Yantao Wei

Background suppression of small target image based on fast local reverse entropy operator

Background suppression is vitally important for the small target detection, which aims to enhance targets and improve the signal-to-noise ratio of small target images. Consequently, the study proposes a background suppression approach based on the fast local reverse entropy operator, which is designed according to the fact that the appearance of a small target could result in the great change of the value of local reverse entropy in the local region. The operator is adopted to suppress complex backgrounds of small target images in order to enhance small targets, and then bring about high probabilities of detection and low probabilities of false alarm in the small target detection. Both quantitative and qualitative analyses contribute to confirm the validity and efficiency of the proposed approach.

Small target detection using quantum genetic morphological filter

Small target detection plays a crucial role in infrared warning and tracking systems. A background suppression method using morphological filter based on quantum genetic algorithm (QGMF) is presented to detect small targets in infrared image. Structure element of morphological filter is encoded and the best structure element is selected using quantum genetic algorithm. The optimized structure element is used for background suppression to detect small target. Experimental results demonstrate that QGMF has good performance in clutter suppression, and obtains higher signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF) than the one using the fixed structure element with the same size.

High School Statistical Graph Classification Using Hierarchical Model for Intelligent Mathematics Problem Solving

High school statistical graph classification is one of the key steps in intelligent mathematics problem solving system. In this paper, a hierarchial classification method is proposed for high school statistical graph classification. Firstly, the dense Scale-invariant Feature Transform (SIFT) features of the input images are extracted. Secondly, the sparse coding of the SIFT features are obtained. Thirdly, these sparse features are pooled in multiscale. Finally, these pooled features are concatenated and then fed into single-hidden layer feedforward neural network for classification. The effectiveness of the proposed method is demonstrated on the constructed dataset, which contains 400 statistical graphs. In contrast to several state-of-the-art methods, the proposed method achieves better performance in terms of classification accuracy, especially when the size of the training samples is small.

Statistical graph classification in intelligent mathematics problem solving system for high school student

In recent years, intelligent mathematics problem solving has aroused the interest of researchers. In the intelligent mathematics problem solving system related to high school, the classification of statistical graph is a key step. Consequently, the classification of statistical graphs has become an urgent problem to be solved. In this paper, a new method is proposed for statistical graphs classification. Firstly, the image features of statistical graphs are obtained by spatial pyramid matching using sparse coding (ScSPM). The extracted features are then fed into classifier: support vector machine (SVM). In this paper, a new statistical graph dataset was established to evaluate the proposed method. It contains 400 statistical graphs including line graphs, histograms, scatter plots, and pie charts. Experimental results on the established dataset demonstrate that the proposed statistical graphs classification method achieves better performance.