Abstract: One embodiment provides a method comprising receiving content for presentation on a display, and obtaining one or more sample frames of the content. The method further comprises generating a set of features based on one or more horizontal edge signals and one or more vertical edge signals of the one or more sample frames. The method further comprises utilizing a classification model to detect, based on the set of features, a region of interest of moving text in the one or more sample frames.

Abstract: A method comprises: collecting data including combinations of images and accompanying text and user feedback of the combinations; building training data sets based on the collected data; training a plurality of neural networks using the training data; generating a creative feature vector based on a specified image using a first network of the trained plurality of neural networks; generating a target audience vector based on a specified target audience using a second network of the trained plurality of networks; generating a sequence of words based on the vectors using a third network of the plurality of trained neural networks; and transmitting the generated sequence of words and the specified image to the target audience over a network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for predicting connections in electric grid models are disclosed. A method includes obtaining geospatial data representing a geographic area that includes an electrical distribution system; and generating, from the geospatial data, asset data that represents characteristics of electrical distribution system assets. The asset data includes: load data representing electrical loads of the electrical distribution system; and node data representing nodes of the electrical distribution system. The method includes processing the asset data using a connection model that is configured to predict electrical connections between assets of the electrical distribution system; and obtaining, from the connection model; output data indicating predicted electrical connections between assets of the electrical distribution system. The geospatial data includes at least one of overhead imagery or street level imagery of the geographic area.

Abstract: Systems and techniques for detecting a railcar identifier are described herein. A method can include receiving railcar image information including at least an image portion that contains, or is likely to contain, a railcar identifier for a first railcar at a data input stage of a railcar identification processor. The method can also include analyzing the railcar image information to identify characters of the railcar identifier for the first railcar at a recognition stage of the railcar identification processor. The analyzing of the railcar image information can include using two or more of a confidence algorithm, a result frequency algorithm, and a per-character algorithm. The method can also include providing a first recognition result with information about the identified characters of the railcar identifier for the first railcar at an output stage of the railcar identification processor.

Abstract: The method for external appearance inspection of a terminal T includes: using a camera 6 being placed to face a front surface or a back surface of a terminal T; capturing images of both left and right side surfaces, an upper surface, and upper left and right corner portions of the terminal, and simultaneously imaging the front surface or the back surface of the terminal. The mirrors 11 to respectively corresponds to both the left and right side surfaces, the upper surface, and the upper left and right corner portions of the terminal. The mirrors each are arranged with a reflective surface being inclined outward with respect to an optical axis of a photographing lens 6L of the camera; and inverting the placed terminal in its up-down direction after the imaging, and imaging the terminal in this posture using the camera.

Abstract: The present invention provides methods for determining a personal improvement potential of physiological characteristics of human faces or other body parts. This may involve determining an objective score of at least one physiological characteristic, in particular an attractiveness and/or youthfulness score, of the face of a human user. More specifically, the invention provides a computer-implemented body part analysis method. The method may comprise an image acquisition step, comprising obtaining one or more digital images of at least one body part of a user, in particular the user's face, captured by an image capturing device. The method may further comprise an image processing step, comprising detecting one or more biometric parameters of the at least one body part in the captured one or more digital images.

Abstract: Disclosed are various embodiments for intelligent text recognition based upon a selected pattern detection mode. First, text can be identified in an image. A pattern detection mode can be selected by a user or autonomously. In some instances, the pattern detection mode can be selected based at least in part on a user account. Next, the text can be parsed for occurrences of a pattern associated with the selected pattern detection mode. A list of occurrences of the pattern can be generated from the text and presented to a user. In some instances, a user can train a model to learn a new pattern.

Abstract: A method for training a machine learning model to detect substances that compromise medical imaging clarity includes receiving imaging of tissue associated with a medical procedure type; receiving imaging of one or more substances that can affect clarity of imaging associated with the medical procedure type; combining at least a portion of the imaging of the tissue and at least a portion of the imaging of the one or more substances to generate machine learning training image data in which at least a portion of the tissue is at least partially obscured by the one or more substances; and training, with the training image data, a machine learning model to detect the one or more substances in imaging generated during a medical procedure of the medical procedure type.

Abstract: A method of processing an image, an electronic device, and a storage medium. The method includes: determining, based on a predetermined color mode, a second rendering color of pixels of an original image according to a first rendering color of the pixels, where the first rendering color corresponds to a first color space; adjusting the second rendering color according to the second rendering color and an original rendering color of the pixels to obtain a third rendering color of the pixels, the original, second, and third rendering colors corresponding to the second color space, the first rendering color being obtained by performing a color space conversion on the original rendering color; and obtaining a target image according to the third rendering color, where a region corresponding to a visual saliency region of the original image in the target image has a color corresponding to the predetermined color mode.

Abstract: Disclosed is an apparatus and method configured for classifying timber logs in a wood processing chain. The apparatus comprises an image processor. The image processor is configured to receive a first log image of a first end of log and determine a first end pattern. The image processor is configured to receive a second log image of a second end of the same log, wherein the second end is opposite the first end. The image processor is further configured to determine a first pattern in the first end and a second pattern in the second end. Lastly, the image processor is further configured to determine a characteristic of the log based on the first pattern and the second pattern.

Abstract: Disclosed is a method and system for separating and recalibrating a feature for adversarial robustness. A feature separation and recalibration method may include disentangling an input feature map into a first feature defined as a robust feature and a second feature defined as a non-robust feature, based on a robustness score; generating a recalibrated feature by recalibrating activation of the second feature; and generating an output feature map by combining the first feature and the recalibrated feature.

Abstract: Methods, systems, and techniques for assigning an image acquisition task to a constellation of satellites may be performed by first receiving a request via an inbound API call to assign the image acquisition task to the constellation of satellites. The request may include a time at or during which at least one image is to be acquired by the constellation. Based on the request, at least one task performance rule specifying how the task is to be performed is retrieved. At least one internal API call is generated to task the constellation of satellites to perform the task. The at least one API call specifies the time at or during which the task is to be performed and the at least one task performance rule. The task is then assigned to be performed by the constellation of satellites by making the at least one internal API call.

Abstract: Embodiments of the disclosure provide an image processing method and an image processing device. The method includes the following. A first image is obtained, and the first image is down-sampled into a second image. A first resolution parameter combination of each second pixel in the second image is obtained. A target pixel among the first pixels is selected, and second pixels corresponding to the target pixel among the second pixels are accordingly obtained. A comparison result is determined between the target pixel and each second pixel corresponding to the target pixel, and a candidate pixel among the second pixels corresponding to the target pixel is accordingly selected. A second resolution parameter combination of the target pixel is selected based on the first resolution parameter combination of the candidate pixel. The target pixel is converted into an output pixel based on the second resolution parameter combination.

Abstract: A method of processing an image by an electronic device, includes: obtaining a label image of a label including at least one symbol associated with handling of clothing item; obtaining, from the label image, text information associated with the handling of the clothing item; identifying, from the label image, the at least one symbol associated with the handling of the clothing item, based on the text information and the label image; and obtaining handling information related to the clothing item based on the identified at least one symbol.

Abstract: A measurement system includes a first feature point data generator, a second feature point data generator, and a calculator. The first feature point data generator generates first feature point data from first image data or from first shape data. The first image data is obtained from imaging of a measurement target and includes a predetermined portion of the measurement target. The first shape data is generated based on the first image data. The second feature point data generator generates second feature point data from second image data different from the first image data or from second shape data. The calculator calculates a positional correspondence of the predetermined portion of the measurement target between the first image data and the second image data or between the first shape data and the second shape data based on the first feature point data and the second feature point data.

Abstract: A method of improving a main output of a main AI model includes processing first video data using a machine learning model includes continuously analyzing incoming video data via a first processing pipeline and concurrently via a second processing pipeline. The first processing pipeline can include preprocessing incoming video data according to first preprocessing parameters that format the incoming video data to create the first video data and processing the first video data by the main AI model to determine the main output that is indicative of a first inference dependent upon the first video data. The second processing pipeline can include identifying first test preprocessing parameters that are predictive to produce a first test output that satisfies a baseline criterion and preprocessing the incoming video data according to the first test preprocessing parameters that format the incoming video data to create first test video data.

Abstract: Hyperspectral imaging (HSI) of biofluids for biometric applications is disclosed. A HSI device scans a region of a human subject's skin with sweat thereon, without requiring the human subject to touch any surface. The resulting HSI data is processed to identify aspects of the data that represent physical and/or chemical features that uniquely identify the subject. Processing the HSI data includes identifying one or more particular metabolites in the subject's sweat by analyzing wavelengths which those metabolites reflect. Processing the HSI data includes identifying one or more glands beneath the outermost surface of the subject's skin based on HSI-detected wavelength reflectance and wavelength penetration. The disclosed system may generate and store an identity profile corresponding to a human's unique identity features, as determined based on the HSI data processing. A subject's identity is verified by comparing subsequent HSI scans of his skin with sweat thereon to his stored identity profile.

Abstract: Systems and methods for face annotation are described. One or more of the systems and methods include receiving a plurality of annotated images, wherein each annotated image of the annotated images comprises a caption; cropping the annotated image based on a face detection algorithm to obtain a face crop; comparing the face crop to the caption corresponding to the annotated image to obtain a caption similarity score; and filtering the plurality of annotated images based on the caption similarity score to obtain a plurality of annotated face images.

Abstract: To utilize a result of image analysis, an image analysis apparatus 100 includes an analysis result acquisition unit 110 and a counting unit 111. The analysis result acquisition unit 110 acquires analysis information being information acquired by analyzing a still image and including an appearance attribute to which a person included in the still image belongs. The counting unit 111 counts, for each appearance attribute, the number of persons belonging to the appearance attribute in the still image by using the analysis information, and generates counting information indicating a result of the counting in units of still images.

Abstract: Methods, systems, and apparatus for receiving, from a provider server and at an image processing server, medical data including at least one image; determining, by the image processing server using a neural network, the at least one image within the medical data contains PII; in response to determining that the at least one image in the medical data contains PII, classifying, by the neural network, at least one medical characteristic of the image; generating, by the image processing server using the medical characteristic classification, at least one metadata tag that describes the medical characteristic; modifying, by the image processing server, the medical data to replace the image with the generated metadata tag; and transmitting, by the image processing server to a transaction server, the modified medical data.