Abstract: An examination paper correction method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining a first image of a standard examination paper; identifying an area and characters of each standard answer in the first image, and using a marking box to mark an answering area where each standard answer is located; determining position information of each marking box; obtaining a second image of an examination paper to be corrected; determining, according to the position information of each marking box of the first image, an answering area in the second image matching a position of the marking box, and using a marking box to mark the determined answering area; identifying characters of an answer to be corrected in each marking box of the second image; and comparing the characters of the standard answer with the characters of the answer to be corrected.

Abstract: Embodiments provide multi-angle screen coverage analysis. In some embodiments, a system obtains at least one image, where the at least one image is a computer graphics generated image, and where the at least one image comprises at least one target object. The system determines screen coverage information for the at least one target object, where the screen coverage information is based on a portion of a screen that is covered by the at least one target object. The system determines depth information for the at least one target object. The system determines an asset detail level for the at least one target object based on the screen coverage information and the depth information, where the asset detail level is adjustable based on the screen coverage information. The system then stores the asset detail level in a database.

Abstract: A non-transitory computer-readable medium includes instructions that when executed by a processor cause the processor to perform a method for selecting available assignments, the method including: receiving an indication of an external assignment, wherein the external assignment is associated with one or more locations external to a plurality of retail stores; based on the indication of the external assignment, selecting a retail store among the plurality of retail stores; based on the selected retail store, selecting at least one available assignment in the selected retail store; and offering the selected at least one available assignment in the selected retail store to a user.

Abstract: A system for categorizing images is provided. The system is programmed to store a first training set of images. Each image of the first training set of images is associated with an image category of a plurality of image categories. The system is further programmed to analyze each image of the first training set of images to determine one or more features associated with each of the plurality of image categories and receive a second training set of images. The second training set of images includes one or more errors. The system is also programmed to analyze each image of the second training set of images to determine one or more features associated with an error category and generate a model to identify each of the image categories based on the analysis such that the model includes the error category in the plurality of image categories.

Abstract: A non-transitory computer readable medium storing instructions to cause one or more processors to acquire an image data sequence containing images of an object in a scene along time and track a pose of the object through an object pose tracking algorithm. The processor may further, acquire a first pose of the object in a first image of the image data sequence, the first pose being a result of tracking a pose of the object through an object pose tracking algorithm, verify the first pose, extract 2D features of the object from the first image when the first pose is verified, and store a training dataset containing the extracted 2D features and the corresponding verified first pose in the one or more memories or other one or more memories.

Abstract: Disclosed is a machine learning architecture for a two-dimensional image protocol detector configured to receive a first image representing at least a portion of a mouth of a user, and output user feedback for capturing a second image representing a portion of the mouth of the user, where the machine learning architecture outputs the user feedback in response to an image quality score of the first image not satisfying an image quality threshold.

Abstract: In one embodiment, a method of determining tag signals from measured intensities, the measured intensities collected by light sensors in a sensor array directed to a sample surface, the sample surface including pixel areas and holding a plurality of clusters during a sequence of sampling events, each light sensor directed to and measuring intensity from one of the pixel areas during each sampling period includes adjustments for background intensity and crosstalk and taking into account signal decay and phasing/pre-phasing. Coefficients for the adjustments can be determined by gradient descent, using as ground truth base calling by the system being characterized or by using reliable base calling of well-characterized sample run through the system being characterized.

Abstract: Systems and methods for aligning digital image datasets to a computer model of a structure. The system receives a plurality of reference images from an input image dataset and identifies common ground control points (“GCPs”) in the reference images. The system then calculates virtual three-dimensional (“3D”) coordinates of the measured GCPs. Next, the system calculates and projects two-dimensional (“2D”) image coordinates of the virtual 3D coordinates into all of the images. Finally, using the projected 2D image coordinates, the system performs spatial resection of all of the images in order to rapidly align all of the images.

Abstract: A method and a device for generating a collection of incorrectly-answered questions are provided. The method includes: acquiring an image of a marked test paper (S101); recognizing regions of respective questions in the marked test paper according to a pre-trained first region recognition model (S102); recognizing a question whose marking result is incorrect in the marked test paper as an incorrectly-answered question according to a pre-trained incorrectly-answered question recognition model (S103); and storing the region of the incorrectly-answered question in an incorrectly-answered question database to generate the collection of incorrectly-answered questions (S104). The above solution may solve the problem of low efficiency in generating the collection of incorrectly-answered questions in the prior art.

Abstract: This disclosure relates to maintaining a feature point map. The maintaining can include selectively updating feature points in the feature point map based on an assigned classification of the feature points. For example, when a feature points is assigned a first classification, the feature point is updated whenever information indicates that the feature point should be updated. In such an example, when the feature point is assigned a second classification different from the first classification, the feature point forgoes being updated whenever information indicates that the feature point should be updated. A classification can be assigned to a feature point using a classification system on one or more pixels of an image corresponding to the feature point.

Abstract: A method of labelling features for image recognition and an apparatus thereof are provided. The method includes inputting an image to be recognized to an image recognition model to obtain a classification result; obtaining a plurality of recognized features and positions of the recognized features from the image to be recognized based on activation maps respectively corresponding to a plurality of preset features in the classification result; and labelling the recognized features and the positions of the recognized features activating the classification result. Accordingly, the features determined by the image recognition model can be showed clearly by the method of labelling features for image recognition and the apparatus thereof.

Abstract: A computer vision system is provided that includes a camera capture component configured to capture an image from a camera, a memory, and an image compression decompression engine (ICDE) coupled to the memory and configured to receive each line of the image, and compress each line to generate a compressed bit stream. To compress a line, the ICDE is configured to divide the line into compression units, and compress each compression unit, wherein to compress a compression unit, the ICDE is configured to perform delta prediction on the compression unit to generate a delta predicted compression unit, compress the delta predicted compression unit using exponential Golomb coding to generate a compressed delta predicted compression unit, and add the compressed delta predicted compression unit to the compressed bit stream.

Abstract: An information processing device includes a memory that stores a known position of a marker, a camera, and a processor. The processor is configured to determine whether the image includes the marker, calculate a relative position of the marker with respect to the camera and store the calculated relative position in the memory if image includes the marker, and determine a position of the camera using the known position of the marker and two or more relative positions calculated for the marker stored in the memory.

Abstract: A parking detection method based on visual difference includes: obtaining a video frame of a predetermined monitoring area captured by each camera in real time, and labeling the video frame corresponding to each camera with time information of a current moment; determining vehicle information of a to-be-detected vehicle in each video frame labeled with the time information through a predetermined convolutional neural network model; determining feature point information of the to-be-detected vehicle in each video frame according to the vehicle information of the to-be-detected vehicle in each video frame; calculating a position relationship between the to-be-detected vehicle in each video frame and respective corresponding camera, and constructing current three-dimensional coordinates of the to-be-detected vehicle according to the position relationship; and determining a parking status of the to-be-detected vehicle according to the current three-dimensional coordinates of the to-be-detected vehicle.

Abstract: The present disclosure relates to a signal processing apparatus and a signal processing method, a program, and a moving body that allow improvement of estimation accuracy for a self-position. One of a plurality of wavelengths of light is selected, and the light with the selected wavelength is projected. Reflected light of the projected light is received that is reflected from a reflector corresponding to an object having a reflectance higher than a predetermined reflectance, and the reflector is detected. This prevents interference of light projected by another vehicle, enabling appropriate detection of the reflector and improvement of estimation accuracy for a self-position. The present disclosure is applicable to an in-vehicle system.

Abstract: A method to detect a roadway edge includes calculating a first likelihood of a roadway edge from an aerial image of a roadway by shifting a centerline of the roadway perpendicular to the centerline and overlapping the centerline with image gradients. A second likelihood of the roadway edge is determined using a vehicle telemetry fitting a probability distribution to telemetry points along the roadway. The first likelihood of the roadway edge and the second likelihood of the roadway edge are fused to identify a final likelihood of the roadway edge.

Abstract: Fingerprint sensors include a plurality of optical sensor elements, a collimator filter disposed above the plurality of optical sensor elements, and a display disposed above the collimator filter, wherein the display is a fingerprint imaging light source. The collimator filter has a plurality of apertures, and the plurality of optical sensor elements is configured to receive light transmitted through one aperture of the plurality of apertures.

Abstract: The present invention relates to a system for steering or guiding a sod harvesting machine (sod harvester) with a high degree of precision, without the need for the sod harvesting machine to have a guide stick or shoe in physical or mechanical contact with the sod to be harvested or to be in connection with remote navigation systems, such as GPS. The system includes a sensor mounted at the front of the harvester and a processor for processing information from the sensor to determine a boundary line and for steering the sod harvester along the boundary line.

Abstract: An image editing program can include a content-aware selection system. The content-aware selection system can enable a user to select an area of an image using a label or a tag that identifies object in the image, rather than having to make a selection area based on coordinates and/or pixel values. The program can receive a digital image and metadata that describes an object in the image. The program can further receive a label, and can determine from the metadata that the label is associated with the object. The program can then select a bounding box for the object, and identify in the bounding box, pixels that represent the object. The program can then output a selection area that surrounds the pixels.

Abstract: The present application discloses a method, device, and system for testing an image to assess whether the image was subject to tampering. The method includes obtaining an image, extracting residual data of the image from the image, obtaining a feature map of at least two neural layers based at least in part on the residual data of the image, obtaining a tamper probability map of the image based at least in part on the feature map, and outputting the tamper probability map or providing an indication of whether the image was subject to tampering based at least in part on the tamper probability map.