In order to improve the 3D reconstruction capability of high-resolution fine-grained 3D images, a fast 3D image reconstruction algorithm based on artificial intelligence technology is proposed. The cross-gradient sharpening detection method is used to collect features and extract information from high-resolution fine-grained three-dimensional images, and establish an edge contour feature detection model for high-resolution fine-grained three-dimensional images. Combining the salient feature analysis method and the subspace feature analysis method to cluster and analyze the high-resolution fine-grained three-dimensional image. In the artificial intelligence environment, the saliency of the three-dimensional image is detected and analyzed, and the multi-dimensional segmentation and gray histogram of the high-resolution fine-grained three-dimensional image are reconstructed through the subspace segmentation method. According to the reconstruction results of the gray histogram, fast 3D image reconstruction and image fusion processing are performed. Finally, the accurate detection and recognition of the reconstructed image is realized. The simulation results show that this method has a good effect on 3D image reconstruction, and the time cost of image reconstruction is relatively short. It improves the recognition and feature analysis capabilities of high-resolution fine-grained 3D images, and has good application value in the reconstruction, detection and recognition of high-resolution fine-grained 3D images.
机构:
China Univ Geosci, Sch Arts & Commun, Wuhan 430074, Peoples R China
Burapha Univ, Fac Fine & Appl Arts, Bang Saen 20131, Chonburi, ThailandChina Univ Geosci, Sch Arts & Commun, Wuhan 430074, Peoples R China
Wu, Siwei
Xiao, Shan
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China Univ Geosci, Int Educ Coll, Wuhan 430074, Peoples R ChinaChina Univ Geosci, Sch Arts & Commun, Wuhan 430074, Peoples R China
Xiao, Shan
Di, Yihua
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Wuhan Text Univ, Coll Mech & Elect Engn, Wuhan 430074, Peoples R ChinaChina Univ Geosci, Sch Arts & Commun, Wuhan 430074, Peoples R China
Di, Yihua
Di, Cheng
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China Univ Geosci, Sch Arts & Commun, Wuhan 430074, Peoples R China
Burapha Univ, Fac Fine & Appl Arts, Bang Saen 20131, Chonburi, ThailandChina Univ Geosci, Sch Arts & Commun, Wuhan 430074, Peoples R China
机构:
Chongqing Radio & TV Univ, Chongqing Technol & Business Inst, Coll Media & Design, Chongqing 400052, Peoples R China
Sangmyung Univ, Grad Sch, Dept Digital Image, Seoul 03016, South KoreaChongqing Radio & TV Univ, Chongqing Technol & Business Inst, Coll Media & Design, Chongqing 400052, Peoples R China
Yin, Jing
Yang, Jong Hoon
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Sangmyung Univ, Grad Sch, Dept Digital Image, Seoul 03016, South KoreaChongqing Radio & TV Univ, Chongqing Technol & Business Inst, Coll Media & Design, Chongqing 400052, Peoples R China