Abstract: An optical dimensioning system includes one or more light emitting assemblies configured to project one or more predetermined patterns on an object; an imaging assembly configured to sense light scattered and/or reflected off the object, and to capture an image of the object while the patterns are projected; and a processing assembly configured to analyze the image of the object to determine one or more dimension parameters of the object. The light emitting assembly may include a single piece optical component configured for producing a first pattern and second pattern. The patterns may be distinguishable based on directional filtering, feature detection, feature shift detection, or the like. A method for optical dimensioning includes illuminating an object with at least two detectable patterns; and calculating dimensions of the object by analyzing pattern separate of the elements comprising the projected patterns. One or more pattern generators may produce the patterns.

Abstract: In one embodiment, a method includes accessing an image of a damaged object. The method further includes determining, using a plurality of image segmentation models, a plurality of objects in the image. The method further includes determining, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image. The repair-relevant property vector includes a plurality of damaged object properties. The method further includes generating a repair report using the repair-relevant property vector and a price catalogue. The repair report includes an indication of the damaged object and a price associated with the repair or replacement of the damaged object. The method further includes providing the generated report for display on an electronic display device.

Abstract: Disclosed herein is a transmitting method and a receiving method of point cloud data. The transmitting 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 point cloud data include at least geometry data, attribute data, or an occupancy map data, the bitstream is stored in multiple tracks of the file, the file further includes signaling data, and the signaling data include spatial region information of the point cloud data.

Abstract: A method comprising: receiving an image of a scene, segmenting the image into a plurality of segments, obtaining at least one depth sample from each of at least some of the segments, and with respect to each of the at least some of the segments, assigning a value of the depth sample to each pixel in the segment, to create a depth image of the image.

Abstract: The Loupe system defines Loupe Visual Art DNA for art images to be presented to a user so as to maximize and customize the user experience in viewing art images delivered onto digital displays, TVs and other screens facilitating the artwork transition with and without human interaction. The Loupe system recommendations engine utilizes both human and machine curated data to determine factors of art images that will appeal to a user viewing the images. The Loupe system gathers data about visual perception, historical and academic provenance, and emotion or intention represented in an image. The gathered data is analyzed through deep learning and AI algorithms to inform recommendations and select art images to be presented to a user. The user may purchase fine art prints or select originals of the artwork image displayed, if the artist elects to make it available for sale, presented from the Loupe integrated electronic marketplace.

Abstract: Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

Abstract: Generally, a system and method for an automatic detection of referee's decisions during a ball-game match are provided. The method may include receiving a plurality of images of a ball-game field generated during the ball-game match; determining, based on predetermined ball-game rules, a first subset of images of the plurality of images representing a first event that is suspected as a specified rule-based event; determining, based on the predetermine ball-game rules, a second subset of images of the plurality of images that represents a second event, wherein the second event is subsequent to the specified rule-based event according to the predetermined ball-game rules; and analyzing, based on the predetermined ball-game rules, the images of the second subset and further determining, based on the analysis thereof, a referee's decision concerning whether the first even is the specified rule-based event.

Abstract: A mobile client device is localized based on a captured image by identifying where the client device is located from a set of known locations. The set of known locations is associated with a set of regions, where each region is associated with a set of key frames representing the important features of the region. Latent vectors and keypoints are calculated for each of the key frames and an image captured by the client device. The system compares the latent vectors of the captured image to the latent vectors associated with the regions to determine a subset of similar regions. The system compares the keypoints of the captured image to the keypoints associated with the regions in the subset to determine a best match. This determined location is considered the region of the client device and may be used with other localization information to maintain localization of the client device.

Abstract: The ultrasonic diagnostic apparatus according to the present embodiment includes processing circuitry. The processing circuitry is configured to: acquire multiple position data associated with respective multiple two-dimensional image data of ultrasonic related to multiple cross sections; smooth the acquired multiple position data; and arrange the multiple two-dimensional image data in accordance with the smoothed multiple position data to generate volume data.

Abstract: A data conversion processing unit converts a second group including a plurality of reflection point data units in which a reflection point corresponding to each reflection point data unit belongs to a three-dimensional object among a first data unit group into a third group including a plurality of projection point data units by projecting the second group onto a horizontal plane in a world coordinate system. A clustering processing unit clusters the plurality of projection point data units of the third group into a plurality of clusters based on positions of these units on the horizontal plane. A space of interest setting unit sets a space of interest for each cluster by using the plurality of reflection point data units corresponding to the plurality of projection point data units included in each cluster.

Abstract: A system and a method are disclosed where an autonomous robot captures an image of an object to be transported from a source to a destination. The robot generates a bounding box within the image surrounding the object. The robot applies a machine-learned model to the image with the bounding box, the machine-learned model configured to identify an object type of the object, and to identify features of the object based on the identified object type and the image. The robot determines which of the identified features of the object are visible to the autonomous robot, and determines a three-dimensional pose of the object based on the features determined to be visible to the autonomous robot.

Abstract: Implementations of the present disclosure include receiving a training image, providing a hash pattern that is representative of the training image, applying a plurality of filters to the training image to provide a respective plurality of filtered training images, identifying a filter to be associated with the hash pattern based on the plurality of filtered training images, and storing a mapping of the filter to the hash pattern within a set of mapping in a data store.

Abstract: An image processing method, an image processing apparatus, an electronic device and a computer readable storage medium are provided. The image processing method includes the following. A background image and a portrait region image of a current user that a preset parameter of the background image matching the preset parameter of the portrait region image are acquired. The portrait region image and the background image are merged to obtain a merged image.

Abstract: An imaging system to be mounted in a vehicle is provided. The imaging system includes an optical wide-angle camera acquiring an image, and an object identifying apparatus. This object identifying apparatus executes a distortion correcting process with the acquired image and apply previously-prepared reference patterns to the image which has been subjected to the distortion correcting process such that objects in the acquired image are recognized.

Abstract: An image processing apparatus includes, an input unit configured to input images, a first detection unit configured to detect a first area based on features in the images input by the input unit, a second detection unit configured to detect a second area based on variations between the images input by the input unit, a generation unit configured to generate a background image by using a result of the first area detection by the first detection unit and a result of the second area detection by the second detection unit, and a processing unit configured to perform image processing for reducing a visibility of a specific region identified through a comparison between a processing target image acquired after a generation of the background image by the generation unit and the background image.

Abstract: A machine device state monitoring system includes an image data acquisition unit that acquires image data of an inside of a housing chamber in which a mechanical part is housed, an image analysis unit that outputs foreign substance data relating to a foreign substance in oil in the housing chamber based on the image data, a storage unit that stores characteristic data indicating a characteristic of the foreign substance, and an estimation unit that estimates a state of a machine device having the mechanical part based on the foreign substance data and the characteristic data to output estimation data.

Abstract: The orientation of imagery relative to a compass bearing may be determined based on the position of the sun or other information relating to celestial bodies captured in the image.

Abstract: Described herein is an object recognition system in low illumination conditions. A 3D InIm system can be trained in the low illumination levels to classify 3D objects obtained under low illumination conditions. Regions of interest obtained from 3D reconstructed images are obtained by de-noising the 3D reconstructed image using total-variation regularization using an augmented Lagrange approach followed by face detection. The regions of interest are then inputted into a trained CNN. The CNN can be trained using 3D InIm reconstructed under low illumination after TV-denoising. The elemental images were obtained under various low illumination conditions having different SNRs. The CNN can effectively recognize the 3D reconstructed faces after TV-denoising.

Abstract: The present disclosure provides a method and apparatus for facial classification, which is applied to the field of image processing. The method includes acquiring a color image of a target face, where the color image includes information of at least one channel, inputting the information into a neural network, and classifying, by the neural network, the target face according to the information and a first parameter. The first parameter includes at least one facial category and first parameter data for identifying a facial category of the target face. The method and device of the present disclosure can analyze facial attributes from the content of a facial image, and automatically classify many facial images according to their facial attributes. This reduces the burden of manual classification, thereby allowing facial images to be stored in a clear and orderly fashion and improving classification efficiency.

Abstract: An image processing method includes: obtaining a first target face image in a reference expression state; obtaining user face key point offset information that includes offsets between face key points of a user in a target expression state and face key points of the user in the reference expression state; adjusting face key points of the first target face image in the reference expression state according to the user face key point offset information; and attaching, in a to-be-processed image including a second target face image, an expression texture image of the adjusted first target face image to the second target face image, the second target face image and the first target face image belonging to a same target.