A system of the present invention for providing additional object information by using a boundary code on an edge comprises: a sign having an edge to which a boundary code is provided; and a smart device which converts the boundary code into additional object information and visually, acoustically, and sensately provides the additional object information. The present invention facilitates pattern recognition even at a long distance and there is almost no probability of the pattern recognition failing.

A method for simultaneous localization and mapping (SLAM) employs dual event-based cameras. Event streams from the cameras are processed by an image processing system to stereoscopically detect surface points in an environment, dynamically compute pose of a camera as it moves, and concurrently update a map of the environment. A gradient descent based optimization may be utilized to update the pose for each event or for each small batch of events.

An image matching method includes: extracting a plurality of feature points from a reference image; selecting a first feature point from the feature points, and selecting a first reference search area comprising the first feature point; setting a first matching candidate search area corresponding to the first reference search area from a target image, and extracting a plurality of feature points from the first matching candidate search area; selecting a second feature point closest to the first feature point in the first reference search area, and selecting a first straight line connecting the first and second feature points; generating a plurality of segments from the feature points extracted from the first matching candidate search area; and determining a first matching straight line matching a length and an angle of the first straight line, from the segments generated from the feature points extracted from the first matching candidate search area.

The present invention discloses a cloud-edge-end cooperative control method of a 5G networked UAV for security rescue, including: an image acquisition step: performing, by a single-chip microcomputer, attitude resolution on data acquired by a detection sensor, to obtain image data; a sparse landmark map building step: performing, by a control platform, front-end feature point matching, local map building and optimization, loopback detection, and frame resolution on the image data, to generate a sparse landmark map; a three-dimensional dense map building step: generating, by an edge cloud, a three-dimensional dense map based on a key frame pose and key frame observation data of the sparse landmark map; a high-precision semantic map building step: obtaining a high-precision semantic map; and a UAV movement step: adjusting, by the driving mechanism, a pose of the UAV according to the three-dimensional dense map or the high-precision semantic map.

A method of dynamically updating a feature database that contains features corresponding to a known target object includes providing an image, extracting a first set of features from within the captured image, and comparing the first set of features to the features stored in the feature database. If it is determined that the target object is present in the image then at least one of the extracted features of the first set that are not already included in the feature database are added to the feature database.

An image processing apparatus processes a first image and a second image so as to detect a corresponding pixel in the second image which corresponds to a target pixel in the first image. The first image has a first parameter value, and the second image has a second parameter value different from the first parameter value. The first parameter value and the second parameter value are values of optical parameters of image capturing systems used to capture the first image and the second image. The image processing apparatus includes an area setter that sets a two-dimensional search area as a partial area in which the corresponding pixel is to be searched in the second image, based on a predetermined range in which each of the first and second parameter values can change, and a detector that detects the corresponding pixel by searching the two-dimensional search area.

An example system includes a feature extraction engine. The feature extraction engine is to determine a plurality of scale-dependent features for a portion of a target. The system also includes a signature-generation engine to select a subset of the plurality of scale-dependent features based on a strength of each feature. The signature-generation engine also is to store a numeric representation of the portion of the target and the subset of the plurality of scale-dependent features.

A three-dimensional position estimation device includes: a feature point detection unit detecting feature points from images captured by an image capturing device mounted in a moving object; a position-posture change amount calculation unit calculating change amounts of a position and a posture of the moving object; a feature point association unit associating feature points using a feature amount indicating a feature of the feature point; a three-dimensional position acquisition unit acquiring three-dimensional positions of the feature points and acquires a position and a posture of the image capturing device; a graph structure data generation unit generating graph structure data configured with feature point and image nodes, and a feature point and image capturing edges; an error function setting unit setting first and second error functions; and a graph structure data optimizing unit optimizing the three-dimensional position and the position and the posture of the image capturing device.

A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing an impartiality assessment operation via an impartiality assessment engine, the impartiality assessment operation detecting a presence of bias in an outcome of the cognitive computing function, the impartiality assessment operation generating a burden score representing the presence of bias in the outcome; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

An object recognition method is provided. The method includes: detecting an occlusion region of an object in an image, to obtain a binary image; obtaining occlusion binary image blocks; querying a mapping relationship between occlusion binary image blocks and binary masks included in a binary mask dictionary to obtain binary masks corresponding to the occlusion binary image blocks; synthesizing the binary masks queried based on each of the occlusion binary image blocks, to obtain a binary mask corresponding to the binary image; and determining a matching relationship between the image and a prestored object image, based on the binary mask corresponding to the binary image, a feature of the prestored object image, and a feature of the to-be-recognized image.