Abstract:Nowadays, people are increasingly using electronic formats to store data. In order to convert the table in paper into digital information, this project implements a handwritten Chinese character recognition system for table pictures, which releases the pressure of manual workload of typing handwritten Chinese character information. This project pre-processes the scanned images, extracts the handwritten text information from the images . Then segment the cell in the table and cuts the individual Chinese characters. In order to improve the accuracy of character cutting, this project adopts the combination of vertical projection and aspect ratio of character to determine the cutting position. The ResNext50 model is used for model training, and the two models are trained to recognize numbers, letters and handwritten Chinese characters respectively. The accuracy of the Chinese character recognition model is more than 90%, and that of the number and letter recognition model is 98%. Based on the contents filled in the table, the list of proper nouns is used to correct the recognition results and improve the accuracy. By calculating Levenshtein distance find the specific nouns with the highest similarity. The method proposed in this paper effectively complete the separation and recognition of handwritten Chinese characters in the table image.

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	1. Design and Implementation of Handwritten Chinese Characters Recognition Based on Deep Learning
	Jingyi Shen
	Computer Science and Technology 24 March 2022
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	Abstract:Nowadays, people are increasingly using electronic formats to store data. In order to convert the table in paper into digital information, this project implements a handwritten Chinese character recognition system for table pictures, which releases the pressure of manual workload of typing handwritten Chinese character information. This project pre-processes the scanned images, extracts the handwritten text information from the images . Then segment the cell in the table and cuts the individual Chinese characters. In order to improve the accuracy of character cutting, this project adopts the combination of vertical projection and aspect ratio of character to determine the cutting position. The ResNext50 model is used for model training, and the two models are trained to recognize numbers, letters and handwritten Chinese characters respectively. The accuracy of the Chinese character recognition model is more than 90%, and that of the number and letter recognition model is 98%. Based on the contents filled in the table, the list of proper nouns is used to correct the recognition results and improve the accuracy. By calculating Levenshtein distance find the specific nouns with the highest similarity. The method proposed in this paper effectively complete the separation and recognition of handwritten Chinese characters in the table image.
	TO cite this article:Jingyi Shen. Design and Implementation of Handwritten Chinese Characters Recognition Based on Deep Learning[OL].[24 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4756976


	2. Compressive Hyperspectral Video Reconstruction Via Multitask Nonparametric Bayesian Learning
	Yang Man,Gao Zhanchun
	Computer Science and Technology 09 February 2022
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	Abstract:Compressive hyperspectral video reconstruction (CHVR) extends snapshot spectral imaging into the temporal dimension, which allows fast hyerspectral observation of dynamic scenes. This paper proposes a multitask learning method for CHVR under the blind compressive sensing framework, characterized by joint inference of the representation atoms and the corresponding coefficients, directly from the compressive measurements. Defining the compressive reconstruction of each frame as a single task, our method employs a common dictionary shared by all tasks, which significantly alleviates the data paucity problem. The complete inference process is fulfilled via a Bayesian nonparametric estimation strategy, which contributes three advantages: reliable generalizability, parameter-tuning free and automatic determination of the model complexity. Simulation results demonstrate the efficacy of the proposed approach.
	TO cite this article:Yang Man,Gao Zhanchun. Compressive Hyperspectral Video Reconstruction Via Multitask Nonparametric Bayesian Learning[OL].[ 9 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756225

Saved Papers

Saved Papers


	3. A generality enhanced forensic towards GAN facial images
	Xiong Xiao-Fang,Yang Gao-Bo
	Computer Science and Technology 13 May 2020
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	Abstract:\justifying Recent advances in GAN technique have made it much easier than ever to generate believable face images, which brings some potential security issues to the public. Currently a variety of architectures have been proposed to detect these generated fake images, but few works address the problem of generalization ability of forensics models, which means most of them need prior knowledge of the structure of GAN model when detecting a specific GAN fake face images, and unable to detect unseen fake images generated by other GAN models. In this work, we tackle the generality enhanced problem by adding a fixed weight convolutional layer before CNN structure, which contain three designed high-pass filters. Firstly, we convert RGB images to YCbCr color space to get more obvious edge information. Then, EfficientNet is adopted as our basic CNN structure to extract inhenrent features and classify them. Finally, a fixed weight layer is added to the first convolutional layer, which could greatly suppress the semantic image content and magnify microscopic characteristics of images, thus help to enhance the generalization ability of the model. A series of experiments demonstrate that our approach achieve superior performance compare with the state-of-the-art work in terms of the generality of forensics model.
	TO cite this article:Xiong Xiao-Fang,Yang Gao-Bo. A generality enhanced forensic towards GAN facial images[OL].[13 May 2020] http://en.paper.edu.cn/en_releasepaper/content/4752048


	4. Super-resolution Reconstruction of Mosaic Face Images Based on GAN
	Xu Yonghui,Yang Gaobo
	Computer Science and Technology 11 May 2020
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	Abstract:With the rapid development of artificial intelligence technologies, image super-resolution which is one of the hottest topics in computer vision community, has achieved attractive progresses. To restore mosaic face images, we present an effective model, namely DemosaicGAN. It combines existing SRGAN and RDN and optimizes the perceptual loss functions. As far as we concerned, our experimental results show that the proposed DemosaicGAN achieves the best results in super-resolution reconstruction of mosaic face images so far.
	TO cite this article:Xu Yonghui,Yang Gaobo. Super-resolution Reconstruction of Mosaic Face Images Based on GAN[OL].[11 May 2020] http://en.paper.edu.cn/en_releasepaper/content/4752050


	5. Class-balanced and Local Median Loss Jointly Supervised for Wild Facial Expression Recognition
	Shi Cong-Cong,TIAN,TIAN Mei
	Computer Science and Technology 07 February 2020
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	Abstract:Over the past few years, Convolutional Neural Networks (CNNs) have shown effective performance on facial expression recognition. However, it is still a challenge problem for facial expression in the wild. The facial expression recognition dataset in the wild usually has the problem that imbalanced distribution of facial expression data and large intra-class differences caused by factors such as pose, lighting and gender. In order to solve this problem, this paper presents a novel loss function -- CALM Loss (Class-balanced and Local Median Loss). CALM Loss contains two parts. The first part is the class-balanced softmax loss function, which is uesd to solve the problem of data imbalance. The data set is divided into two classes, with two expressions with less data as one class and the other five as one class. During the network training process, the weight of the class with less data is adaptively increased. The second part is the local median loss function, which uses the median of serveral neatest neighbors in the same class as the center of class, weakens the influence of difficult samples on the selection of class center. Finally, the CALM Loss training network was adopted in this paper. The average recognition accuracy on the RAF dataset reaches 77.34$\%$, which proves the effectiveness of the proposed method.
	TO cite this article:Shi Cong-Cong,TIAN,TIAN Mei. Class-balanced and Local Median Loss Jointly Supervised for Wild Facial Expression Recognition[OL].[ 7 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750634


	6. Adaptive Clustering Algorithm based small Group Detection and Tracking
	CHENG Zhongbin,ZOU Qi,TIAN Mei
	Computer Science and Technology 02 September 2016
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	Abstract:We propose to detect and track small groups of individuals who are traveling together in surveiuance videos. Coherent groups are dynamically updated through merge and split events. To handle these challenges, we propose to discover groups by adaptive clustering (AGD). Experiments on challenging videos (FM dataset) which have complex motions and occlusions show that the proposed method based on trajectory-level similarity can correctly discover dynamic changes of groups. The effectiveness of the proposed approach is shown through comparison with classical methods.
	TO cite this article:CHENG Zhongbin,ZOU Qi,TIAN Mei. Adaptive Clustering Algorithm based small Group Detection and Tracking[OL].[ 2 September 2016] http://en.paper.edu.cn/en_releasepaper/content/4703372


	7. Procedural Texture Generation Based on Semantic Descriptions
	DONG Jun-Yu, WANG Li-Na, LIU Jun,SUN Xin
	Computer Science and Technology 10 May 2016
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	Abstract:Procedural textures are normally generated from mathematical models and have been widely used in computer games and animations for efficient rendering of natural elements, such as wood, marble, stone and other materials. Although the intuitive way to describe procedural texture is to use semantic attributes, there is no connection between procedural models, model parameters and texture semantic descriptions. In this paper, we propose a novel framework for generating procedural textures according to semantic descriptions. First a vocabulary of semantic attributes is collected for describing procedural textures based on extensive psychophysical experiments. Then a multi-label learning method is employed to label more new textures using the semantic attributes. We construct a procedural texture dataset with semantic attributes and further learn a low-dimensional semantic texture space. Finally, for a set of input semantic descriptions, we are able to find a generation model with proper parameters in this space. This model can be used to generate procedural textures that retain the input semantic attributes. Experimental results show that the proposed framework is effective and the generated procedural textures are correlated with the corresponding input semantic descriptions.
	TO cite this article:DONG Jun-Yu, WANG Li-Na, LIU Jun, et al. Procedural Texture Generation Based on Semantic Descriptions[OL].[10 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4688895


	8. Detecting Spatial-Temporal Subject Caption for News Video
	Wang Xihan,Feng Xiaoyi,Peng Jinye
	Computer Science and Technology 23 November 2015
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	Abstract:With the increasing popularity of Internet news video analysis, indexing, and retrieval, locate both spatial and temporal positions of captions in news video becomes a challenging task. Most existing methods have focused on detecting texts in staitc frame and tracking in different frames. In this paper, we propose an effective and efficient system for detecting news video subject caption by using spatial and temproal imformation. An classification scheme is equipped in Our alogrithm and a set of spatial-temporal features specially designed for eliminating false caption candidates. The experimental results on our datasets have shown the robustness and efficiency of the proposed method.
	TO cite this article:Wang Xihan,Feng Xiaoyi,Peng Jinye. Detecting Spatial-Temporal Subject Caption for News Video[OL].[23 November 2015] http://en.paper.edu.cn/en_releasepaper/content/4665038


	9. A perception-motivated Interpolation algorithm for dynamic image sequences
	WANG Qian,DU Junping
	Computer Science and Technology 21 October 2013
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	Abstract:Image interpolation is a classical problem in image processing. In this paper, we propose a perception-motivated interpolation algorithm for dynamic image sequences, which attempts to acquire high quality interpolation results in accordance with human visual perception. This algorithm mainly consists of two stages. First, the salient region detection method based on histogram contrast is used to capture attention regions of images. Then a partition interpolation model is presented to improve the interpolation quality of attention regions. Conducted experiments have shown that our algorithm can spend less time to produce satisfactory image sequences.?????
	TO cite this article:WANG Qian,DU Junping. A perception-motivated Interpolation algorithm for dynamic image sequences[OL].[21 October 2013] http://en.paper.edu.cn/en_releasepaper/content/4562236


	10. Small unmanned helicopter landing system based on image
	Wang Qiang,An Jie,Hu Fei
	Computer Science and Technology 15 December 2012
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	Abstract:The small unmanned helicopter landing systems usually identify a specific shape on the ground to calculate the distance between helicopter and the target. In this paper, we use a new method. When the unmanned helicopter is landing, the aerial system identifies the QR code on the ground, parses out the information contained, then determines whether the QR code is the place to land. If the helicopter should land on the QR code, the height and distance between helicopter and QR code should be calculated. Using the geometric relationship of the three find patterns of QR code in different coordinate systems, we can calculate the height and distance between the camera and QR code. As the positional relationship between the camera and helicopter is known, the height and distance between helicopter and QR code can be calculated. The information of height and distance will be transmitted to the flight control system on the board of the small unmanned helicopter, which controls the helicopter land.
	TO cite this article:Wang Qiang,An Jie,Hu Fei. Small unmanned helicopter landing system based on image[OL].[15 December 2012] http://en.paper.edu.cn/en_releasepaper/content/4502053

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