The present disclosure provides an electronic device. The electronic device comprises a memory circuitry, an interface circuitry, and a processor circuitry. The processor circuitry is configured to obtain first image data associated with a first image. The processor circuitry is configured to obtain a primary object based on the first image data. The processor circuitry is configured to generate one or more secondary objects based on a first augmentation operation of the primary object. The processor circuitry is configured to obtain primary background data from the first image data. The processor circuitry is configured to generate secondary background data based on a second augmentation operation of the primary background data. The processor circuitry is configured to provide a first data set by combining the primary object and/or the one or more secondary objects with the primary background data and/or the secondary background data.

A method, including receiving a two-dimensional (2D) image of a three-dimensional (3D) object recorded at a first angle of rotation of the object, and identifying, in the 2D image, a set of image descriptors, each of the image descriptors including an image keypoint and one or more image features. The set of image descriptors are compared against sets of template descriptors for respective previously captured 2D images, each of the template descriptors comprising a template keypoint and one or more template features. Using a threshold, a given set of template descriptors matching the set of image descriptors are identified, the given set of template descriptors corresponding to a given previously captured 2D image of the 3D object recorded at a second angle of rotation of the object. Any of the image descriptors not in the given set of the template descriptors are added to the given set of template descriptors.

The present invention relates to an infrared thermometer (1) able to project the detected temperature directly on the surface (6a) of the body (2) to be measured. The determination of the ideal distance of the thermometer from the body, necessary for the correct detection of the temperature thereof, being visually identifiable by means of the relative position of luminous shapes (8a, 8b) projected on the body to be measured (6).

A method includes: determining at least one typical object ratio from a first training data set by counting ratios of objects in training pictures of the first training data set; determining at least one picture scaling size based at least on the at least one typical object ratio; scaling the training pictures of the first training data set according to the at least one picture scaling size; obtaining a second training data set by slicing the scaled training pictures; training an object detection model using the second training data set; and performing object detection on a to-be-detected picture using the trained object detection model. The object detection method according to the embodiments of the present disclosure can be used to complete, without manual intervention, a task of detecting an extremely small object.

At least some example embodiments disclose a device and a method for generating a synthetic image and a different-angled image and eliminating noise. The method may include receiving input images, extracting feature values corresponding to the input images using an image learning model, the image learning model permitting an input and an output to be identical and generating a synthetic image based on the feature values corresponding to the input images using the image learning model.

The disclosure relates to system for processing an image of at least one camera. The camera has predetermined camera parameters including a lens distortion and a camera pose with respect to a predefined reference frame. The system comprises: a trained neural network with a predefined architecture, the neural network being configured to receive the image of the camera as input and to predict in response at least one characteristic, wherein the neural network architecture comprises at least one static feature map configured to encode the predetermined camera parameters including the lens distortion and/or the camera pose.

LK-SURF, Robust Kalman Filter, HAR-SLAM, and Landmark Promotion SLAM methods are disclosed. LK-SURF is an image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking using stereo images to produce 3D features can be tracked and identified. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis and the X84 outlier rejection rule. Hierarchical Active Ripple SLAM is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple tracked objects, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of tracked objects, landmarks, and estimated object locations. In Landmark Promotion SLAM, only reliable mapped landmarks are promoted through various layers of SLAM to generate larger maps.

The present invention provides a method for quantitatively rating the degree of similarity of images by using orientation, resizing of the digital image, pixilation of the digital image, creation of an image string suitable for generating a hit-score. In the generation of the image string color codes e.g. by the RGB system is used. The hit-score can be a percentage identity or homology between the image strings of the two digital images, said hit-score being the rating of the similarity between the two digital images. The invention also related to the use of the method in process of assessing insurance claims. Further the invention to the use of said method and for a computer program with instruction for carrying out the method.

The disclosure relates to system for processing an image of at least one camera. The camera has predetermined camera parameters including a lens distortion and a camera pose with respect to a predefined reference frame. The system comprises: a trained neural network with a predefined architecture, the neural network being configured to receive the image of the camera as input and to predict in response at least one characteristic, wherein the neural network architecture comprises at least one static feature map configured to encode the predetermined camera parameters including the lens distortion and/or the camera pose.

At least some example embodiments disclose a device and a method for generating a synthetic image and a different-angled image and eliminating noise. The method may include receiving input images, extracting feature values corresponding to the input images using an image learning model, the image learning model permitting an input and an output to be identical and generating a synthetic image based on the feature values corresponding to the input images using the image learning model.