In recent years, along with the computer operation speed unending enhancement, the computer is employed to carry on the dangerous cargos the examination and the recognition to obtain the more and more widespread applications. Aiming at the disadvantage of high false detection rate in target classification detection using existing feature training classifiers, the work proposes a detection algorithm for hazardous articles with convolutional neural network on the basis of deep learning. For the image to be checked, sliding windows of different scales are used to determine whether there is an object window. For object detection, a convolutional neural network is trained with a large number of positive and negative samples. In order to better adapt to object detection, the topology of the convolutional neural network is improved. The window of suspected hazardous article is input into the improved convolutional neural network for dangerous object detection, and the false detection rate is reduced while maintaining the original detection rate.
For the fast touching imaging of the capacitive touch screens (CTS) with a very large size, the conventional methods can only be achieved at the expense of the analog hardware complexity and power consumption. Firstly, the sparse feature of variation within the capacitance of CTS being touched was analyzed. Secondly, the principal for measuring the capacitance of CTS was introduced, and then the scheme of sparse touching imaging was given. Moreover, the hardware implementation for sparse sensing was presented. An algorithm of touching sensing for CTS and its implementation were proposed. The test result indicated the validity of sparse touching imaging for CTS. The proposed method can achieve touching imaging efficiently with lower hardware complexity and lower power consumption.
The conventional compressive sensing for videos based on the non-adaptive linear projections, and the measurement times is usually set empirically. As a result, the quality of videos reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was described. Then an estimation method for the sparsity of multi-view videos was proposed based on the two dimensional discrete wavelet transform (2D DWT). With an energy threshold given beforehand, the DWT coefficients were processed with both energy normalization and sorting by descending order, and the sparsity of the multi-view video can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of video frame effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparsity estimated with the energy threshold provided, the proposed method can ensure the reconstruction quality of multi-view videos.
The conventional compressive sensing works based on the non-adaptive linear projections, and the parameter of its measurement times is usually set empirically. As a result, the quality of image reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was given. Then an estimation method for the sparsity of image was proposed based on the two dimensional discrete cosine transform (2D DCT). With an energy threshold given beforehand, the DCT coefficients were processed with both energy normalization and sorting in descending order, and the sparsity of the image can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of image effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparse degree estimated with the energy threshold provided, the proposed method can ensure the quality of image reconstruction.
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