A method for deriving accurate body size measures of a user from a sequence of 2D images includes: a) automatically guiding the user through a sequence of body poses and motions; b) scanning the body of said user by obtaining a sequence of raw 2D images of said user as captured by at least one camera during said guided sequence of poses and motions; c) analyzing the behavior of said user to ensure that the user follows the provided instructions; d) extracting and encoding 2D shape data descriptors from said sequence of images by using a 2D shape analyzer (2DSA); and e) integrating said 2D shape descriptors and data representing the user's position, pose and rotation into a 3D shape model.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

A method for increasing the resolution of a series of low resolution frames of a low resolution video sequence to a series of high resolution frames of a high resolution video sequence includes receiving the series of low resolution frames of the video sequence. The system determines a first plurality of semantically relevant key points of a first low resolution frame of the series of low resolution frames and determines a second plurality of semantically relevant key points of a second low resolution frame of the series of low resolution frames. The system temporally processes the first plurality of key points based upon the second plurality of key points to determine a more temporally consistent set of key points for the first plurality of key points.

A method of automatic defect classification (ADC) includes detecting defective parts from a substrate wherein at least one unit process is performed; and classifying defect types of the respective defective parts, wherein the classifying includes obtaining a scanning electron microscope (SEM) image of each of the defective parts; registering information about the substrate in a graphic data system (GDS) image corresponding to each SEM image; defining a plurality of defects of interest (DOIs) categorizing defects of the respective defective parts; defining a DOI rule that is a criterion for determining which defects of the respective defective parts correspond to which DOI from among the DOIs; and analyzing the image to classify which defects of the respective defective parts correspond to which DOI from among the DOIs according to the DOI rule.

Locating morphology in a tissue sample is achieved with devices and methods involving storage of a plurality of feature vectors, each associated with a specific named superpixel of a larger image of a tissue sample from a mammalian body. A microscope outputs, in some embodiments, a live image of an additional tissue sample or a digitized version of the output is used. At least one superpixel of the image is converted into a feature vector and a nearest match between the first feature vector and the plurality of stored feature vectors is made. A first name suggestion is then made based on the nearest match comparison to a store feature vector. Further, regions of interest within the image can be brought to a viewer's attention based on their past history of selection, or that of others.

Disclosed are embodiments of in-situ display monitoring and calibration systems and methods. An image acquisition system captures images of the viewing plane of the display. Captured images may then be processed to characterize various visual performance characteristics of the display. When not in use capturing images of the display, the image acquisition system can be stored in a manner that protects it from environmental hazards such as dust, dirt, precipitation, direct sunlight, etc. A calibration image in which a plurality of light emitting elements is set to a particular color and intensity may be displayed, an image then captured, and then a difference between what was expected and what was captured may be developed for each light emitting element. Differences between captured images and expected images may be used to create a calibration data set which then may be used to adjust the display of further images upon the display.

A hand-held or otherwise portable or spatial or temporal performance-based image capture device includes one or more lenses, an aperture and a main sensor for capturing an original main image. A secondary sensor and optical system are for capturing a reference image that has temporal and spatial overlap with the original image. The device performs an image processing method including capturing the main image with the main sensor and the reference image with the secondary sensor, and utilizing information from the reference image to enhance the main image. The main and secondary sensors are contained together within a housing.

According to one embodiment, a computer-implemented method includes: capturing an image of a document using a camera of a mobile device; performing optical character recognition (OCR) on the image of the document; extracting an identifier of the document from the image based at least in part on the OCR; comparing the identifier with content from one or more reference data sources, wherein the content from the one or more reference data sources comprises global address information; and determining whether the identifier is valid based at least in part on the comparison. The method may optionally include normalizing the extracted identifier, retrieving additional geographic information, correcting OCR errors, etc. based on comparing extracted information with reference content. Corresponding systems and computer program products are also disclosed.

A source deconvolutional network is adaptively trained to perform semantic segmentation. Image data is then input to the source deconvolutional network and outputs of the S-Net are measured. The same image data and the measured outputs of the source deconvolutional network are then used to train a target deconvolutional network. The target deconvolutional network is defined by a substantially fewer numerical parameters than the source deconvolutional network.

A fault diagnosis method for an electrical fused magnesia furnace includes steps of: 1) arranging six cameras; 2) obtaining video information by the six cameras and sending the video information to a control center; then analyzing the video information by a chip of the control center; wherein a multi-view-based fault diagnosis method is used by the chip, comprising steps of: 2-1) comparing a difference between two consecutive frame histograms for shots segmentation; 2-2) computing a set of characteristic values for each shot obtained by the step 2-1), and then computing color, texture, and motion vector information; finally, evaluating shot importance via entropy; 2-3) clustering shots together by calculating similarity; 2-4) generating and optimizing a multi-view video summarization with a multi-objective optimization model; and 2-5) providing fault detection and diagnosis; and 3) displaying results of the fault detection and diagnosis on a host computer inter face of the control center.