A method to facilitate extraction of display objects for industrial diagrams are disclosed herein. The method comprises: receiving user input indicating a first display object within an industrial diagram; extracting the first display object to generate a first graphic extraction template; identifying one or more regions within the first graphic extraction template; masking the text information; linking each of the one or more regions with at least a portion of an object name of the first display object; extracting all the display objects, from the industrial diagram, that are of the type of the first display object using the first graphic extraction template to generate a first set of extracted graphic objects; and for each of the first set of extracted graphic objects, matching text information within each of the one or more regions with at least a portion of an object name.

A system and method for confidence aided upsampling of categorical maps. In some embodiments, the method includes: determining a category of a first pixel of an image, the first pixel having a plurality of neighboring pixels, each of the neighboring pixels having a category; and processing the image based on the determined category. The determining may include: calculating a confidence weighted metric for each of the neighboring pixels, the confidence weighted metric being based on a maximum confidence value among each of the neighboring pixels; and determining the category of the first pixel based on the confidence weighted metric of each of the neighboring pixels and based on the category of one of the neighboring pixels.

Systems and methods for image exploration are provided. One aspect of the systems and methods includes identifying a set of images; reducing the set of images to obtain a representative set of images that is distributed throughout the set of images by removing a neighbor image based on a proximity of the neighbor image to an image of the representative set of images; arranging the representative set of images in a grid structure using a self-sorting map (SSM) algorithm; and displaying a portion of the representative set of images based on the grid structure.

Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.

An illustrative image descriptor generation system determines a subset of image descriptors from a plurality of image descriptors that each correspond to a different feature point included within an image. The subset of image descriptors is determined based on geometric proximity, within the image, of respective feature points of the subset of image descriptors to a feature point of a primary image descriptor. The image descriptor generation system then selects a secondary image descriptor from the subset of image descriptors and combines the primary image descriptor and the secondary image descriptor to form a composite image descriptor. Corresponding methods and systems are also disclosed.

In some implementations, a computer-implemented method includes: obtaining a captured image including, (i) a latent fingerprint to be captured, and (ii) a template, surrounding the latent fingerprint, that contains plurality of symbols; extracting, using an image processing technique, one or more characteristics for each of the plurality of symbols; calculating a resolution for the captured image based at least on the extracted one or more characteristics for each of the plurality of symbols; generating a reconstructed desired image, based at least on (i) the calculated resolution of the captured image, and (ii) the one or more extracted characteristics for each of the plurality of symbols; and providing the reconstructed desired image to a fingerprint identification system to perform a particular fingerprint operation.

An object detection system that can achieve both low delay and object detection accuracy is provided. A first detection unit identifies labels of objects reflected in a input frame and locations of bounding boxes of the objects. A history information generation unit assigns the same ID to the bounding boxes that share the same object, and generates history information that is information indicating a history of combination of a frame number and a location of a bounding box for each ID. A prediction unit predicts regions of the bounding boxes in latest frame, based on the history information, according to a delay that is a time required for the first detection unit to identify the labels and the locations of the bounding boxes in the input frame.

Feature descriptor matching is reformulated into a graph-matching problem. Keypoints from a query image and a reference image are initially matched and filtered based on the match. For a given keypoint, a feature graph is constructed based on neighboring keypoints surrounding the given keypoint. The feature graph is compared to a corresponding feature graph of a reference image for the matched keypoint. Relocalization data is obtained based on the comparison.

A technique identifies regions of an image characterized by constant pixel intensity in a resource-efficient, latency-efficient, and scalable manner. The technique involves: obtaining a candidate image; determining whether the candidate image contains a contiguous region of pixels having intensity values within a specified range of intensity values; assessing whether the contiguous region satisfies a prescribed test; and selecting or excluding the candidate image for further processing based on a result of the assessing. The operation of determining involve two phases. First, the technique determines a distribution of intensity values within the candidate image. Second, the technique leverages the distribution to search the candidate image for neighboring pixels having intensity values within the specified range of intensity values, beginning from a selected starting pixel in a qualifying subset of pixels. In some examples, the technique is applied to the task of combining supplemental content with the candidate image.

A system for recognizing objects and/or text in image data may use context data to perform object/text recognition. The system may also use context data when determining potential functions to execute in response to recognizing the object/text. Context data may be gathered based on device sensor data, user profile data such as the behavior of a user or the behavior of those in a user's social network, or other factors. Recognition processing and/or function selection may be configured to account for context data when operating to improve output results.