4results about How to "Guaranteed segmentation effect" patented technology

The invention relates to the field of image generation, in particular to an AIGC-based advertising map generation method, which comprises the following steps of: periodically detecting an associated element proportion and an associated element complex coefficient of a target generated image in each target image combination so as to determine a combination state of each target image combination; determining an image analysis strategy according to the combination state; when an element combination analysis mode is adopted to extract optimal generation elements, the optimal generation elements are determined according to the reference evaluation coefficients, and the combination coefficients of the key analysis elements are adjusted; when the preferential generation elements are extracted in a preference analysis mode, the preferential generation elements of the target image combination are determined according to the reference evaluation coefficient and the reference use frequency; under the image generation condition, the target combination elements are determined according to the optimal indexes, the target element set is determined according to the reference combination coefficients, and the effectiveness of the image synthesis result and the image synthesis efficiency are improved.

The application is a medical image segmentation method based on multi-modal self-supervision. First, the multi-modal medical image of the lesion tissue is obtained, including A-mode image and B-mode image, and the image is preprocessed. Then, a cycle-consistent modal contrast domain translation network is constructed, including two generators and two discriminators. The generator is used to convert the image of one mode into the image of another mode, including an encoder, an intermediate shared module and a decoder. The discriminator is used to judge the source of the input. Then, the cycle-consistent modal contrast domain translation network is pre-trained, the training loss is calculated, and the loss function includes multi-modal semantic consistency loss, adversarial loss, cross-domain translation loss and cycle consistency loss. Finally, the A-mode segmentation network and the B-mode segmentation network are constructed, the pre-trained weights are migrated to the two segmentation networks, and the trained two segmentation networks are respectively used for medical image segmentation of the corresponding mode. The contrast cross-domain translation is used as a multi-modal self-supervised pre-training task to learn more comprehensive modal features, promote the network to better learn modal characteristics and common knowledge, and improve the segmentation ability.

A power transmission line equipment three-dimensional model segmentation method based on deep learning comprises the following steps: performing over-segmentation on a three-dimensional model of power transmission line equipment to obtain a plurality of sub-patches, performing feature extraction on each sub-patch, clustering the extracted features, and inputting a clustering result into a neural network prediction model to obtain a neural network prediction model; the neural network prediction model is a bidirectional long-short-term memory network improved based on a principal component analysis network and an ScSR sparse dictionary, and hyper-parameters of the bidirectional long-short-term memory network are optimized by adopting a whale optimization algorithm improved by a hybrid strategy; and carrying out segmentation prediction by using the obtained optimal hyper-parameter, and finally adjusting the segmentation labels of the sub-patches by using a graph cut algorithm. According to the invention, on the basis of the prediction result of the neural network prediction model, the segmentation labels of the sub-patches are adjusted by using the graph cut algorithm, and the segmentation is further optimized, so that the segmentation effect of the segmentation edge is ensured.

The invention discloses an image shelter segmentation method, system and device based on low-rank adaptation fine tuning, and belongs to the technical field of image analys.The method comprises the following steps that feature extraction is conducted on an input image through a feature extraction assembly of an image encoder, and the feature extraction assembly comprises a self-attention mechanism with LoRA branches; and segmenting the image through the mask decoder and the features to obtain a segmented image of the shielding object. The LoRA branch allows the SAM to only update a small part of parameters in the training process of the medical image, so that the calculation and storage overhead is saved, the segmentation performance is ensured, and the fine adjustment difficulty of the model is reduced; the improvement on the SAM ensures that automatic segmentation can be completed without depending on any prompt.