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 evaluation result acquiring unit acquiring an evaluation result obtained by evaluating matching eligibility of the feature in the database, and a processing unit processing the database on the basis of the evaluation result acquired by the evaluation result acquiring unit.

A dismantling procedure selecting apparatus includes a dismantling information storage unit that stores a plurality of pieces of dismantling information respectively including a plurality of predetermined dismantling procedures for a plurality of dismantled objects each used as a reference for dismantling an object to be dismantled, a whole detector that captures a whole image of the object to be dismantled, a detail detector that captures an image of at least a portion of the object to be dismantled, and a dismantling procedure deriving unit that extracts a first feature as a feature of the object to be dismantled based on data obtained by capturing an image by at least one of the whole detector and the detail detector, obtains a degree of matching between the first feature and each of a plurality of second features that are respectively features of a plurality of dismantled objects, and selects a dismantling procedure associated with one of a plurality of the dismantled objects having a second feature having a highest degree of matching among a plurality of second features.

Disclosed in the present invention are a method and apparatus for the identification of fetal cross-sections based on ultrasound dynamic images; the method includes: inputting sequentially each frame of fetal ultrasound images from acquired multiple consecutive frames of fetal ultrasound images into a predetermined feature-detecting model for analysis; acquiring a sequentially exported analysis from the feature-detecting model as feature information for each frame of fetal ultrasound images; corresponding to each frame of fetal ultrasound images, identifying a cross-section by the categories of the part and the structural feature. Obviously, for a fetal ultrasound image, the implementation of the present invention may improve the identified accuracy, identified efficiency, and the standardization of the cross-section, by acquiring the part features and structural features from consecutive multi-frame fetal ultrasound images and identifying the cross-section by combining the part features and structural features.

A signal processing device and an image display apparatus including the same are disclosed. The signal processing device includes a scaler configured to scale input images of various resolutions to a first resolution, a resolution enhancement processor configured to perform learning on the input images and to output a residual image of the first resolution, and an image output interface configured to output an output image of the first resolution based on a scaling image from the scaler and the residual image from the resolution enhancement processor, and the image output interface changes a weight and an application strength of the residual image according to the area of the input image.

This disclosure is related to an image generation method and apparatus. The method includes: obtaining a first body image including a target body and a first clothes image including target clothes; transforming the first clothes image based on a posture of the target body in the first body image to obtain a second clothes image, the second clothes image including the target clothes, and a posture of the target clothes matching the posture of the target body; performing feature extraction on the second clothes image, an image of a bare area in the first body image, and the first body image to obtain a clothes feature, a skin feature, and a body feature respectively; and generating a second body image based on the clothes feature, the skin feature, and the body feature, the target body in the second body image wearing the target clothes.

Described herein is a computer-implemented method. The method includes: providing digital images and respective formulas for coating compositions with known pigments and/or pigment classes associated with the respective digital images, classifying, using an image annotation tool, for each digital image, each pixel, by visually reviewing the respective digital image pixel-wise, providing, for each digital image, an associated pixel-wise annotated image, training a first neural network with the provided digital images as input and the associated pixel-wise annotated images as output, making the trained first neural network available for applying the trained first neural network to at least one unknown input image of a target coating and for assigning a pigment label and/or a pigment class label to each pixel in the at least one unknown input image, and determining and/or outputting, for each unknown input image, a statistic of corresponding identified pigments and/or pigment classes, respectively.

The method for generating a road edge line includes: acquiring a road image; recognizing lane line information from the road image; recognizing key point information related to the road edge from the road image; and generating the road edge line according to the lane line information and the key point information.

A method for determining a location of a trailer in an image includes obtaining at least one real-time image from a vehicle. The at least one real-time image is processed with a controller on the vehicle to obtain a feature patch describing at least one real-time image. A convolution is performed of the feature patch and each filter from a set of filters with the filter being based on data representative of known trailers. A location of a trailer is determined in the at least one real-time image based on the convolution between the feature patch and each filter from the set of filters.

Providing an object detection engine, training the object detection engine to identify a weed, training the object detection engine to identify a crop, providing an image from a sensor to the object detection engine, discerning with the object detection engine the weed from the crop, and plotting a path from the weed to a spot spray assembly upon identification of the weed by the object detection engine.

A method and system is provided for determining the denomination and related data for a currency item using a personal computing device, such as a mobile phone. The device includes or is connected to an image capture device that is preferably a digital video camera. At least one image of a target currency item is captured then processed for image quality. A further processing of the image includes a coordinate mapping. A comparison is made between individual pixels of the processed image based on the assigned coordinate mapping with a database of reference currency images to determine the currency denomination. Additional processing of the currency image provides the date and other data regarding the target currency item. A market value for the target currency item is identified by reference to a valuation database using the data determined for the currency item.