Abstract: An image processing system includes a plurality of imaging units configured to capture optical images including a low-distortion region and a high-distortion region; a first image recognition unit configured to perform image recognition on at least a partial region out of image data obtained from the imaging unit and output a first image recognition result; a second image recognition unit configured to perform image recognition on image data in a wider region than the partial region out of the image data obtained from at least one of the imaging units and output a second image recognition result; and an integration processing unit configured to output an image recognition result integrated on the basis of the first image recognition result and the second image recognition result.

Abstract: Alignment of a 2D image to a corresponding 3D image is provided by writing a pattern into a 3D sample. The pattern is at known positions in the 3D image, and provides visible reference features in the 2D image. This permits accurate determination of the plane in the 3D image that corresponds to the 2D image.

Abstract: A data processing system is implemented for receiving an image of a canvas, and detecting design features within the canvas by analyzing the image. The data processing system is implemented for determining an execution of a command by comparing the image to a current image of the canvas, and modifying the design features within the canvas to generate layouts in response to determining the execution of the command. Further, the data processing system is implemented for determining parameters of the modified design features, and determining a value for each layout by applying rules to the parameters of the modified design features. In addition, the data processing system is implemented for displaying the layouts based on the determined value for each layout to a user.

Abstract: One or more aspects of the present disclosure enable high accuracy computer vision and image processing techniques with decreased system resource requirements (e.g., with decreased computational load, shallower neural network designs, etc.). As described in more detail herein, one or more aspects of the described techniques may leverage key layers (e.g., certain key layers of a neural network) and compressed tensor comparisons to efficiently exploit temporal redundancy in videos and other slow changing signals (e.g., to efficiently reduce neural network inference computational burden, with only minor increase in data transfer power consumption). For example, key layers of a neural network may be identified, and temporal/spatial redundancy across frames may be efficiently leveraged such that only a computation region in a subsequent frame n+1 is re-computed in layers between identified key layers, while remaining feature-map calculations may be disabled in the layers between the identified key layers.

Abstract: A tomographic imaging system comprises a support carrying an image data acquisition system and defining a reference coordinate frame. A scan plan control sets the image-data acquisition system to acquire image-data from a selected imaging zone in the reference coordinate system. A motion detection system to detect movement and includes (i) a dynamic camera system to receive dynamic image information registered in the image coordinate frame of the dynamic camera system, (ii) an arithmetic unit configured to transform the selected imaging zone from the reference coordinate frame to the image coordinate-frame and a (iii) motion analyser to derive motion information from the registered dynamic image information in the transformed selected imaging zone. In the event of motion detected by the motion analyser in or near the imaging zone, the detected motion may be employed for motion correction.

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: 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 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 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: 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, 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: 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: 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: 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.

Abstract: Embodiments of this application provide a method for obtaining depth information and an electronic device, which is applied to the field of image processing technologies and can help an electronic device improve accuracy of obtaining depth information. The electronic device includes a first camera, a second camera, and an infrared projector. The electronic device receives a instruction to obtain depth information of a target object; transmits infrared light with a light spot by an infrared projector; collects first image information of the target object by using the first camera; collects second image information by using the second camera, where the first and second image information include a feature of the target object and a texture feature formed by infrared light; and calculates depth information of the target object based on the first and second image information, the first length, lens focal lengths of the first camera and the second camera.

Abstract: The subject matter discloses a method of asset change detection using images, the method comprising steps executed by processing circuitry, the steps comprising: receiving at least one image of an asset captured by an image capturing device; receiving at least one attribute of a task of detecting a change in the asset using the received at least one image, at least one of the at least one attribute being one of the group consisting of: an attribute measured by a sensor, an attribute extracted from a website, an attribute retrieved from a database, an attribute input by a user, and an attribute encoded in computer code; selecting a reference image among a plurality of reference images of the asset according to at least one criterion based on the received at least one attribute of the task of detecting the change in the asset; computing an asset-difference pixel map, using the selected reference image and the image captured by the image capturing device; and detecting the change in the asset, using the computed

Abstract: A system is for in-situ monitoring and recording of fish health of fish in a fish cage. The system has at least one camera housing. The camera housing is provided with a camera group having at least two cameras arranged to take synchronized pictures for digital close-range photogrammetry. The system has a central data-processing unit, the central data-processing unit being arranged to calculate a three-dimensional model of an object photographed synchronously by the at least two cameras. The data-processing unit is arranged to report the number of structures deviating from the smooth surface of the object in the three-dimensional model.