Abstract: A system includes a processor configured to request capture of image data of an environment surrounding the user, responsive to a margin of error of a detected location of a user being above a predefined threshold. The processor is also configured to process the image data to determine an actual user location relative to a plurality of objects, having known positions, identifiable in the image data and replace the detected location with the determined actual user location.

Abstract: An optical characterization system and a method of using the same are disclosed. The system comprises a controller configured to be communicatively coupled with one or more detectors configured to receive illumination from a sample and generate image data. One or more processors may be configured to receive images of dies on the sample, calculate dissimilarity values for all combinations of the images, perform a cluster analysis to partition the combinations of the images into two or more clusters, generate a reference image for a cluster of the two or more clusters using two or more of the combinations of the images in the cluster; and detect one or more defects on the sample by comparing a test image in the cluster to the reference image for the cluster.

Abstract: A system for verifying a machine-readable label comprises a scan table processing device comprising a first input for receiving a list of items with machine-readable labels; a second input for receiving a list of stores that have an inventory of the items in the list of items and that have at least one sensing device for capturing images of the items; and an output that includes a plurality of electronic records. The system further comprises a data repository that stores the captured images of the items and that updates the electronic records to include an association to the captured images; a graphical user interface (GUI) processing apparatus that modifies the captured images in preparation for training an artificial intelligence apparatus to identify the items in the images; and a machine language (ML) model processor that determines whether the images training the artificial intelligence apparatus are correctly identified with machine-readable labels associated with the items.

Abstract: The disclosure analyzes moon-earth relationship and influencing factors for geometric distortion of a moon-based earth image, focuses on influences of position change of a sublunar point, a curvature of the earth, a terrain fluctuation on a large-scale hemisphere image, and the problem of small number of ground control points and uneven distribution thereof, and proposes a projection polar coordinate geometric expression method for the moon-based earth observation image, which takes into account of movement of the sublunar point, while considering multi-platform earth observation data, to realize accurate automatic geometric correction of the moon-based platform earth observation data.

Abstract: An automatic guided vehicle (AGV) comprising a mobile base including a drive train and configured to drive the AGV in a self-navigation mode within a facility, a first camera configured to capture first image data of objects within the facility, a second camera configured to capture second image data of objects within the facility, the second camera including a content filter, and a main control module configured to receive the first and second image data from the first and second cameras. The main control module executes a recognition neural network program. The neural network program recognizes targets in the first image data. The main control module also executes a supervisor program under user control. The supervisor program is receives the second image data and recognizes markers attached to targets in the second image data. The supervisor program produces a supervised outcome in which targets to which markers are attached are associated with categories based on user commands.

Abstract: A computer-implemented method for assessing if characters in a sample image are formed from a predefined font. The method comprises forming a first embedded space representation for the predefined font, extracting sample characters from the sample image, forming a second embedded space presentation of the sample characters, and comparing the first and second embedded space representation to assess if the sample characters are of the predefined font.

Abstract: There are provided methods and apparatus for in-loop artifact filtering. An apparatus includes an encoder for encoding an image region. The encoder has at least two filters for successively performing in-loop filtering to respectively reduce at least a first and a second type of quantization artifact.

Abstract: A system, according to various embodiments, receives images such as photographs and videos from one or more portable computing devices associated with one or more individuals (e.g., construction workers or landscapers) while the portable computing devices are in a particular position within a particular location at a particular time. The system determines a virtual position within a 3-D representation of the particular location that generally corresponds to the particular position and combines the images with the 3-D representation to generate an enhanced 3-D representation of the particular location. This may allow, for example, owners of a particular property to track and quickly understand construction and landscaping work that has been done on their property and to easily contact those individuals regarding that work.

Abstract: Described is a system for controlling autonomous platform. Based on an input image, the system generates a motor control command decision for the autonomous platform. A probability of the input image belonging to a set of training images is determined, and a reliability measure for the motor control command decision is generated using the determined probability. An exploratory action is performed when the reliability measure is above a predetermined threshold. Otherwise, an exploitation action corresponding with the motor control command decision is performed when the reliability measure is below a predetermined threshold.

Abstract: Lossless compression of fragmented image data is disclosed. In some embodiments, a stream of information comprising data elements having statistical characteristics is received. An encoded output is produced by an encoder comprising a data compressor that implements a variable length code that is adapted to the statistical characteristics of the data elements. The output and information from which the variable length code can be derived are stored.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, by a pixelation device, an arrangement of pixels for generating representations of data for a document; adjusting, by an increment module and based on the arrangement of pixels, an optical density of one or more pixels included in the arrangement; associating, by the pixelation device, a plurality of distinct types of credential data with one or more line patterns included in the arrangement of pixels; and generating, by the pixelation device, multiple line patterns at a document, the multiple line patterns encoding the plurality of distinct types of credential data.

Abstract: Embodiments of the disclosure provide methods and systems for acquiring map data. The system may include a mounting structure configured to adjustably mount a sensor to a vehicle. The sensor may be configured to capture data indicative of at least one surrounding object as the vehicle travels along a path. The system may further include a controller configured to dynamically determine a mounting angle based on the captured data, and cause the mounting structure to adjust the sensor according to the dynamically determined mounting angle.

Abstract: The present disclosure relates to methods and systems for data visualization. The systems may perform the methods to obtain a video having a plurality of frames including a plurality of objects; identify a target object from the plurality of objects according to the plurality of frames; determine one or more track points of the target object, each of the one or more track points being corresponding to the target object in one of the plurality of frames; determine a first track of the target object based on the track points, the first track including at least one of the one or more track points of the target object; determine a second track of the target object based on the first track, the second track including at least one of the track points of the first track; generate a video analysis result by analyzing the second track; and visualize the video analysis result.

Abstract: According to the present invention, switching of the monitoring images matching the intention of the observer can be automatically performed for images from a plurality of image capturing apparatus, and the load about the job of the observer can be reduced. The image monitoring apparatus includes an estimating unit configured to estimate attention degrees of a user for a plurality of images acquired from the plurality of image capturing apparatuses, a designating unit configured to designate one of the acquired images as an image to be displayed in accordance with an instruction from the user, a learning unit configured to cause the estimating unit to learn so as to increase an attention degree of the designated image, and a selecting unit configured to select one of the plurality of images based on an attention degree of each estimated image.

Abstract: Lossless compression of fragmented image data is disclosed. In some embodiments, a stream of information is received, wherein the stream of information comprises a sequence of tuples and wherein each of the tuples comprises data elements corresponding to one of a plurality of input channels. A channel transformer is employed to rearrange the data elements into a plurality of output channels for an output stream wherein the output channels have higher compressibility than the input channels. The compressed output stream is stored.

Abstract: A method of identifying a living creature includes training a convolutional neural network model using pretrained convolutional neural networks to generate proposals about the regions where there might be an anatomical object within a digital image. Introducing a residual connection to get the input from the previous layer to the next layer helps in solving gradient vanishing problem. The next step is to design an object detector network that does three tasks: classifying the boxes with respective anatomies, tightening the boxes, and generating a mask (i.e., pixel-wise segmentation) of each anatomical component. In constructing the architecture of the object detector network, the network uses per-pixel sigmoid, and binary cross-entropy loss function (to identify the k anatomical components) and rigorously train them.

Abstract: Embodiments of the present specification provide a method and system for improving the quality of a vehicle damage image on the basis of a GAN network. During operation, the system obtains a first vehicle damage image and inputs the first vehicle damage image to a machine-learning model to obtain a second vehicle damage image. The machine-learning model is trained using a plurality of labeled samples of vehicle damage images, and the second vehicle damage image has a better quality than the first vehicle damage image.

Abstract: Implementations of the present disclosure can include a method and apparatus for processing point cloud data. Specifically, the method for processing point cloud data can be provided, including: acquiring a first frame and a second frame respectively from the point cloud data; extracting a first candidate object from the first frame and a second candidate object corresponding to the first candidate object from the second frame, respectively; determining a first location of the first candidate object and a second location of the second candidate object in a coordinate system of the point cloud data, respectively; and identifying any one of the first candidate object and the second candidate object as a moving object, in response to an offset between the first location and the second location.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: An image-processing apparatus including: an image processor including circuitry or a hardware processor that operates under control of a stored program, the image processor being configured to execute processes including: a saliency-map calculating process that calculates saliency maps on a basis of at least one type of feature quantity obtained from an input image; a salient-region-identifying process that identifies a salient region by using the saliency maps; a salient-region-score-calculating process that calculates a score of the salient region by comparing a distribution of values of the saliency map in the salient region and a distribution of values of the saliency map in a region other than the salient region; and a saliency-evaluating process that evaluates the saliency of the salient region on a basis of the score.