A system and method for high-confidence error overbounding of multiple optical pose solutions receives a set of candidate correspondences between 2D image features captured by an aircraft camera and 3D constellation features including at least one ambiguous correspondence. A candidate estimate of the optical pose of the camera is determined for each of a set of candidate correspondence maps (CMAP), each CMAP resolving the ambiguities differently. Each candidate pose estimate is evaluated for viability and any non-viable estimates eliminated. An individual error bound is determined for each viable candidate pose estimate and CMAP, and based on the set of individual error bounds a multiple-pose containment error bound is determined, bounding with high confidence the set of candidate CMAPs and multiple pose estimates where at least one is correct. The containment error bound may be evaluated for accuracy as required for flight operations performed by aircraft-based instruments and systems.

The present invention relates to imaging a hollow organ. In order to provide an improved and facilitated imaging of a hollow organ of interest, a device (10) for providing three-dimensional data of a hollow organ is provided that comprises a measurement input (12), a data processor (14) and an output interface (16). The measurement input is configured to receive a plurality of local electric field measurements (18) of at least one electrode on a catheter inserted in a lumen of a hollow organ of interest. The measurement input is also configured to receive geometrical data (20) representative of the location of the at least one electrode inside the lumen during the measurements. The data processor is configured to receive pre-set electric field characteristics (22) associated with predetermined anatomical landmarks of the hollow organ expectable in the lumen in dependency of a type of the hollow organ. The data processor is also configured to compare at least one of the plurality of local electric field measurements with the pre-set electric field characteristics to determine matching electric field measurements. The data processor is further configured to allocate local electric field measurements to matching electric field characteristics based on the geometrical data to identify anatomical landmarks of the hollow organ by identifying those local field measurements in the plurality of measurements that correspond to landmarks of the hollow organ. The data processor is still further configured to generate a three-dimensional image data cloud (24) by transforming the allocated electric field measurements into portions of the three-dimensional image data cloud based on the identified anatomical landmarks. The output interface is configured to provide the three-dimensional image data cloud.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.

A device that executes a program stored in the memory to perform generating a local graph for the 3D point cloud based on distances and angles between 3D points in the 3D point cloud; matching the local graph using a similarity function and determining a feature matching pair in a matched local graph; and estimating a rigid body transformation matrix using the feature matching pair is provided.

Processor implemented systems and methods for matching color and appearance of a target coating are provided herein. A system includes a storage device for storing instructions, and one or more data processors. The data processor(s) are configured to execute instructions to receive a target image of a target coating. The data processor(s) are also configured to apply a feature extraction analysis process that divides the target image into a plurality of target pixels for image analysis.

In implementations of answering questions during video playback, a video system can receive a question related to a video at a timepoint of the video during playback of the video, and determine audio sentences of the video that occur within a segment of the video that includes the timepoint. The video system can generate a classification vector from words of the question and the audio sentences, and determine an answer to the question utilizing the classification vector. The video system can obtain answer candidates, and the answer to the question can be selected as one of the answer candidates based on matching the classification vector to one of the answer vectors.

Methods and systems for classifying an image. For example, a method includes: inputting a medical image into a recognition model, the recognition model configured to: generate one or more attribute distributions that are substantially Gaussian when inputted with a normal image; and generate one or more attribute distributions that are substantially non-Gaussian when inputted with an abnormal image; generating, by the recognition model, one or more attribute distributions corresponding to medical image; generating a marginal likelihood corresponding to the likelihood of a sample image substantially matching the medical image, the sample image generated by sampling, by a generative model, the one or more attribute distributions; and generating a classification by at least: if the marginal likelihood is greater than or equal to a predetermined likelihood threshold, determining the image to be normal; and if the marginal likelihood is less than the predetermined likelihood threshold, determining the image to be abnormal.

An identification apparatus includes processing circuitry configured to determine whether or not a first image and a second image are similar based on feature points extracted from each of the first image and the second image, and determine whether or not the first image and the second image are similar by comparing neighborhood graphs generated for each of the first image and the second image, the feature points being as nodes.

A method of determining a state of a target object, an electronic device, and a storage medium, relate to fields of a computer technology, cloud computing and Internet of things, and apply to smart cities. The method includes: receiving a transmitted first moving point sequence for the target object, the first moving point sequence including a plurality of target moving point elements, and each target moving point element containing a timestamp information and a displacement information that indicate a stay state of the target object; determining, from the first moving point sequence, a target stay point of the target object, according to the timestamp information and the displacement information; and determining that the state of the target object at the target stay point is an abnormal stay state, in response to a distance between the target stay point and a first preset position being less than a first preset threshold.