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Recognition of Natural Road Sign Based on the Improved Curvature Feature

Recognition of Natural Road Sign Based on the Improved Curva...

The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Yanqing Wang Hao Zheng Weiwei Chen Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing Xiaozhuang University

To solve the recognition of road sign with an intelligent vehicle in vision-based navigation, road sign extraction and matching techniques required in outdoor scene was proposed in this paper. The method of the improved curvature based on feature extraction and binary description took the advantage of reasonable features distribution to overcome the problems of traditional features uneven distribution. Binary description method was represented to solve the real-time problem of feature matching. Through the validity and real-time performance of different algorithms are compared by experiments and indicate that the method can not only overcome negative influences from the disturb of non-targets, while spending on average only 46 ms processing each frame, but also meet the requirements of robustness, real-time, and accuracy.

关键词： Feature matching Feature extraction Road sign Curvature algorithm

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Research on Target Extraction Technology of Fruit and Vegetable Images in the Complex Environment

Research on Target Extraction Technology of Fruit and Vegeta...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Yanqing Wang Hao Zheng Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing Xiaozhuang University

Considering the difficulty of fruit and vegetable images with uneven illumination and uncontrolled backgrounds, this paper proposed an image preprocess algorithm based on visual subject detection. Firstly, we can use manifold ranking to significance test and to acquire significance images, then use gradient images and position weighting to get cutting images, finally adjust the image brightness and remove image noise to complete the image *** experiment results demonstrated the robustness and real-time of the proposed algorithm which can segment images accurately and the accuracy is higher than 91%.

关键词： Manifold ranking Significance images Gradient images Image segment

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Intelligent Question and Answering System Based on SVM and Cosine Similarity

Intelligent Question and Answering System Based on SVM and C...

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2017 International Conference on Mathematics, Modelling and Simulation Technologies and Applications（MMSTA 2017)

作者： Wan-li SONG Wei-wei CHEN Ming-zhu ZHANG Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information EngineeringNanjing Xiao Zhuang University

It is a tough task for teachers to answer all questions from students effectively and *** this paper,we design and implements an intelligent question answering system using Natural Language Processing,template classification,support vector *** system also calculates the similarity between the question and answer pairs by cosine similarity algorithm,and returns the most similar *** the user is not satisfied with the answer,the system will write the question into the public section to fall back on other *** answer will be evaluated and added to the QA base if it is passed with the corresponding *** that the questions and answers in the QA base continue to *** use the QA base of a network forum as the basic library to carry out the *** implementation and experimental results indicate that the proposed approach is achievable.

关键词： Intelligent system Question classification Question similarity Cosine similarity

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Flame detection via quaternion discrete cosine transform based spectral saliency

International Journal of Control and Automation

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International Journal of Control and Automation 2016年第9期9卷 343-352页

作者： Zhang, Haiyan Gao, Shangbing Xue, Jianxun Zheng, Hao Faculty of computer and software Jiangsu Provincial Key Laboratory for advanced manufacturing technology Huaiyin Institute of Technology Huai'an China The Key Laboratory for traffic and transportation security of Jiangsu Province Huai'an China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

To address the problem of detecting the specific object of flame, this paper proposes quaternion discrete cosine transform based video flame detection algorithm in conjunction with the bottom-up visual attention mechanisms and the flame features. According to the characteristics of the flame to be detected, the brightness, color, texture and motion information of the image are respectively defined and extracted to form a quaternion image, and then the flame saliency map is produced by Quaternion discrete cosine Transform with this quaternion image. Extensive experimental results verify the accuracy and robustness of the proposed method on flame detection. © 2016 SERSC.

关键词： Discrete cosine transforms

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Weighted Discriminative Dictionary Learning based on Low-rank Representation

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Journal of Physics: Conference Series 2017年第1期787卷

作者： Heyou Chang Hao Zheng Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing XiaoZhuang University Nanjing China

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.

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Exploring deep gradient information for biometric image feature representation

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Neurocomputing 2016年 213卷 162-171页

作者： Jianjun Qian Jian Yang Ying Tai Hao Zheng School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University China

This paper presents a novel but simple biometric image feature representation method, called exploring deep gradient information (DGI). DGI first captures the local structure of an image by computing the histogram of gradient orientation of each macro-pixel (local patch around the reference pixel). Thus, one image can be decomposed into L sub-images (sub-orientation images) according to the gradient information of each macro-pixel since there are L bins in the local histogram. To enrich the gradient information, we also consider the gradient orientation and magnitude of original image as sub-images. For each sub-image, histogram of oriented gradient (HOG) is used to further explore the gradient orientation information. All HOG features are concatenated into one augmented super-vector. Finally, fisher linear discriminate analysis (FLDA) is applied to obtain the low-dimensional and discriminative feature vector. We evaluated the proposed method on the real-world face image datasets NUST-RWFR, Pubfig and LFW, the PolyU Finger-Knuckle-Print database and the PolyU Palmprint database. Experimental results clearly demonstrate the effectiveness of the proposed DGI compared with state-of-the-art algorithms, e.g., SIFT, HOG, LBP, POEM, LARK and IDLS.

关键词： Image representation Deep gradients Feature extraction Biometrics

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Qian, Jin Chang, Yu-Hui Lv, Ping Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou213015 China Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781509003891

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets. © 2016 IEEE.

关键词： Decision tables

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Sentiment analysis of Tourism Micro-blog Comments

Sentiment Analysis of Tourism Micro-blog Comments

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2016 International Conference on Artificial Intelligence: Techniques and Applications(AITA 2016)

作者： Wan-li SONG Jun-hua WANG Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information EngineeringNanjing XiaoZhuang University Nanjing Vocational Institute of Transport Technology College of Computer and Information Hohai University

Recent years,an amount of tourism micro-blog comments on the Internet have become an important source of information for potential customers and to improve the service quality. These micro-blog comments do help to research tourism resources or services before making decisions. Thus,sentiment analysis of tourism micro-blog comments has become a hot issue in the field of natural language processing and text mining. We designed a system called SASTMC by using web crawler,Chinese words segmentation,emotion words dictionary and an improved TF-IDF algorithm. It enhances expression ability of sentiment information of text words. Experiments on Sina micro-blog comments datasets demonstrate that our method can do the task well.

关键词： Micro-blog comment Web crawler Words segmentation Sentiment analysis TF-IDF

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Micro-Expression Recognition based on 2D Gabor Filter and Sparse Representation

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Journal of Physics: Conference Series 2017年第1期787卷

作者： Hao Zheng Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing XiaoZhuang University Nanjing China MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering Southeast University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Micro-expression recognition is always a challenging problem for its quick facial expression. This paper proposed a novel method named 2D Gabor filter and Sparse Representation (2DGSR) to deal with the recognition of micro-expression. In our method, 2D Gabor filter is used for enhancing the robustness of the variations due to increasing the discrimination power. While the sparse representation is applied to deal with the subtlety, and cast recognition as a sparse approximation problem. We compare our method to other popular methods in three spontaneous micro-expression recognition databases. The results show that our method has more excellent performance than other methods.

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Yu-Hui Chang Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781509003914

关键词： Cybernetics Acceleration Time complexity Rough sets Heuristic algorithms data mining Algorithm design and analysis

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