Abstract: Various examples of the disclosure pertain to using whole-slide images that depict healthy tissue for a training process for at least one machine-learning algorithm for digital pathology. For instance, an autoencoder neural network can be trained based on the healthy tissue.

Abstract: A system for detecting disk blade damage on an agricultural implement includes a disk blade configured to rotate relative to soil within a field across which the agricultural implement is traveling. Moreover, the system includes an imaging device configured to generate image data depicting an aft portion of the field located rearward of the disk blade relative to a direction of travel of the agricultural implement, with the aft portion of the field including a lane of the field to be worked by the disk blade. In addition, a computing system is configured to analyze the image data generated by the imaging device to identify when vegetation is present within the lane of the field. Furthermore, the computing system is configured to determine that the disk blade is damaged when the vegetation is present within the lane of the field.

Abstract: The diffusing member includes a diffusing surface with protruding portions arrayed in a first direction. The protruding portions include a first group of protruding portions in which tops thereof are arranged on a first line parallel to the first direction when they are projected on a first cross section parallel to the first direction.

Abstract: The subject disclosure relates to techniques for tracking keypoints on an object represented in a Light Detection and Ranging (LiDAR) point cloud. A process of the disclosed technology can include receiving, for each of a plurality of frames in a series, an identification of at least one keypoint on the object represented in LiDAR point clouds and a confidence score for the respective keypoint, wherein each of the plurality of frames includes LiDAR point clouds including the object at different times represented in the series, and determining kinematics for the object from a determined movement of the keypoint across the plurality of frames.

Abstract: A method for an interactive medical record review, said method comprising the steps of: displaying at-a-glance anatomical imagery with health code and/or date of event associated with a patient interaction on a whole-body graphic; and configuring the anatomical imagery on the whole-body graphic for pointing-device control enabling a requester to access and interact with the code-standardized data related to the patient interaction.

Abstract: An information processing apparatus includes a processor configured to perform display corresponding to progress of processing accepted by an image forming apparatus only in a case where a second user having a possibility of using the image forming apparatus other than a first user who is operating an operation panel is detected.

Abstract: An image processing apparatus includes a processor configured to: extract a first target image corresponding to a reading target having a glossy portion and a non-glossy portion from a first read image acquired by optically reading the reading target in a first reading environment, and extract a second target image corresponding to the reading target from a second read image acquired by optically reading the reading target in a second reading environment in which specular light acquired from the reading target by an image sensor is of higher intensity compared to the first reading environment; execute an alignment of the first target image and the second target image if positions of the first and second target images do not coincide with each other and if the first and second target images are alignable by translating or rotating at least one of the first target image or the second target image; and form a composite image by executing a composition process that composites a non-glossy region corresponding to

Abstract: Disclosed herein are systems and methods that provide an end-to-end approach for performing multi-dimensional object pose estimation in the context of machine learning models. In an implementation, processing circuitry of a suitable computer inputs image data to a machine learning model that predicts a parameterized rotation vector and a parameterized translation vector for an object in the image. Next, the processing circuitry converts the parameterized rotation vector and the parameterized translation vector into a non-parameterized rotation vector and a non-parameterized translation vector respectively. Finally, the processing circuitry updates the image data based on the non-parameterized rotation vector and the non-parameterized translation vector.

Abstract: Described herein are an imaging system and method. Specifically, the imaging system includes a positioning image acquisition unit configured to acquire positioning images of a scanned object from a plurality of angles, a contour estimation unit configured to estimate a contour of the object in each positioning image in a scanning direction when truncation is present in at least one positioning image, and a display field of view determination unit configured to select a maximum value of an estimated contour as a display field of view of the image. A contour of a scanned object in each positioning image in a scanning direction is estimated when truncation is present in at least one positioning image, thereby determining a suitable display field of view. An appropriate display field of view can be set, so that a reconstructed image can cover the entire contour of the object and have a higher resolution.

Abstract: A vehicle drivable area detection system includes a vehicle, at least one 3D sensor and an electronic controller. The at least one 3D sensor is installed to the vehicle and is configured to scan and capture data using laser imaging, detection and distance ranging relative to the vehicle. The data collected represents ground surface features including vertical obstacles, non-vertical obstacles and a drivable area proximate the vehicle within a line-of-sight of the 3D sensor. The electronic controller is installed within the vehicle and is electronically connected to the 3D sensor and at least one driver assist component. The electronic controller conducts the following: a vertical obstacle extraction from the data; terrain estimating from the data; curb detection from the data; and generating a plurality of data elements identifying vertical obstacles including curbs and the drivable area to the at least one driver assist component.

Abstract: The current disclosure provides systems and methods for reducing clutter artifacts in medical images. In one example, a method for an image processing system comprises receiving a sequence of medical images including an amount of clutter artifacts; reducing the amount of clutter artifacts present in the sequence of medical images using a trained data-driven model, the data-driven trained on image sequence pairs including a first, lower-clutter image sequence as a target image, and a second, higher-clutter image sequence as an input image, the higher-clutter image sequence generated by superimposing an artifact overlay on the lower-clutter image sequence, the lower-clutter image sequence generated by an imaging device during a medical exam of a subject; and displaying an artifact-reduced version of the sequence of medical images on a display device of the image processing system, the artifact-reduced version outputted by the data-driven model.

Abstract: An information processing device according to the present invention performs operations including: selecting a base image from a base dataset including a target region including an object to be subjected to machine learning and a background region not including an object to be subjected to machine learning, and generating a processing target image; selecting the target region included in another image included in the base dataset; combining an image of the selected target region and information on an object to be subjected to machine learning included in the image of the target region with the processing target image; generating a dataset of the processing target images obtained by combining a predetermined number of the target regions; calculating a feature of an image included in the dataset; generating a learned model using first machine learning using the feature and the dataset; and outputting the generated learned model generated.

Abstract: Provided are an image analysis apparatus, an image analysis system, an image analysis method, a program, and a recording medium for supporting a user who reads a printed matter and uses data of a read image. An image analysis apparatus includes a processor, in which the processor executes a process of reading a printed matter on which a captured image is printed to acquire image data of a read image, a process of analyzing a first content related to reading of the printed matter based on the image data, and a process of acquiring first support information based on an analysis result of the first content.

Abstract: An embodiment vehicle control device includes a LiDAR sensor mounted in a vehicle and configured to sense an environment surrounding the vehicle, an audio video navigation (AVN) unit mounted in the vehicle and configured to generate an event signal when a predetermined event area is sensed during driving of the vehicle, and a processor configured to acquire information on the environment surrounding the vehicle based on LiDAR data obtained by the LiDAR sensor and the event signal and to control a view angle of a camera mounted in the vehicle in response to the information on the environment.

Abstract: Disclosed herein is an apparatus for rear seat detection. The apparatus may include: an indoor camera having a field of view for a vehicle interior and configured to provide image data; an indoor radar having a sensing area for the vehicle interior and configured to provide radar data; a controller including a first processor and a second processor. The first processor configured to obtain information on a change in a front passenger seat and a rear seat of a vehicle based on processing the image data The second processor configured to obtain motion information on an object in the rear seat of the vehicle based on processing the radar data. The first controller may identify a change of a detection area in the rear seat based on the information on the change in the front passenger seat and the rear seat of the vehicle, and determine whether a human is seated in the rear seat of the vehicle based on the motion information on the object in the detection area where the change is identified.

Abstract: An image reading device (100) includes a lens array (4), a light receiver (6), and at least one light blocking member. The lens array (4) includes first lens bodies arranged in a line in a main scanning direction with predetermined spacing therebetween to converge light from a reading target. The light receiver (6) receives light converged by each first lens body. The at least one light blocking member is disposed between the light receiver (6) and an end of the lens array (4) proximate to the reading target at at least one position corresponding to the predetermined spacing in the main scanning direction. The at least one light blocking member blocks light from the reading target propagating between the first lens bodies, and each of the at least one light blocking member separates optical paths of light converged by ones of the first lens bodies adjacent to each other.

Abstract: Systems and methods for segmenting 3D images are provided. In an embodiment, the system includes a neural network having a low-resolution module trained to infer a complete low-resolution segmentation from an input low-resolution 3D image and to generate corresponding low-resolution feature maps; and a high-resolution module trained to infer a complete high-resolution segmentation from an input high-resolution 3D image and the feature maps from the low-resolution module. Methods for training the neural network and measuring a volume of an object using the 3D segmentations are also described.

Abstract: An image processing apparatus comprises: an acquiring unit configured to acquire image data generated by reading an original; a specifying unit configured to specify an object region including a predetermined object in the image data; a removing unit configured to remove, from the object region, noise with a size smaller than a size specified by a first threshold; and a setting unit configured to set the first threshold, for each object region specified by the specifying unit, in accordance with the size of the object region.

Abstract: An information processing apparatus includes a processor, the processor extracting, from at least a part of a document, a feature of the part, extracting related elements which are elements related to the feature from a question and an answer about a first target object stored in a storage unit using the extracted feature, and combining the extracted related elements and the feature to generate a question about a second target object specified in the document.

Abstract: An image reading apparatus includes a transparent member having a placement surface on which a document is to be placed, a reading unit configured to read an image of the document placed on the transparent member via the transparent member, and a reading mode in which an image of a document, placed in a holding member with a transparent portion, is read. The document to be read is placed in the holding member so as to be visible via the transparent portion and the holding member is placed on the placement surface with the transparent portion of the holding member in contact with the placement surface. A processor detects an edge in an image acquired by the reading unit in the reading mode, and to extract an image surrounded by an edge detected in an area surrounded by an edge corresponding to the holding member in the reading mode.