Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to identify a surface feature of a portion of a three-dimensional (3D) point cloud. Data indicative of a path along a 3D point cloud is received, wherein the 3D point cloud comprises a plurality of 3D data points. A plurality of lists of 3D data points are generated, wherein: each list of 3D data points extends across the 3D point cloud at a location that intersects the received path; and each list of 3D data points intersects the received path at different locations. A characteristic associated with a surface feature is identified in at least some of the plurality of lists of 3D data points. The identified characteristics are grouped based on one or more properties of the identified characteristics. The surface feature is identified based on the grouped characteristics.

Abstract: According to an embodiment of the present disclosure, there may be provided an operation method of a server for estimating the size of damage in disaster affected areas. In this instance, the operation method of the server may include acquiring at least one first disaster image, deriving an affected area from each of the at least one first disaster image, acquiring affected area related information through labeling based on the derived affected area, and training a first learning model using the at least one first disaster image and the affected area related information.

Abstract: An image processing device combines a plurality of time-series images to generate a composite image with a higher resolution than a resolution of the plurality of images. The image processing device includes a processor that includes hardware and that is configured to: detect depth information of at least one image of the plurality of images; and set the number of images to be combined for generating the composite image on the basis of the detected depth information. The depth information indicates a depth of a captured scene of the at least one image. The processor is configured to reduce the number of images as the depth of the captured scene increases.

Abstract: A substrate inspection apparatus for inspecting a substrate, includes: an acquisition part configured to acquire an estimated image of an inspection target substrate after a process by a substrate processing apparatus, based on an image estimation model created by a machine learning by using a captured image before the process by the substrate processing apparatus and a captured image after the process by the substrate processing apparatus for each of a plurality of substrates, and a captured image of the inspection target substrate before the process by the substrate processing apparatus; and a determination part configured to determine the presence or absence of a defect in the inspection target substrate, based on a captured image of the inspection target substrate and the estimated image of the inspection target substrate after the process by the substrate processing apparatus.

Abstract: There is provided a vehicle sensor system, comprising: a plurality of sensors with mostly overlapping field of views that simultaneously acquire temporary images, a processing circuitry that: analyzes the temporary images to identify at least one blocked image area in at least one temporary image of the plurality of temporary images which is less or not blocked in at least one spatially corresponding image area of at least one other temporary image of the plurality of temporary images, selects visual data from the at least one spatially corresponding image areas over corresponding visual data from the at least one blocked images area, merges the plurality of temporary images into a final image using the selected visual data and excludes the at least one blocked image area, and an output interface that forwards the final image to a vehicle controller.

Abstract: A trained model is retrained for video quality assessment and used to identify sets of adaptive compression parameters for transcoding user generated video content. Using transfer learning, the model, which is initially trained for image object detection, is retrained for technical content assessment and then again retrained for video quality assessment. The model is then deployed into a transcoding pipeline and used for transcoding an input video stream of user generated content. The transcoding pipeline may be structured in one of several ways. In one example, a secondary pathway for video content analysis using the model is introduced into the pipeline, which does not interfere with the ultimate output of the transcoding should there be a network or other issue. In another example, the model is introduced as a library within the existing pipeline, which would maintain a single pathway, but ultimately is not expected to introduce significant latency.

Abstract: Methods and systems are presented for providing a machine learning model framework configured to perform complex data classifications. Upon receiving a request for classifying data, the data is recursively assigned to one or more clusters. During each iteration of clustering assignment, a set of clusters is selected based on a previously assigned cluster for the data, and the data is then assigned to a particular cluster from the selected set of clusters. The machine learning model framework also includes a plurality of machine learning models configured to perform simple data classifications. A particular machine learning model is selected from the plurality of machine learning model based on the one or more clusters to which the document is assigned. The particular machine learning model is then used to classify the document.

Abstract: The present invention is applicable to the fields of panoramic images and videos, and provides a panoramic image and video splicing method, and a panoramic camera. In the present invention, fisheye photos captured by two adjacent cameras are mapped to corresponding seam areas of a sphere model, so as to form two strip graphs having overlapping areas; performing block template matching on the two strip graphs to obtain an initial template matching result; performing matching filtering on the initial template matching result by using an area expansion-based matching filtering algorithm, so as to obtain a final matching result; and updating a mapping relationship between the fisheye photos and the corresponding seam areas of the sphere model, and performing panoramic splicing according to the updated mapping relationship to obtain a seamless panoramic image.

Abstract: One variation of a method for automatically generating a common measurement across multiple assembly units includes: displaying a first image—recorded at an optical inspection station—within a user interface; receiving manual selection of a particular feature in a first assembly unit represented in the first image; receiving selection of a measurement type for the particular feature; extracting a first real dimension of the particular feature in the first assembly unit from the first image according to the measurement type; for each image in a set of images, identifying a feature—analogous to the particular feature—in an assembly unit represented in the image and extracting a real dimension of the feature in the assembly unit from the image according to the measurement type; and aggregating the first real dimension and a set of real dimensions extracted from the set of images into a digital container.

Abstract: Presented herein are embodiments of systems and methods for training a system and for using a trained system to generate super-resolution imagery from low-resolution imagery. Embodiments for generating super-resolution imagery from low-resolution imagery include obtaining an input trade-off parameter that indicates a preference regarding low distortion or high perceptual quality for a generated SR image and obtaining a latent variable from a distribution defined, at least in part, by a trade-off parameter. Embodiments include inputting an LR image and the latent variable into an embodiment of an invertible rescaling network (IRNN) in an inverse upscaling direction of the IRNN to generate an output SR image that comprises accuracy and perception qualities conditioned by the input trade-off parameter. In one or more embodiments, a trained IRNN uses a trade-off parameter that indicates a desired trade-off between whether the trained IRNN generates an SR image having lower distortion or higher perceptual quality.

Abstract: The learning device includes a first trainer, a candidate area determinator and a second trainer. On the basis of a training image and a training label including the correct coordinate value relating to the feature point included in the training image, the first trainer generates a first discriminator learned to output the predicted coordinate value relating to a feature point from an input image. The candidate area determinator determines a candidate area of the feature point in the training image based on the predicted coordinate value outputted by inputting the training image to the first discriminator. The second trainer generates the second discriminator, which is learned to output the reliability map indicating the reliability to the feature point at each block in an input image, on the basis of a cut-out image that is the candidate area cut out from the training image.

Abstract: An inspection device includes a processor configured to acquire image information of each of a correct image and a target image as an inspection target, extract edge information of each of the correct image and the target image by using the acquired image information, obtain a difference image between the correct image and the target image, and change a threshold value for detecting a defect by using brightness information or color information of the correct image along with the edge information and detect a defect of the target image by using the difference image and the threshold value.

Abstract: A medical image processing apparatus comprises processing circuitry configured to: receive volume data; set a symmetry plane for the volume data; generate mirrored volume data by mirroring the volume data using the symmetry plane; and generate symmetrized volume data based on the volume data and the mirrored volume data.

Abstract: An image enhancement method is provided. The method includes obtaining an image, extracting high-frequency components of the image, calculating an average pixel value of pixels corresponding to the extracted high-frequency components, and performing enhancement on pixels in the image that have pixel values greater than or equal to the calculated average pixel value to obtain an enhanced image. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also provided.

Abstract: A computing device is configured to detect inconsistencies on a vehicle that are typically small and difficult to detect using conventional inspection techniques. Particularly, a spotlight illuminates a target area on the vehicle. The color of the spotlight is the same color as the ambient light illuminating the vehicle but is a higher intensity. Additionally, the color of the spotlight and the ambient light is selected based on the type of inconsistencies expected to be detected. Images of the target area are then captured, digitally processed, and stored in memory, such as a database, for example. Based on the processing of these images, a weighting coefficient is computed and used to generate an enhanced contrast feature map. Inconsistencies are then detected based on the enhanced contrast feature map.

Abstract: A video target tracking method is provided to a computing device, the method includes: obtaining a partial detection map corresponding to a target image frame in a to-be-detected video; obtaining a relative motion saliency map corresponding to the target image frame; determining constraint information corresponding to the target image frame according to the partial detection map and the relative motion saliency map; adjusting a parameter of an image segmentation model by using the constraint information, to obtain an adjusted image segmentation model; and extracting a target object from the target image frame by using the adjusted image segmentation model.

Abstract: A method for identifying an individual of an oplegnathus punctatus based on a convolutional neural network is provided. Target initial positioning involves three continuous convolutional layers and an average pooling layer. A region of feature interest point is obtained, a hyperparameter candidate box is set to obtain a region, thereby obtaining an approximate position of a target object. II_Net backbone convolutional neural network includes six convolutional layers and four pooling layers, which includes a LeakyReLU activation function used as an activation function of the first convolutional layer, convolutional network layers of Alexnet and parameter data, and a maximum pooling layer of an overlapped pooling structure. Fully connected layers use a genetic algorithm to improve data transmission between layers. With the established model, identification of the individual of the oplegnathus punctatus is performed by using test data.

Abstract: Techniques performed by a data processing system include establishing an online presentation session for conducting an online presentation, receiving first media streams comprising presentation content from the first computing device, receiving second media streams from the second computing devices of a subset of the plurality of participants, the second media streams including audio content, video content, or both of the subset of the plurality of participants, analyzing the first media streams using first machine learning models to generate feedback results, analyzing the set of second media streams to identify first reactions by the participants to obtain reaction information, automatically analyzing the feedback results and the reactions to identify discrepancies between the feedback results and the reactions, and automatically updating one or more parameters of the machine learning models based on the discrepancies to improve the suggestions for improving the online presentation.

Abstract: To improve a determination accuracy when determining each particle contained in an image of an object. An image analysis apparatus according to an embodiment of the present invention includes: a shape determination unit configured to determine a shape of a particle included in a particle image that is extracted from an image of an object, so that an OK particle image which is a particle image of an OK particle that satisfies a predetermined standard for shape and a provisional NG particle image which is a particle image of a provisional NG particle that does not satisfy the predetermined standard, are obtained; a pseudo image generation unit configured to generate a pseudo image; and a similarity determination unit configured to determine whether the provisional NG image and the pseudo image are similar.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for three-dimensional object tracking includes, in at least one aspect, a method including: obtaining three-dimensional positions of objects registered by a detection system configured to allow more false positives so as to minimize false negatives, forming hypotheses using a filter that allows connections between registered objects when estimated three-dimensional velocity vectors roughly correspond to an object in motion in three-dimensional space, eliminating a proper subset of the hypotheses that are not further extended during the forming, specifying at least one three-dimensional track of at least one ball in motion in three-dimensional space by applying a full three-dimensional physics model to data for the three-dimensional positions used in the forming of at least one hypothesis that survives the eliminating, and outputting for display the at least one three-dimensional track of the at least one ball in motion in thr