Aspects of the present disclosure relate to processing irregularly arranged characters. An image is received. An irregularly arranged character within the image is detected. A direction of the irregularly arranged character is modified to a proper direction to obtain a properly oriented character. The properly oriented character is recognized to obtain a first identified character. The image is then rebuilt by replacing the irregularly arranged character with the first identified character, the first identified character in a machine-encoded format.

A method of rectifying a text image, a training method, an electronic device, and a medium, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, deep learning technology, intelligent transportation and high-precision maps. An exemplary implementation includes: performing, based on a gating strategy, a plurality of first layer-wise processing on a text image to be rectified, so as to obtain respective feature maps of a plurality of layer levels, wherein each of the feature maps includes a text structural feature related to the text image to be rectified, and the gating strategy is configured to increase an attention to the text structural feature; and performing a plurality of second layer-wise processing on the respective feature maps of the plurality of layer levels, so as to obtain a rectified text image corresponding to the text image to be rectified.

A system 100 and computerized method for real time videographic digital and production ready art 118 having a computer capable of: retrieving video files and photographic files from storage 114; automatically selecting patterns or prints, deriving patterns or prints from the files; extracting complementary and/or similar patterns and/or prints as videographic digital and production ready art 118 from files; applying the extracted patterns, prints or both patterns and prints to real world products; and creating targeted still images from the production ready art 118 for commercial use and licensing.

The present disclosure relates to augmented reality, virtual reality, mixed reality, and extended reality systems, and more specifically, to systems and methods for a visual positioning system.

The present invention relates to a system for monitoring a non-static clinical scenario, preferably a surgical field, such that different elements of interest can be located throughout the entire clinical event; in particular, a high-precision monitoring and distinction of critical biological tissues, such as nerves or blood vessels, is sought.

An image processing apparatus obtains image data of a plurality of frames, aligns the image data of the plurality of frames, and combine the image data of the plurality of frames that have been aligned. The image data includes first data and second data, the first data being data of pixels of an effective pixel area of an image sensor and constituted by signals corresponding to a predetermined arrangement of color components, and the second data being data of pixels outside the effective pixel area of the image sensor. The image processing apparatus is capable of aligning the first data independently from the second data.

Techniques for automated facial measurement are provided. A set of coordinate locations for a set of facial landmarks on a face of a user are extracted by processing a first image using one or more landmark-detection machine learning models. An orientation of the face of the user is determined. It is determined that impedance conditions are not present in the set of images, and a reference distance on the face of the user is estimated based on the first image, where the first image depicts the user facing towards the imaging sensor. A nose depth of the user is estimated based on a second image of the set of images based at least in part on the reference distance, where the second image depicts the user facing at an angle relative to the imaging sensor. A facial mask is selected for the user based on the nose depth.

A three-dimensional object detection method includes: extracting a target in a two-dimensional image by a pre-trained deep convolutional neural network to obtain a plurality of target objects; determining a point cloud frustum in a corresponding three-dimensional point cloud space based on each target object; segmenting the point cloud in the frustum based on a point cloud segmentation network to obtain a point cloud of interest; and estimating parameters of a 3D box in the point cloud of interest based on a network with the weighted channel features to obtain the parameters of the 3D box for three-dimensional object detection. According to the present invention, the features of the image can be learned more accurately by the deep convolutional neural network and the parameters of the 3D box in the point cloud of interest are estimated based on the network with the weighted channel features.

A method trains a recognition system for recognizing an object in an interior space of a household appliance. The method includes the steps of capturing images from a plurality of predetermined perspectives of the object placed on an alignment sheet; producing training data on the basis of the images; and training the adaptive recognition system using the training data.

Systems and methods are provided for processing and extracting content from an image captured using a mobile device. In one embodiment, an image is captured by a mobile device and corrected to improve the quality of the image. The corrected image is then further processed by adjusting the image, identifying the format and layout of the document, binarizing the image and extracting the content using optical character recognition (OCR). Multiple methods of image adjusting may be implemented to accurately assess features of the document, and a secondary layout identification process may be performed to ensure that the content being extracted is properly classified.