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 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: 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: 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: 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: 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: 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.

Abstract: An information processing apparatus includes a processor configured to: obtain plural images each including any of plural objects; and determine, based on an analysis result regarding the plural objects in the plural images, according to which of two or more objects, among the plural objects, included in an image the image is corrected.

Abstract: A maintenance support system prevents leakage of confidential information by excluding a maintenance target while imposing few restrictions on imaging conditions. A maintenance support system 100 includes a wearable terminal 1 including an imaging device 12, a first identifying device 31 configured to identify a first three-dimensional area TA including a maintenance target T with reference to a predetermined reference point M, and a second identifying device 32 configured to identify a mask pixel area excluding an effective pixel area corresponding to the first three-dimensional area for a captured image acquired by the imaging device in a post-movement state in which the wearable terminal has moved. A processed-image generating device 33 is configured to generate a processed image in which the mask pixel area is made invisible and a communication device 34 is configured to transmit the processed image to a support terminal 2.

Abstract: It is possible to easily monitor work performed by a worker with a detection unit detects a marker reflected in an image obtained by imaging a predetermined imaging space by a camera. A first determination unit determines a distance from the camera to the marker detected by the detection unit. A second determination unit determines, in consideration of the distance determined by the first determination unit, an article arranged in a predetermined arrangement space as a work target by a worker to which the marker detected by the detection unit is attached if an object virtualized at a predetermined position relative to the marker detected by the detection unit and the arrangement space have a predetermined positional relation.

Abstract: A three-dimensional data encoding method is a three-dimensional data encoding method of encoding a three-dimensional point having attribute information. The three-dimensional data encoding method includes: calculating a predicted value of the attribute information of the three-dimensional point; calculating a prediction residual which is a difference between the attribute information of the three-dimensional point and the predicted value; binarizing the prediction residual to generate binary data; and arithmetic encoding the binary data. For example, the three-dimensional data encoding method may use, in the arithmetic encoding, a different coding table for each bit included in the binary data.

Abstract: This application discloses a face anti-spoofing recognition method and apparatus, a device, and a storage medium, and relates to the field of artificial intelligence (AI) technologies. The method is performed by a computer device, including: acquiring face video data including a to-be-detected face; detecting eye contours of the to-be-detected face from image frames of the face video data, and generating an eye contour sequence; performing eye movement probability prediction based on the eye contour sequence, and generating an eye movement state sequence; acquiring a first dataset and a second dataset in the eye movement state sequence; determining a probability of existence of an abnormal blink behavior of the to-be-detected face according to the first dataset and the second dataset; and determining that the to-be-detected face is a real face in a case that the probability is less than a predetermined threshold.

Abstract: There is provided a program causing a computer to execute processing including: acquiring an endoscopic image of a subject from an endoscope; acquiring a three-dimensional medical image obtained by capturing an image of an internal body portion of the subject by means of X-ray CT, X-ray cone beam CT, MRI-CT, or an ultrasonic diagnosis apparatus configured to capture a three-dimensional image of an internal body portion of the subject; generating a virtual endoscopic image reconfigured from the three-dimensional medical image, based on the acquired endoscopic image; calculating distance image information in the endoscopic image based on the virtual endoscopic image and the endoscopic image; and outputting operation assistance information on an operation of the endoscope based on the distance image information and the three-dimensional medical image.

Abstract: One or more embodiments of the present disclosure provide an intelligent vase system, a flower recognition and presentation method for an intelligent vase system, and an electronic apparatus. The intelligent vase system includes: a vase body; a flower recognition device configured to: acquire a flower image of a flower inserted into the vase body, perform an object extraction on the flower image to obtain at least one object flower image, perform a flower recognition on the object flower image to obtain a flower recognition result, and acquire presentation information of an object flower based on the flower recognition result; and a display device, which is on the vase body, in communication with the flower recognition device, and configured to display the presentation information.

Abstract: An embodiment of the present disclosure discloses a training method for a multi-object tracking model and a multi-object tracking method. The multi-object tracking method comprises: constructing an object graph according to objects to be tracked in a current frame, wherein the vertexes of the object graph correspond to the objects to be tracked, and edge features of the edges between the two vertexes comprise an attribute relationship between the two vertexes; performing graph matching on the object graph and a tracklet graph to calculate matching scores between the object to be tracked and the tracked tracklet in the tracklet graph, wherein the vertexes of the tracklet graph correspond to tracked tracklets, and the edge features of the edges between the two vertexes comprise an attribute relationship between the two vertexes; and determining the matched tracklet of the object to be tracked according to the matching scores.

Abstract: An example computing device includes: a three-dimensional (3D) sensor configured to capture point cloud data from a field of view; an auxiliary sensor configured to capture a reference depth measurement corresponding to a surface within the field of view; a processor interconnected with the 3D sensor and the auxiliary sensor, the processor configured to: control the 3D sensor and the auxiliary sensor to capture the point cloud data and the reference depth measurement; select a region of the point could data including a position of the reference depth measurement; determine a representative depth value of the region of the point cloud data to compare to the reference depth measurement; determine whether the representative depth value is within a similarity threshold to the reference depth measurement; and determine, based on whether the representative depth value is within the similarity threshold to the reference depth measurement, whether to use the point cloud data for a dimensioning operation.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a generative inpainting neural network to accurately generate inpainted digital images via object-aware training and/or masked regularization. For example, the disclosed systems utilize an object-aware training technique to learn parameters for a generative inpainting neural network based on masking individual object instances depicted within sample digital images of a training dataset. In some embodiments, the disclosed systems also (or alternatively) utilize a masked regularization technique as part of training to prevent overfitting by penalizing a discriminator neural network utilizing a regularization term that is based on an object mask. In certain cases, the disclosed systems further generate an inpainted digital image utilizing a trained generative inpainting model with parameters learned via the object-aware training and/or the masked regularization.