Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for training a data classification model. The method includes generating a first training rule based on probabilities of classifying a plurality of sample data into corresponding classes by a data classification model. The method also includes generating a second training rule based on relevances of the plurality of sample data to the corresponding classes. In addition, the method also includes training the data classification model using the first training rule and the second training rule. With this method, a data classification model is trained, so that the data classification accuracy of the data classification model and the robustness to noise can be improved.

An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

A method for tracking a lane on a road is presented. The method comprises receiving, by one or more processors from an imaging system, a set of pixels associated with lane markings. The method further includes generating, by the one or more processors, a predicted spline comprising (i) a first spline and (ii) a predicted extension of the first spline in a direction in which the imaging system is moving. The first spline describes a boundary of a lane and is generated based on the set of pixels. The predicted extension of the first spline is generated based at least in part on a curvature of at least a portion of the first spline.

Example implementations include a method, apparatus and computer-readable medium for configuring profiles for a camera, comprising receiving video from the camera. The implementations further include classifying a first scene of the first video stream. Additionally, the implementations further include determining a first metadata for the first scene. Additionally, the implementations further include selecting a first profile for the camera based on the first metadata, wherein the first profile comprises one or more configuration parameters, wherein values of each of the one or more configuration parameters of the first profile are based on the first metadata. Additionally, the implementations further include configuring the camera with the first profile.

A method performed by a computing device includes obtaining a set of image segment identigens for image segments of an image to produce sets of image segment identigens. A set of image segment identigens is a set of possible interpretations of a first image segment of the image segments. The method further includes identifying a subset of valid image segment identigens of each set of image segment identigens by applying identigen rules to the sets of image segment identigens to produce subsets of valid image segment identigens. Each valid image segment identigen of a subset of valid image segment identigens represents a most likely interpretation of a corresponding image segment. The method further includes generating an image entigen group utilizing the subsets of valid image segment identigens, where the image entigen group represents a most likely interpretation of the image.

The present disclosure relates to a moving body, a control method, and a program that enable realization of safer movement and stop. A safety degree estimation unit estimates a safety degree according to a lapse of time of its own machine in a moving state on the basis of external environmental information regarding an external environment, and a movement control unit controls movement of the own machine on the basis of the estimated safety degree. Technology according to the present disclosure can be applied to, for example, a moving body such as a drone.

A method is provided for training at least one classifier model used by an artificial intelligence (AI) system to recognize each of a set of objects and to assign each of the set of objects to a class. The method includes training the at least one classifier model on a training dataset, thereby producing at least one trained classifier model; using the at least one trained classifier model to detect and classify each member of a set of objects, thereby generating a set of inferences, wherein each inference includes (a) a cropped image of a classified object, (b) the classified object's inferred class, and (c) a confidence score associated with the inferred classification; examining the set of inferences with a machine implemented audit trigger, wherein the audit trigger identifies a subset of the set of inferences whose members have (i) a confidence score that falls below a predetermined threshold value, or (ii) a missing classification; and if the identified subset has at least one member, subjecting the identified subset to a human audit, thereby yielding a corrected set of observations, wherein, for each member of the corrected set of observations, the inferred class of the corresponding member of the set of inferences is replaced with a corrected class. The corrected set of observations is then added to a training dataset and used to improve the future accuracy of the classifier model.

The present invention relates to a method, device, and robot for classifying an outlier during object recognition learning using artificial intelligence. The method or device for classifying an outlier during object recognition learning according to an embodiment of the present invention sets an inlier region and an outlier region through learning using unlabeled data and labeled data.

A data generating method includes obtaining first sample data, determining a type of the first sample data and a corresponding data expansion method, expanding the first sample data according to the determined data expansion method to generate second sample data, and dividing the first sample data and the second sample data into a training set and a verification set according to a preset rule. A data model is trained according to the training set, and the data model is verified according to the verification set after training.

Embodiments provide systems, methods, and non-transitory computer storage media for providing search result images based on associations of keywords and depth-levels of an image. In embodiments, depth-levels of an image are identified using depth-map information of the image to identify depth-segments of the image. The depth-segments are analyzed to determine keywords associated with each depth-segment based on objects, features, or content in each depth-segment. An image depth-level data structure is generated by matching keywords generated for the entire image with the keywords at each depth-level and assigning the depth-level to the keyword in the image depth-level data structure for the entire image. The image depth-level data structure may be queried for images that contain keywords and depth-level information that match the keywords and depth-level information specified in a search query.