Abstract: A method of tracking subjects in an area. The method including receiving a plurality of sequences of images of corresponding fields of view in the area of real space, using a plurality of trained inference engines that process respective sequences of images in the plurality of sequences of images to locate features of subjects in the corresponding fields of view of the respective sequences, combining the located features from more than one of the trained inference engines which process respective sequences of images having overlapping fields of view to generate data locating subjects in three dimensions in the area of real space during identification intervals, and matching located subjects from a plurality of identification intervals to identify tracked subjects, including comparing located subjects with tracked subjects.

Abstract: An apparatus to perform or control obtaining or causing obtaining an image of at least a part of a surface of a sealed unit. The apparatus derives or causes to derive a representation of the at least a part of the surface from the image. The representation includes a set of values representing height levels of corresponding sections of the part of the surface. The apparatus generates or causes generating a string of characters based on the representation of the part of the surface. The string of characters includes a first portion representative of the set of height levels and a second portion generated based on a function of the representation and/or of the first portion of the string of characters. The apparatus associates or causes associating information representing the string of characters with identification information of the sealed unit.

Abstract: An image processing apparatus includes first, second, and third acquisition units, a setting unit, a calculation unit, and a processing unit. The first acquisition unit acquires information regarding a setting for inspecting an image. The second acquisition unit acquires first data that is data of a reference image representing a target print result. The third acquisition unit acquires second data that is data of a printed image to be inspected. The setting unit sets a comparison area including a pixel of interest in each of the reference image and the printed image based on the information and the first data. The pixels of interest correspond to an identical pixel position. The calculation unit calculates a density difference between the comparison areas based on the first data and the second data. The processing unit inspects the printed image based on the first data, the second data, and the density difference.

Abstract: In order to acquire recognition environment information impacting the recognition accuracy of a recognition engine, an information processing device 100 comprises a detection unit 101 and an environment acquisition unit 102. The detection unit 101 detects a marker, which has been disposed within a recognition target zone for the purpose of acquiring information, from an image captured by means of an imaging device which captures images of objects located within the recognition target zone. The environment acquisition unit 102 acquires the recognition environment information based on image information of the detected marker. The recognition environment information is information representing the way in which a recognition target object is reproduced in an image captured by the imaging device when said imaging device captures an image of the recognition target object located within the recognition target zone.

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: The present invention acquires a local enlargement ratio in an image transformed based on a transformation parameter and then acquires a transformed image which is a captured image transformed based on the transformation parameter. The present invention acquires a sharpening parameter for sharpening the transformed image based on the lens information, which indicates the resolution of a lens used at the time of capturing the captured image, and the enlargement ratio, and performs sharpening of the transformed image based on the sharpening parameter.

Abstract: Concepts and technologies directed to surrogate metadata aggregation for dynamic content assembly are disclosed. Embodiments can include a system that comprises a processor and a memory that stores computer-executable instructions that configure a processor to perform operations. The operations can include obtaining a first visual content from a digital data store, where the first visual content is configured to digitally represent a first scene. The operations can include performing image recognition on the first visual content so as to identify a second visual content that is digitally configured to represent a second scene. The operations can include determining that native original metadata cannot be obtained for the first visual content, where the native original metadata includes information about the first scene digitally represented by the first visual content.

Abstract: A method is disclosed that includes receiving, by a processing device, a plurality of images of a test assembly. The processing device selects a component in the test assembly and an image of the plurality of images of the test assembly as received. For the component as selected and the image as selected, the processing device compares a plurality of portions of the image as selected to a corresponding plurality of portions of a corresponding profile image and computing a matching score for each of the plurality of portions. The processing device selects a largest matching score from the matching score for each of the plurality of portions as a first matching score for the component as selected and the image as selected. The first matching score is stored for the component as selected and the image as selected.

Abstract: The present invention is directed to a method for quantitative and qualitative evaluation of laser printed samples, the method comprising the following steps: a) providing (S1) a laser printed sample, b) capturing (S2) a digital raster image of the laser printed part of the laser printed sample and thereon providing digital image information that constitutes the digital raster image, c) identifying (S3) at least one distinct part within the digital image information, d) obtaining at least one image histogram for the identified at least one distinct part of the digital image information, e) fitting (S4) a probability density function on the obtained at least one image histogram, and f) determining (S5) at least one parameter of the probability density function.

Abstract: Systems and methods are provided for receiving a plurality of images corresponding to a listing in an online marketplace, generating a scene type for each image of the plurality of images, and grouping each image into a scene type group of a set of predefined scene types. Each group of images are input into a respective machine learning model specific to the scene type of the group of images to generate a visual score for each image in each group of images, and an attractiveness score is generated for the listing in the online marketplace based on the visual scores for each image in each group of images.

Abstract: The present disclosure relates to a machining station for machining platelike workpieces (1) by means of at least one tool, in particular a drilling station for machining at least one circuit board, as well as to a method for controlling or identifying a platelike workpiece (1). The machining station has at least one X-ray radiation source (3), at least one detector (4) and a table (2) that can be positioned between the X-ray radiation source (3) and the detector (4), on which the workpiece (1) to be machined can be fastened. The table (2) has a receiving plate (6) made out of a material permeable to X-rays (5), in particular a plastic. The receiving plate (6) has at least one suction port (11) for extracting air and a distribution grid, which is connected in terms of flow with the at least one suction port (11) and consists of several channels (10) unilaterally open in a support surface (9) with beveled and/or rounded edges (13) and/or of inclinedly running through openings (14).

Abstract: In variants, the method can include: determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations.

Abstract: Accurately detection of logos in media content on media presentation devices is addressed. Logos and products are detected in media content produced in retail deployments using a camera. Logo recognition uses saliency analysis, segmentation techniques, and stroke analysis to segment likely logo regions. Logo recognition may suitably employ feature extraction, signature representation, and logo matching. These three approaches make use of neural network based classification and optical character recognition (OCR). One method for OCR recognizes individual characters then performs string matching. Another OCR method uses segment level character recognition with N-gram matching. Synthetic image generation for training of a neural net classifier and utilizing transfer learning features of neural networks are employed to support fast addition of new logos for recognition.

Abstract: A machine learning model training method includes: training a machine learning model using features of samples in a training set, where a sample in the training set corresponds to an initial first weight and an initial second weight. In one iteration, the method includes: determining a first sample set comprising one or more samples whose corresponding target variables are incorrectly predicted; determining an overall predicted loss of the first sample set based on the predicted losses and corresponding first weights of samples in the first sample set; updating the first weights and second weights of the samples in the first sample set based on the overall predicted loss of the first sample set; and inputting the second weights, the features, and the target variables of the samples in the training set to the machine learning model, and initiating a next iteration of training the machine learning model.

Abstract: An image processing apparatus capable of analyzing a cause of an abnormality of a system efficiently. The image processing apparatus includes a monitoring unit and an analysis unit. The monitoring unit monitors a monitoring object to detect occurrence of an abnormality in the monitoring object by applying an image process to a first area in each of images of the monitoring object that are photographed at different time points. The analysis unit analyzes a cause of an abnormality detected by the monitoring unit by applying an image process to a second area that is different from the first area in an image of a time point preceding a time point at which the abnormality is detected.

Abstract: The invention relates to a computer-implemented method for the measurement of an object, wherein the method comprises the following steps: ascertainment of measurement data using a radiographic measurement of the object, wherein the measurement data generates a digital representation of the object with a large number of items of image information of the object; and carrying out the following steps at least before ending the ascertainment of measurement data: analysis of at least one portion of the digital representation of the object; optimization of at least one recording parameter of the radiographic measurement using the analysed portion of the digital representation of the object; and adaptation of the step of ascertainment of measurement data taking the at least one recording parameter into consideration. The invention thus provides a computer-implemented method that has an increased efficiency.

Abstract: A drain pump clog prediction device predicts the clogging of a drain pump, which is configured to drain water out of a drain pan of an air conditioner. The drain pump clog prediction device includes an obtaining portion that obtains information related to an object in the drain pan and a prediction portion calculates at least one of an object amount, an object size, and an object inflow amount based on information obtained by the obtaining portion and predicts occurrence of the clogging of the drain pump based on at least one of the object amount, the object size, and the object inflow amount.

Abstract: A system for visual inspection, which includes a processor that receives a sensitivity level relating to an image of an item on an inspection line, the image being displayed on the user interface device, and calculates a minimal defect size visible in the image at the received sensitivity level. The calculated minimal defect size is then displayed on a user interface. This visual demonstration of minimal detectable size, which changes as the user changes sensitivity level, enables the user to easily obtain a desired minimal detectable defect size.

Abstract: A system and method is provided for displaying surfaces of an object from a vantage point different from the vantage point from which imagery of the object was captured. In some aspects, imagery may be generated for display by combining visual characteristics from multiple source images and applying greater weight to the visual characteristics of some of the source images relative to the other source images. The weight may be based on the orientation of the surface relative to the location from which the image was captured and the location from which the object will be displayed.

Abstract: An information processing device includes a displacement calculation means and a motion estimation means. The displacement calculation means acquires time-series images obtained by capturing images of a measurement target region of a structure supported by a supporting member. The displacement calculation means calculates a three-dimensional displacement of the measurement target region from the acquired time-series images. The motion estimation means estimates a motion of the supporting member in the structure based on the three-dimensional displacement of the measurement target region.