Abstract: There is provided mechanisms for joint visual object detection and object mapping to a 3D model. A method is performed by an image processing device. The method includes obtaining a first sequence of digital images of a scene as captured by a first image capturing unit, and obtaining a second sequence of digital images of the scene as captured by a second image capturing unit. The second sequence of digital images is time-wise synchronized with the first sequence of digital images by being captured time-wise in parallel with the first sequence of digital images. The first image capturing unit has a narrower field of view than the field of view of the second image capturing unit. The first image capturing unit and the second image capturing unit have a known spatial relation. The method includes performing joint visual object detection and object mapping to the 3D model.

Abstract: A method for analyzing at least one sequence of movements performed by a person, including steps of segmenting the sequence of movements into time units, each associated with a time (t0 to tn), determining for each instant, positions of at least some characteristic points of the person using an acquisition system providing raw data, assigning at each instance at least one position code for the characteristic points as a function for each of them of the determined position to form a combination of position codes at each instant, assigning at least one elementary action code for each given instant corresponding to the combination of position codes at the given instant, and syntactically verifying the elementary action codes and/or position codes from at least one structured language to refine the raw data relating to the positions of the characteristic points.

Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, detecting one or more image regions from a background of the one or more digital images, determining a prediction, using a machine learning system, of whether at least one first image region of the one or more image regions comprises at least one external contaminant, the machine learning system having been trained using a plurality of training images to predict a presence of external contaminants and/or a location of any external contaminants present in the tissue specimen, and determining, based on the prediction of whether a first image region comprises an external contaminant, whether to process the image region using an processing algorithm.

Abstract: A method for processing image data according to an aspect of the present disclosure includes obtaining first image data indicating a hyperspectral image of a target captured in first background light, generating, on a basis of the first image data, first spectral data indicating an estimated spectrum of the first background light, and generating, from the first image data, at least one piece of second image data indicating at least one image of the target in at least one type of second background light, which is different from the first background light, using at least one piece of second spectral data indicating at least one spectrum of the at least one type of second background light and the first spectral data.

Abstract: The present disclosure includes a multiple of object detection devices including a transmission system that transmits a transmission wave and a receiving system that receives a reflected wave from an object, and a control device into which detected information is input from each of the multiple of object detection devices, wherein the control device includes a representative value calculation unit that calculates a representative value of reception strength of the reflected wave obtained by each of the multiple of object detection devices detecting a peripheral object at regular intervals, and a performance decline determination unit that determines that a decline in performance of the multiple of object detection devices has occurred.

Abstract: The disclosure relates to devices, systems and methods for image registration and annotation. The devices include computer software products for aligning whole slide digital images on a common grid and transferring annotations from one aligned image to another aligned image on the basis of matching tissue structure. The systems include computer-implemented systems such as work stations and networked computers for accomplishing the tissue-structure based image registration and cross-image annotation. The methods include processes for aligning digital images corresponding to adjacent tissue sections on a common grid based on tissue structure, and transferring annotations from one of the adjacent tissue images to another of the adjacent tissue images.

Abstract: A method and apparatus are provided for detecting respective potential presences of respective different cellular fluorescence pattern types on a biological cellular substrate including human epithelioma cells (HEp cells), wherein the fluorescence pattern types include different antinuclear antibody fluorescence pattern types. A method is also provided for detecting potential presences of different cellular fluorescence pattern types on a biological cellular substrate including human epithelioma cells by means of digital image processing, as well as a computing unit, a data network device, a computer program product and a data carrier signal therefor.

Abstract: An artificial intelligence server includes: a communication interface configured to communicate with a terminal, and a processor. The processor is configured to receive, from the terminal through the communication interface, a video of a home appliance, acquire a first feature vector by inputting image data extracted from the video to an image classification model, acquire a second feature vector by inputting sound data extracted from the video to a voice classification model, acquire a result value by inputting a data set obtained by combining the first feature vector and the second feature vector to an abnormality classification model, and transmit, to the terminal through the communication interface, a failure type acquired based on the result value.

Abstract: An information processing device includes at least one memory storing a program, and at least one processor that, by executing the program, causes the information processing apparatus to acquire an image group obtained by imaging a subject under a plurality of imaging conditions, to select at least one inference model from a plurality of inference models on the basis of at least one imaging condition among the plurality of imaging conditions, and to perform inferencing on images in the acquired image group by using the selected inference model.

Abstract: A method is used to generate a distortion model for a structured illumination microscopy (SIM) optical system. A sliding window is moved in relation to a plurality of images to define a plurality of sub-tiles. Each sub-tile represents a portion of the corresponding image. Parameters are estimated for each sub-tiles. The parameters include two or more parameters selected from the group consisting of modulation, angle, spacing, phase offset, and phase deviation. A full width at half maximum (FWHM) value associated with each sub-tile is estimated. A distortion model is estimated, based at least in part on a combination of the estimated parameters and FWHM values stored in the predetermined format and an estimated center window parameter. A two-dimensional image may be generated, based at least in part on the estimated distortion model. The two-dimensional image may include representations indicating where distortions occur in the optical system.

Abstract: Apparatus and methods are described for use with an output device, and a blood sample that was drawn from a subject. A microscope system acquires first and second images of the blood sample at respective times. A computer processor determines whether, between acquisitions of the first and second images, there was relative motion between at least one erythrocyte within the sample and at least one entity within the sample, by comparing the first and second images to one another. At least partially in response thereto, the computer processor determines whether the entity is an extra-erythrocytic or an intra-erythrocytic entity, and generates an output on the output device, at least partially in response thereto. Other applications are also described.

Abstract: A computer-assisted method for analyzing person-related accident data of one or more vehicle occupants, the person-related accident data including at least image data of a video sequence of the vehicle occupants during an accident. The computer-assisted method includes at least one first step of detecting a pattern on the basis of the image data, and at least one second step of comparing the detected pattern with a number of previously stored patterns.

Abstract: A method for visualization of dynamic objects using a generative manifold includes steps of: acquiring a set of measurements associated with the dynamic objects using sensors; estimating parameters of a generator using the set of measurements and estimating latent variables using the set of measurements; modeling using a computing device the dynamic objects as a smooth non-linear function of the latent variables using the generator such that points in a latent subspace are mapped to a manifold in a generative manifold model; and generating a visualization of the dynamic objects using the generative manifold model. The set of measurements may include multi-slice data. The generative manifold model may provide for modeling deformations.

Abstract: A method for extracting an oil storage tank based on a high-spatial-resolution remote sensing image is provided, including: acquiring an oil storage tank sample, and randomly dividing the oil storage tank sample into a training set and a testing set; building an oil storage tank extraction model based on a Res2-Unet model structure, wherein the Res2-Unet is a deep learning network based on a UNet semantic segmentation structure, and a Res2Net convolution block is configured to change a feature interlayer learning to a granular learning and is arranged in a residual mode; and performing a precision verification on the testing set.

Abstract: An image processing device includes a rotation processor and an image processor. The rotation processor receives an input image and generates a temporary image according to the input image. The image processor is coupled to the rotation processor and outputs a processed image according to the temporary image, wherein the image processor has a predetermined image processing width, a width of the input image is larger than the predetermined image processing width, and a width of the temporary image is less than the predetermined image processing width.

Abstract: Described are methods and systems for training a machine learning (ML) model to detect anomalies in images of documents. A first image of a first set of images of documents is obtained. Each first image relates to a region of the document and the first set of images comprises an image of a document containing an anomaly and an image of a document not containing an anomaly. Signal processing algorithms are applied to the first images to generate a signal for each first image and each algorithm, and a discriminative power of each algorithm is evaluated. Based on the discriminative power, a signal processing algorithm is selected and ML model input data is generated using signals generated by applying the algorithm to second digital images. The ML model is trained using the input data to produce output indicating whether an image of a document contains an anomaly.

Abstract: Devices and methods for capturing vehicle undercarriage images are described. In some instances, a mirror assembly may be used to reflect images of portions of a vehicle undercarriage into a field of view of a camera to be captured, e.g., as a vehicle passes over the mirror assembly. Composite images may be reconstructed from the reflected portions of the vehicle undercarriage, and analysis may be performed on those reconstructed, composite images to identify features in the composite vehicle undercarriage images.

Abstract: The present disclosure relates to the field of artificial intelligence technology, and to a method of estimating a pose of a device and a related device. The method of estimating a pose of a device includes identifying a similar key frame that is similar to a current frame collected by the device from a key frame set, based on a vector distance between the similar key frame and the current frame; obtaining data-related information between image frames based on a feature matching relationship between the current frame and the similar key frame; and obtaining the pose of the device based on the data-related information.

Abstract: A system for high definition (HD) image recognition of criminals is disclosed. The system includes a plurality of cameras, an image recognition server, investigator user devices, a computing device, a database, and a network. At least one processor of the image recognition server is configured to receive a plurality of photographs of a first individual, perform image processing of the plurality of photographs to extract a first set of physical features, store feature data regarding the first set of physical features in the database, receive suspect data regarding a suspected individual from a first investigator user device, match the suspect data with the feature data stored in the database, and transmit an alert to the computing device in the prison, wherein the alert activates a mobile application on each investigator user device to display match data identifying the suspected individual as the first individual based on the feature data.

Abstract: A problem of imbalanced big data is solved by decoupling a classifier into a neural network for generation of representation vectors and into a classification model for operating on the representation vectors. The neural network and the classification model act as a mapper classifier. The neural network is trained with an unsupervised algorithm and the classification model is trained with a supervised active learning loop. An acquisition function is used in the supervised active learning loop to speed arrival at an accurate classification performance, improving data efficiency. The accuracy of the hybrid classifier is similar to or exceeds the accuracy of comparative classifiers in all aspects. In some embodiments, big data includes an imbalance of more than 10:1 in image classes. The hybrid classifier reduces labor and improves efficiency needed to arrive at an accurate classification performance, and improves recognition of previously-unrecognized images.