Abstract: The present invention discloses a bionic SERS substrate of a metal-based compound eye bowl structure, a construction method and application. The bionic SERS substrate of the metal-based compound eye bowl structure of the present invention consists of a metal bowl and a cone-shaped structure substrate in an ordered hierarchy manner. The metal bowl is of a continuously and closely arranged single-layer bowl structure. A height of the metal bowl is 0.01-10 ?m, and a bowl opening diameter is 0.01-10 ?m. A cone is a micron pyramid cone, and a height of the micron pyramid cone is 1-100 ?m. The present invention assembles the metal bowl on a surface of the substrate of the micron pyramid cone structure with great fluctuation by a solid-liquid interface chemical reduction method and a small ball template method, and further constructs a 3D SERS substrate with a bionic compound eye structure.

Abstract: The present invention discloses an SERS substrate of a metal-modified semiconductor-based bionic compound eye bowl structure and a construction method, and belongs to the technical field of nano materials. The present invention is based on a multi-time interface self-assembly method. Firstly, a small ball template is constructed by using a gas-liquid interface assembly process. Then, a semiconductor bowl structure array is induced to be formed by the template by using a solid-liquid interface assembly process. Next, a semiconductor bowl is assembled to a surface of a pyramid-shaped cone to form a bionic compound eye structure by using a transfer process. Finally, a surface of the bionic compound eye structure is modified with a layer of uniformly distributed metal particles by a physical deposition method or a chemical deposition method, thereby forming the SERS substrate of the metal-modified semiconductor-based bionic compound eye bowl structure.

Abstract: The present invention discloses a bionic SERS substrate of a metal-based compound eye bowl structure, a construction method and application. The bionic SERS substrate of the metal-based compound eye bowl structure of the present invention consists of a metal bowl and a cone-shaped structure substrate in an ordered hierarchy manner. The metal bowl is of a continuously and closely arranged single-layer bowl structure. A height of the metal bowl is 0.01-10 ?m, and a bowl opening diameter is 0.01-10 ?m. A cone is a micron pyramid cone, and a height of the micron pyramid cone is 1-100 ?m. The present invention assembles the metal bowl on a surface of the substrate of the micron pyramid cone structure with great fluctuation by a solid-liquid interface chemical reduction method and a small ball template method, and further constructs a 3D SERS substrate with a bionic compound eye structure.

Abstract: The present invention discloses an SERS substrate of a metal-modified semiconductor-based bionic compound eye bowl structure and a construction method, and belongs to the technical field of nano materials. The present invention is based on a multi-time interface self-assembly method. Firstly, a small ball template is constructed by using a gas-liquid interface assembly process. Then, a semiconductor bowl structure array is induced to be formed by the template by using a solid-liquid interface assembly process. Next, a semiconductor bowl is assembled to a surface of a pyramid-shaped cone to form a bionic compound eye structure by using a transfer process. Finally, a surface of the bionic compound eye structure is modified with a layer of uniformly distributed metal particles by a physical deposition method or a chemical deposition method, thereby forming the SERS substrate of the metal-modified semiconductor-based bionic compound eye bowl structure.

Abstract: A method of generating a text line classifier including generating text line samples by use of a present terminal system font reservoir. The method also includes extracting features from the text line samples and pre-stored marked-up samples. The method further includes training models by use of the extracted features to generate a text line classifier for recognizing text regions. With the system font reservoir being utilized for generating text line samples, the generated text line classifiers can target different scenes or different requirements for text region recognition with a high degree of applicability and wide application in addition to ease of implementation. Together with the combinational use of the marked up samples for extracting features from the text line samples, the generated text line classifiers provide for enhanced classification efficiency and accuracy.

Abstract: An example target acquisition method includes obtaining, according to a global feature of each video frame of a plurality of video frames, a target pre-estimated position of each scale in the video frame; clustering the target pre-estimated position in each video frame to obtain a corresponding target candidate region; and determining a target actual region in the video frame according to all the target candidate regions in each video frame in combination with confidence levels of the target candidate regions and corresponding scale processing. The techniques of the present disclosure quickly and effectively acquire one or multiple targets, and, more particularly, achieve accurately distinguishing and acquiring the multiple targets.

Abstract: A method of generating a text line classifier including generating text line samples by use of a present terminal system font reservoir. The method also includes extracting features from the text line samples and pre-stored marked-up samples. The method further includes training models by use of the extracted features to generate a text line classifier for recognizing text regions. With the system font reservoir being utilized for generating text line samples, the generated text line classifiers can target different scenes or different requirements for text region recognition with a high degree of applicability and wide application in addition to ease of implementation. Together with the combinational use of the marked up samples for extracting features from the text line samples, the generated text line classifiers provide for enhanced classification efficiency and accuracy.

Abstract: An example target acquisition method includes obtaining, according to a global feature of each video frame of a plurality of video frames, a target pre-estimated position of each scale in the video frame; clustering the target pre-estimated position in each video frame to obtain a corresponding target candidate region; and determining a target actual region in the video frame according to all the target candidate regions in each video frame in combination with confidence levels of the target candidate regions and corresponding scale processing. The techniques of the present disclosure quickly and effectively acquire one or multiple targets, and, more particularly, achieve accurately distinguishing and acquiring the multiple targets.