Abstract: A method of decoding a bitstream by an electronic device is provided. A block unit having a block size is determined from an image frame according to the bitstream. The number of one or more reference lines neighboring to the block unit is determined based on the block size. One of the one or more reference lines is selected based on the number of the one or more reference lines. The block unit is reconstructed based on the selected one of the one or more reference lines.

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: 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 of decoding a bitstream by an electronic device is provided. A block unit having a prediction mode is determined from an image frame according to the bitstream. The prediction mode is selected from a plurality of linear modes each including at least one model prediction. A reference set of the block unit is selected from a plurality of candidate sets. Each of the candidate sets includes a plurality of candidate locations selected from a plurality of neighboring locations neighboring to the block unit. A plurality of reference samples is determined from the selected candidate locations in the reference set. A linear model is derived for each of the at least one model prediction in the prediction mode based on the reference samples. The block unit is reconstructed based on the at least one linear model.

Abstract: A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.

Abstract: A method of decoding a bitstream by an electronic device is provided. A block unit having an intra prediction mode is determined from an image frame according to the bitstream. The intra prediction mode is determined based on a first one of a plurality of mode indices, when a reference line index (Refidx) is different from a first predefined value. The intra prediction mode is determined based on the first mode index, when the Refidx is equal to the first predefined value and a most probable mode (mpm) flag is equal to a second predefined value. The intra prediction mode is determined based on a second one of the mode indices, when the Refidx is equal to the first predefined value and the mpm flag is different from the second predefined value. Then, the block unit of the image frame is reconstructed based on the intra prediction mode.

Abstract: In scan data retrieval, a mesh is fit (32) to surface data of a current patient, such as data from an optical or depth sensor (18). Meshes are also fit (48) to medical scan data, such as fitting (48) to skin surface segments of computed tomography data. The meshes or parameters derived from the meshes may be more efficiently compared (34) to identify (36) a previous patient with similar body shape and/or size. The scan configuration (38) for that patient, or that patient as altered to account for differences from the current patient, is used. In some embodiments, the parameter vector used for searching (34) includes principle component analysis coefficients. In further embodiments, the principle component analysis coefficients may be projected to a more discriminative space using metric learning.

Abstract: A method is disclosed for adjusting a collimator of an X-ray source. In an embodiment, the method includes detecting an arrangement of an X-ray detector with respect to the X-ray source; automatically determining an adjustment for the collimator based on the detected position of the X-ray detector with respect to the X-ray source; and automatically adjusting the collimator based on the determined adjustment for the collimator. An X-ray device and computer readable medium are also disclosed.

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 method of characterizing a specimen for HILN (H, I, and/or L, or N). The method includes capturing images of the specimen at multiple different viewpoints, processing the images to provide segmentation information for each viewpoint, generating a semantic map from the segmentation information, selecting a synthetic viewpoint, identifying front view semantic data and back view semantic data for the synthetic viewpoint, and determining HILN of the serum or plasma portion based on the front view semantic data with an HILN classifier, while taking into account back view semantic data. 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 cap to identify the container cap. The method includes providing a specimen container including a container cap; capturing backlit images of the container cap taken at different exposures lengths and using a plurality of different nominal wavelengths; selecting optimally-exposed pixels from the images at different exposure lengths at each nominal wavelength to generate optimally-exposed image data for each nominal wavelength; classifying the optimally-exposed pixels as at least being one of a tube, a label or a cap; and identifying a shape of the container cap based upon the optimally-exposed pixels classified as being the cap and the image data for each nominal wavelength. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are numerous other aspects.

Abstract: A video encoding method includes receiving a source video frame, dividing the source video frame into a coding tree unit, determining a coding unit from the coding tree unit, enabling or disabling a coding mode of the coding unit, determining, if the coding mode is enabled, whether to evaluate a size of a transform unit for the enabled coding mode, and determining a transform unit of the coding unit for the enabled coding mode, wherein the size of the coding unit is defined by a number (N) of samples.

Abstract: A device, structure, and method are provided whereby an insert layer is utilized to provide additional support for surrounding dielectric layers. The insert layer may be applied between two dielectric layers. Once formed, trenches and vias are formed within the composite layers, and the insert layer will help to provide support that will limit or eliminate undesired bending or other structural motions that could hamper subsequent process steps, such as filling the trenches and vias with conductive material.

Abstract: A resistance sensing mechanism includes a resistance adjusting unit including a holder frame, a locating sleeve disposed above the holder frame, an adjustment screw rod rotatably inserted through the locating sleeve and a first screw nut threaded onto a first thread segment of the adjustment screw rod and pivotally connected to the holder frame, and a sensor unit including a linkage pivotally connected to the holder frame, a second screw nut threaded onto a second thread segment of the adjustment screw rod and pivotally connected to the linkage, a sensor mounted at one of the holder frame and the linkage and a sensible member mounted at the other of the holder frame and the linkage to face toward the sensor. Subject to the relative movement between the first and second screw nuts, the holder frame and the linkage relatively biased to change the distance between the sensor and sensible member.

Abstract: A method for training a learning-based medical scanner including (a) obtaining training data from demonstrations of scanning sequences, and (b) learning the medical scanner's control policies using deep reinforcement learning framework based on the training data.

Abstract: A method of decoding a bitstream by an electronic device is provided. A block unit is determined from an image frame according to the bitstream. A plurality of candidate modes included in a mode list are separated in a first mode group and a second mode group. The electronics device determines whether a specific one of the candidate modes in the first mode group is replaced by one of the candidate modes in the second mode group. When the specific candidate mode in the first mode group is replaced, a prediction mode is determined from the candidate modes in the second mode group. When the specific candidate mode in the first mode group remains unchanged, the specific candidate mode in the first mode group is determined as the prediction mode. Then, the block unit of the image frame is reconstructed based on the prediction mode.

Abstract: A method for estimating a body surface model of a patient includes: (a) segmenting, by a computer processor, three-dimensional sensor image data to isolate patient data from environmental data; (b) categorizing, by the computer processor, a body pose of the patient from the patient data using a first trained classifier; (c) parsing, by the computer processor, the patient data to an anatomical feature of the patient using a second trained classifier, wherein the parsing is based on a result of the categorizing; and (d) estimating, by the computer processor, the body surface model of the patient based on a result of the parsing. Systems for estimating a body surface model of a patient are described.

Abstract: Embodiments are directed to classifying barcode tag conditions on sample tubes from top view images to streamline sample tube handling in advanced clinical laboratory automation systems. The classification of barcode tag conditions leads to the automatic detection of problematic barcode tags, allowing for a user to take necessary steps to fix the problematic barcode tags. A vision system is utilized to perform the automatic classification of barcode tag conditions on sample tubes from top view images. The classification of barcode tag conditions on sample tubes from top view images is based on the following factors: (1) a region-of-interest (ROI) extraction and rectification method based on sample tube detection; (2) a barcode tag condition classification method based on holistic features uniformly sampled from the rectified ROI; and (3) a problematic barcode tag area localization method based on pixel-based feature extraction.

Abstract: A method for detecting properties of sample tubes is provided that includes extracting image patches substantially centered on a tube slot of a tray or a tube top in a slot. For each image patch, the method may include assigning a first location group defining whether the image patch is an image center, a corner of an image or a middle edge of an image, selecting a trained classifier based on the first location group and determining whether each tube slot contains a tube. The method may also include assigning a second location group defining whether the image patch is from an image center, a left corner of the image, a right corner of the image, a left middle of the image; a center middle of the image or a right middle of the image, selecting a trained classifier based on the second location group and determining a tube property.

Abstract: A method of decoding a bitstream by an electronic device is provided. A block unit having a block height and a block width is determined from an image frame according to the bitstream. A mode list including a plurality of intra modes is determined for reconstructing the block unit. At least one of the intra modes is selected from the mode list based on a comparison between the block height and the block width, and removed from the mode list to generate an adjusted list including a plurality of unselected modes. The block unit included in the image frame is reconstructed based on the adjusted list.