Abstract: A method for correcting projection images in CT image reconstruction is provided. The method may include obtaining a plurality of projection images of a subject. Each of the plurality of projection images may correspond to one of the plurality of gantry angles. The method may further include correcting a first projection image of the plurality of projection images according to a process for generating a corrected projection image. The process may include performing, based on the first projection image and a second projection image of the plurality of projection images, a first correction on the first projection image to generate a preliminary corrected first projection image. The process may also include performing, based on at least part of the preliminary corrected first projection image, a second correction on the preliminary corrected first projection image to generate a corrected first projection image corresponding to the first gantry angle.

Abstract: A pixel value calibration method includes: obtaining input image data generated by pixels, the input image data including a first group of pixel values in a first color plane and a second group of pixel values in a second color plane, generated by a first portion and a second portion of the pixels respectively; determining a difference function associated with filter response values and target values, the filter response values being generated by utilizing characteristic filter coefficients to filter first and second estimated pixel values of estimated pixel data in the first and second color planes, respectively; determining a set of calibration filter coefficients by calculating a solution of the estimated pixel data, the solution resulting in a minimum value of the difference function; and filtering the input image data, by a filter circuit using the set of calibration filter coefficients, to calibrate the first group of pixel values.

Abstract: Automated camera-based optical assessment involves color assessment of a physical object using conventional and inexpensive computer hardware such as a smartphone. A specially-configured test card includes a body supporting a reagent pad configured to change to an expected color in response to an enzymatic reaction, and an imaging key adjacent the reagent pad. The imaging key includes color fields including at least one field of the expected color. The hardware captures an image of the test card, and processes the image to identify the reagent pad and color fields, to process a brightness calibration target, to determine color values for the reagent pad and color fields, to calibrate the color values as a function of brightness and/or color by comparison to the brightness and color calibration targets, and to identify a color field most closely matching the reagent pad's color to determine a corresponding test result.

Abstract: A system and method for calibrating a machine vision system on the undercarriage of a rail vehicle while the rail vehicle is in the field is presented. The system enables operators to calibrate the machine vision system without having to remove the machine vision system from the undercarriage of the rail vehicle. The system can capture, by a camera of an image capturing module, a first image of a target. The image capturing module and a drum can be attached to a fixture and the target can be attached to the drum. The system can also determine a number of lateral pixels in a lateral pitch distance of the image of the target, determining a lateral object pixel size based on the number of lateral pixels, and determining a drum encoder rate based on the lateral object pixel size. The drum encoder rate can be programmed into a drum encoder.

Abstract: A debanding determination method for an image and a debanding determination circuit thereof are provided. The method includes: calculating change amounts between color components respectively corresponding to a start pixel, an end pixel, and at least one intermediate pixel in a plurality of pixels of the image; adjusting the change amounts according to weight values corresponding to the color components, and calculating a weighted total change amount according to the adjusted change amounts; calculating change counts between the color components of the start pixel, the end pixel, and the at least one intermediate pixel; and determining whether to perform debanding compensation on each of the color components of the start pixel by using the end pixel as a reference pixel.

Abstract: This application relates to apparatus and methods for optimizing hyperparameters for machine learning models. In some examples, a computing device configures a machine learning model with a first set of hyperparameters from a pool of hyperparameters. The computing device may execute the machine learning model to generate a validation score, and may update parameters of a probability determination model based on the validation score. Further, the computing device may execute the probability determination model to generate probabilities corresponding to the first set of hyperparameters. The computing device may also determine a second set of hyperparameters from the pool of hyperparameters based on the generated probabilities, and may configure the machine learning model with the second set of hyperparameters.

Abstract: A portable terminal includes an extraction unit and a display control unit. The extraction unit extracts a part of a display image displayed on a touch panel display, as an extraction image used as a compositing target image. The display control unit, in a case where a plurality of regions having different areas are included in the extraction image used as the compositing target image, removes at least one of the plurality of regions in order of area based on an operation amount of an operation input by a user.

Abstract: Systems, apparatuses, and methods for converting data to a tiling format when implementing convolutional neural networks are disclosed. A system includes at least a memory, a cache, a processor, and a plurality of compute units. The memory stores a first buffer and a second buffer in a linear format, where the first buffer stores convolutional filter data and the second buffer stores image data. The processor converts the first and second buffers from the linear format to third and fourth buffers, respectively, in a tiling format. The plurality of compute units load the tiling-formatted data from the third and fourth buffers in memory to the cache and then perform a convolutional filter operation on the tiling-formatted data. The system generates a classification of a first dataset based on a result of the convolutional filter operation.

Abstract: A method includes generating a delicate area map by performing a morphological function on a portion of a received first image and identifying a plurality of edges in the first image, the plurality of edges comprising a plurality of pixels. The method also includes verifying a first contrast metric for a first subset of pixels that are in the plurality of pixels but not in the delicate area map, verifying a second contrast metric for a second subset of pixels that are in the plurality of pixels and in the delicate area map, and generating a validation result based on the verifying of the first contrast metric and the verifying of the second contrast metric.

Abstract: A computer-implemented method for estimating a vehicle pose for a moving vehicle is described includes obtaining, via a processor disposed in communication with a monochromatic camera, a monochromatic image of an operating environment, and detecting in the monochromatic image an event patch showing a plurality of pixels associated with the moving vehicle. The method further includes generating an optical flow map using an unsupervised optical flow prediction network to predict an optical flow for each pixel in the monochromatic image. The optical flow map includes a Red-Green-Blue (RGB) patch having color information associated with a velocity for the moving vehicle. The system generates a pixel-level event mask that includes the RGB patch, and estimates the vehicle pose for the moving vehicle.

Abstract: Initial low-quality images of a progressively-displayed high-definition image are masked with corresponding progressively-revealing mask filters or masking algorithms to realistically obscure such low quality and therefore to provide a realistically appearing progressive presentation of the high-definition image.

Abstract: A method, system and computer-program product for identifying neural network inputs for a neural network that may have been incorrectly processed by the neural network. A set of activation values (of a subset of neurons of a single layer) associated with a neural network input is obtained. A neural network output associated with the neural network input is also obtained. A determination is made as to whether a first and second neural network input share similar sets of activation values, but dissimilar neural network outputs or vice versa. In this way a prediction can be made as to whether one of the first and second neural network inputs has been incorrectly processed by the neural network.

Abstract: A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.

Abstract: An image processing method includes image inputting, auxiliary information inputting, image processing, feature quantity extracting, and discriminating. The image inputting is inputting a cell image. The auxiliary information inputting is inputting auxiliary information on a color of the cell image. The image processing is generating multiple processed images by performing a different image-processing process on the cell image based on the auxiliary information. The feature quantity extracting is extracting a feature quantity of a discrimination target from each of the multiple processed images. The discriminating is discriminating the discrimination target in the cell image based on the feature quantity.

Abstract: A method (300) for obtaining one or more oral images (90) using an imaging device or system (100, 200) includes: (i) obtaining (320), using an imager, an oral image; (ii) extracting (330), by a controller, one or more features from the oral image, wherein the extracted one or more features comprises at least an image quality feature and an object recognition feature; (iii) determining (340), by a decision module of the controller, whether the one or more extracted features satisfy a predetermined feature threshold; (iv) providing (350) feedback to a user regarding the obtained oral image if the one or more extracted features does not satisfy the predetermined feature threshold, or authorizing (370) transmission of the oral image to a dental professional if the one or more extracted features satisfy the predetermined feature threshold; and (v) transmitting (380), if transmission is authorized, the obtained oral image to a dental professional.

Abstract: A rendered image is aligned with a scanning electron microscope (SEM) image to produce an aligned rendered image. A reference image is aligned with the SEM image to produce an aligned reference image. A threshold probability map also is generated. Dynamic compensation of the SEM image and aligned reference image can produce a corrected SEM image and corrected reference image. A thresholded defect map can be generated and the defects of the thresholded probability map and the signal-to-noise-ratio defects of the thresholded defect map are filtered using a broadband-plasma-based property to produce defect-of-interest clusters.

Abstract: A contact image sensor having an illumination source; a first SBG array device; a transmission grating; a second SBG array device; a waveguiding layer including a multiplicity of waveguide cores separated by cladding material; an upper clad layer; and a platen. The sensor further includes: an input element for coupling light from the illumination source into the first SBG array; a coupling element for coupling light out of the cores into output optical paths coupled to a detector having at least one photosensitive element.

Abstract: An access point barrier system having a barrier that extends in such a way as to be movable between an enabled position and a non-enabled position preventing a vehicle from crossing the access point is provided. The image-capture device is integrated to the arm of the barrier and a computing device coupled to a database of registered vehicles, wherein the computing device is adapted to establish a match between the vehicle adjacent the barrier against the database of registered vehicles based on in part at least one image captured by the image-capture device so that an established match moves the barrier to the enabled position.

Abstract: There is provided an information processing device, an information processing method, and a program that make it possible to provide proper exposure control of a captured image. A matching section matches a feature point extracted from a captured image against a registered feature point stored in a predetermined storage section, and extracts a corresponding feature point in the captured image that corresponds to the registered feature point. A control section sets an exposure control value in accordance with an area image that is the image of an area near the corresponding feature point in the captured image. The present technology is applicable, for example, to an exposure control device that provides exposure control of a camera mounted on a mobile body.

Abstract: The present disclosure relates to a simple, fast, and low cost method for 3D microvascular imaging, termed “scatter labeled imaging of microvasculature in excised tissue” (SLIME). The method can include perfusing a contrast agent through vasculature of a tissue sample with a contrast perfusing unit (22). The contrast agent can include colloids and a dispersant. After the contrast agent is perfused through the vasculature, the vasculature of the tissue sample can be treated with a cross-linking agent delivery unit (24) providing a molecule that cross links with at least a portion of the dispersant to form a sticky, non-Newtonian polymer that prevents leakage of the contrast agent out of the vasculature of the tissue sample. The tissue sample can then be immersed in a solution comprising a clearing agent with an optical clearing unit (26) and subsequently imaged.