6results about How to "Reduce false matches" patented technology

The invention relates to the technical field of three-dimensional reconstruction, in particular to a three-dimensional reconstruction method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a plurality of two-dimensional images of a to-be-reconstructed scene and corresponding camera parameters, the two-dimensional images comprising a plurality of source images and a plurality of reference images; inputting the plurality of two-dimensional images into a convolutional neural network, and performing feature extraction on the plurality of two-dimensional images by the convolutional neural network to generate a feature map; calculating the matching cost of the feature map of each source image and the feature map of the target reference image under different parallaxes, and generating a cost body corresponding to each source image based on the matching cost; optimizing the cost body by using the stacked cyclic hourglass network, and processing the optimized cost body to obtain a disparity map; and calculating a three-dimensional coordinate of each pixel point in the target reference image based on the disparity map and the camera parameters of the target reference image so as to complete reconstruction of the scene to be reconstructed. Therefore, the problems of poor three-dimensional reconstruction effect and the like in the prior art are solved.

The invention relates to a liver focal lesion image analysis method based on unbalanced optimal transmission. The analysis method comprises the following steps: extracting original image sample features, and embedding Gaussian distribution into each image to encode uncertainty; and then unbalanced optimal transmission loss calculation and classification loss calculation are carried out to realize accurate pairwise matching so as to reduce negative migration, and adaptive learning is carried out by using identification type perception information, so that an optimal transmission strategy is determined, and classification prediction is carried out. According to the analysis method, an unsupervised field adaptive network module, a feature uncertainty estimation mechanism module and a UOT strategy module are adopted. Compared with the prior art, the method has the advantages that the problem of pairwise wrong matching during feature distribution alignment in the existing optimal transmission strategy can be relieved, the uncertainty estimation of the noisy training sample is emphasized, and the migration performance, the robustness and the adaptability of the model can be remarkably improved by combining the feature uncertainty estimation and the UOT strategy.

The embodiment of the invention discloses an unmanned aerial vehicle cruise shot image fusion method and related equipment, the local and global features of an infrared image and a visible light image are fully utilized, the extracted features are more representative and distinctive, matching is carried out based on the local features and the global features to obtain an affine transformation matrix, and the fusion efficiency is improved. According to the method, high-quality matching pairs can be quickly screened in an early stage, unnecessary calculation amount is reduced, resampling is performed on the infrared image by using the affine transformation matrix, the optimized infrared image is obtained, the optimized infrared image and the target visible light image are fused, wrong matching is effectively reduced, and the accuracy of feature matching is improved.

The present invention discloses a method for positioning electronic components based on an improved SURF algorithm, comprising the following steps: 1) obtaining an image of electronic components on a production line through a binocular camera, and then inputting the image into a computer for image preprocessing to eliminate the influence of factors such as illumination and noise in image acquisition; 2) using the SURF algorithm to detect and describe feature points of the workpiece image; 3) using the K-means clustering algorithm to segment the image; 4) matching the segmented images; 5) finally using the target edge algorithm to calculate the centroid, locate the centroid position of the workpiece in the left and right images, and perform three-dimensional reconstruction in combination with the calibration results of the camera to achieve spatial positioning of the workpiece. The beneficial effects of the present invention are: the proposed algorithm reduces the total algorithm running time, and the matching results are combined with binocular vision technology to achieve the positioning of electronic components, which has great application value for improving the level of intelligent manufacturing.

The invention discloses a false star filtering and star map recognition method based on multi-sky-area parallelism, which is applied to autonomous attitude determination of a star sensor and comprises the following steps: constructing a multi-sky-area parallelism index database of a whole celestial sphere, dividing a whole celestial sphere space represented by a unit spherical surface, and generating a plurality of candidate sky-area sets which cover the whole celestial sphere and are allowed to be overlapped with one another; the method for extracting star pattern features based on the discretized radial angular distance comprises the following steps: extracting position parameter information of observation star points based on star map data acquired by a star sensor, and constructing a radial feature vector for representing a local spatial distribution relationship of fixed stars for each observation star point in a star map; performing feature matching of the multi-day-area parallel star mode feature vectors, and calculating a matching evaluation index based on the feature matching; and a final star map recognition result is obtained based on iterative screening recognition and false star elimination. And outputting a final star map recognition result for subsequent attitude calculation. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The application discloses a video image data intelligent gathering and full life cycle management method, relates to the technical field of video monitoring and intelligent security and protection, and discloses the following technical scheme: low light and shielding state are explicitized through working condition information, a degeneration area mask and a reliability weight graph are generated, video degeneration is converted from uncontrollable input into available control quantity, subsequent processing can adopt differentiated treatment on the degeneration area and the non-degeneration area, the interference of the degeneration area on detection output is reduced, and effective detail information of the non-degeneration area is reserved; the reliability weight is introduced into a detection model inference link, intermediate features are gate weighted, confidence penalty is applied to candidate frames falling into the degeneration area, a result reliability mark and an alarm linkage rule are generated, low reliability results are stored in a database, real-time alarm is inhibited or delayed triggering, alarm noise is reduced, and review resources are focused on high reliability events.