In this paper, we put forward a novel approach based on hierarchical teaching-and-learning-based optimization (HTLBO) algorithm for nonlinear camera calibration. This algorithm simulates the teaching-learning ability of teachers and learners of a classroom. Different from traditional calibration approach, the proposed technique can find the nearoptimal solution without the need of accurate initial parameters estimation (with only very loose parameter bounds). With the introduction of cascade of teaching, the convergence speed is rapid and the global search ability is improved. Results from our study demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness. The HTLBO can also be used to solve many other complex non-linear calibration optimization problems for its good portability.
IC marking provides information about the integrated circuit chips, such as product function and classification. So IC marking inspection is one of the essential processes in semiconductor fabrication. A real-time IC chip marking defect inspection method is presented in this paper. The method comprises the following steps: chip position detection, characters segmentation, feature extraction and classification. The extracted features are used in a back propagation neural network for classifying the types of marking errors such as illegible characters, missing characters and misprinted characters. Character segmentation is an essential part of the inspection method. It is a considerable challenge to segment touching and broken characters correctly, due to uneven illumination, motion blur, as well as problems in the printing process. In order to segment the characters rapidly and accurately, a novel approach for character segmentation based on vertical projection and the character features is proposed. Experiments using a TSSOP20 packaging chip demonstrate that our method can inspect an IC marking with 17 different characters in just 130ms. The system achieves a maximum recognition rate of 98.5%. As a result, it is an ideal solution for a real-time IC marking recognition and defects inspection system.
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