Erroneous detection due to erroneous parallax measurement is suppressed to accurately detect a step present on a road. An in-vehicle environment recognition device 1 includes a processing device that processes a pair of images acquired by a stereo camera unit 100 mounted on a vehicle. The processing device includes a stereo matching unit 200 that measures a parallax of the pair of images and generates a parallax image, a step candidate extraction unit 300 that extracts a step candidate of a road on which the vehicle travels from the parallax image generated by the stereo matching unit 200, a line segment candidate extraction unit 400 that extracts a line segment candidate from the images acquired by the stereo camera unit 100, an analysis unit 500 that performs collation between the step candidate extracted by the step candidate extraction unit 300 and the line segment candidate extracted by the line segment candidate extraction unit 400 and analyzes validity of the step candidate based on the collation result and an inclination of the line segment candidate, and a three-dimensional object detection unit 600 that detects a step present on the road based on the analysis result of the analysis unit 500.

A client device receives a first map tile, a second map tile, and map terrain data from a mapping system, the first and second map tiles together including map feature having a geometric base with a height value, the geometric base represented by a set of vertices split across the first and second map tiles. The client device identifies edges of the geometric base that intersect a tile border between the first and second map tiles. The client device determines a set of sample points based on the identified edges and determines a particular sample elevation value corresponding to a sample point in the set. The client device renders the map feature based on the particular sample elevation value and displays the rendering of the map feature.

Each input image from a plurality of input images is divided into a plurality of image tiles. A feature point map including a plurality of feature point tiles respectively corresponding to the plurality of image tiles is generated by extracting feature points included in each image tile of the plurality of image tiles. A descriptor map including a plurality of descriptor tiles respectively corresponding to the plurality of feature point tiles is generated by generating descriptors of feature points included in the feature point map. Mapping information containing matching relationships between feature points included in different input images of the plurality of input images is generated based on a plurality of descriptor maps respectively corresponding to the plurality of input images. Image merging performance may be enhanced by dividing the input image into the plurality of image tiles to increase distribution uniformity of the feature points.

One exemplary implementation facilitates object detection using multiple scans of an object in different lighting conditions. For example, a first scan of the object can be created by capturing images of the object by moving an image sensor on a first path in a first lighting condition, e.g., bright lighting. A second scan of the object can then be created by capturing additional images of the object by moving the image sensor on a second path in a second lighting condition, e.g., dim lighting. Implementations determine a transform that associates the scan data from these multiple scans to one another and use the transforms to generate a 3D model of the object in a single coordinate system. Augmented content can be positioned relative to that object in the single coordinate system and thus will be displayed in the appropriate location regardless of the lighting condition in which the physical object is later detected.

An image processing method for identifying text on production line components obtains an image to be recognized and a standard image for reference and extracts a first text area of the image to be recognized. A second text area of the standard image is obtained, and a text window is extracted based on the second text area. The method further obtains a target text area of the image to be recognized based on the first text area and the text window, and obtains a first set of first text sub-areas, and obtains a second set of second text sub-areas, by dividing the second text area into sub-windows of the text window. The method further marks the image to be recognized as a qualifying image when each first text sub-area of the first set is the same as a corresponding second text sub-area of the second set.

An own-position estimating device for estimating an own-position of a moving body by matching a feature extracted from an acquired image with a database in which position information and the feature are associated with each other in advance, includes an estimating unit estimating the own-position of the moving body by matching the feature extracted by the extracting unit with the database, and a determination threshold value adjusting unit adjusting a determination threshold value for extracting the feature, in which the determination threshold value adjusting unit acquires the database in a state in which the determination threshold value is adjusted, and adjusts the determination threshold value on the basis of the determination threshold value linked to each of the position information items in the database, and the extracting unit extracts the feature from the image by using the determination threshold value adjusted by the determination threshold value adjusting unit.

A tree crown extraction method based on UAV multi-source remote sensing includes: obtaining a visible light image and LIDAR point clouds, taking a digital orthophoto map (DOM) and the LIDAR point clouds as data sources, using a method of watershed segmentation and object-oriented multi-scale segmentation to extract single tree crown information under different canopy densities. The object-oriented multi-scale segmentation method is used to extract crown and non-crown areas, and a tree crown distribution range is extracted with the crown area as a mask; a preliminary segmentation result of single tree crown is obtained by the watershed segmentation method based on a canopy height model; a brightness value of DOM is taken as a feature, the crown area of the DOM is performed secondary segmentation based on a crown boundary to obtain an optimized single tree crown boundary information, which greatly increases the accuracy of remote sensing tree crown extraction.

A computing system including a processing circuit in communication with a camera having a field of view. The processing circuit is configured to perform operations related to detecting, identifying, and retrieving objects disposed amongst a plurality of objects. The processing circuit may be configured to perform operations related to object recognition template generation, feature generation, hypothesis generation, hypothesis refinement, and hypothesis validation.

Even if an object to be detected is not remarkable in images, and the input includes images including regions that are not the object to be detected and have a common appearance on the images, a region indicating the object to be detected is accurately detected. A local feature extraction unit 20 extracts a local feature of a feature point from each image included in an input image set. An image-pair common pattern extraction unit 30 extracts, from each image pair selected from images included in the image set, a common pattern constituted by a set of feature point pairs that have similar local features extracted by the local feature extraction unit 20 in images constituting the image pair, the set of feature point pairs being geometrically similar to each other. A region detection unit 50 detects, as a region indicating an object to be detected in each image included in the image set, a region that is based on a common pattern that is omnipresent in the image set, of common patterns extracted by the image-pair common pattern extraction unit 30.

A coding amount estimation device includes: a feature vector generation unit that generates a feature vector on the basis of a feature map generated by an estimation target image and at least one filter set in advance; and a coding amount evaluation unit that evaluates a coding amount of the estimation target image on the basis of the feature vector.