Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for a secure edge platform that uses image classification machine learning models. An edge platform can include at least one camera and can identify image classification models that generate classification output data from image data generated by the cameras. The edge platform can receive image data generated by the camera, and provide the image data to the models. In response to providing the image data classification models, the edge platform can receive classification output data. In response to receiving the classification output data from the image classification models, the edge platform can generate augmentation data that is associated with the image data, then transmit detection data to a central server platform. The detection data can include (i) the classification output data and (ii) the augmentation data associated with the image data. Data can be made recordable, reportable, searchable, and alarmable.

Abstract: A method (400) and a system (200) for generating a chromatically modified image of one or more components on a microscopic slide (303) is disclosed. In one aspect of the invention, the method includes obtaining the image of the one or more components on the microscopic slide (303). Additionally, the method (400) includes processing the image to identify the one or more components. The method (400) further includes segmenting at least one part of the one or more components identified from the image. Furthermore, the method (400) includes chromatically modifying the at least one part of the one or more components and generating a chromatically modified image of the one or more components.

Abstract: An image processing device includes: a determination means for determining an image capturing means used for capturing at least one particular portion in a combined image in which a first partial image captured using a first image capturing means and a second partial image captured using a second image capturing means are stitched together; and a correction means for applying image correction to the combined image based on a determination result of the determination means.

Abstract: An imaging system can receive an image of a portion of an environment. The environment can include an object, such as a hand or a display. The imaging device can identify a data stream from an external device, for instance by detecting the data stream in the image or by receiving the data stream wirelessly from the external device. The imaging device can detect a condition based on the image and/or the data stream, for instance by detecting that the object is missing from the image, by detecting that a low resource at the imaging device, and/or by detecting visual media content displayed by a display in the image. Upon detecting the condition, imaging device automatically determines a location of the object (or a portion thereof) using the data stream and/or the image. The imaging device generates and/or outputs content that is based on the location of the object.

Abstract: An information processing device of the present invention includes a detection means that detects the content of an image, a determination means that determines a processing mode for the image based on the result of detection of the content of the image, and an execution means that executes processing for a captured image corresponding to the processing mode.

Abstract: Provided is an object detection device or the like which efficiently generates good-quality training data. This object detection device is provided with: a detection unit which uses a dictionary to detect objects from an input image; a reception unit which displays, on a display device, the input image accompanied by a display emphasizing partial areas of detected objects, and receives, from one operation of an input device, a selection of a partial area and an input of the class of the selected partial area; a generation unit which generates training data from the image of the selected partial area and the inputted class; and a learning unit which uses the training data to learn the dictionary.

Abstract: An information processing device of the present invention includes a detection means that detects the content of an image, a determination means that determines a processing mode for the image based on the result of detection of the content of the image, and an execution means that executes processing for a captured image corresponding to the processing mode.

Abstract: A method of generating lane information using a neural network includes generating a lane probability map based on an input image, generating lane feature information and depth feature information by applying the lane probability map to a second neural network, generating depth distribution information by applying the depth feature information to a third neural network, generating spatial information based on the lane feature information and the depth distribution information, generating offset information including a displacement between a position of a lane and a reference line by applying the spatial information to a fourth neural network, and generating three-dimensional (3D) lane information using the offset information.

Abstract: Disclosed is an image processing method, electronic device and storage medium. The method includes obtaining feature information of first region in a current image frame, wherein first region includes a region that is determined by performing motion estimation on the current and previous image frames based on optical flow; obtaining feature information of second region in the current image frame, wherein second region includes a region corresponding to pixel points among first pixel points of the current image frame, where its association with pixel points among second pixel points of the previous image frame satisfies a condition; and based on the feature information of first region and that of second region, fusing the previous and current image frames to obtain a processed current image frame, which is used as a previous image frame for a next image frame.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: There are provided a learning recommendation apparatus and method for detecting a problem from an image through character recognition and providing at least one sub-topic learning among a plurality of sub-topic learnings related to the detected problem. The provided learning recommendation apparatus recommends, as a recommendation target, a plurality of learning topics including the concept of a formula which has been read through the character recognition for an image, wherein a priority order is set to the plurality of learning topics based on the concept distance between the learning topic and the learning history, and the learning topics are recommended so that the learning topic having a higher priority order is located at a higher position.

Abstract: A method to train a model for feature point detection involves obtaining a first image and a second image. The method involves generating a first score map for the first image and a second score map for the second image using the model. The method involves selecting a first plurality of interest points in the first image based on the first score map. The method involves selecting a second plurality of interest points in the second image based on the second score map. Pairwise matching of a first interest point among the first plurality of interest points with a second interest point among the second plurality of interest points is performed. Correctness of the pairwise matches is checked based on a ground truth, to generate a reward map. The score map and the reward map are compared and used to update the model.

Abstract: An image processing method and apparatus, a device, and a storage medium. The image processing method includes: performing preliminary segmentation recognition on a raw image by using a first segmentation model to obtain a candidate foreground image region and a candidate background image region of the raw image; recombining the candidate foreground image region, the candidate background image region, and the raw image to obtain a recombined image, pixels in the recombined image being in a one-to-one correspondence with pixels in the raw image; and performing region segmentation recognition on the recombined image by using a second segmentation model to obtain a target foreground image region and a target background image region of the raw image.

Abstract: Devices, methods, and systems for occupancy detection are described herein. One device includes instructions to receive an image of a portion of a facility captured by an imaging device, the image defined by a field of view, and set a first occupancy detection threshold for a first part of the field of view and a second occupancy detection threshold for a second part of the field of view.

Abstract: In learning processing performed before sample observation processing (steps S705 to S708), the sample observation device acquires a low-picture quality learning image under a first imaging condition for each defect position indicated by defect position information, determines an imaging count of a plurality of high-picture quality learning images associated with the low-picture quality learning image for each defect position and a plurality of imaging points based on a set value of the imaging count, acquires the plurality of high-picture quality learning images under a second imaging condition (step S702), learns a high-picture quality image estimation model using the low-picture quality learning image and the plurality of high-picture quality learning images (step S703), and adjusts a parameter related to the defect detection in the sample observation processing using the high-picture quality image estimation model (step S704).

Abstract: According to some embodiments, the process includes the steps of: a) taking a sagittal preoperative x-ray of the vertebral column of the patient to be treated, extending from the cervical vertebrae to the femoral heads; b) on that x-ray, identifying points on S1, S2, T12 et C7; c) depicting, on the said x-ray, curved segments beginning at the center of the plate of S1 et going to the center of the plate of C7; e) identifying, on that x-ray, the correction(s) to be made to the vertebral column, including the identification of posterior osteotomies to make; f) pivoting portions of said x-ray relative to other portions of that x-ray, according to osteotomies to be made; g) performing, on said x-ray, a displacement of the sagittal curvature segment extending over the vertebral segment to be corrected; h) from a straight vertebral rod (TV), producing the curvature of that rod according to the shape of said sagittal curvature segment in said displacement position.

Abstract: A first artificial neural network (ANN) model implemented on a memory device can be executed on first data from an imaging device corresponding to a first image. A second ANN model implemented on the memory device can be executed on second data from the imaging device corresponding to a second, subsequent image. Whether an accuracy value of results yielded from the execution of the second ANN model on the second data is less than a threshold accuracy value can be determined by the memory device. Responsive to determining that the accuracy value is less than the threshold accuracy value, the first ANN model can be executed on third data from the imaging device corresponding to a third image subsequent to the second image. Such selection of ANN models can reduce excess power consumption of a memory device on which the ANN models are implemented.

Abstract: Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images and video with multiple subjects. Some of these subjects may vary in priority for various tasks. It may be desired to increase or decrease the compression on each subject in order to more efficiently store the image data. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. Inference data can be associated with the type, motion and/or priority of the subjects as desired. This inference data can be utilized along with other subject data to generate one or more variable compression regions within the image data. The image data can be subsequently processed to compress different areas of the image based on a desired application. The variably compressed image can reduce file sizes and allow for more efficient storage and processing.

Abstract: There is provided an information processing device, an information processing method, and a computer program capable of highly accurately designating a discretionary position in a three-dimensional model.

Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to receive, at a deep neural network, an image captured by an image capture device. The processor is also programmed to generate a predicted distortion map based on the image and update at least one weight of the deep neural network based on a loss function of the deep neural network.