Abstract: There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

Abstract: A non-transitory computer-readable storage medium storing a set of program instructions for an image processing apparatus is disclosed. The set of program instructions, when executed by a controller of the image processing apparatus, causes the image processing apparatus to: acquire input image data indicating an input image; and input the input image data to a trained machine learning model configured to perform calculation processing on the input image data and to generate output image data corresponding to the input image data. The machine learning model is trained to generate the output image data indicating a first area and a second area. The first area is an area in the input image in which it is preferable to perform first image processing. The second area is an area in the input image in which it is preferable to perform second image processing different from the first image processing.

Abstract: A method and apparatus for identifying blood vessels in ultrasound images and displaying blood vessels in ultrasound images are described. In some embodiments, the method is implemented by a computing device and includes assigning, with a neural network implemented at least partially in hardware of the computing device, one of a vein classification and an artery classification to one or more blood vessels in ultrasound images. The method also includes determining a misclassification for one blood vessel that denotes the neural network assigning the one of the vein classification and the artery classification to the one blood vessel in one ultrasound image and the other of the vein classification and the artery classification to the one blood vessel in additional ultrasound images. The method includes displaying, in the one ultrasound image, an indication of the other of the vein classification and the artery classification for the one blood vessel.

Abstract: An image correction method includes: acquiring band images obtained by imaging a subject, and a high-resolution image having a resolution higher than that of the band images; acquiring a position difference between the object band image and the reference band image among the band images; by using a pixel of the object band image as an object pixel, for each object pixel, determining a pixel value of each sub-region obtained by dividing the imaging region of the object pixel into a plurality of regions, based on the pixel value of the object pixel and a relationship between pixel values of the pixels of the high-resolution image corresponding to the object pixel; and creating a corrected band image that holds a pixel value of light on the object band image at the pixel position of the reference band image, from the determined pixel value of each sub-region and the position difference.

Abstract: A method is used to generate a report presenting parameter values corresponding to a structured illumination microscopy (SIM) optical system. The parameter values are based at least in part on the performed modulation calculation corresponding to an image set captured with the SIM optical system. A minimum FWHM slice is identified, based at least in part on an average FWHM value across the images in the first image set. Parameter estimation is performed on the identified minimum FWHM slice. Best in-focus parameters are identified based at least in part on the performed estimation. A phase estimate is performed for each image in the set. A modulation calculation is performed based at least in part on the identified best in-focus parameters. The report is based at least in part on the performed modulation calculation.

Abstract: A system comprising a drilling rig having a rig floor, a derrick, a master control computer system and at least one camera, the at least one camera capturing a master image of at least a portion of the rig floor, sending the master image to the master control computer, the master control computer system mapping said master image into a model to facilitate control of items on said drilling rig.

Abstract: A belt examination system includes a defect candidate detecting processor that detects a candidate for a belt defect of an intermediate transfer belt of an image forming apparatus from a belt image, the defect candidate detecting processor executes a background pattern reduction step to reduce a texture-pattern like background noise present in the belt image and detects the candidate based on the belt image generated during the background pattern reduction step, the background pattern reduction step is to execute a locally adaptive binarization process on the belt image in a range having a specific size based on a typical size of the belt defect to generate a binary image and subtract the binary image from the belt image, and the binary image includes a first value into which a lowest color value of the belt defect is converted and a second value larger than the first value.

Abstract: A self-adaptive point cloud stripe division method. The method comprises: firstly, carrying out space division with a certain depth on a point cloud to obtain a plurality of local point clouds; then, counting the number of points in each of the local point clouds, comparing same with an upper and lower limit for the number of stripe points, and determining whether the number of points satisfies a requirement; and after a series of re-segmentation or re-fusion operations on the local point clouds, adjusting the number of points in each of the local point clouds until the number of points satisfies a range, thereby obtaining a final point cloud stripe. A plurality of local structures capable of being independently coded and decoded are obtained by means of division of a point cloud stripe, and this supports parallel processing, enhances system fault tolerance, and improves coding efficiency.

Abstract: A method for acquiring shadow-free images of a document for scanning or other purposes is applied in a device. The method includes training a shadow prediction model based on sample documents of a sample library and inputting a background color and a shadow mask of each of the sample documents extracted by the shadow prediction model into a predetermined shadow removing network for training, to obtain a shadow removing model. The method further includes obtaining a background color and a shadow mask of the document through the shadow prediction model and removing the shadows of the document based on the shadow removing model. The device utilizing the method is also disclosed.

Abstract: An abnormality behind a shielding object is discovered. There is provided a monitoring system including a video acquirer, a detector, and a notifier. The video acquirer of the monitoring system acquires a video. The detector of the monitoring system detects entering of a target object into a blind spot generated by a shielding object in the video and appearance of the target object from the blind spot. If the target object does not appear from the blind spot even after a first time elapses since entering of the target object into the blind spot, the notifier of the monitoring system makes a notification.

Abstract: The present invention generally relates to a technology of detecting a stationary object in a static video, i.e., a video which is generated by a camera with a fixed view, such as a CCTV video. More specifically, the present invention relates to a technology of identifying a stationary object by use of inference information (Tensor, Activation Map) of Deep Learning object detector among objects which have been detected in a static video such as a CCTV video. According to the present invention, it is possible to lower the priority of stationary objects (e.g., parked vehicles) in processing a static video so that computing resource of the video analysis system can be effectively utilized in the process after object detection so as to enhance the performance of image analysis.

Abstract: Provided is a dental image registration device comprising: an outermost boundary detection unit for detecting, from first teeth image data, a first outermost boundary region which is the outermost boundary region of dentition, and detecting, from second teeth image data, a second outermost boundary region which is the outermost boundary region of the dentition; and an image registration unit which registers the first and second teeth image data on the basis of a first inscribed circle inscribed within the first outermost boundary region and a second inscribed circle inscribed within the outermost boundary region, or registers the first and second teeth image data on the basis of a first central point of the first outermost boundary region and a second central point of the second outermost boundary region.

Abstract: A color correction method is applied to an image correction apparatus having an image sensor, and includes searching a color deviation area within a detection image, analyzing the detection image to estimate a correction color value of the color deviation area, and calibrating the color deviation area by the correction color value to generate a calibrated detection image without color deviation.

Abstract: Apparatus and methods automate selection of patient interface(s) according to their size, such as with processing in a processor(s) or in a server(s). Image data captured by an image sensor may be received. The captured image data may contain facial feature(s) of an intended user of the patient interface. The facial features may be captured in association with a predetermined reference feature of known dimension(s). The user's facial feature(s) and the reference feature may be detected in the captured image data. Image pixel data of the image may be processed to measure an aspect of the detected facial feature(s) based on the reference feature. A patient interface size may be detected from standard patient interface sizes based on a comparison between the measured aspect of the facial feature(s) and a data record relating sizing information of the standard patient interface sizes and the measured aspect of the facial feature(s).

Abstract: This application provides an image dehazing method, apparatus, and device, and a computer storage medium. The method includes: in response to obtaining an image dehazing instruction, acquiring a first image and a second image corresponding to a target scene at the same moment. The method also includes calculating, based on a first pixel value of each pixel of the first image and a second pixel value of each pixel of the second image, haze density information of the each pixel; generating an image fusion factor of the each pixel according to the haze density information, the image fusion factor indicating a fusion degree between the first image and the second image; and fusing the first image and the second image according to the image fusion factor to obtain a dehazed image.

Abstract: An object identification method is disclosed. The method includes training a first neural network for a first set of conditions regarding a first plurality of objects, training a second neural network for a second set of conditions regarding a second plurality of objects, receiving a plurality of target images associated with a target set of conditions in which to identify objects, analyzing the plurality of target images using the first and second neural networks to identify objects in the plurality of target images resulting in object identification information, and selecting the first neural network or the second neural network as a preferred neural network for the target set of conditions based on an analysis of the object identification information.

Abstract: A process for assessing image quality metrics includes the steps of receiving image data from an image capture device, wherein the image data includes a skin portion; extracting, via a machine learning system, a first set of information from the image data; extracting, via the machine learning system, a second set of information from the image data; scoring the first and second sets of information of the image data to reflect image quality metrics; identifying a threshold score reflecting acceptable image quality metrics; when the score is greater than the threshold score, submit the image data; and when the score is less than the threshold score, prompt the user to retake the image data.

Abstract: Various techniques are provided to reduce noise in captured images using frame-to-frame shifts of scene information. In one example, a system includes an imager and a processor. The imager is configured to capture a plurality of image frames comprising scene information. The image frames exhibit frame-to-frame shifts of the scene information caused by a motion pattern associated with the imager. The processor is configured to distinguish noise in the image frames from the frame-to-frame shifts of the scene information. The processor is also configured to update non-uniformity correction terms to reduce the noise. Additional systems and methods are also provided.

Abstract: A method of reducing radiation dose for radiology imaging modalities and nuclear medicine by using a convolutional network to generate a standard-dose nuclear medicine image from low-dose nuclear medicine image, where the network includes N convolution neural network (CNN) stages, where each stage includes M convolution layers having K×K kernels, where the network further includes an encoder-decoder structure having symmetry concatenate connections between corresponding stages, downsampling using pooling and upsampling using bilinear interpolation between the stages, where the network extracts multi-scale and high-level features from the low-dose image to simulate a high-dose image, and adding concatenate connections to the low-dose image to preserve local information and resolution of the high-dose image, the high-dose image includes a dose reduction factor (DRF) equal to 1 of a radio tracer in a patient, the low-dose PET image includes a DRF of at least 4 of the radio tracer in the patient.

Abstract: A portable checkout unit automatically generates training data for an automatic checkout system as a customer collects items in a store. A customer uses an item scanner of portable checkout unit to generate a virtual shopping list of items collected in the shopping cart. When the customer adds a new item to the shopping cart or on some regular interval, the portable checkout unit captures images of the items contained by the shopping cart and can generate bounding boxes for each product in each image. The bounding boxes can be associated with item identifiers from previously-generated bounding boxes to identify the items captured by the bounding boxes. Each bounding box paired with an item identifier can then be used as training data for an automated checkout system.