Abstract: A system and method for image reconstruction are provided. A first region of an object may be determined. The first region may correspond to a first voxel. A second region of the object may be determined. The second region may correspond to a second voxel. Scan data of the object may be acquired. A first regional image may be reconstructed based on the scan data. The reconstruction of the first regional image may include a forward projection on the first voxel and the second voxel and a back projection on the first voxel.

Abstract: The embodiments of the disclosure provide a method for transmitting reduced depth information and an electronic device. The method includes: obtaining a first depth map of an image frame, wherein the first depth map includes a plurality of first depth pixels corresponding to multiple image pixels in the image frame; downscaling the first depth map as a second depth map, wherein the second depth map includes multiple second depth pixels, and each second depth pixel has a depth value; adjusting the second depth map as a third depth map by scaling up the depth value of each second depth pixel based on a specific ratio; dividing the third depth map into multiple fourth depth maps; appending the fourth depth maps to the image frame; encoding the image frame appended with the fourth depth maps; and transmitting the encoded image frame to a client device.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: A pigment detection method includes: extracting a first image from a to-be-detected RGB skin image, where the first image is used to represent a body reflection component in the RGB skin image, and the RGB skin image is photographed by a device having an RGB image photographing function; extracting a pigment from an R channel, a B channel, and a G channel of the first image based on a correspondence between a first spectral response curve of the pigment and a second spectral response curve of the device having the RGB image photographing function; and generating a pseudo-color image based on the extracted pigment, and displaying the pseudo-color image.

Abstract: At least one storage stores a dictionary having information corresponding to crowd state images. At least one processor extracts rectangular regions, a size of the rectangular regions is predetermined, from the given image, and recognizes crowd states in the extracted rectangular regions based on the dictionary. The dictionary is acquired by machine learning by use of a plurality of pairs of crowd state images and training label for the crowd state image.

Abstract: Disclosed is a cross-modality person re-identification method based on local information learning, the method comprising the following steps: acquiring a standard data set and performing data enhancement on the standard data set; dividing the enhanced standard data set into a training set and a test set; constructing a cross-modality person re-identification training network based on a dual-stream ResNet50 convolutional neural network architecture; inputting the training set into the cross-modality person re-identification training network to obtain a cross-modality person re-identification test network through training; randomly selecting an image to be queried from the test set, and inputting the image to be queried and a candidate database from the test set into the cross-modality person re-identification test network to obtain an identification accuracy value corresponding to the image to be queried.

Abstract: A method for evaluating the effectiveness of a skin treatment on a skin feature includes correcting the color of a first image before the skin treatment, correcting the color of a second image after the skin treatment, determining the sizes of the skin feature in the first and second images, and comparing the corrected colors and sizes of the skin feature in the first and second images. In some embodiments, the first and second images include the skin feature and an indicator adjacent to the skin feature, where the indicator has a standard color and a known size.

Abstract: When removing a block distortion occurring in a local decoded image, a loop filtering part 11 of an image coding device carries out a filtering process on each of signal components (a luminance signal component and color difference signal components) after setting the intensity of a filter for removing the block distortion for each of the signal components according to a coding mode (an intra coding mode or an inter coding mode) selected by a coding controlling part 1.

Abstract: Disclosed herein is a method of transmitting point cloud data. The method may include acquiring point cloud data, encoding geometry information including positions of points of the point cloud data, generating one or more LODs based on the geometry information and selecting one or more neighbor points of each point to be attribute-encoded based on the one or more LODs, wherein the selected one or more neighbor points of each point are located within a maximum neighbor point distance, encoding attribute information of each point based on the selected one or more neighbor points of each point, and transmitting the encoded geometry information, the encoded attribute information, and signaling information.

Abstract: Disclosed herein is a method of transmitting point cloud data. The method may include acquiring point cloud data, encoding geometry information including positions of points of the point cloud data, generating one or more levels of detail (LODs) based on the geometry information and selecting one or more neighbor points of each point to be attribute-encoded based on the one or more LODs, encoding attribute information of each point based on the selected one or more neighbor points of each point, and transmitting the encoded geometry information, the encoded attribute information, and signaling information.

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: Methods and systems for generating artificial parcel data are provided. An example method involves accessing geospatial imagery depicting one or more buildings and surrounding areas, applying a machine learning model to the geospatial imagery to generate artificial parcel data in a form of a distance-transform raster map that represents a legal land parcel for each of the one or more buildings, and converting the distance-transform raster map into a vector map containing one or more polygons that represent the boundaries of each of the legal land parcels.

Abstract: An image processing device includes a setting unit configured to estimate a disparity between a first image and a second image based on a method different from stereo matching and set, based on the disparity thus estimated, a search range for a corresponding point for the stereo matching, a disparity map generating unit configured to search only the search range thus set for a corresponding point of each pixel between the first image and the second image and generate a disparity map, and a disparity map compositing unit configured to composite a plurality of the disparity maps each generated from a corresponding one of a plurality of image pairs.

Abstract: Provided are a training data collection apparatus, a training data collection method, and a program that easily collect training data for use in training of a model that discriminates a lesion from an endoscopic image, a training system, a trained model, and an endoscopic image processing apparatus. Endoscopic images are associated with respective pieces of findings-diagnosis information. When the endoscopic images and the respective piece of findings-diagnosis information that are associated with each other are recorded in a recording apparatus as training data for use in training of a model that discriminates a lesion from an endoscopic image, the endoscopic images associated with the respective pieces of findings-diagnosis information are classified into corresponding groups of the respective pieces of findings-diagnosis information. The endoscopic images are displayed and switched on a group-by-group basis by using tabs.

Abstract: In a point cloud data set represented by an image, a first object can be caused to be distinguished from a second object. Current positions of points in the point cloud data set can be arguments for functions of a set of functions. The set of functions can include attraction functions for the points and repulsion functions for the points. Results of the set of functions can be calculated. Based on the results, at least some of the points can be caused to move from the current positions to new positions. In the point cloud data set represented by the image with the at least some of the points being at the new positions, the first object can be distinguished from the second object.

Abstract: The present approach relates to determining a reference value based on image data that includes a non-occluded vascular region (such as the ascending aorta in a cardiovascular context). This reference value is compared on a pixel-by pixel basis with the CT values observed in the other vasculature regions. With this in mind, and in a cardiovascular context, the determined FFR value for each pixel is the ratio of CT value in the vascular region of interest to the reference CT value.

Abstract: Provided is provide a robotic device, including: a chassis; a set of wheels; a control system; a battery; one or more sensors; a processor; a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing, with the one or more sensors, data of an environment of the robotic device and data indicative of movement of the robotic device; generating or updating, with the processor, a map of the environment based on at least a portion of the captured data; and generating or updating, with the processor, a movement path of the robotic device based on at least the map of the environment.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: capturing two or more images of an item in a sequence; extracting one or more groups of images from the two or more images of the item in the sequence; converting at least one group of the one or more groups of images into a video file; and coordinating displaying, on a user electronic device of a user, a virtual display of the item, the virtual display of the item comprising the video file. Other embodiments are disclosed herein.

Abstract: An image recognition method and system based on deep learning are provided. The image recognition system includes a first recognizing engine, at least one second recognizing engine and a processing circuit. The second recognizing engine is activated to recognize a testing image when the first recognizing engine is recognizing the testing image. The processing circuit determines whether to interrupt the first recognizing engine recognizing the testing image according to a result outputted by the second recognizing engine after the second recognizing engine completes recognition of the testing image.

Abstract: The disclosure relates to techniques for perming chemical exchange saturation transfer (CEST) imaging correction. The present disclosure improves the speed of correcting CEST images.