graphical comparison technologies.

This invention is related to a method to improve the performance of a deep neural network (10) for the identification of a segmentation target (111) in a medical image (12, 110), comprising the steps of performing n training steps on said deep neural network (10) for the identification of said region of interest on two different representations (13, 14) of the same segmentation target (111), said representations (13,14) being definitions of the same segmentation target (111).

Disclosed are an information providing method and system. In an embodiment, the method includes: acquiring product data and/or historical operation data; determining at least one feature description tag of an industrial product based upon the product data, and taking the at least one feature description tag of the industrial product as a feature of the industrial product; and/or determining at least one feature description tag of a user based upon the historical operation data, and taking the at least one feature description tag of the user as a feature of the user. The method further includes sending the feature of the industrial product and/or the feature of the user to a human-machine interactive device for display on a human-machine interactive interface. An embodiment further provides a reference suggestion for operative control of the industrial product by the user.

A method (100) for comparing a first video shot (Vs1) comprising a first set of first images (I1(s)) with a second video shot (Vs2) comprising a second set of second images (I2(t)), at least one between the first and the second set comprising at least two images. The method comprises pairing (110) each first image of the first set with each second image of the second set to form a plurality of images pairs (IP(m)), and, for each image pair, carrying out the operations a)-g): a) identifying (120) first interest points in the first image and second interest points in the second image; b) associating (120) first interest points with corresponding second interest points in order to form corresponding interest point matches; c) for each pair of first interest points, calculating (130) the distance therebetween for obtaining a corresponding first length; d) for each pair of second interest points, calculating (130) the distance therebetween for obtaining a corresponding second length; e) calculating a plurality of distance ratios (130), each distance ratio corresponding to a selected pair of interest point matches and being based on a ratio of a first term and a second term or on a ratio of the second term and the first term, said first term corresponding to the distance between the first interest points of said pair of interest point matches and said second term corresponding to the distance between the second interest points of said pair of interest point matches; f) computing (140) a first representation of the statistical distribution of the plurality of calculated distance ratios; g) computing (150) a second representation of the statistical distribution of distance ratios obtained under the hypothesis that all the interest point matches in the image pair are outliers. The method further comprises generating (160) a first global representation of the statistical distribution of the plurality of calculated distance ratios computed for all the image pairs based on the first representations of all the image pairs; generating (170) a second global representation of the statistical distribution of distance ratios obtained under the hypothesis that all the interest point matches in all the image pairs are outliers based on the second representations of all the image pairs; comparing (180) said first global representation with said second global representation, and assessing (190) whether the first video shot contains a view of an object depicted in the second video shot based on said comparison.

Disclosed embodiments facilitate keypoint selection in part by assigning a similarity score to each candidate keypoint being considered for selection. The similarity score may be based on the maximum measured similarity of an image patch associated with a keypoint in relation to an image patch in a local image section in a region around the image patch. A subset of the candidate keypoints with the lowest similarity scores may be selected and used to detect and/or track objects in subsequent images and/or to determine camera pose.

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.

Comprehensive 2D learning images are collected for learning subjects. Standardized 2D gallery images of many gallery subjects are collected, one per gallery subject. A 2D query image of a query subject is collected, of arbitrary viewing aspect, illumination, etc. 3D learning models, 3D gallery models, and a 3D query model are determined from the learning, gallery, and query images. A transform is determined for the selected learning model and each gallery model that yields or approximates the query image. The transform is at least partly 3D, such as 3D illumination transfer or 3D orientation alignment. The transform is applied to each gallery model so that the transformed gallery models more closely resemble the query model. 2D transformed gallery images are produced from the transformed gallery models, and are compared against the 2D query image to identify whether the query subject is also any of the gallery subjects.

A computer-implemented method and system register plural images using a computer processor and computer memory, where computer code stored in the computer memory causes the computer processor to perform the registration. The registration includes receiving a reference image; receiving a sensed image; computing an approximate image transformation between the reference image and the sensed image; finding plural candidate tie points by utilizing template matching; applying radiometric filtering to at least a portion of the plural candidate tie points; applying geometric filtering to at least a portion of the plural candidate tie points; computing a calculated image transformation using candidate points of the plural candidate points that were not filtered out by the radiometric and geometric filtering; transforming the sensed image using the calculated image transformation; and registering the sensed image with the reference image.