Abstract: A method and device for 3D object detection. The method comprises the steps of: generating one or more fused images(201) based on a pair of images(530), the pair of images (530) including a left view image(101,710a) and a right view image(102,710b); extracting one or more fused features from the fused images(201) by a single backbone network(210) of a share network with feature unmixing (SNFU,540); unmixing the fused features to a left view-aware feature and a right view-aware feature by a feature unmixing sub-network(220) of the SNFU(540); predicting the 3D object based on the left view-aware feature and the right view-aware feature; and determining spatial features of the predicted 3D object. The proposed method and device can reduce the computation complexity to the level of that in monocular based 2D object detection so as to improve the computation efficiency while keeping high precision.

Abstract: Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing an atrous self-attention operation based on a plurality of dilation rates for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.

Abstract: Examples described herein relate to rendering an image using a reduced fidelity of shading for portions of a triangle that are potentially occluded by other objects. A computer device may pre-populate a potentially occluded depth map in a graphics processing unit (GPU) for a current frame based on depth data of a previous frame, a low resolution depth pre-pass, or a rasterizing occlusion geometry. The GPU may determine, at a rasterization stage, that a depth of a portion of a triangle being processed is further than a depth of a corresponding tile in the potentially occluded depth map. The GPU may decrease a fidelity of shading applied to at least the portion of the triangle in comparison to a specified fidelity for the triangle in response to determining that the depth of the portion of the triangle is further than the depth of the corresponding tile.

Abstract: An image processing apparatus includes an acquisition unit configured to acquire an image captured by an imaging unit, and image capturing information at the time of image capturing of the image, and a calculation unit configured to calculate an object side pixel dimension of a target subject in the image based on the image capturing information and a pixel dimension of the imaging unit, wherein the acquisition unit acquires in-focus information indicating an in-focus state of a subject in an image, as the image capturing information, and wherein the calculation unit calculates the object side pixel dimension based on the in-focus information.

Abstract: A method for self-determination of shot geometry for use in open-configuration portable x-ray computed tomography (CT) includes inputting one or more x-ray shot images into a computing system. The method also include determining, by the computing system, one or more shot geometries from the one or more inputted x-ray shot images. The method further includes reconstructing, by the computing system, a volume image from the one or more inputted x-ray shot images and the one or more shot geometries. The method also includes outputting, by the computing system, a reconstructed volume image.

Abstract: A method for evaluating edge quality in an image includes receiving an image from a camera of a vehicle, detecting an edge in the image captured by the camera, fitting a polynomial curve to the edge in the image captured by the camera, executing a numerical optimizer to determine a minimum distance from each pixel in the image of the edge to the polynomial curve, determining a modulation transfer function (MTF) value using the minimum distance, determining whether the MTF value is greater than predetermined threshold, and providing an alert in response to determining that the MTF value is not greater than the predetermined threshold.

Abstract: Provided is a dual-neighborhood wear angle automatic detection method, including constructing a proximal neighborhood and establishing grayscale differences between a pixel in a center area and pixels having first directions in the proximal neighborhood; calculating, based on the grayscale differences, deviation degrees of all pixels in the center area in the first directions; calculating a similar direction value based on the deviation degrees; constructing a distal neighborhood and focus pixels having second directions in the distal neighborhood; calculating grayscale differences between the pixel and the focus pixels; calculating deviation degrees of all pixels in the center area, in the second directions based on the grayscale differences between the pixel and the focus pixels; calculating a direction value of a wear angle based on the deviation degrees in the second directions; and calculating the wear angle based on the direction value of the wear angle.

Abstract: A computer tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (116) configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor (118) configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor (132) configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module (136) to produce sequential dense time line perfusion volumetric image data.

Abstract: A detection device according to an aspect of the present disclosure includes a detection portion that contains a substance that emits fluorescence in a case of being irradiated with light when reacting with a plant hormone.

Abstract: Embodiments described herein provide methods and systems for predicting a subject response to a therapeutic. The methods and systems generally operate by using a machine learning (ML) component trained using time-series responses and image features to generate time-series responses of a subject to a therapeutic that has been administered to the subject.

Abstract: A method for video stabilization may include obtaining a target frame of a video; obtaining a plurality of first feature points associated with the target frame; determining whether the plurality of first feature points include at least one feature point relating to a moving object in the video; in response to a determination that the plurality of first feature points include at least one feature point relating to the moving object in the video, removing the at least one feature point relating to the moving object; performing video stabilization to the target frame based on remaining first feature points of the plurality of first feature points; determining, in the target frame, at least one supplement feature point; and designating the at least one supplement feature point and the remaining first feature points as second feature points associated with a frame immediately following the target frame in the video.

Abstract: An object pose estimation system, an execution method thereof and a graphic user interface are provided. The execution method of the object pose estimation system includes the following steps. A feature extraction strategy of a pose estimation unit is determined by a feature extraction strategy neural network model according to a scene point cloud. According to the feature extraction strategy, a model feature is extracted from a 3D model of an object and a scene feature is extracted from the scene point cloud by the pose estimation unit. The model feature is compared with the scene feature by the pose estimation unit to obtain an estimated pose of the object.

Abstract: The present disclosure provides a method of analyzing an image captured from an imaging device. The method includes receiving one or more images captured from a camera over a predetermined time period. Feature extraction and tracking are executed with respect to the received images. One or more features are detected as untracked features during the feature extraction and tracking. A geometrical prediction is executed with respect to the untracked features to predict one or more features and thereby achieving the one or more untracked features as the one or more tracked features.

Abstract: A medical image processing apparatus of an embodiment includes processing circuitry acquiring an X-ray image about a subject, acquiring an ultrasonic image data about the subject, extracting an object contained in the X-ray image, and performing processing based on the position of the extracted object on the ultrasonic image data in accordance with the relative positional relation between a coordinate system in the X-ray image and a coordinate system in the ultrasonic image data to generate a composite image as a combination of a processed ultrasonic image data after being subjected to the processing and the X-ray image.

Abstract: An image processing method and a device configured to implement the same are disclosed. The device comprises: a hybrid imaging device configured to obtain optical input; and a processing device in signal communication with the hybrid imaging device. The processing device comprises: a motion detection circuit that performs feature tracking based on a first component of an obtained optical input; a motion estimation circuit that performs motion compensation based on output of the motion detection unit; a frame reconstruction circuit that reconstructs image frame based on both the output of the motion estimation unit and a second component of the optical input; and an output unit that outputs image frame at a predetermined global frame rate.

Abstract: An image processing method includes: converting a red yellow blue RYB image into a grid image based on a red green blue RGB format; generating a first brightness layer of the grid image, and determining a reference gain compensation array used to adjust the first brightness layer; obtaining, based on a preset compensation array correspondence, a target gain compensation array that is associated with the reference gain compensation array and based on an RYB format; and adjusting a second brightness layer of the RYB image by using the target gain compensation array, to generate a corrected image.

Abstract: A method and a system of designing a memristor-based naive Bayes classifier and a classifier belonging to the field of information technology are provided. The method includes: constructing a naive Bayes classifier including a memristor array of M rows by 2N columns, where M is the number of classification types, and N is the number of pixels in a picture; calculating the number hj,2i-1 of the pixel value of 0 and the number hj,2i of the pixel value of 1 in an ith pixel in the jth training sample, where j=1, 2, . . . , and M; and applying hj,2i-1 pulses to a memristor Rj,2i-1 in a jth row and a 2i-1th column to modulate the conductance of the memristor Rj,2i-1 and applying hj,2i pulses to a memristor Rj,2i in the jth row and a 2ith column to modulate the conductance of the memristor Rj,2i.

Abstract: Systems and methods are provided for augmenting digital analysis of lesions. An image of tissue having a glandular epithelial component is generated. The image represents a plurality of medium-scale epithelial components. For each of a plurality of cells within the image, a representative point is identified to provide a plurality of representative points for each of the plurality of medium-scale epithelial components. For each of a subset of the plurality of medium-scale epithelial components, a graph connecting the plurality of representative points is constructed. A plurality of classification features is extracted for each of the subset of medium-scale epithelial components from the graph constructed for the medium-scale epithelial component. A clinical parameter is assigned to each medium-scale epithelial component according to the extracted plurality of classification features.

Abstract: An image alteration system includes a memory and one or more processors. The processors receive input image data that depicts a background environment, and segment the input image data to locate objects depicted in the background environment. The processors identify a set of one or more of the objects to modify, and generate output image data that depicts a modified version of the background environment for display on a display device. The processors change an appearance of the one or more objects in the set as depicted in the output image data, relative to the appearance of the one or more objects as depicted in the input image data, without changing an appearance of one of more other objects located in the background environment. The input image data and the output image data represent video streams.

Abstract: A method, computer system, and a computer program product for text detection is provided. The present invention may include training a text detection model. The present invention may include performing text detection on an inputted image using the trained text detection model. The present invention may include determining whether at least one of a plurality of bounding boxes generated using the inputted image has an aspect ratio above a threshold. The present invention may include based upon determining that at least one of the plurality of bounding boxes generated using the inputted image has the aspect ratio above the threshold, upscaling any text within the at least one bounding box and performing text detection on a new image using the trained text detection model. The present invention may include outputting an output image.