Abstract: Provided are a device and method for analyzing spatiotemporal data of a geographical space including one or more regions. The method includes imaging spatiotemporally varying data, selecting a representative image according to each transition state based on a spatiotemporal change in the imaged data, grouping pixels in the selected image by clustering the pixels, and performing group-specific data analyses.

Abstract: Automated stain finding. In an embodiment, an image of a sample comprising one or more stains is received. For each of a plurality of pixels in the image, an optical density vector for the pixel is determined. The optical density vector comprises a value for each of the one or more stains, and represents a point in an optical density space that has a number of dimensions equal to a number of the one or more stains. The optical density vectors are transformed from the optical density space into a representation in a lower dimensional space. The lower dimensional space has a number of dimensions equal to one less than the number of dimensions of the optical density space. An optical density vector corresponding to each of the one or more stains is identified based on the representation.

Abstract: The present disclosure provides a sight line detection method, a sight line detection device, a computer readable storage medium, and a computer device. The sight line detection method comprises: receiving an image of a user taken by a camera; acquiring a plurality of face feature points according to the image of the user; calculating an eye center position and a pupil position based on the plurality of face feature points; obtaining an eye sight line based on the eye center position and obtaining a camera sight line base on the pupil position; and determining whether the eye sight line coincides with the camera sight line, and in a case where the eye sight line coincides with the camera sight line, performing a sight line detection with the eye sight line coincided with the camera sight line as a reference line.

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 receives image frames of the angled-view images. The tracking subsystem detects that a trigger event has occurred. A set of one or more image frames from the image feed are determined that are associated with the detected trigger event. A region-of-interest of the image frame is determined based on the pixel position of the wrist of the person. The region-of-interest includes a subset of the pixels of the image frame. A first item in the determined region-of-interest using an object detection algorithm. The identified first item is assigned to the person.

Abstract: A weighting for a roadmap method is automatically determined. A first or a second weighting image is generated from an anatomical image and an object image. For this purpose, a prespecified first weighting value is assigned to pixels belonging to a prespecified anatomical feature or to an instrument. Other pixels are assigned increasingly small weighting values at increasing distances from the anatomical feature or from the instrument toward an edge of a respective recording region according to a prespecified monotonously decreasing function in dependence upon the location. An overall weighting image is generated by combining the first and the second weighting images with one another and/or a region of interest determined using the overall weighting image are then provided as input data for an image processing algorithm.

Abstract: The embodiments of the present disclosure disclose a method and apparatus for extracting an edge of an object in a depth image, a storage medium, and a computer device, to provide a solution which is simple to implement and accurate in edge extraction. The method for extracting an edge of an object in a depth image includes: calculating at least two sets of edge pixels of a first depth image using at least two edge detection algorithms; and acquiring a final set of edge pixels by combining the at least two sets of edge pixels. The solution according to the embodiments of the present disclosure is simple to implement, and may obtain a more accurate set of edge pixels, thereby obtaining an accurate pixel-level segmentation result.

Abstract: A motion detection method using an image processing device may include: based on the noise level of a current frame, obtaining a weighted average of a sum of absolute differences (SAD) value and an absolute difference of sums (ADS) value; setting the weighted average as an initial motion detection value of each pixel; and selectively performing max filtering on motion detection values of the pixels to obtain a final motion detection value.

Abstract: The present invention relates to image processing for enhancing ultrasound images. In order to provide image data showing the current situation, for example in a region of interest of a patient, an image processing device (10) for enhancing ultrasound images is provided that comprises an image data input unit (12), a central processing unit (14), and a display unit (16). The image data input unit is configured to provide an ultrasound image of a region of interest of an object, and to provide an X-ray image of the region of interest of the object. The central processing unit is configured to select a predetermined image area in the X-ray image, to register the ultrasound image and the X-ray image, to detect the predetermined area in the ultrasound image based on the registered selected predetermined image area, and to highlight at least a part of the detected area in the ultrasound image to generate a boosted ultrasound image.

Abstract: A subtitle extraction method includes decoding a video to obtain video frames; performing adjacency operation in a subtitle arrangement direction on pixels in the video frames to obtain adjacency regions in the video frames; and determining certain video frames including a same subtitle based on the adjacency regions, and subtitle regions in the certain video frames including the same subtitle based on distribution positions of the adjacency regions in the video frames including the same subtitle. The method also includes constructing a component tree for at least two channels of the subtitle regions and using the constructed component tree to extract a contrasting extremal region corresponding to each channel; performing color enhancement processing on the contrasting extremal regions of the at least two channels to form a color-enhanced contrasting extremal region; and extracting the subtitle by merging the color-enhanced contrasting extremal regions of at least two channels.

Abstract: In a method for reconstructing magnetic resonance (MR) image data from k-space data, k-space data of an image region of a subject are provided to a computer that is also provided with multiple navigator signals for the image region of the subject. The computer sorts the k-space data into multiple bins, the multiple bins representing different motion states of the subject. For each of the multiple bins, the computer executes a compressed sensing procedure to reconstruct the MR image data from the k-space data in the respective bin. Execution of the compressed sensing procedure includes solving an optimization problem comprising a data consistency component and a transform sparsity component. Motion information is incorporated by the computer into at least one of the data consistency component and the transform sparsity component of the optimization problem.

Abstract: This disclosure relates generally to multi instance visual tracking based on observer motion modelling. Conventional state-of-the-art methods have major challenges in dealing with the error rate, accuracy of tracking the moving objects when observer motion is also considered. Embodiments of the present disclosure provide a method for multi instance visual tracking considering the motion of observer. The method tracks moving objects in an input video comprising a set of image frames, by detecting the moving objects in each image frame. The method detects the moving objects in each image frame by calculating trifocal tensors and fundamental matrix of the previous frames. The disclosed multi instance visual tracking can be used in remote surveillance problem for tracking moving objects.

Abstract: Systems and methods for magnetic resonance imaging acceleration. The systems may perform the methods to obtain imaging data of a subject, or a portion thereof, captured by the MRI system according to an undersampling pattern; execute a first iterative procedure; determine that the first iteration number meets a first threshold; execute, in response to the determination that the first iteration number meets the first threshold, a second iterative procedure; determine that a sum of the first iteration number and the second iteration number meets a second threshold; and generate a reconstruction image of the subject, or a portion thereof, according to the processed imaging data, wherein the first threshold is lower than the second threshold.

Abstract: A lesion tracking system is operable to receive a first medical scan and second medical scan associated with a patient ID. A lesion area calculation is performed on a first subset of image slices determined to include a lesion detected in the first medical to generate a first set of lesion area measurements. The lesion area calculation is performed on a second subset of image slices determined to include the lesion in the second medical scan to generate a second set of lesion area measurements. A lesion volume calculation is performed on the first set of lesion area measurements and the second set of lesion area measurements to generate a first lesion volume measurement and a second lesion volume measurement, respectively, and the first and second lesion volume measurements are utilized to calculate a lesion volume change for transmission to a client device for display via a display device.

Abstract: A system for estimating a pose of one or more persons in a scene includes a camera configured to capture an image of the scene; and a data processor configured to execute computer executable instructions for: (i) receiving the image of the scene from the camera; (ii) extracting features from the image of the scene for providing inputs to a keypoint subnet and a person detection subnet; (iii) generating one or more keypoints using the keypoint subnet; (iv) generating one or more person instances using the person detection subnet; (v) assigning the one or more keypoints to the one or more person instances by learning pose structures from the image data; and (vi) determining one or more poses of the one or more persons in the scene using the assignment of the one or more keypoints to the one or more person instances.

Abstract: A contrast parameter learning system is operable to generate contrast significance data for a computer vision model, where the computer vision model was generated by performing a training step on a training set of medical scans. Significant contrast parameters are identified based on the contrast significance data. A re-contrasted training set is generated by performing an intensity transformation function that utilizes the significant contrast parameters on the training set of medical scans. A re-trained model is generated by performing the training step on the first re-contrasted training set. Re-contrasted image data of a new medical scan is generated by performing the intensity transformation function. Inference data is generated by performing an inference function that utilizes the first re-trained model on the re-contrasted image data. The inference data is transmitted via the transmitter to a client device for display via a display device.

Abstract: An image is obtained by using a charged particle beam, and a design layout information is generated to select patterns of interest. Grey levels among patterns can be compared with each other to identify abnormal, or grey levels within one pattern can be compared to a determined threshold grey level to identify abnormal.

Abstract: A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.

Abstract: The present invention discloses a grid map obstacle detection method fusing probability and height information, and belongs to the field of image processing and computer vision. A high-performance computing platform is constructed by using a GPU, and a high-performance solving algorithm is constructed to obtain obstacle information in a map. The system is easy to construct, the program is simple, and is easy to implement. The positions of obstacles are acquired in a multi-layer grid map by fusing probability and height information, so the robustness is high and the precision is high.

Abstract: Disclosed are various embodiments for patent analysis applications. A computing device may be directed to parse an electronic version of a patent document having a detailed description, a claims section, and at least one drawing. In various embodiments, parsing the electronic version of the patent document may include applying an OCR process to the electronic document, obtaining a list of claim terms used in the claims section, identifying instances of the claim terms used in the detailed description, identifying a reference numeral corresponding to the claim terms from the detailed description, and identifying portions of the drawing that includes the reference numeral. In response to user interaction with a claim term, a dialog may be shown proximate to the claim term, where the dialog includes a portion of the detailed description that includes the claim term and/or the drawing that comprises the reference numeral corresponding to the claim term.

Abstract: A method for studying the behavior of an animal in an experimental area including stimulating the animal using a stimulus device; collecting data from the animal using a data collection device; analyzing the collected data; and developing a quantitative behavioral primitive from the analyzed data. A system for studying the behavior of an animal in an experimental area including a stimulus device for stimulating the animal; a data collection device for collecting data from the animal; a device for analyzing the collected data; and a device for developing a quantitative behavioral primitive from the analyzed data. A computer implemented method, a computer system and a nontransitory computer readable storage medium related to the same. Also, a method and apparatus for automatically discovering, characterizing and classifying the behavior of an animal in an experimental area. Further, use of a depth camera and/or a touch sensitive device related to the same.