Abstract: A system and method includes creation of a combined network comprising an image segmentation network and an image representation network, the combined network to generate an image descriptor based on an input query image, training of the combined network based on a plurality of first images and a segmentation mask associated with each of the plurality of first images, reception of a first input query image, use of the combined network to generate an image descriptor based on the first input query image, determination of a matching image descriptor from a plurality of stored image descriptors, determination of a camera pose associated with the matching image descriptor, registration of the first input query image with image data based on the determined camera pose, generation of a composite image based on the registered first input query image and image data, and presentation of the composite image.

Abstract: A system and method includes creation of a combined network comprising an image segmentation network and an image representation network, the combined network to generate an image descriptor based on an input query image, training of the combined network based on a plurality of first images and a segmentation mask associated with each of the plurality of first images, reception of a first input query image, use of the combined network to generate an image descriptor based on the first input query image, determination of a matching image descriptor from a plurality of stored image descriptors, determination of a camera pose associated with the matching image descriptor, registration of the first input query image with image data based on the determined camera pose, generation of a composite image based on the registered first input query image and image data, and presentation of the composite image.

Abstract: The present embodiments relate to localizing a mobile device in a complex, three-dimensional scene. By way of introduction, the present embodiments described below include apparatuses and methods for using multiple, independent pose estimations to increase the accuracy of a single, resulting pose estimation. The present embodiments increase the amount of input data by windowing a single depth image, using multiple depth images from the same sensor, and/or using multiple depth image from different sensors. The resulting pose estimation uses the input data with a multi-window model, a multi-shot model, a multi-sensor model, or a combination thereof to accurately estimate the pose of a mobile device.

Abstract: A system and method includes generation of a first map of first descriptors based on pixels of a first two-dimensional depth image, where a location of each first descriptor in the first map corresponds to a location of a respective pixel of a first two-dimensional depth image, generation of a second map of second descriptors based on pixels of the second two-dimensional depth image, where a location of each second descriptor in the second map corresponds to a location of a respective pixel of the second two-dimensional depth image, upsampling of the first map of descriptors using a first upsampling technique to generate an upsampled first map of descriptors, upsampling of the second map of descriptors using a second upsampling technique to generate an upsampled second map of descriptors, generation of a descriptor difference map based on differences between descriptors of the upsampled first map of descriptors and descriptors of the upsampled second map of descriptors, generation of a geodesic preservation m

Abstract: A model-based method of inspecting a specimen for presence of an interferent (H, I, and/or L). The method includes capturing images of the specimen at multiple different exposures times and at multiple spectra having different nominal wavelengths, selection of optimally-exposed pixels from the captured images to generate optimally-exposed image data for each spectra, identifying a serum or plasma portion of the specimen, and classifying whether an interferent is present or absent within the serum or plasma portion. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.

Abstract: A model-based method of classifying a specimen in a specimen container. The method includes capturing images of the specimen and container at multiple different exposures times, at multiple different spectra having different nominal wavelengths, and at different viewpoints by using multiple cameras. From the captured images, 2D data sets are generated. The 2D data sets are based upon selection of optimally-exposed pixels from the multiple different exposure images to generate optimally-exposed image data for each spectra. Based upon these 2D data sets, various components are classified using a multi-class classifier, such as serum or plasma portion, settled blood portion, gel separator (if present), tube, air, or label. From the classification data and 2D data sets, a 3D model can be generated. Specimen testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.

Abstract: A model-based method of determining characteristics of a specimen container. The method includes providing a specimen container, capturing images of the specimen container at different exposures times and at different spectra having different nominal wavelengths, selecting optimally-exposed pixels from the images at different exposure times at each spectra to generate optimally-exposed image data for each spectra, and classifying the optimally-exposed pixels as at least being one of tube, label or cap, and identifying a width, height, or width and height of the specimen container based upon the optimally-exposed image data for each spectra. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.

Abstract: A system includes: a movable X-ray tube scanner; a range sensor movable with the X-ray tube scanner; an X-ray detector positioned to detect X-rays from the X-ray tube passing through a standing subject between the X-ray tube and the X-ray detector; and a processor configured for automatically controlling the X-ray tube scanner to transmit X-rays to a region of interest of the patient while the subject is standing between the X-ray tube and the X-ray detector.

Abstract: Methods and systems for detecting properties of sample tubes in a laboratory environment include a drawer vision system that can be trained and calibrated. Images of a tube tray captured by at least one camera are analyzed to extract image patches that allow a processor to automatically determine if a tube slot is occupied, if the tube has a cap, and if the tube has a tube top cup. The processor can be trained using a random forest technique and a plurality of training image patches. Cameras can be calibrated using a three-dimensional calibration target that can be inserted into the drawer.

Abstract: Reconstructing 3-D vessel geometry of a vessel includes: receiving a plurality of 2-D rotational X-ray images of the vessel; extracting vessel centerline points for normal cross sections of each of the plurality of 2-D images; establishing a correspondence of the centerline points from a registration of the centerline points with a computed tomography (CT) 3-D centerline, the registration being an affine or deformable transformation; constructing a 3-D centerline vessel tree skeleton of the vessel from the centerline points of the 2-D images; constructing an initial 3-D vessel surface having a uniform radius normal to the 3-D centerline vessel tree skeleton; defining sample points based sampling on median radii to the 3-D centerline vessel tree skeleton of the initial 3-D vessel surface; and constructing a target 3-D vessel surface by deforming the initial vessel surface using the sample points to provide a reconstructed 3-D vessel geometry of the vessel.

Abstract: A system and method includes acquisition of first surface data of a patient in a first pose using a first imaging modality, acquisition of second surface data of the patient in a second pose using a second imaging modality, combination of the first surface data and the second surface data to generate combined surface data, for each point of the combined surface data, determination of a weight associated with the first surface data and a weight associated with the second surface data, detection of a plurality of anatomical landmarks based on the first surface data, initialization of a first polygon mesh by aligning a template polygon mesh to the combined surface data based on the detected anatomical landmarks, deformation of the first polygon mesh based on the combined surface data, a trained parametric deformable model, and the determined weights, and storage of the deformed first polygon mesh.

Abstract: Embodiments of a medical imaging device and a method controlling one or more parameters of a medical imaging device are disclosed. In an embodiment, a method includes receiving image data representing a first image of an object to be imaged using the radiation source and detecting a plurality of positions of respective predetermined features in the first image. Based upon the detected positions, a boundary of an imaging area of the object to be imaged is determined. Based on the determined boundary, one or more parameters of the radiation source unit are controlled.

Abstract: An automation method is disclosed for an X-ray tube scanner having an X-ray tube and an X-ray detector. The method allows the X-ray tube scanner to detect the X-ray detector's plane with an object to be imaged placed on the X-ray detector; determine a boundary box of the object to be imaged on the X-ray detector; determine the object's center position and orientation on the X-ray detector's plane; transfer the object's center position from the object's coordinate system to the X-ray tube's coordinate system; and estimate the X-ray tube control parameters for aligning the X-ray field emitted from the X-ray tube's collimator to the object's center position and the object's orientation on the X-ray detector.

Abstract: A database stores reference photographs of an assembly. The reference photographs are from different orientations relative to the assembly. By matching the query photograph to one or more of the reference photographs, the pose of the assembly in the query photograph is determined. Based on the pose, the pixels of the two-dimensional query photograph are related to a three-dimensional representation from engineering data. Using labeled parts from the engineering data, the parts represented in the query photograph are identified, and part information (e.g., segmentation, number, or other metadata) is provided relative to the query photograph.

Abstract: A method for inspecting an object to assist in determining whether the object has a surface defect. The method includes moving the object in a first direction and illuminating the object under ambient lighting conditions. The method also includes capturing at least one image of the object under the ambient lighting conditions while the object moves in the first direction. In addition, the object is illuminated under object lighting conditions and at least one image of the object under the object lighting conditions is captured while the object moves in the first direction to provide at least one object image. Further, the method includes selecting at least one object image having at least one indication of a possible defect to provide images having defect candidates and comparing the defect candidates with previously defined characteristics associated with the defect to facilitate determination of whether a defect exists.

Abstract: Multi-source, multi-type image registration is provided. Images are received from a plurality of image devices, and images are received from a medical imaging device. A pre-existing diagram of a probe of the medical imaging device is received. A four-dimensional model is determined based on the received images from the image devices. A pose of the probe of the medical imaging device is determined based on the pre-existing diagram of the probe and the received images from the image devices. The plurality of images from the medical imaging device are registered with the four-dimensional model based on a common coordinate system and the determined pose of the probe.

Abstract: A method and system for classification of endoscopic images is disclosed. An initial trained deep network classifier is used to classify endoscopic images and determine confidence scores for the endoscopic images. The confidence score for each endoscopic image classified by the initial trained deep network classifier is compared to a learned confidence threshold. For endoscopic images with confidence scores higher than the learned threshold value, the classification result from the initial trained deep network classifier is output. Endoscopic images with confidence scores lower than the learned confidence threshold are classified using a first specialized network classifier built on a feature space of the initial trained deep network classifier.

Abstract: Barcode tag conditions on sample tubes are detected utilizing side view images of sample tubes for streamlining handling in clinical laboratory automation systems. The condition of the tags may be classified into classes, each divided into a list of additional subcategories that cover individual characteristics of the tag quality. According to an embodiment, a tube characterization station (TCS) is utilized to obtain the side view images. The TCS enables the simultaneous or near-simultaneous collection of three images for each tube, resulting in a 360 degree side view for each tube. The method is based on a supervised scene understanding concept, resulting in an explanation of each pixel into its semantic meaning. Two parallel low-level cues for condition recognition, in combination with a tube model extraction cue, may be utilized. The semantic scene information is then integrated into a mid-level representation for final decision making into one of the condition classes.

Abstract: A method and a system for automatically aligning a positionable X-ray source of an X-ray system in alignment with a mobile X-ray detector is disclosed where the X-ray system detects the position of the mobile X-ray detector using a 3D camera and then driving the positionable X-ray source to a position in alignment with the mobile X-ray detector.

Abstract: A method for performing cellular classification includes generating a plurality of local dense Scale Invariant Feature Transform (SIFT) features based on a set of input images and converting the plurality of local dense SIFT features into a multi-dimensional code using a feature coding process. A first classification component is used to generate first output confidence values based on the multi-dimensional code and a plurality of global Local Binary Pattern Histogram (LBP-H) features are generated based on the set of input images. A second classification component is used to generate second output confidence values based on the plurality of LBP-H features and the first output confidence values and the second output confidence values are merged. Each of the set of input images may then be classified as one of a plurality of cell types using the merged output confidence values.