Abstract: Systems and methods of tracking chain of custody of relevant electronic documents are provided. An example method begins with receiving an electronic document collection request. In response, a set of relevant electronic documents is retrieved, a tracking unit is generated, and the tracking unit is assigned to the set of relevant electronic documents. The tracking unit includes: a state machine having at least two stages including a specification stage for specifying the electronic document collection request and a review stage for displaying the relevant electronic documents, a plurality of Chain-Of-Custody (COC) statuses, and a plurality of number of relevant document values. The chain of custody of the set of relevant electronic documents is tracked. The set of relevant electronic documents generated by the electronic document collection request is displayed by a graphical user interface.

Abstract: According to some embodiments, a system may include an additive manufacturing platform that provides additive manufacturing capability data. A customer platform, associated with a customer, may transmit an industrial asset item request for an industrial asset item. A digital transaction engine may receive the additive manufacturing capability data and the industrial asset item request. The digital transaction engine may then associate the industrial asset item request with an industrial asset definition file, and, based on the additive manufacturing capability data and the industrial asset definition file, assign the industrial asset item request to the additive manufacturing platform. The assignment of the industrial asset item request may be recorded via a secure, distributed transaction ledger. Responsive to the assignment, the additive manufacturing platform may create the industrial asset item (e.g., via an additive manufacturing printer) and provide the item to the customer.

Abstract: A computer-enabled system and related method to enable a user to edit, author and implement educational curricula and associated coursework. The system includes a content identification and parsing into instructional objects function, an instructional object organization function, a tool and template configuration function and a data gathering and analysis function. The instructional object organization function is configured to organize the instructional objects through metadata tags into a complete course content, wherein the organization of the instructional objects is modifiable.

Abstract: A computer system for identifying medical care providers outside a process-of-care standard for a field of health care is configured to perform steps that include retrieving claim line item information including procedure/service codes, and retrieving definitions for marker code groups associated with each of a set of medical conditions; deriving, for each marker-condition pair, an actual rate of utilization of the marker code group for episodes of the associated medical condition; and assigning a status to each marker-condition pair in response to the actual rate of utilization respectively exceeding or not exceeding a target rate. The steps also include aggregating the statuses across the marker-condition pairs to obtain an overall score, and causing an output to be displayed in a viewable format. The output includes the overall score, each marker-condition pair of the set of medical conditions, and the status for each marker-condition pair of the set of medical conditions.

Abstract: An information processing apparatus includes a controller configured to accept a reservation for use of a vehicle charging service from a user of a vehicle, notify the user of a first joining point and a first joining time as a target joining point and a target joining time at which the vehicle joins a power supply vehicle, determine whether the vehicle can arrive at the first joining point by the first joining time based on positional information and traffic congestion information for the vehicle, determine a second joining point closer to the vehicle than the first joining point upon determining that the vehicle cannot arrive, and notify the user of a proposal to change the target joining point to the second joining point.

Abstract: An apparatus for determining a passenger status comprising a data storage unit 1150; a beacon ID translation module 1111 configured to read a position ID and an attribute of a received beacon signal; a beacon signal recognition module 1112 configured to determine a moving object beacon signal strength of beacon signals received from beacons installed on a moving object and a stationary object beacon signal strength of beacon signals received from beacons installed on a stationary object, wherein the beacon signal strength is the sum of the number of beacon IDs stored in a beacon signal management table 1102; a passenger status presumption module 1113 configured to determine a passenger current status based on the moving object beacon signal strength, the stationary object beacon signal strength and/or a previous passenger status.

Abstract: System, methods, and non-transitory computer-readable medium are provided for watermarking a diffusion model. For example, a method for watermarking a diffusion model may include generating one or more training data elements. In some aspects, the one or more trainings data elements may include target images. Moreover, the target images may include pre-defined watermark information. Further, the method may include training the diffusion model to predict the target images using training data including the one or more training data elements.

Abstract: A system may receive a frame, divide the frame into objects, select an object from the objects, divide the object into regions, determine a set of attributes for a target region of the regions, assign a priority to the target region, and queue, based on an assignment of a low priority, the target region to a discount rendering instance.

Abstract: In some implementations, a method includes: obtaining a reference image and forward flow information; identifying a neighborhood of pixels corresponding to a pixel within a target image based on the forward flow information; in accordance with a determination that a characterization vector for the neighborhood of pixels satisfies a background condition, generating a warp result for the pixel based on a first warp type; in accordance with a determination that the characterization vector satisfies a foreground condition, generating the warp result for the pixel based on a second warp type; and in accordance with a determination that the characterization vector does not satisfy the foreground or background conditions, generating the warp result for the pixel based on a third warp type; and populating pixel information for the pixel within the target image based on pixel information for a reference pixel within the reference image that corresponds to the warp result.

Abstract: An electronic device and control method for selecting a scaler based on image characteristics are provided. The electronic device includes at least one processor, a display, and a plurality of scalers including a first scaler and a second scaler. The at least one processor generates a first temporary upscaling image of an image by using an algorithm of the first scaler and generate a second temporary up scaling image of the image by using an algorithm of the second scaler. The at least one processor identifies a difference value of a pixel between the first temporary upscaling image and the second temporary upscaling image. The at least one processor selects one scaler based on the identified difference value and a preconfigured threshold value. The at least one processor upscales the image and controls the display to display the upscaled image.

Abstract: An electronic device uses a multidimensional (e.g., 3D) scaler to process multiple-viewing-angle (e.g., 3D-aware) images by resampling each view image and processing image data of each view image according to a view map to change resolution or improve perceived image quality. After being processed, each view image of the multiple-viewing-angle image is used to rebuild a final processed multiple-viewing-angle (e.g., 3D-aware image) with all views for displaying on the electronic device.

Abstract: The present application provides a method for invoking a panoramic picture, and a panoramic camera system. The method includes steps of: collecting multiple camera pictures; unfolding and stitching the collected multiple camera pictures to obtain the panoramic picture; mapping the panoramic picture into a target sphere model to obtain a panoramic sphere picture; and creating a virtual camera, where a third-party application is enabled to directly invoke the virtual camera to obtain the panoramic picture. The present application can facilitate the third-party application to directly invoke the panoramic picture in the panoramic camera system.

Abstract: CNN-based methods for cross-view image geo-localization rely on polar transform and fail to model global correlation. A pure transformer-based approach (TransGeo) is described to address these limitations from a different perspective. TransGeo takes full advantage of the strengths of the transformer related to global information modeling and explicit position information encoding. The claimed invention further leverages transformer input's flexibility and discloses an attention-guided non-uniform cropping method so that uninformative image patches are removed with a negligible drop in performance to reduce computation cost. The saved computation can be reallocated to increase resolution only for informative patches, resulting in performance improvement with no additional computation cost. This “attend and zoom-in” strategy is highly similar to human behavior when observing images.

Abstract: A subject extraction device (10) according to the present disclosure includes: a resolution reducing unit (111) that reduces resolution of an image to generate a low resolution image; a subject possibility extraction unit (112) that extracts possibilities of a subject portion from the low resolution image; a resolution increasing unit (113) that increases resolution of a boundary portion between the subject portion and a non-subject portion, judges whether the boundary portion is the non-subject portion pixel by pixel, and decides the subject portion and the non-subject portion on the basis of a judgement result; and a calculation resource assignment unit (114) that determines a value of the calculation resource to be assigned to the subject possibility extraction unit (112) as a first value and a value of the calculation resource to be assigned to the resolution increasing unit (113) as a second value such that the values are equal to or less than an upper limit value of the calculation resource that can be

Abstract: A device and a method that can be used to perform a correspondence analysis in image data in a particularly low-noise and efficient manner. The device and method include selecting image patches from individual images and generating a plurality of one-dimensional signals using even and odd convolution kernels in the spatial window in each case, and processing differences between the convolution results in a non-linear manner and accumulating these differences to form a correspondence function and evaluating this function.

Abstract: An image processing device has circuitry, which is configured to obtain input image data being represented by a number of color channels and to input the input image data into a neural network for generating output multispectral image data, wherein the neural network is configured to generate at least first and second multispectral image data on the basis of the input image data, wherein a number of spectral channels of the second multispectral image data is larger than the number of spectral channels of the first multispectral image data.

Abstract: A method of processing an image, an electronic device and a storage medium. The method includes: generating a to-be-processed image according to a first target image and a second target image, where an identity information of an object in the to-be-processed image is matched with an identity information of an object in the first target image; generating a set of disentangled images according to the second target image and the to-be-processed image, where the set of disentangled images includes a head-disentangled image and a disentangled repair image; and generating a fusion image according to the set of disentangled images, where an identity information and a texture information of an object in the fusion image are matched with the identity information and the texture information of the object in the to-be-processed image, respectively, and a to-be-repaired information related to the object in the fusion image is repaired.

Abstract: A system and method and for automatically enhancing an input image includes detecting a genre for the input image using a genre identification machine-learning model and identifying one or more objects in the input image using an object identification machine-learning model. The identified genre and objects are then compared to a list of genre and object tags for images in an image library to identify a plurality of genre and object tags that are similar to the identified genre and objects. A list of edits corresponding to each of the identified similar genre and object tags is then to the input image to generate a plurality of enhanced images for the input image. An aesthetic value is measured for the plurality of enhanced images and at least one of the plurality of enhanced images is provided as a recommendation for enhancing the input image, based on the aesthetic value.

Abstract: An image superimposition system (100) according to the present disclosure includes: a control device (4) that generates face region image information indicating a face region image obtained by trimming a face region (R1) in a captured image, and generates superimposition object region image information indicating a superimposition object region image obtained by trimming a superimposition object region (R2) in a captured image; a face recognition device (2) that recognizes a face image and generates face image information indicating the face image; a superimposition object recognition device (3) that recognizes a superimposition image corresponding to the superimposition object region image; and a combining device (5) that detects the face image of the user from a combining region image that is an image of a combining region (R3) larger than the face region (R1) and the superimposition object region (R2) in a captured image, and generates combined image information indicating a combined image formed by superi

Abstract: An apparatus and method for processing an image are disclosed. The method includes: segmenting both a first image and a second image and generating segmentation mask pairs, each segmentation mask pair having a segmentation mask of the first image and a segmentation mask of the second image; generating local homography matrices of the first image with respect to the second image, based on the segmentation mask pairs, the first image, and the second image; and generating a synthetic image obtained by aligning the first image with the second image, wherein the aligning is performed based on the local homography matrices, the segmentation mask pairs, the first image, and the second image.

Abstract: Embodiments relate to an image processing circuit that performs noise reduction on image data. The image processing circuit includes a noise reduction circuit with a kernel calculation circuit, a noise filtering circuit, and a blending circuit. The kernel calculation circuit generates a machine learning (ML) kernel for at least one pixel of an image and a bilateral kernel for the at least one pixel of the image. The noise filtering circuit performs noise filtering of the image using the ML kernel to generate a first de-noised version of the image, and performs noise filtering of the image using the bilateral kernel to generate a second de-noised version of the image. The blending circuit blends each color component of the first de-noised version of the image with a corresponding color component of the second de-noised version of the image to generate a de-noised multi-color version of the image.

Abstract: This disclosure provides systems, methods, and devices for image signal processing that support adjusting a Bokeh effect in image data. In a first aspect, a method of image processing includes receiving a first user input specifying a first depth of focus. A camera is configured with a first aperture size of a plurality of aperture sizes of the camera that is nearest a second aperture size that corresponds to the first depth of focus. The first aperture size is smaller than the second aperture size. Image data representing a scene is received that is captured with the first aperture size of the camera. First modified image data is determined by applying a first blur kernel to the image data. The first modified image data represents the scene at the depth of focus corresponding to the second aperture size. Other aspects and features are also claimed and described.

Abstract: A medical image processing method, an X-ray diagnostic apparatus, and a method of generating a learned model includes receiving first X-ray image data, inputting the first X-ray image data to a trained model, outputting, from the trained model, an X-ray image having an image quality higher than an image quality of the first X-ray image data. The learned model was trained using contrastive learning using second X-ray image data as input data, third X-ray image data and fourth X-ray image data as label data, the third X-ray image data being negative label data having worse image quality than the second X-ray image data, and the fourth image data being positive label data having better image quality than the second X-ray image data.

Abstract: Sensor data processing systems and techniques are described. In some examples, a sensor data processing system receives sensor data (e.g., image data). The system receives a sensor data processing parameter that is associated with a sensor data processing function, such as denoising, and that is consistent across the sensor data. The system adjusts a trained machine learning model based on the sensor data processing parameter to generate an adjusted machine learning model. The system processes the sensor data using the adjusted machine learning model to apply the sensor data processing function to the sensor data according to the sensor data processing parameter and to generate processed sensor data (e.g., denoised image data).

Abstract: An image processing method, an electronic device, and a readable storage medium are provided. The method includes: obtaining an original image; denoising the original image using an image filtering algorithm to form a preprocessed image S; performing contrast enhancement in the preprocessed image S to form a contrast-enhanced image J; adjusting saturation of the contrast-enhanced image J to form an enhanced display image M; sharpening the enhanced display image M to form a sharpened image N; and performing dynamic range enhancement on the sharpened image N using a dynamic range image enhancement algorithm to form an output image. The image processing method, electronic device, and readable storage medium enable multi-level processing on the original image and improve the display accuracy of the output image.

Abstract: Training a neural network to extract a degradation map from a degraded image comprises generating training data comprising pairs of images, each pair of images comprising a clean source image and a degraded source image by, for each clean source image, generating a corresponding noisy image by adding spatially invariant noise to the clean source image, and blending the noisy image with the clean source image according to varying intensity levels defined by a spatially variant mask to obtain the degraded image. The training data is used to train the neural network by inputting each degraded source image to the neural network and extracting a degradation map from the degraded source image such that when the degradation map is applied to its corresponding clean source image the loss between the degraded source image and its corresponding clean source image after the degradation map is applied is minimised.

Abstract: Provided is a noise reduction method of a lightmap, including: obtaining a lightmap (202); determining, by using each pixel in the lightmap as a target pixel, a noise reduction reference region corresponding to the target pixel in the lightmap, the noise reduction reference region including a reference pixel for noise reduction reference (204); determining a distance in three-dimensional space between the reference pixel and the target pixel (206); determining a light contribution parameter value of the reference pixel to the target pixel based on the distance (208); obtaining light data recorded for the reference pixel in the lightmap (210); and performing, based on the light contribution parameter value and the light data of the reference pixel, noise reduction on light data recorded for the target pixel in the lightmap, to obtain a noise-reduced lightmap (212).

Abstract: A data processing apparatus for processing a digital input image is configured to receive the digital input image. The digital input image includes input photon arrival-time data at input image locations. The data processing apparatus is further configured to compute a digital output image based on the digital input image by deconvolution. The digital output image includes output photon arrival-time data at output image locations. The output photon arrival-time data represent an estimate of an unblurred ground-truth of the input photon arrival-time data. The data processing apparatus is further configured to compute the deconvolution by an iterative algorithm using an update function. The update function depends on a point-spread function, a previous estimate of the ground-truth, and the input photon arrival-time data.

Abstract: The invention provides systems, apparatus and methods for digital image processing providing enhanced display of and radiomics generated from images generated from x-ray and other medical imaging data. Such systems, apparatus and methods capture and use that data to identify distinct gradations, e.g., of grayscale within and beyond the spectrum of human vision, then delineate borders based on ranges of gradation, forming irregular multi-layer visual objects with delineated internal contouring and an outer boundary, and then enhance the delineated layers and superimpose the enhancing display over and/or determine radiomic measures of the corresponding areas of the original image, thereby revealing underlying morphology of masses previously obscured, hidden or “masked” from human vision.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for inpainting a digital image using a hybrid wire removal pipeline. For example, the disclosed systems use a hybrid wire removal pipeline that integrates multiple machine learning models, such as a wire segmentation model, a hole separation model, a mask dilation model, a patch-based inpainting model, and a deep inpainting model. Using the hybrid wire removal pipeline, in some embodiments, the disclosed systems generate a wire segmentation from a digital image depicting one or more wires. The disclosed systems also utilize the hybrid wire removal pipeline to extract or identify portions of the wire segmentation that indicate specific wires or portions of wires. In certain embodiments, the disclosed systems further inpaint pixels of the digital image corresponding to the wires indicated by the wire segmentation mask using the patch-based inpainting model and/or the deep inpainting model.

Abstract: A method includes receiving an image from an image capture device, determining a mask for the image, and determining a depth representation including a pixelwise depth estimation for a scene represented by the image. A respective inpainting region in the mask includes pixels that represent two or more features of the scene. The two or more features have different depth estimates in the depth representation, and a respective feature of the two or more features overlaps with the non-inpainting region. The method includes refining the respective inpainting region based on (i) the two or more features having different depth estimates, and (ii) the respective feature of the two or more features overlapping with the non-inpainting region, and inpainting the image in accordance with refining the respective inpainting region such that the portion of the respective feature that overlaps with the non-inpainting region is not inpainted.

Abstract: Provided is a method of training a neural radiance field and producing a rendering of a 3D scene from a novel viewpoint with view-dependent effects. The neural radiance field is initially trained using a first loss associated with a plurality of unmasked regions associated with a reference image and a plurality of target images. The training may also be updated using a second loss associated with a depth estimate of a masked region in the reference image. The training may also be further updated using a third loss associated with a view-substituted image associated with a respective target image. The view-substituted image is a volume rendering from the reference viewpoint across pixels with view-substituted target colors. In some embodiments, the neural radiance field is additionally trained with a fourth loss. The fourth loss is associated with dis-occluded pixels in a target image.

Abstract: The present invention relates to a method for creating a neural network model which can imitate retouching styles of a plurality of experts, and creating a black-and-white photo which gives aesthetics by inputting a color photo into the neural network model.

Abstract: In a method for processing an image, statistical information is obtained by performing histogram statistics on an image frame included in a video sequence. An overall compression ratio of the image frame and mid luminance information of the image frame are obtained based on the statistical information. A compression curve is obtained based on the overall compression ratio and the mid luminance information. Dynamic range compression is performed on the image frame by adjusting a shape of the compression curve based on the overall compression ratio, the mid luminance information and adjustment ratios.

Abstract: An image processing method includes: determining a brightness value of an image; and enhancing a brightness of the image when the brightness value of the image is less than an image brightness threshold. Enhancing the brightness of the image includes: determining each pixel of the image as a target pixel; determining a brightness enhancement value of the target pixel based on the brightness value of the image, an initial brightness value of the target pixel, and initial brightness values of neighboring pixels of the target pixel; and using the brightness enhancement value as an enhanced brightness value of the target pixel.

Abstract: Techniques for improving image quality of a HDR image by increasing the contrast in the dark portion of the image while preserving the bright parts of the image. The methods preserve the input dynamic range. An image with a luminance histogram gap between a main portion with low brightness and a small portion with high brightness. A first tone mapping curve is determined for the low brightness portion of the image. A second tone mapping curve is determined from a selected point on the first tone mapping curve to a maximum brightness level of the input image. A final tone mapping curve is generated including the first tone mapping curve from a minimum brightness input to the selected point and the second tone mapping curve from the selected point to a maximum brightness level. The method can increase overall image quality and contrast.

Abstract: In an example embodiment, two different neural networks are used in conjunction with each other to identify defects in items appearing in images. More particularly, a defect neural network is trained on images labeled with an indication of a defect type (or no defect) appears in an item in the image (with optionally an identification of the area in which the defect appears). Separately, a location neural network is trained on images that have a specific geometric shape (e.g., point, line, circle) labeled in the image. The labels identify not just the pixels that are part of the shape, but also provide a value that indicates a degree of the identification of the location. Surrounding pixels to the identified shapes are then given values according to a distribution that is based on distance from the shape, with the peak values being those pixels that are exactly on the identified shape.

Abstract: A computer system may include at least one memory and at least one processor in communication with the at least one memory. The processor may be programmed to: (1) receive photographic data including one or more images of a structure; (2) in response to receiving the photographic data, apply the photographic data to a structure assessment model configured to determine a structural status of the structure, wherein the structure assessment model is trained using historical photographic data including a plurality of historical images of structures; (3) receive an output from the structure assessment model, wherein the output at least one determined characteristic of the structure; and/or (4) based upon the output, transmit a message to a user computing device associated with the structure that causes display of the determined characteristic.

Abstract: Devices and methods for conducting a remote photography session are described. In some instances, a computing device at a photography station receives messages from a photography station controller which is remote from the photography station. In some examples, the computing device receives one or more messages instructing the computing device to capture an image from an image capture device. The computing device can also receive one or more messages instructing the computing device to adjust the image capture device. Additionally, the messages may instruct the computing device to present instructions to a subject of the photography session. These instructions can prompt the subject to make adjustments to meet a criteria for a photograph in the photography session.

Abstract: Technique for calculating cord parameters of a textile reinforced rubber sheet moving in the machine direction includes: (a) illuminating an area on a side of the sheet with radiation; (b) obtaining a digital image of an illuminated area; (c) generating a digital image of the outer surface; and (d) calculating cord parameters. The elongated textile cords are preferably parallel to each other the elongated textile cords are orientated in a cross direction which is perpendicular to the machine direction. Scanning or static camera with a light source can be employed; the light is directionally perpendicular to the plurality of cords. The digital image comprises a 2D profile, 3D map, or gray scale profile or map and the images are analyzed typically with Fast Fourier transform or sinusoidal fitting techniques. Cord count per unit length, cord spacing, the presence of missing cords, and/or the presence of shifted cords can be determined.

Abstract: The present disclosure relates to an image recognition method and system for a display panel, a training method, and an electronic device and a non-volatile computer-readable storage medium. The image recognition method includes: acquiring an image of a display panel, wherein the image includes gate lines extending in a first direction and data lines extending in a second direction, the gate lines and the data lines intersecting to define a plurality of sub-pixel regions, and the image further includes a defect pattern; and recognizing the defect pattern in the image by using an image recognition model to obtain defect information, wherein the defect information includes at least one of a defect type or a defect position of the defect pattern, the image recognition model comprises a first attention model configured to learn a weight proportion of a feature of the defect pattern in the image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction aggregation. In some implementations, an exemplary method includes obtaining images of fish captured by an underwater camera; providing data of the images to a trained model; obtaining output of the trained model indicating the likelihoods that the biomass of fish are within multiple ranges; combining likelihoods of the output based on one or more ranges common to likelihoods of two or more fish to generate a biomass distribution; and determining an action based on the biomass distribution.

Abstract: Various embodiments include a method for analyzing an electrode layer for a battery storage device in an electrode layer production facility. The method may include: acquiring an image comprising two pixels of the electrode layer, wherein a first pixel represents a first location of the electrode layer and a second pixel represents a second location of the electrode layer, wherein the first location and the second location are disposed adjacent to each other; determining a first value for a material property of the electrode layer at the first location based on the first pixel; determining a second value for the material property of the electrode layer at the second location based on the second pixel; comparing the first value and the second value and determining a comparison value; and determining characteristic properties of the electrode layer based on the comparison value.

Abstract: A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Abstract: An anomaly display device provided with a starting information acquisition unit that acquires starting information including information for starting anomaly detection for detecting an anomaly in an image included in an input picture; an input picture acquisition unit that acquires the input picture; an anomaly detection unit that executes the anomaly detection, based on the acquired starting information, by comparing the acquired input picture with information based on a prestored normal picture; and a display unit that displays, in overlay on the input picture, information based on information detected by the anomaly detection unit.

Abstract: One or more example embodiments of the present invention relates to a computer-implemented method for assessing a component quality of an electron emitter as a function of an ascertained degree of similarity with at least one further electron emitter.

Abstract: Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for simulating, predicting, or estimating, based on machine learning neural networks, wall stress of a body part. In one approach, a first neural network automatically detects features in multiple images of a body part. For example, the first neural network may detect, for each image, a lumen and a wall of an aorta. According to the detected features, a second neural network may simulate, estimate, or predict wall stress of the body part in response to pressure applied to the body part. For example, a model generator can generate a three-dimensional model of the body part according to the detected features in the multiple images, and the second neural network can simulate, estimate, or predict wall stress of the body part according to the three-dimensional model.

Abstract: A computer-implemented method for estimating cartilage includes pre-processing, via a processor, a segmented image of region of interests (ROIs) of a subject to separate the ROIs into individual ROI volumes, wherein the ROI volumes comprise at least one cartilage region. The computer-implemented method also includes performing, via the processor, surface separation between a respective subchondral surface and a respective articular surface for each ROI volume to extract a respective separate subchondral surface and a respective separate articular surface for each ROI volume. The computer-implemented method further includes estimating, via the processor, cartilage statistics for the at least one cartilage region utilizing a nearest neighbor algorithm based on the respective separate subchondral surface and the respective separate articular surface for each ROI volume.

Abstract: The system and method of the present invention processes a raw image of a person's teeth captured through a camera according to given specifications by using a uniquely trained convolutional neural network (CNN). The system and method identify early erosions and their location on the raw image of the teeth.

Abstract: Provided is a technology for reducing calculation cost for identifying a region that has a high tumor content ratio and is used for gene panel testing. This testing support device comprises an acquisition means that acquires a pathological specimen image, a region reception means that receives designation of a region for the acquired pathological specimen image, a detection means that detects the position of a tumor cell and the position of a non-tumor cell in the designated region of the pathological specimen image, and a calculation means that calculates a tumor content ratio for each divided region, which is obtained through division a predetermined size, on the basis of the detected position of a tumor cell and the detected position of a non-tumor cell.