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recognition algorithm of athletes' partially occluded face based on a deep learning algorithm

INTERNATIONAL JOURNAL OF BIOMETRICS 2021年第2-3期13卷 305-321页

作者： Li, Wenjuan Millsap, Kevin Wuxi Taihu Univ Dept Phys Educ Wuxi 214000 Jiangsu Peoples R China Univ Iowa Coll Engn Iowa City IA 52242 USA

Because there are a lot of noise data in the partially occluded face image, the existing recognition methods have the problems of low recall rate and long time consumption. In this paper, a new recognition algorithm based on a deep learning algorithm is proposed. This method uses boosting algorithm to locate face information, based on which the face image is greyed and denoised. The local binary pattern is used to extract face features, and the convolution neural network in deep learning algorithm is used to realise face feature recognition. The experimental results show that compared with the traditional face feature recognition algorithm, the proposed method has significantly improved recognition accuracy and recall rate, and the feature recognition time is shorter, which proves that the proposed algorithm has stronger application performance.

关键词： deep learning algorithm convolution neural network athlete local occlusion face feature recognition algorithm

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recognition algorithm for Sleep Postures using a Smart Fabric Pad with Multiple Pressure Sensors 2019

Recognition Algorithm for Sleep Postures using a Smart Fabri...

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11th International Conference on Computer and Automation Engineering (ICCAE)

作者： Lee, Sang Hun Ahn, Jung-uk Lee, Byung Mun Nayuntech Co Ltd Seoul South Korea Gachon Univ 1342 Seongnamdaero Seongnam 13120 Gyeonggi Do South Korea

ISBN: (纸本)9781450362870

The existing studies suggest the following methods for measuring sleep postures: installing cameras and record the sleep postures then analyze the postures or measuring the change of the posture by attaching the sensor to the user's body. However, the installation of high-cost devices such as a camera or direct attachment of sensors on the subject's body present issues relating to the cost and convenience. As a solution, this paper develops a recognition algorithm for analyzing sleep postures using a smart pad with embedded fabric type pressure sensors. This algorithm is applied using a fabric-made smart pad with multiple pressure sensors. The sensors detect the distribution of the body pressure on the pad during sleeping, and the collected body pressure distribution data determines the sleep postures of the users. Further, this smart fabric pad does not require any additional analytical devices. This pad allows the users to monitor own sleep postures continuously. With the analyzed sleep posture data, the user can recognize one's typical sleep postures. In order to verify the effectiveness of the algorithm, this paper conducts an experiment to validate using the sleep posture data defined as nine categories. As a result, the algorithm had an average of 91.4% accuracy rate.

关键词： body pressure analysis pressure sensing sleep disorder sleep posture recognition algorithm

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An Explicable Keystroke recognition algorithm for Customizable Ring-Type Keyboards

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IEEE ACCESS 2020年 8卷 22933-22944页

作者： Sha, Xiaopeng Lian, Chao Zhao, Yuliang Yu, Jianing Wang, Shuyu Li, Wen Jung Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China City Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China

In our previous work, we developed an IMU (Inertial Measurement Unit) based smart ring that allows users to type characters without a physical keyboard and adopt well-known pattern recognition algorithms, such as Support Vector Machine (SVM), and Naive Bayes (NB), for keystroke recognition. However, these algorithms always require intensive computing resources or offer limited recognition accuracy. Moreover, they are often seen as black boxes incapable of providing readily comprehensible and visible clues for classification. This hampers the improvement of keystroke recognition accuracy and the ring-type virtual keyboard's character layout design. Here we present a novel algorithm to recognize keystrokes in a fast and accurate manner. Firstly, the standard feature vector, including five attitude angle features and one acceleration feature, is built to express a specific stroke. Then, the feature vector of the testing keystroke is compared with the standard features. The most similar keystroke is matched and recognized after three times of voting. Based on this algorithm, we can identify the easily confused keystrokes and understand the mechanisms behind it. With this interpretability, we will be able to achieve the customized ring-type virtual keyboard application if necessary. The performance of this algorithm was evaluated by using a dataset with 1500 keystrokes of three different subjects. The results show that our algorithm is more effective in keystroke recognition than traditional algorithms for this ring-type keyboard. In addition to its application on virtual keyboards, this algorithm can also be potentially applied on other classification tasks with easy-to-understand results.

关键词： Virtual keyboard feature analysis keystroke recognition motion detection human-computer interaction recognition algorithm

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Corrosion monitoring of the RC structures in time domain: Part II. recognition algorithm based on fractional derivative theory

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MEASUREMENT 2015年 67卷 84-91页

作者： Qiao, Guofu Hong, Yi Ou, Jinping Guan, Xinchun Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Heilongjiang Peoples R China Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China Harbin Inst Technol Ctr Composite Mat & Struct Harbin 150090 Peoples R China

To identify the corrosion status of reinforced concrete (RC) structures, the fractional derivative (FD) theory is used to establish the integrated recognition algorithm. The electrochemical corrosion characteristics including dispersion and diffusion effects can be directly obtained in time domain. The effectiveness and accuracy of FD algorithm are verified numerically based on the results of complex-function-approximation algorithm. Furthermore, the robustness of FD algorithm is tested by the interference experiments of the white noise. The results indicate that the recognition algorithm established based on FD can successfully identify the corrosion status of RC structures in time domain. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： RC structures Corrosion monitoring recognition algorithm Time domain Fractional derivative theory

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An O(n(3)) recognition algorithm for bithreshold graphs

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algorithmICA 1997年第4期17卷 416-425页

作者： DeAgostino, S Petreschi, H Sterbini, A Department of Computer Science University “La Sapienza” Rome Italy

A bithreshold graph is the edge intersection of two threshold graphs such that every independent set is independent in at least one of the threshold components. Recognizing a bithreshold graph is polynomially equivalent to recognizing its complement, i.e., a cobithreshold graph. In this paper we introduce a coloring of the vertices and oi the edges of a cobithreshold graph that leads to a recognition and decomposition algorithm. This algorithm works in O(n(3)) time improving the previously known a(n(4)) result [HM].

关键词： recognition algorithm bithreshold graph threshold cover

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A Four-Sweep LBFS recognition algorithm for Interval Graphs

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DISCRETE MATHEMATICS AND THEORETICAL COMPUTER SCIENCE 2014年第3期16卷 23-50页

作者： Li, Peng Wu, Yaokun Shanghai Jiao Tong Univ Dept Math Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MOE LSC Shanghai 200240 Peoples R China

In their 2009 paper, Corneil et al. design a linear time interval graph recognition algorithm based on six sweeps of Lexicographic Breadth-First Search (LBFS) and prove its correctness. They believe that their corresponding 5-sweep LBFS interval graph recognition algorithm is also correct. Thanks to the LBFS structure theory established mainly by Corneil et al., we are able to present a 4-sweep LBFS algorithm which determines whether or not the input graph is a unit interval graph or an interval graph. Like the algorithm of Corneil et al., our algorithm does not involve any complicated data structure and can be executed in linear time.

关键词： chordal graph interval graph interval representation Lexicographic Breadth-First Search perfect ordering recognition algorithm

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A recognition algorithm for simple-triangle graphs

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DISCRETE APPLIED MATHEMATICS 2020年 282卷 196-207页

作者： Takaoka, Asahi Kanagawa Univ Dept Informat Syst Creat Kanagawa Ku Rokkakubashi 3-27-1 Yokohama Kanagawa 2218686 Japan

A simple-triangle graph is the intersection graph of triangles that are defined by a point on a horizontal line and an interval on another horizontal line. The time complexity of the recognition problem for simple-triangle graphs was a longstanding open problem, which was recently settled. This paper provides a new recognition algorithm for simple-triangle graphs to improve the time bound from O(n(2)(m) over bar) to O(nm), where n, (m) over bar, and m are the number of vertices, edges, and non-edges of the graph, respectively. The algorithm uses the vertex ordering characterization that a graph is a simple-triangle graph if and only if there is a linear ordering of the vertices containing both an alternating orientation of the graph and a transitive orientation of the complement of the graph. We also show, as a byproduct, that an alternating orientation can be obtained in O(nm) time for cocomparability graphs, and it is NP-complete to decide whether a graph has an orientation that is alternating and acyclic. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Alternately orientable graphs Cocomparability graphs PI graphs recognition algorithm Simple-triangle graphs Vertex ordering characterization

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Automatic recognition algorithm of traffic signs based on convolution neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第17-18期79卷 11551-11565页

作者： Xu, Hao Srivastava, Gautam Xinyang Vocat & Tech Coll Sch Math & Comp Sci Xinyang 464000 Peoples R China Brandon Univ Dept Math & Comp Sci Brandon MB R7A 6A9 Canada China Med Univ Res Ctr Interneural Comp Taichung 40402 Taiwan

Because of the hierarchical significance of traffic sign images, the traditional methods do not effectively control and extract the brightness and features of layered images. Therefore, an automatic recognition algorithm for traffic signs based on a convolution neural network is proposed in this paper. First, the histogram equalization method is used to pre-process the traffic sign images, with details of the images being enhanced and contrast of the images improved. Then, the traffic sign images are recognized by a convolution neural network and the large scale structure of information in the traffic sign images are obtained by using a hierarchical significance detection method based on graphical models. Next, the area of interest in the traffic sign images are extracted by using the hierarchical significance model. Finally, the Softmax classifier is selected to classify the input feature images to realize the automatic recognition of traffic signs. Experimental results show that the proposed algorithm can control the brightness of traffic sign images, which can accurately extract image regions of interest and complete the automatic recognition of traffic signs.

关键词： Convolution neural network Traffic signs recognition algorithm Feature extraction

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A recognition algorithm for adjusted interval digraphs

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DISCRETE APPLIED MATHEMATICS 2021年 294卷 253-256页

作者： Takaoka, Asahi Kanagawa Univ Fac Engn Dept Informat Syst Creat Kanagawa Ku Rokkakubashi 3-27-1 Yokohama Kanagawa 2218686 Japan

The class of adjusted interval digraphs is a generalization of interval graphs. Although an O(n(4)) time recognition algorithm is known (where n is the number of vertices of the graph), finding a more efficient algorithm remains an open question. This paper presents a new recognition algorithm with running time O(n(3)). Adjusted interval digraphs are characterized by the existence of a min ordering and by the absence of invertible pairs. The proposed algorithm produces a min ordering if the given graph is an adjusted interval digraph;otherwise, it finds an invertible pair. As a byproduct, an alternative proof for the characterization is presented. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Adjusted interval digraphs Min ordering recognition algorithm

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Balanced 0, ± 1 matrices II.: recognition algorithm

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JOURNAL OF COMBINATORIAL THEORY SERIES B 2001年第2期81卷 275-306页

作者： Conforti, M Cornuéjols, G Kapoor, A Vuskovic, K Univ Padua Dipartimento Matemat Pura & Applicata I-35131 Padua Italy Carnegie Mellon Univ GSIA Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Math Sci Pittsburgh PA 15213 USA Univ Leeds Sch Comp Studies Leeds LS2 9JT W Yorkshire England

关键词： balanced matrix decomposition recognition algorithm 2-join 6-join extended star cutset

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