Dr. Yunhua Yin Profile

Dr. Yunhua Yin

Professor at Northwestern Polytechnical Univ

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Proceedings Article | 6 May 2024 Paper

A new method for establishing ballistic range spatial benchmark system using total station

Yin Yunhua, Xie Bing, Fu Wei, Zheng Qian

Proceedings Volume 13107, 1310721 (2024) https://doi.org/10.1117/12.3029151

KEYWORDS: Calibration, Matrices, 3D modeling, Photography, Data conversion, Prisms, Performance modeling, Mathematical modeling, Error analysis, Equipment

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Several measuring instruments, including CCD photography stations and velocity-coordinate measuring equipment, were arranged at various distances in the ballistic range. To ensure consistency and accuracy, the coordinates of each instrument need to be unified into the ballistic range coordinate system. This paper presents a new method that utilizes a high-precision total station for measuring and positioning, establishing a spatial benchmark system for the ballistic range. The three-dimensional coordinate values of all the equipment's datum points were obtained through the total station and the measuring equipment. The transformation matrix, derived from the coordinates of three non-collinear points in the calibration system, is used to convert between the two coordinate systems. The 3D coordinate transformation process is implemented using the Rodrigues matrix, resulting in direct calculation formulas for the seven parameters. The calibration bracket was used to calibrate all the measuring equipment in the ballistic range, ultimately leading to the establishment of a 1,000-meter benchmark system for light weapons. Experimental results indicate that the spatial benchmark system, based on the total station, effectively eliminates errors caused by human operation and achieves a spatial coordinate precision of less than 15mm. The method's simplicity and efficiency render it suitable for 3D coordinate transformation at any given angle.

Proceedings Article | 15 November 2017 Paper

Multi-model convolutional extreme learning machine with kernel for RGB-D object recognition

Yunhua Yin, Huifang Li, Xinling Wen

Proceedings Volume 10605, 106051Z (2017) https://doi.org/10.1117/12.2292428

KEYWORDS: Object recognition, Convolutional neural networks

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With new depth sensing technology such as Kinect providing high quality synchronized RGB and depth images (RGB-D data), learning rich representations efficiently plays an important role in multi-modal recognition task, which is crucial to achieve high generalization performance. To address this problem, in this paper, we propose an effective multi-modal convolutional extreme learning machine with kernel (MMC-KELM) structure, which combines advantages both the power of CNN and fast training of ELM. In this model, CNN uses multiple alternate convolution layers and stochastic pooling layers to effectively abstract high level features from each modality (RGB and depth) separately without adjusting parameters. And then, the shared layer is developed by combining these features from each modality. Finally, the abstracted features are fed to the extreme learning machine with kernel (KELM), which leads to better generalization performance with faster learning speed. Experimental results on Washington RGB-D Object Dataset show that the proposed multiple modality fusion method achieves state-of-the-art performance with much less complexity.

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