A target detection method includes: determining detection points corresponding to regions in an image to be detected and a probability value of a target in a region corresponding to each detection point according to the image to be detected; screening out a first detection point having a maximum probability value, and second detection point(s) having probability value(s) less than the probability value of the first detection point and greater than or equal to a probability threshold; if a first distance between each second detection point and the first detection point is greater than or equal to a distance threshold, updating an original probability value of the second detection point to obtain an updated probability value; comparing the updated probability value with the probability threshold to obtain a comparison result; and determining whether a new target in a region corresponding to the second detection point according to the comparison result.

A target detection method includes: determining detection points corresponding to regions in an image to be detected and a probability value of a target in a region corresponding to each detection point according to the image to be detected; screening out a first detection point having a maximum probability value, and second detection point(s) having probability value(s) less than the probability value of the first detection point and greater than or equal to a probability threshold; if a first distance between each second detection point and the first detection point is greater than or equal to a distance threshold, updating an original probability value of the second detection point to obtain an updated probability value; comparing the updated probability value with the probability threshold to obtain a comparison result; and determining whether a new target in a region corresponding to the second detection point according to the comparison result.

A method of recognizing animals includes recognizing a plurality of body parts of a plurality of animals based on at least one image of the animals, in which the plurality of body parts include a plurality of types of body parts, including determining first estimated positions of the recognized body parts in the at least one image. The method includes estimating a plurality of first associations of body parts based on the at least one image of the animals, each first association of body parts associates a body part of an animal with at least one other body part of the same animal, including determining relative positions of the body parts in each estimated first association of body parts in the at least one image. The method includes determining, based on the first estimated positions of the recognized body parts and the relative positions of the body parts in the estimated first associations of body parts, second associations of body parts in which each second association of body parts associates a recognized body part of an animal with at least one other recognized body part of the same animal; and recognizing individual animals in the at least one image based on the second associations of body parts of the animals.

A method of recognizing animals includes recognizing a plurality of body parts of a plurality of animals based on at least one image of the animals, in which the plurality of body parts include a plurality of types of body parts, including determining first estimated positions of the recognized body parts in the at least one image. The method includes estimating a plurality of first associations of body parts based on the at least one image of the animals, each first association of body parts associates a body part of an animal with at least one other body part of the same animal, including determining relative positions of the body parts in each estimated first association of body parts in the at least one image. The method includes determining, based on the first estimated positions of the recognized body parts and the relative positions of the body parts in the estimated first associations of body parts, second associations of body parts in which each second association of body parts associates a recognized body part of an animal with at least one other recognized body part of the same animal; and recognizing individual animals in the at least one image based on the second associations of body parts of the animals.

According to a method for correcting a 2D measurement value is described, 2D image data of an examination object is received. Landmarks in the 2D image data are detected, and 2D positions of the landmarks are calculated. A corrected measurement value of the examination object is predicted, using a trained model, which depends on the received 2D image data, the estimated 2D positions of the landmarks and a reference parameter of a reference 3D orientation of the examination object.

Systems and methods are provided for identifying an item that is counterfeit in the marketplace. Sales of counterfeit items in marketplaces, particularly at auction places, have been an issue. There has been a need to automatically detect an item that is counterfeit when a seller submits the item for sale in the marketplace. The disclosed technology receives information about an item for transaction. The information associated with the item includes metadata related to the item and an image of the item. The method uses a database of items that are known to be not for sale (e.g., a stock photo) or is otherwise a counterfeit item. The method matches the data of the item against the data in the database and identifies the item as counterfeit based on the matched result. The matching operation includes analyzing the data (e.g., image analyses using features of the image data). Use of the disclosed technology enables automatic and efficient detection of counterfeit items as a seller submits the item for sale in the marketplace, thereby increasing reliability of the marketplace from buyers' perspective.

Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.

Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.

A facility identifies anatomical objects visualized by a medical imaging image. The facility applies two machine learning models to the image: a first trained to predict a view probability vector that, for each of a list of views, attributes a probability that the image was captured from the view, and a second trained to predict an object probability vector that, for each of a list of anatomical objects, attributes a probability that the object is visualized by the image. For each object, the facility: (1) accesses a list of views in which the object is permitted; (2) multiplies the predicted probability that the object is visualized by the image by the sum of the predicted probabilities that the accessed image was captured from views in which the object is permitted; and (3) where the resulting probability exceeds a threshold, determines that the object is visualized by the accessed image.

A system and method for obtaining a 3D pose of an object using 2D images from a 2D camera and a learned-based neural network. The neural network extracts a plurality of features on the object from the 2D images and generates a heatmap for each of the extracted features that identify the probability of a location of a feature point on the object by a color representation. The method provides a feature point image that includes the feature points from the heatmaps on the 2D images, and estimates the 3D pose of the object by comparing the feature point image and a 3D virtual CAD model of the object.