Abstract: A pattern inspection system inspects an image of an inspection target pattern of an electronic device using an identifier constituted by machine learning, based on the image of the inspection target pattern of the electronic device and data used to manufacture the inspection target pattern. The system includes a storage unit which stores a plurality of pattern images of the electronic device and pattern data used to manufacture a pattern of the electronic device, and an image selection unit which selects a learning pattern image used in the machine learning from the plurality of pattern images, based on the pattern data and the pattern image stored in the storage unit.

Abstract: A method, computer system, and a computer program product for case-adaptive image quality assessment is provided. The present invention may include detecting a current set of features in a current exam associated with a patient. The present invention may also include calculating a current set of quality measurements for the current exam based on the detected current set of features. The present invention may further include in response to determining that the calculated current set of quality measurements for the current exam is below a patient-specific image quality threshold defined by at least one prior exam associated with the patient, automatically registering a negative quality assessment for the current exam associated with the patient.

Abstract: Systems and methods are described herein for tracking an elongate instrument or other medical instrument in an image.

Abstract: A method for scanning and analyzing a surface, the method comprising: receiving a piece of equipment with a target surface for inspection; receiving an input from a user; determining at least one scan parameter based on the user input; scanning the target surface using an optical detector in accordance with the at least one scan parameter; generating an image of the target surface; correcting the image of the target surface to remove at least one undesired feature to generate a corrected image based on the at least one scan parameter; and analyzing the corrected image to determine at least one geometric parameter of the target surface.

Abstract: In some implementations, a method may be used for matching palm print images. A search template that identifies at least a plurality of distinctive search orientation blocks within a search orientation field may be initially generated for a search palm print image. A reference template may be obtained. A mated distinctive reference orientation block may be identified for each of the distinctive search orientation blocks. One or more regions of the search orientation field that include the distinctive search orientation blocks may be compared against one or more corresponding regions of the reference orientation field. An orientation similarity score between the search palm print image and the reference palm print image may be computed based on the comparison. A match may finally be determined if the computed orientation score satisfies a predetermined threshold value.

Abstract: A network-connected security device is communicatively coupled to an audio/video (A/V) recording and communication device having a camera and a speaker. A method receives video data captured by the camera, and performs an object recognition algorithm upon the received video data to identify an object therein. The method performs a table lookup using the identified object, into a data structure that associates objects with at least one description of a predefined voice message. The method selects a description of a predefined voice message associated with the identified object, and transmits the selected description's predefined voice message to the A/V recording and communication device for output through the speaker.

Abstract: Embodiments of object detection method, device, apparatus and a computer-readable storage medium are provided. The method can include: obtaining an enclosing frame of a target object in an input image; according to the enclosing frame, determining a reference frame from a predetermined candidate frame set comprising a plurality of candidate frames; generating a size-related feature according to a size of the reference frame and a size of the enclosing frame; and detecting an object in the input image by applying the size-related feature in a machine learning model. In an embodiment of the present application, the object detection is performed by using a feature related to an object size, that is, the prediction criterion related to the object size is added to an original feature in a machine learning model, thereby further improving the accuracy of the object detection.

Abstract: An adaptive approach to image bracket determination and a more memory-efficient approach to image fusion, which are designed to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions, are described. An incoming preview image stream may be obtained from an image capture device. When a capture request is received, an analysis may be performed on an image from the preview image stream that has a predetermined temporal relationship to the image capture request. Based on the analysis, a set of images (and their respective capture parameters, e.g., exposure time) may be determined for the image capture device to capture. As the determined set of images are captured, they may be registered and fused in a memory-efficient manner that, e.g., places an upper limit on the overall memory footprint of the registration and fusion operations—regardless of how many images are captured in total.

Abstract: An image sensor is positioned such that a field-of-view of the image sensor encompasses at least a portion of a rack storing items. The image sensor generates angled-view images of the items stored on the rack. A tracking subsystem determines that a person has interacted with the rack and receives image frames of the angled-view images. The tracking subsystem determines that the person interacted with a first item stored on the rack. A first image is identified associated with a first time before the person interacted with the first item, and a second image is identified associated with a second time after the person interacted with the first item. If it is determined, based on a comparison of the first and second images, that the item was removed from the rack, the first item is assigned to the person.

Abstract: There is provided a method of locating a tip of a metallic instrument inserted in a body, utilizing a baseline sinogram comprising projections in N exposure directions and derived from a prior computerized tomography (CT) scanning of the body, the method comprising: performing three-dimensional Radon space registration of a sparse repeat sinogram to the baseline sinogram, the repeat CT scanning having the metallic instrument inserted into the body, the metallic instrument having an attached marker located at a known distance from the instrument tip; subtracting the baseline sinogram from the repeat sinogram in accordance with the registration parameters to obtain projection difference images; and using the projection difference images and the known distance of the attached marker from the metallic instrument tip to determine a three-dimensional location of the metallic instrument tip.

Abstract: The present application provides a medical imaging method and system and a non-transitory computer-readable storage medium. The medical imaging method comprises identifying an image quality type of a medical image based on a trained learning network, and generating, based on the identified image quality type, a corresponding control signal for controlling a medical imaging system.

Abstract: In one embodiment a medical scan correction system includes a medical instrument having a distal end and an image sensor disposed at the distal end, and configured to be inserted into a body-part of a living subject, and a controller to register a medical scan of the body-part with a coordinate frame of the medical instrument, the medical scan including initial tissue-type indications, render a first image including a representation of the body-part and the initial tissue-type indications based on the medical scan, and a representation of the medical instrument, render a second image captured by the image sensor of a portion of the body-part, and responsively to a comparison between an appearance of a tissue in the second image of the portion of the body-part and an initial tissue-type indication of the tissue, update the medical scan to modify the tissue-type indication associated with the tissue.

Abstract: A method and system for detecting a fall risk condition, the system comprising a surveillance camera configured to generate a plurality of frames showing an area in which a patient at risk of falling is being monitored, and a computer system comprising memory and logic circuitry configured to store motion feature patterns that are extracted from video recordings, the motion feature patterns are representative of motion associated with real alarm cases and false-alarm cases of fall events, receive a fall alert from a classifier, determine motion features of one or more frames from the plurality of frames that correspond to the fall alert; compare the motion features of the one or more frames with the motion feature patterns, and determine whether to confirm the fall alert based on the comparison.

Abstract: A method configured to be implemented on at least one image processing device for enhancing edges in images includes obtaining, by the at least one imaging processing device, image data of an image, wherein the image includes a plurality of pixels, and each of the plurality of pixels has a luminance value and a motion intensity value. The method also includes performing at least one filtering operation to the image to obtain, by the at least one imaging processing device, one or more filtered values for each pixel. The method further includes performing a first logical operation to the one or more filtered values of each pixel in the image to obtain, by the at least one imaging processing device, an edge value and an edge enhancement coefficient for each pixel in the image.

Abstract: System and methods are provided for predicting a roadway feature. A geometric pattern in a roadway network is selected. The geometric pattern comprises one or more first links and one or more first nodes. A similar pattern to the geometric pattern is identified that comprises one or more second links and one or more second nodes in the roadway network. One or more features for the geometric pattern are identified. An absence of a feature in the similar pattern is determined based on the one or more features for the geometric pattern. A notification relating to the absence of the feature is generated.

Abstract: A radiographic imaging apparatus includes an image generation unit that generates radiation images, and an image processing unit that synthesizes a plurality of radiation images to generate an enhanced image. The image processing unit is configured to synthesize the plurality of radiation images by weighting each of the plurality of radiation images in accordance with a feature point distance.

Abstract: A gesture recognition method includes determining a palm connected domain based on an acquired depth image. The method includes determining a tracking frame corresponding to the palm connected domain. The method includes recognizing a gesture within a region of a to-be-recognized image corresponding to the tracking frame, based on a location of the tracking frame. In this arrangement, a palm connected domain and a tracking frame corresponding to the palm connected domain are acquired, and a gesture is recognized within a region of a to-be-recognized image corresponding to the tracking frame.

Abstract: A noise detection method for time-series vegetation index (TSVI) derived from remote sensing images. Firstly, unit root test is used to classify observation values of each pixel into a stationary series or a non-stationary series; for the non-stationary, an appropriate mathematical model is used to model discrete TSVI, then differences between actual observation values and prediction values of the model are calculated and recorded as a deviation. As the deviation has removed seasonal components, the non-stationary series is transformed into a stationary series. For a stationary series or deviation data, noise detection is performed based on the assumption that observation values are distributed within a certain range around mean values; then model fitting and noise detection are iteratively carried out with remained observation values—until the iterations reached the maximum number or no noise is detected at one iteration.

Abstract: The present disclosure is directed to a traffic light recognition system and method for advanced driver assistance systems (ADAS) and robust to variations in illumination, partial occlusion, climate, shape and angle at which traffic light is viewed. The solution performs a real time recognition of traffic light by detecting the region of interest, where extracting the region of interest is achieved by projecting the sequence of frames into a kernel space, binarizing the linearly separated sequence of frames, identifying and classifying the region of interest as a candidate representative of traffic light. With the aforesaid combination of techniques used, traffic light can be conveniently recognized from amidst closely similar appearing objects such as vehicle headlights, tail or rear lights, lamp posts, reflections, street lights etc. with enhanced accuracy in real time.

Abstract: A determination processing unit determines a disease region in a medical image including an axisymmetric structure. A first determination section of the determination processing unit generates a feature amount map of the medical image from the medical image. A second determination section second inverts the feature amount map with reference to a symmetry axis to generate an inverted feature amount map. A third determination section superimposes the feature amount map and the inverted feature amount map on each other and determines the disease region in the medical image using the feature amount map and the inverted feature amount map superimposed on each other.