Abstract: Various approaches discussed herein enable browsing groups of visually similar items to an item of interest, wherein the item of interest may be identified in a query image, for example. One or more visual attributes associated with the item of interest are identified, and the visually similar items matching at least one of the visual attributes are grouped together, wherein the group is ranked according to the visually similar items' overall visual similarity to the item of interest, for example by using a visual similarity score and/or metric.

Abstract: Machine learning-based approaches are used to identify complementary sets of items, such as articles of clothing and accessories that “match,” and suggest items that would complement a given item of interest. A simulated representation of how the item of interest and the identified complementary items would look together is then generated. For example, given a particular piece of clothing or other apparel item of interest, additional items that complement the item of interest can be identified and suggested to a potential purchaser of the item. Additionally, a three-dimensional (3D) or pseudo-3D representation of a human body can be generated to model the apparel item of interest and the identified complementary apparel items to give the user an idea of how the suggested outfit would look on a user. The representation can be modified to more closely resemble a particular user.

Abstract: Various approaches discussed herein enable browsing groups of visually similar items to an item of interest, wherein the item of interest may be identified in a query image, for example. One ore more visual attributes associated with the item of interest are identified, and the visually similar items matching at least one of the visual attributes are grouped together, wherein the group is ranked according to the visually similar items' overall visual similarity to the item of interest, for example by using a visual similarity score and/or metric.

Abstract: A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

Abstract: A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

Abstract: An auxiliary device can be used to display a fiducial that contains information useful in determining the physical size of the fiducial as displayed on the auxiliary device. A primary device can capture image data including a representation of the fiducial. The scale and orientation of the fiducial can be determined, such that a graphical overlay can be generated of an item of interest that corresponds to that scale and orientation. The overlay can then be displayed along with the captured image data, in order to provide an augmented reality experience wherein the image displayed on the primary device represents a scale-appropriate view of the item in a location of interest corresponding to the location of the auxiliary device. As the primary device is moved and the viewpoint of the camera changes, changes in relative scale and orientation to the fiducial are determined and the overlay is updated accordingly.

Abstract: Approaches introduce a pre-processing and post-processing framework to a neural network-based approach to identify items represented in an image. For example, a classifier that is trained on several categories can be provided. An image that includes a representation of an item of interest is obtained. Rotated versions of the image are generated and each of a subset of the rotated images is analyzed to determine a probability that a respective image includes an instance of a particular category. The probabilities can be used to determine a probability distribution of output category data, and the data can be analyzed to select an image of the rotated versions of the image. Thereafter, a categorization tree can then be utilized, whereby for the item of interest represented the image, the category of the item can be determined. The determined category can be provided to an item retrieval algorithm to determine primary content for the item of interest.

Abstract: Methods and apparatus for analyzing, mapping and structuring healthcare data are disclosed. An example method includes parsing a clinical report to identify a parameter within the clinical report and associated with an image. The clinical report is associated with the image. The example method includes, when the parameter is identified in the parsing, analyzing the image to determine a value of the parameter, mapping the value, the image, and the clinical report to a model and generating an output to display one or more of the value, the image, or the clinical report.

Abstract: A method for obtaining multi-material decomposition images of an object is presented. The method includes acquiring an image pair from a dual energy computed tomography scan of the imaged object. The method then includes selecting a material basis for multi-material decomposition of the image pair. The method further includes applying a physicochemical model for the material basis. Also, the method includes performing multi-material decomposition using at least one constraint imposed by the physicochemical model.

Abstract: A method and system for visualizing regions in an image is provided. The method comprises computing a regional response around a region in the image, deriving a region score based on from the regional response for the region and labeling the region in the image by comparing the region score to a plurality of probabilistic models.

Abstract: An apparatus for multi-energy tissue quantification includes an x-ray imaging system comprises an x-ray source configured to emit a beam of x-rays toward an object to be imaged, a detector configured to receive the x-rays attenuated by the object, and a data acquisition system (DAS) operably coupled to the detector. A computer operably connected to the x-ray source and the DAS is programmed to cause the x-ray source to emit x-rays at each of a first kVp and a second kVp toward the detector, acquire x-ray data from x-rays emitted at the first and second kVp through a region of interest (ROI), and perform a first multi-material decomposition based on the acquired x-ray data. The computer is also programmed to quantify a volume fraction of a first material in the ROI based on the first multi-material decomposition and display the volume fraction of the first material to a user.

Abstract: Systems and methods for segmenting images comprising cells, wherein the images comprise a plurality of pixels; one or more three dimensional (3D) clusters of cells are identified in the images; and the 3D clusters of cells are automatically segmented into individual cells using one or more models.

Abstract: An apparatus for multi-energy tissue quantification includes an x-ray imaging system comprises an x-ray source configured to emit a beam of x-rays toward an object to be imaged, a detector configured to receive the x-rays attenuated by the object, and a data acquisition system (DAS) operably coupled to the detector. A computer operably connected to the x-ray source and the DAS is programmed to cause the x-ray source to emit x-rays at each of a first kVp and a second kVp toward the detector, acquire x-ray data from x-rays emitted at the first and second kVp through a region of interest (ROI), and perform a first multi-material decomposition based on the acquired x-ray data. The computer is also programmed to quantify a volume fraction of a first material in the ROI based on the first multi-material decomposition and display the volume fraction of the first material to a user.

Abstract: Methods and systems for determining whether a particular individual is to be granted, among other things, access to information, access to a particular room or other space, or permission to perform a given act or transaction are disclosed. For example, disclosed is an exemplary method that includes performing multiple biometrics tests on the individual to retrieve multiple biometrics test results, performing at least a first data fusion operation on the multiple biometrics test results to determine an identity result, performing at least one biological state test to determine one or more biological state test results of the individual, and conditionally granting authority to the individual based on the identity result and the one or more biological state results.

Abstract: A device for conducting rail car inspections including an inspection module, an imaging module, a scanner module, a location module, a printer module and a communication module.

Abstract: A device for conducting rail car inspections including an inspection module, an imaging module, a scanner module, a location module, a printer module and a communication module.

Abstract: An imaging system includes an x-ray source configured to emit a beam of x-rays toward an object to be imaged, a detector configured to receive x-rays that are attenuated by the object, a data acquisition system (DAS) operably coupled to the detector, and a computer operably coupled to the DAS and programmed to obtain scan data with two or more incident energy spectra, decompose the obtained scan data into at least three basis materials, generate an image of one of the at least three basis materials using the decomposed scan data, and replace at least one pixel in the image using decomposed data of another of the at least three basis materials.

Abstract: A device for conducting rail car inspections including an inspection module, an imaging module, a scanner module, a location module, a printer module and a communication module.

Abstract: A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

Abstract: The present invention aims at providing a method for detecting a color of a signal light from a moving vehicle such as railroad train. The method includes capturing an image from a camera mounted on the moving vehicle and extracting candidate regions in the image that contain light region. The method further includes classifying the candidate regions as a signal light region or a non-signal light region and identifying the color of the signal light region.