Abstract: Two vehicles—an ego vehicle and an other vehicle—can share sensor data in a streamlined manner. One or more sensors can be configured to acquire first environment data of an external environment of the ego vehicle. A data summary based on second environment data of an external environment of the other vehicle can be received. Whether there is a common region of sensor coverage between the ego vehicle and the other vehicle can be determined. In response to there being a common region, the first environment data that is located within the common region can be identified and the resolution level of the identified first environment data can be reduced. The first environment data that has the reduced resolution level and a remainder of the first environment data excluding the identified first environment data can be transmitted to the other vehicle.

Abstract: A method for point cloud decoding includes receiving a bitstream. The method also includes decoding the bitstream into multiple frames that include pixels. A portion of the pixels are organized into patches and correspond to respective clusters of points of a 3D point cloud. The method further includes decoding, from the bitstream, an occupancy map frame. The occupancy map frame indicates the portion of the pixels included in the multiple frames that represent the points of the 3D point cloud. In addition, the method includes reconstructing the 3D point cloud using the multiple frames and the occupancy map frame. The method also includes determining whether to perform smoothing to the 3D point cloud based at least in part on properties of the multiple frames. Based on determining to perform the smoothing, the method includes performing the smoothing to the 3D point cloud.

Abstract: An image processing method is disclosed. The image processing method may include: partitioning an original gridded image to obtain a plurality of regional grid images, wherein the regional grid images are distributed along a direction away from a geometric center of the original gridded image (S300); and adjusting grid vertices of the regional grid images based on anti-distortion parameters to obtain a plurality of regional correction grid images forming a grid correction image (S400). A grid density of each of the regional correction grid images may be smaller than or equal to a grid density of a corresponding regional grid image, and the grid density of each of the plurality of the regional correction grid images may gradually increase along the direction away from the geometric center of the original gridded image.

Abstract: An object is to provide an endoscopic image learning device, an endoscopic image learning method, an endoscopic image learning program, and an endoscopic image recognition device that appropriately learn a learning model for image recognition for recognizing an endoscopic image in which a treatment tool for an endoscope appears. The object is achieved by an endoscopic image learning device including an image generation unit and a machine learning unit. The image generation unit generates a superimposed image where a foreground image in which a treatment tool for an endoscope is extracted is superimposed on a background-endoscopic image serving as a background of the foreground image, and the machine learning unit performs the learning of a learning model for image recognition using the superimposed image.

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; inferring, with the one or more processors of the robotic device, a current location of the robotic device, and generating or updating, with the processor, a movement path of the robotic device based on at least the map of the environment, at least a portion of the captured data, and the inferred current location of the robotic device.

Abstract: A computing device includes a memory for storing vector graphics including information about graphic objects including locations of the graphic objects, a display for displaying a representation of the vector graphics and a surface or pointing device for detecting an indication of a change in the vector graphics. In response to detecting the indication, the computing device is configured to automatically change a geometry of at least one other graphic object connected to at least one other graphic object at one or more locations of the at least one graphic object, the change includes a change in at least one location of the at least one graphic object, and at least one location of the at least one other graphic object is unchanged, and automatically change at least a portion of the representation, and the display is configured to display at least a portion of the changed representation.

Abstract: Provided is an image generating device including a first lighting unit configured to emit light to an object, a second lighting unit configured to emit light to the object and located at a position different from a position of the first lighting unit, a light receiver configured to receive light reflected from the object when light is emitted to the object from the first lighting unit or the second lighting unit, and an image processor configured to obtain image data based on the light received by the light receiver and process the obtained image data, wherein the image processor obtains a first image based on light reflected from the object when light is emitted from the first lighting unit, obtains a second image based on light reflected from the object when light is emitted from the second lighting, generates a processed image by synthesizing the first image and the second image to correct a pixel value of a light noise region of the first image and/or the second image.

Abstract: Disclosed herein are a point cloud data transmission method including encoding point cloud data, and transmitting a bitstream containing the point cloud data, and a point cloud data reception method including receiving a bitstream containing point cloud data, and decoding the point cloud data.

Abstract: Disclosed herein is a transmitting method and a receiving method of point cloud data. The transmitting method may include encoding point cloud data, and transmitting a bitstream that includes the point cloud data and signaling information. The receiving method may include receiving a bitstream that includes point cloud data and signaling information, decoding the point cloud data, and rendering the decoded point cloud data.

Abstract: A system for recording HIPAA compliant medical imagery may be configured as follows. A medical imaging system comprising a special purpose computer. The special purpose computer comprising: a processor, a permanently installed hard drive, and a random access memory. The permanently installed hard drive is configured such that it stores all instructions necessary for the processor to process medical files transmitted from the first medical system. The permanently installed hard drive is further configured such that it stores all instructions necessary for the processor to encrypt and store all medical files, as they are created, on a removable drive only. The removable drive is an encrypted removable flash storage drive.

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: A method for secure access to a mobile edge-computing system device based on a subscriber location fingerprint may comprise receiving a request to access the mobile edge-computing system, a first personal authorization credential record, and an encrypted token from a requesting client, associating the first personal authorization credential record with a block chain location fingerprint for the subscribing client, including a plurality of time-stamped records of a plurality of estimated or measured location state variables of the subscribing client and an associated confidence interval representing an accuracy of those variables, decrypting the location fingerprint, receiving a requesting client location measurement, predicting a current location for the subscribing client and an associated current confidence interval based on recent location state variables in the location fingerprint, and allowing the requesting client access to the mobile edge-computing system when the received requesting client location m

Abstract: Disclosed herein is a point cloud data transmission method. The transmission method may include encoding point cloud data, and transmitting point cloud data. Disclosed herein is a point cloud data reception device. Disclosed herein is a point cloud data reception method. The reception method may include receiving point cloud data, decoding the point cloud data, and rendering the point cloud data.

Abstract: A method and an electronic device are disclosed. The method includes obtaining an image, obtaining information of the image, obtaining content information of content included in the image, obtaining related information which relates to the image based on at least one of the information of the image and the content information, and classifying the image into at least one category based on a plurality of defined information/data elements and a relation among the information/data elements and metadata of the image.

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: According to embodiments, point cloud data transmission method may include encoding point cloud data, encapsulating a bitstream that includes the encoded point cloud data into a file, and transmitting the file, the bitstream is stored either in a single track or in multiple tracks of the file, the file further includes signaling data, wherein the signaling data include at least one parameter set and spatial region information, and the encoded point cloud data include geometry data and attribute data.

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: 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: An image processing method and an image processing circuit are provided. The method and circuit are applied to motion estimation. The method includes the steps of: performing low-pass filtering on a first image and a second image, wherein the first image is part of a first frame, the second image is part of a second frame, and the first frame is different from the second frame; calculating a first characteristic value of the first image and calculating a second characteristic value of the second image; calculating a sum of absolute difference (SAD) between the first image and the second image; blending the difference between the first characteristic value and the second characteristic value and the SAD to generate a blended result; and estimating a motion vector between the first image and the second image according to the blended result.

Abstract: An information processing apparatus includes an acquisition section that acquires at least two first evaluation values obtained by evaluating a motion of a worker in time series and a reference evaluation value obtained by evaluating a motion of an expert as an exemplar of the worker, a calculation section that calculates at least two motion levels indicating similarity between the at least two first evaluation values and the reference evaluation value, and an output section that outputs a development degree indicating a degree of development of the worker based on the at least two motion levels.