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	A signal analysis and identification scheme for an online multiphase micron-sized particle analyzer system 期刊论文 OAI收割 Measurement Science and Technology, 2021, 卷号: 32, 期号: 8 作者: Wang,Fuhai; Cao,Hongjian; Wang,Pingping; Guo,Xiaokang; Han,Jinlong \| 收藏 \| 浏览/下载：66/0 \| 提交时间：2021/08/31 Electrical Sensing Zone Multiphase Microparticle Detection Wavelet Threshold Denoising Morphological Filtering Feature Recognition Hierarchical Clustering
	Lower recognition thresholds for sad facial expressions in subthreshold depression: a longitudinal study 期刊论文 OAI收割 PSYCHIATRY RESEARCH, 2020, 卷号: 294, 页码: 7 作者: Mei, Gaoxing; Li, Yufeng; Chen, Shiyu; Cen, Mofen; Bao, Min \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2021/03/01 Subthreshold depression facial expression perception recognition threshold perceptual sensitivity QUEST
	Incremental threshold learning for classifier selection 期刊论文 OAI收割 neurocomputing, 2012, 卷号: 89, 页码: 89-95 作者: Pang, Yanwei; Deng, Junping; Yuan, Yuan 收藏 \| 浏览/下载：35/0 \| 提交时间：2012/09/03 Incremental learning Threshold-based classifier Classifier fusion Object detection Pattern recognition
	Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China Wu Z.-G.; Wang M.-J.; Han G.-L. 收藏 \| 浏览/下载：84/0 \| 提交时间：2013/03/25 Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.
	Affine object recognition and affine parameters estimation based on covariant matrix (EI CONFERENCE) 会议论文 OAI收割 2008 International Symposium on Information Science and Engineering, ISISE 2008, December 20, 2008 - December 22, 2008, Shanghai, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：152/0 \| 提交时间：2013/03/25 A new method of affine object recognition and affine parameters estimation is presented. For a real-time image and a group of templates in addition firstly on the basis of correct recognition we segment the object regions in them and compute their covariant matrices. Secondly it can estimate affine parameters exactly normalize the ellipse regions defined by covariant matrices to circle regions to get rotational invariants and the estimated error is within 3%. 2008 IEEE. and compute the similarity function value between rotational invariants of real-time image and every template respectively. Then compare the values with threshold set in advance if more than one value is larger than threshold take the corresponding templates as candidates and compute affine matrix between real-time image and every candidate. Finally transform the realtime image with every affine matrix and match the result with corresponding candidate by classical matching methods. Experimental results show that the presented method is robust to illumination with low computational complexity and it can realize recognition of different affine objects