A detection object analysis unit (4) is a parameter determination apparatus that determines parameters of a plurality of anchor boxes to be used in a sliding window method when a bounding box and a class of an object in an image are detected using a neural network and the sliding window method. The detection object analysis unit (4) includes a distribution generation unit (11) that generates distribution information of parameters of bounding boxes indicated by object specifying information of a plurality of pieces of learning data. The detection object analysis unit (4) includes a clustering processing unit (12) that generates a plurality of clusters by clustering the distribution information. The detection object analysis unit (4) includes a parameter determination unit (13) that determines the parameters of the plurality of anchor boxes based on the plurality of clusters.

An object detection apparatus according to the present invention includes an object detecting unit configured to perform, for each region in an image set based on a size of a specific object detected in the image, a process of detecting the specific object on a new image.

To enhance identification accuracy for the external environment of a movable object.

Acquired is image data having an image feature (such as area, date and time, and weather) corresponding to a movement scene of the movable object. Learning with the image data is performed to acquire an inference DNN coefficient for identification of the external environment of the movable object from the image data of the movement scene. For example, the external environment is identified on the basis of, for example, semantic segmentation or depth. The inference DNN to which the inference DNN coefficient is set enables accurate identification of the external environment of the movable object from the image data of the movement scene.

A method for detecting a display panel defect, including: collecting a panel image of a to-be-detected display panel, a plurality of first pixels of the display panel corresponding to a plurality of second pixels in the panel image; converting the panel image into a binary image; dilating each bright spot region in the binary image such that adjacent bright spot regions communicate with each other to form at least one closed communication region in the binary image; determining a region of interest mask image in the binary image in accordance with the at least one closed communication region; determining a region of interest in accordance with the region of interest mask image and the panel image; and performing feature identification on the region of interest to determine a defect of the display panel.

A method for detecting a display panel defect, including: collecting a panel image of a to-be-detected display panel, a plurality of first pixels of the display panel corresponding to a plurality of second pixels in the panel image; converting the panel image into a binary image; dilating each bright spot region in the binary image such that adjacent bright spot regions communicate with each other to form at least one closed communication region in the binary image; determining a region of interest mask image in the binary image in accordance with the at least one closed communication region; determining a region of interest in accordance with the region of interest mask image and the panel image; and performing feature identification on the region of interest to determine a defect of the display panel.

A computer-implemented method for automated classification of 3D image data of teeth includes a computer receiving one or more of 3D image data sets where a set defines an image volume of voxels representing 3D tooth structures within the image volume associated with a 3D coordinate system. The computer pre-processes each of the data sets and provides each of the pre-processed data sets to the input of a trained deep neural network. The neural network classifies each of the voxels within a 3D image data set on the basis of a plurality of candidate tooth labels of the dentition. Classifying a 3D image data set includes generating for at least part of the voxels of the data set a candidate tooth label activation value associated with a candidate tooth label defining the likelihood that the labelled data point represents a tooth type as indicated by the candidate tooth label.

Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

One or more images of a portion of a wafer with fabricated devices are acquired using an imaging tool. A pattern of repeating features in an input image of a wafer is identified using various methods, such as correlation and clustering of neighboring vectors. A template is generated based on the found pattern of repeating features. The template is aligned with the acquired image to identify target locations. The target locations are then isolated from the original image for performing detailed metrology.

One or more images of a portion of a wafer with fabricated devices are acquired using an imaging tool. A pattern of repeating features in an input image of a wafer is identified using various methods, such as correlation and clustering of neighboring vectors. A template is generated based on the found pattern of repeating features. The template is aligned with the acquired image to identify target locations. The target locations are then isolated from the original image for performing detailed metrology.