5 results about "Aspect ratio (image)" patented technology

This disclosure provides a character recognition method, apparatus, electronic device, and storage medium. The character recognition method includes: acquiring an image of a target character to be tested, wherein the target image includes at least one character to be tested; extracting and determining the grayscale value and aspect ratio of each character to be tested based on the image of the target character to be tested; extracting and determining the directional gradient, geometric invariant moments, and gray-level co-occurrence matrix of each character to be tested based on the grayscale value; determining the geometric features of each character to be tested based on the aspect ratio; determining the edge features of each character to be tested based on the directional gradient and the geometric invariant moments; determining the texture features of each character to be tested based on the gray-level co-occurrence matrix; inputting the geometric features, the edge features, and the texture features into a character recognition model; and outputting the recognition result of each character to be tested through the character recognition model.

This invention relates to a method and apparatus for object detection and determining training samples for object detection. The method, when dividing positive and negative samples, determines positive samples of target boxes based on the aspect ratio of the target boxes in the training image. Furthermore, when the aspect ratio of a target box is large, a first preset threshold inversely proportional to the aspect ratio is set. This results in a smaller first preset threshold when the aspect ratio of the target box is large. Using the intersection-union ratio (IUU) of candidate boxes and target boxes, along with the first preset threshold, can also effectively determine whether a candidate box is a positive sample, avoiding missed detections. This lays the foundation for training an object detection model based on positive and negative samples of target boxes in the training image. The trained object detection model can better learn the position and feature information of the object to be detected, reducing the missed detection rate of objects with large aspect ratios, thereby improving the accuracy of object detection.

The article to which the design pertains in this design registration application (hereinafter referred to as "the Application") is an image generation GUI using AI technology. The image diagram shows a screen that displays a list of images in order to visually recognize the history of how the generated images were created. The image diagram shows a state in which a total of five images have been generated by performing a process four times in which one image with a 1:1 aspect ratio is generated by inputting prompts, and then another image with a 1:1 aspect ratio is generated based on that image by inputting prompts. The number of images generated, the aspect ratio, and other conditions of the generated images vary depending on the content of the prompts entered and the number of times the prompts are entered.

This invention discloses a geometry-guided method and system for identifying and sorting objects in a virtual-to-physical migration environment. The method includes: generating a synthetic reference image that ignores material photometric properties using geometric abstract rendering based on the object's CAD model; performing isotropic scaling and filling on a real-time acquired physical object image while maintaining the original aspect ratio, and outputting a standardized image; extracting feature vectors from the synthetic reference image and the standardized image using the same convolutional neural network; adjusting the batch normalization layer statistical parameters in the convolutional neural network online using at least one real-time acquired physical image to align the feature distributions of the synthetic and physical domains; calculating feature similarity and outputting the recognition result. This invention, through subtractive geometric prior rendering, affine invariant preprocessing, and single-sample dynamic statistical calibration, can be deployed on edge computing devices to meet high-speed sorting cycle requirements.

The article to which the design pertains in this design registration application (hereinafter referred to as "the Application") is an image generation GUI using AI technology. The image diagram shows the state in which five images with a 1:1 aspect ratio have been generated by inputting prompts. The user can generate further images, as shown in the changed image diagram, by clicking the "edit" button located below the generated images and entering additional prompts. The changed image diagram shows the state in which five images have been generated based on the first image generated and the additional prompts. In each generation phase, the number of images generated, the aspect ratio, and other conditions of the generated images will vary depending on the input prompts, so the aspect ratio is not limited to 1:1 and the number of generated images is not limited to five.