This paper proposes a Hilbert stereo reconstruction algorithm based on depth feature and stereo matching to solve the problem of occlusive region matching errors, namely, the Hilbert stereo network. The traditional stereo network pays more attention to disparity itself, leading to the inaccuracy of disparity estimation. Our design network studies the effective disparity matching and refinement through reconstruction representation of Hilbert’s disparity coefficient. Since the Hilbert coefficient is not affected by the occlusion and texture in the image, stereo disparity matching can conducted effectively. Our network includes three sub-modules, namely, depth feature representation, Hilbert cost volume fusion, and Hilbert refinement reconstruction. Separately, texture features of different depth levels of the image were extracted through Hilbert filtering operation. Next, stereoscopic disparity fusion was performed, and then Hilbert designed to refine the difference regression stereo matching solution was used. Based on the end-to-end design, the structure is refined by combining the depth feature extraction module and Hilbert coefficient disparity. Finally, the Hilbert stereo matching algorithm achieves excellent performance on standard big data set and is compared with other advanced stereo networks. Experiments show that our network has high accuracy and high performance.
Different areas of eyes reflect different states of internal organs of human body. That can assist to detect the disease internal organs, facilitate the prevention and cure by analyzing the image of human eyes. In this paper, we propose a new algorithm, Deep Composite Predict Network (DCP-Net), combined deep learning technique with traditional Chinese medicine theory, which can segment interesting areas of the human eyes, detect the disease eyes texture and then predict internal lesion organ. Aimed at helping doctors to diagnose quickly. The proposed algorithm consists three sub-modules, i.e., multiple segmentation sub-module, fast detection sub-module and predict eye disease module. The multiple segmentation module is solved by the improved retraining U-network, which can obtain an accurate eye to segment interesting areas. The fast detection module is based on enhanced Yolo framework. The predication disease method combined Chinese traditional theory with computer vision technology. Finally, our network can accurately detect the disease of human eyes and predict lesions of internal organs, which can help doctors quickly diagnose lesion organs by human eyes.
Thermodynamics is devoted to the study of energy or materials, and few researchers have combined the laws of thermodynamics with stereoscopic vision. Thus, we propose Depth Multiple Hierarchical StereoNet (DMH-Net) by using thermodynamic color guidance, which present end-to-end deep architecture for real-time stereo matching, realize sub-pixel precision and produce high-quality refinement disparity on dataset benchmarks. At the same time, we use color thermodynamics to guide the image output of an accurate Hill disparity image. Our DMH-Net consists of four modules: the first module uses a deep spatial pooling method to extract multiple features that work on the initial left and right images. The second module use color guided to produce high-quality cost volume. We use the ahead of two modules to calculate depth disparity precision, which can segment coarse to fine to obtain background and edge of objects information. Thirdly module integrates the information about 3D cost volume and color channel to frame a 4D hierarchical cost volume. The fourth module is designed by depth hierarchical refinement to regress the final disparity. Finally, we achieve sub-pixel precision and real-time performance on benchmarks, which can produce high-quality thermodynamic disparity regression maps.
Event extraction, as one of the difficult tasks of information extraction, can quickly obtain valuable information from the massive information on the Internet. This paper proposes a joint event extraction model based on RoBERTa-wwm-ext and gating mechanism for document-level long text data, which not only uses the prior knowledge from event types and pre-trained language models, but also uses gated fusion module to aggregate information in the event argument extraction tasks to enhance entity representation and splices entity type embedding, thereby enhancing the correlation among events, arguments and argument roles in the text, and improving the recognition accuracy of the arguments of each event in the document. Finally, the effectiveness of the model is verified on the public dataset.
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