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	1. Fusion algorithm of infrared and visible images based on LatLRR and image pyramid
	LI Ning,LI Liqun
	Computer Science and Technology 23 March 2022
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	Abstract:Aiming at the loss of high-frequency information components of visible light and infrared images fused by Latent Low-Rank Representation (LatLRR) algorithm, this paper proposes a multi-level image fusion algorithm based on the combination of LatLRR and Gauss-Laplace Pyramid. Firstly, the image is decomposed into Gaussian pyramid and Laplace pyramid, then the highest level image of Gaussian pyramid and each layer image of Laplace pyramid are decomposed into low-rank part and Significant part, meanwhile the low-rank part and significant part of infrared and visible image pyramid are blended, then the fused low-rank part and significant part are fused at all levels, and finally the image is restored by Gaussian-Laplace pyramid. The algorithm has been tested on the public data set, this method retains more high-frequency information components compared with LatLRR algorithm.
	TO cite this article:LI Ning,LI Liqun. Fusion algorithm of infrared and visible images based on LatLRR and image pyramid[OL].[23 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4757034


	2. THMobile : An Improved Network For Garbage Classification Based on MobileNet
	Zhou Jialan,Bian Jiali
	Computer Science and Technology 22 February 2022
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	Abstract:With the rapid development of deep learning, more and more image recognition models are applied to daily life. For the current neural network model, the recognition accuracy of large model is higher and higher, but the more resources are needed. The lightweight of neural network model is more conducive to the application in life. In this paper, a THMobile model with smaller size and higher accuracy is proposed based on MobileNet. On the self-made garbage dataset, the classification accuracy of it reaches 91.2%, obtaining better performance than MobileNet. And it also performs better on CIFAR-10 than MobileNet.
	TO cite this article:Zhou Jialan,Bian Jiali. THMobile : An Improved Network For Garbage Classification Based on MobileNet[OL].[22 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756338


	3. The Research of Active Contour Optimization Model Based on Fast Marching Algorithm
	Liu Changzheng,Guo Junfei,Xu Lei
	Computer Science and Technology 16 January 2015
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	Abstract:The Active Contour Model is one of the mainstreams of medical image segmentation method. During the image segmentation method of the Active Contour Model, the iteration needs to calculate each pixel in the image, which leads to large amount of calculation and the slow segmentation speed, so could not reach the requirements of real-time clinical operation. In this paper, we take the simple and quick advantages of the Fast Marching to conduct the simulation experiment of optimization model on ITK platform finally. The analysis of experimental results: the optimization model can keep the smoothness of the boundary, make the common anatomical and topology structure in the medical images to process well, the complexity of the algorithm to reduce, and the robustness of the algorithm to improve. The research results also do a good bedding for the real-time clinical application of medical image segmentation algorithm.
	TO cite this article:Liu Changzheng,Guo Junfei,Xu Lei. The Research of Active Contour Optimization Model Based on Fast Marching Algorithm[OL].[16 January 2015] http://en.paper.edu.cn/en_releasepaper/content/4626915


	4. License Plate Recognition System Using a Coarse-to-fine Strategy
	Li Ang,Liu Liang
	Computer Science and Technology 23 December 2013
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	Abstract:This paper deals with problematic from field of image processing, artificial intelligence and machine vision in construction of a license plate recognition system. This issue includes mathematical principles and algorithms. The significant of this system is its robustness for Chinese license plate detection and recognition. Authors represent a coarse-to-fine strategy: license plate region's rough detection and accurate localization of the region of interest (ROI). For the Optical Character Recognition (OCR) task, a Probabilistic Neural Network (PNN) is trained to identify Chinese and alphanumberic characters. It turns out a high accuracy is achieved in experiments and the system has a good application prospect.
	TO cite this article:Li Ang,Liu Liang. License Plate Recognition System Using a Coarse-to-fine Strategy[OL].[23 December 2013] http://en.paper.edu.cn/en_releasepaper/content/4577610


	5. Texture Feature Clustering and Segmentation Algorithm based on Pixel
	Zhu Hong,Zhang Guoying
	Computer Science and Technology 10 August 2010
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	Abstract:As the coverage area of the sub-block is too large, the texture feature clustering based on sub-block often produces the mosaic phenomenon of inaccurate boundary. In this paper, the texture clustering algorithm based on pixel extracts the texture feature vector of its central pixel point from sub-block. The image texture feature is standardized, then the normalized feature vector is clustered and the clustering result is used to realize the segmentation of complex image. Compared with the traditional segmentation methods such as gradient method, threshold method and so on, the segmentation boundary is accurate, and the phenomenon of under-segmentation and over-segmentation also reduce significantly.
	TO cite this article:Zhu Hong,Zhang Guoying. Texture Feature Clustering and Segmentation Algorithm based on Pixel[OL].[10 August 2010] http://en.paper.edu.cn/en_releasepaper/content/4381224


	6. The Capturing of Water Level by Using the technology of Image Pattern Recognition
	Xiayang Zhao,Jinshui Chen
	Computer Science and Technology 01 February 2007
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	Abstract:Two methods can be used to measure water level. One is creating a water-level measuring post, then read it by human each time. Another one is using sensor to derive the analog water level, then transform it to digital value. There are many different sensors for water level measuring, such as float model, pressure model, supersonic model, etc. This paper provides a method that derives the image of water level by camera firstly, then by employing the methodology of image preprocessing, we can get the number of the calibrations above the water, in the end conduct the water level value. Using this method, the devices are easily to install, can save cost, and has high precision, so it is suitable to apply in irrigation district.
	TO cite this article:Xiayang Zhao,Jinshui Chen. The Capturing of Water Level by Using the technology of Image Pattern Recognition[OL].[ 1 February 2007] http://en.paper.edu.cn/en_releasepaper/content/11006

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