3results about How to "Improve recognition results" patented technology

The present application relates to the field of digital medical treatment, and discloses a kind of identification method of image text in test sheet, comprising: obtaining the position information of different regions in test sheet image, convert the text information in different position regions in check sheet image into text feature vector, using the first network model trained by target detection algorithm, test sheet image is encoded into visual feature vector, each visual feature vector represents the visual feature in the preset image region in test sheet image, after text feature vector and visual feature vector are combined into multimodal sequence feature, linear network mapping is carried out to obtain the feature representing entity in test sheet image, using the feature representing entity in test sheet image, the text in test sheet image and the corresponding relationship of text are identified, and the text array contained in test sheet is obtained.The present application enriches text feature by the mode of multimodal feature, avoids introducing cascade error prediction, and improves the identification result of text in test sheet image.

This invention relates to the field of computer image processing technology, and particularly to an optimized method for measuring the volume between intraocular implants and anterior segment structures. The invention utilizes anterior segment OCT grayscale images, preprocesses the OCT images using specific image processing methods, generates a high-resolution OCT grayscale image mask using a specific mask generation method, and combines mathematical methods to automatically identify, 3D reconstruct, and measure the volume between the posterior surface of the intraocular implant and the anterior surface of the lens in the image. This invention fills the gap in the fully automated measurement of the volume between intraocular implants and anterior segment structures based on anterior segment OCT grayscale image recognition, proposes a new image preprocessing workflow, optimizes the speed and accuracy of Mask RCNN for high-resolution anterior segment OCT grayscale image recognition, and simultaneously enables 3D reconstruction and automatic measurement of the spatial volume between the posterior surface of the intraocular implant and the anterior surface of the lens.

The application discloses a traffic sign category determination method and device, a vehicle and an electronic device, and relates to the technical field of vehicles. The method comprises the following steps: acquiring a to-be-tested image, wherein the to-be-tested image is an image comprising a traffic sign; performing gray scale processing on the to-be-tested image to obtain a position image, wherein the position image is used for representing the position of the traffic sign in the to-be-tested image; and determining a recognition result according to the to-be-tested image and the position image, wherein the recognition result is used for representing the category of the traffic sign. The application solves the technical problem that, in the prior art, the category of a traffic sign is identified by using a traditional computer graphics method, a machine learning method and other deep learning algorithms, so that the neural network model is complex, the recognition rate is relatively low, and the classification effect is not ideal.