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Journal articles on the topic 'Model-based recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Tran, Chi-Kien. "Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model." International Journal of Machine Learning and Computing 9, no. 3 (June 2019): 357–62. http://dx.doi.org/10.18178/ijmlc.2019.9.3.810.

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2

Wang, Yi Qiang, Rui Jian Huang, Tian Yi Xu, and Ke Hong Tang. "Vehicle Model Recognition Based on Fuzzy Pattern Recognition Method." Advanced Materials Research 383-390 (November 2011): 4799–802. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4799.

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The method based on the theory of Fuzzy Pattern Recognition is divided into three parts. Firstly, use Hough transformation to extract the feature points of vehicles, and use the ratio between two absolute distance of adjacent feature points as the characteristic values of vehicles; secondly, use Fuzzy C-mean Classification to handle feature data of 75 car model, then establish a degree of membership matrix as the sample space; thirdly, consider the classification algorithm based on fuzzy approach degree and the credibility of the vehicle feature to propose a weighted close- degree recognition algorithm. This recognition method has a good effect.

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3

Imamverdiyev, Yadigar, and Lyudmila Sukhostat. "DIALECTS RECOGNITION BASED ON ACOUSTIC MODEL." Problems of Information Technology 07, no. 2 (July 19, 2016): 34–38. http://dx.doi.org/10.25045/jpit.v07.i2.04.

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4

Russo, Mladen, Maja Stella, Marjan Sikora, and Vesna Pekić. "Robust Cochlear-Model-Based Speech Recognition." Computers 8, no. 1 (January 1, 2019): 5. http://dx.doi.org/10.3390/computers8010005.

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Accurate speech recognition can provide a natural interface for human–computer interaction. Recognition rates of the modern speech recognition systems are highly dependent on background noise levels and a choice of acoustic feature extraction method can have a significant impact on system performance. This paper presents a robust speech recognition system based on a front-end motivated by human cochlear processing of audio signals. In the proposed front-end, cochlear behavior is first emulated by the filtering operations of the gammatone filterbank and subsequently by the Inner Hair cell (IHC) processing stage. Experimental results using a continuous density Hidden Markov Model (HMM) recognizer with the proposed Gammatone Hair Cell (GHC) coefficients are lower for clean speech conditions, but demonstrate significant improvement in performance in noisy conditions compared to standard Mel-Frequency Cepstral Coefficients (MFCC) baseline.

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5

Ahn, J. S., and B. Bhanu. "Model-based recognition of articulated objects." Pattern Recognition Letters 23, no. 8 (June 2002): 1019–29. http://dx.doi.org/10.1016/s0167-8655(02)00033-8.

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6

Song, Mingliang. "Vehicle Model Recognition Based on SURF." Journal of Information and Computational Science 12, no. 17 (November 20, 2015): 6249–56. http://dx.doi.org/10.12733/jics20107056.

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7

KIM, SUNGHO, GIJEONG JANG, WANG-HEON LEE, and IN SO KWEON. "COMBINED MODEL-BASED 3D OBJECT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 07 (November 2005): 839–52. http://dx.doi.org/10.1142/s0218001405004368.

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This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

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8

Chin, Roland T., and Charles R. Dyer. "Model-based recognition in robot vision." ACM Computing Surveys 18, no. 1 (March 1986): 67–108. http://dx.doi.org/10.1145/6462.6464.

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9

Lamdan, Y., J. T. Schwartz, and H. J. Wolfson. "Affine invariant model-based object recognition." IEEE Transactions on Robotics and Automation 6, no. 5 (1990): 578–89. http://dx.doi.org/10.1109/70.62047.

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10

Schweitzer, Haim, and Sanjeev R. Kulkarni. "Computational limitations of model-based recognition." International Journal of Intelligent Systems 13, no. 5 (May 1998): 431–43. http://dx.doi.org/10.1002/(sici)1098-111x(199805)13:5<431::aid-int4>3.0.co;2-n.

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11

Gheni, Eman A., Zahraa M. Ali, and Dalia N. Abul-Wadood. "Model-Based Active Appearance Model Approach For Face Recognition." Journal of Engineering and Applied Sciences 14, no. 9 (December 31, 2019): 2988–92. http://dx.doi.org/10.36478/jeasci.2019.2988.2992.

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12

Yanqiu Wang, Xiaofei Yan, and Hua Hu. "A FSVM-Based Corn Varieties Recognition Model." International Journal of Digital Content Technology and its Applications 6, no. 23 (December 31, 2012): 586–92. http://dx.doi.org/10.4156/jdcta.vol6.issue23.67.

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13

GU, Jun-Xia, Xiao-Qing DING, and Sheng-Jin WANG. "Human 3D Model-based 2D Action Recognition." Acta Automatica Sinica 36, no. 1 (April 19, 2010): 46–53. http://dx.doi.org/10.3724/sp.j.1004.2010.00046.

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14

Webster, Rodney G., and Masaki Nakagawa. "A recognition based on a dynamic model." Pattern Recognition 31, no. 2 (February 1998): 193–203. http://dx.doi.org/10.1016/s0031-3203(97)00036-8.

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15

Hanmandlu, M., and O. V. Ramana Murthy. "Fuzzy model based recognition of handwritten numerals." Pattern Recognition 40, no. 6 (June 2007): 1840–54. http://dx.doi.org/10.1016/j.patcog.2006.08.014.

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16

Murray, D. W. "Model-based recognition using 3D shape alone." Computer Vision, Graphics, and Image Processing 40, no. 2 (November 1987): 250–66. http://dx.doi.org/10.1016/s0734-189x(87)80118-4.

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17

Kim, Jeong-Won. "Ubiquitous healthcare model based on context recognition." Journal of the Korea Society of Computer and Information 15, no. 9 (September 30, 2010): 129–36. http://dx.doi.org/10.9708/jksci.2010.15.9.129.

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18

Dingrui Wan and Jie Zhou. "Fingerprint recognition using model-based density map." IEEE Transactions on Image Processing 15, no. 6 (June 2006): 1690–96. http://dx.doi.org/10.1109/tip.2006.873442.

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19

Zhu, Shaoping. "Pain Expression Recognition Based on pLSA Model." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/736106.

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We present a new approach to automatically recognize the pain expression from video sequences, which categorize pain as 4 levels: “no pain,” “slight pain,” “moderate pain,” and “ severe pain.” First of all, facial velocity information, which is used to characterize pain, is determined using optical flow technique. Then visual words based on facial velocity are used to represent pain expression using bag of words. Final pLSA model is used for pain expression recognition, in order to improve the recognition accuracy, the class label information was used for the learning of the pLSA model. Experiments were performed on a pain expression dataset built by ourselves to test and evaluate the proposed method, the experiment results show that the average recognition accuracy is over 92%, which validates its effectiveness.

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20

Qiao, Xiuquan, and Xiaofeng Li. "Bayesian Network-Based Service Context Recognition Model." International Journal of Distributed Sensor Networks 5, no. 1 (January 2009): 80. http://dx.doi.org/10.1080/15501320802571830.

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21

Wen, Chenglin, Guangfu Zhou, Jingli Gao, Hongwei Li, and Xiaobin Xu. "Object Recognition Based on Improved Context Model." Chinese Journal of Electronics 27, no. 3 (May 1, 2018): 573–81. http://dx.doi.org/10.1049/cje.2018.03.014.

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22

Yanyi, Xu, and Lv Jinhua. "State Recognition Based on Hidden Markov Model." International Journal of Multimedia and Ubiquitous Engineering 11, no. 2 (February 28, 2016): 389–98. http://dx.doi.org/10.14257/ijmue.2016.11.2.38.

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23

Parveen, Rahila, Wei Song, Baozhi Qiu, Mairaj Nabi Bhatti, Tallal Hassan, and Ziyi Liu. "Probabilistic Model-Based Malaria Disease Recognition System." Complexity 2021 (January 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/6633806.

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In this paper, we present a probabilistic-based method to predict malaria disease at an early stage. Malaria is a very dangerous disease that creates a lot of health problems. Therefore, there is a need for a system that helps us to recognize this disease at early stages through the visual symptoms and from the environmental data. In this paper, we proposed a Bayesian network (BN) model to predict the occurrences of malaria disease. The proposed BN model is built on different attributes of the patient’s symptoms and environmental data which are divided into training and testing parts. Our proposed BN model when evaluated on the collected dataset found promising results with an accuracy of 81%. One the other hand, F1 score is also a good evaluation of these probabilistic models because there is a huge variation in class data. The complexity of these models is very high due to the increase of parent nodes in the given influence diagram, and the conditional probability table (CPT) also becomes more complex.

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24

Huang, Chung-Lin, and Sheng-Hung Jeng. "A model-based hand gesture recognition system." Machine Vision and Applications 12, no. 5 (March 1, 2001): 243–58. http://dx.doi.org/10.1007/s001380050144.

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25

Murray, D. W. "Model-based recognition using 3D shape alone." Computer Vision, Graphics, and Image Processing 38, no. 3 (June 1987): 363. http://dx.doi.org/10.1016/0734-189x(87)90125-3.

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26

Holzmann, Oscar J. "Revenue recognition convergence: The contract-based model." Journal of Corporate Accounting & Finance 22, no. 6 (August 25, 2011): 87–92. http://dx.doi.org/10.1002/jcaf.20727.

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27

Rodríguez, Carla, Albert Van Eeckhout, Laia Ferrer, Enrique Garcia-Caurel, Emilio González-Arnay, Juan Campos, and Angel Lizana. "Polarimetric data-based model for tissue recognition." Biomedical Optics Express 12, no. 8 (July 15, 2021): 4852. http://dx.doi.org/10.1364/boe.426387.

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28

Jiang, Lipei. "GROOVE RECOGNITION BASED ON 2-D WAVELET TRANSFORM AND MODEL RECOGNITION." Chinese Journal of Mechanical Engineering 41, no. 02 (2005): 162. http://dx.doi.org/10.3901/jme.2005.02.162.

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29

Hamdan, Yasir Babiker, and Sathish. "Construction of Statistical SVM based Recognition Model for Handwritten Character Recognition." June 2021 3, no. 2 (June 8, 2021): 92–107. http://dx.doi.org/10.36548/jitdw.2021.2.003.

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There are many applications of the handwritten character recognition (HCR) approach still exist. Reading postal addresses in various states contains different languages in any union government like India. Bank check amounts and signature verification is one of the important application of HCR in the automatic banking system in all developed countries. The optical character recognition of the documents is comparing with handwriting documents by a human. This OCR is used for translation purposes of characters from various types of files such as image, word document files. The main aim of this research article is to provide the solution for various handwriting recognition approaches such as touch input from the mobile screen and picture file. The recognition approaches performing with various methods that we have chosen in artificial neural networks and statistical methods so on and to address nonlinearly divisible issues. This research article consisting of various approaches to compare and recognize the handwriting characters from the image documents. Besides, the research paper is comparing statistical approach support vector machine (SVM) classifiers network method with statistical, template matching, structural pattern recognition, and graphical methods. It has proved Statistical SVM for OCR system performance that is providing a good result that is configured with machine learning approach. The recognition rate is higher than other methods mentioned in this research article. The proposed model has tested on a training section that contained various stylish letters and digits to learn with a higher accuracy level. We obtained test results of 91% of accuracy to recognize the characters from documents. Finally, we have discussed several future tasks of this research further.

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30

Sampath, A. K., and N. Gomathi. "Probabilistic Model Based Hybrid Classifier for Character Recognition." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 04 (July 14, 2017): 621–47. http://dx.doi.org/10.1142/s0218488517500271.

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Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient features i.e. histogram oriented feature and grid level feature, an additional feature called GLCM feature is extracted from the input image in the proposed recognition system and are concatenated for the image recognition procedure to encode color, shape, texture, local as well as the statistical information. These extracted features considered are given to the hybrid classifier which recognises the character. In the test set, recognition accuracy of 95% is achieved. The proposed probabilistic model based hybrid classifier tends to contribute more accurate character recognition rate compared to the existing character recognition system.

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31

Mu, Ye, Yuheng Sun, Tianli Hu, He Gong, Shijun Li, and Thobela Louis Tyasi. "Improved Model of Eye Disease Recognition Based on VGG Model." Intelligent Automation & Soft Computing 28, no. 3 (2021): 729–37. http://dx.doi.org/10.32604/iasc.2021.016569.

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32

Soodamani, R., and Z. Q. Liu. "GA-based learning for a model-based object recognition system." International Journal of Approximate Reasoning 23, no. 2 (February 2000): 85–109. http://dx.doi.org/10.1016/s0888-613x(99)00036-5.

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33

Yang, D., Abeer Alsadoon, P. W. C. Prasad, A. K. Singh, and A. Elchouemi. "An Emotion Recognition Model Based on Facial Recognition in Virtual Learning Environment." Procedia Computer Science 125 (2018): 2–10. http://dx.doi.org/10.1016/j.procs.2017.12.003.

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34

YOU, DATAO, JIQING HAN, TIERAN ZHENG, and GUIBIN ZHENG. "SPARSE-BASED AUDITORY MODEL FOR ROBUST SPEAKER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 07 (November 2012): 1250015. http://dx.doi.org/10.1142/s0218001412500152.

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The mismatch between the training and the testing environments greatly degrades the performance of speaker recognition. Although many robust techniques have been proposed, speaker recognition in mismatch condition is still a challenge. To solve this problem, we propose a sparse-based auditory model as the front-end of speaker recognition by simulating auditory processing of speech signal. To this end, we introduce narrow-band filter-bank instead of the widely used wide-band filter-bank to simulate the basilar membrane filter-bank, use sparse representation as the approximation of basilar membrane coding strategy, and incorporate the frequency selectivity enhance mechanism between tectorial membrane and basilar membrane by practical engineering approximation. Compared with the standard Mel-frequency cepstral coefficient approach, our preliminary experimental results indicate that the sparse-based auditory model consistently improve the robustness of speaker recognition in mismatched condition.

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35

Gorai, Kumari Piu, and Thomas Abraham. "A GAUSSIAN MIXTURE MODEL-BASED SPEAKER RECOGNITION SYSTEM." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 140. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19596.

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A human being has lot of unique features and one of them is voice. Speaker recognition is the use of a system to distinguish and identify a person from his/her vocal sound. A speaker recognition system (SRS) can be used as one of the authentication technique, in addition to the conventional authentication methods. This paper represents the overview of voice signal characteristics and speaker recognition techniques. It also discusses the advantages and problem of current SRS. The only biometric system that allows users to authenticate remotely is voice-based SRS, we are in the need of a robust SRS.

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36

MATSUNO, Takayuki, Daichi TAMAKI, Fumihito ARAI, and Toshio FUKUDA. "Shape Recognition Method of Rope Using Topological Model and Knot Invariant Based on Image Information for Manipulation(Vision and Recognition 2,Session: MP1-D)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 30. http://dx.doi.org/10.1299/jsmeicam.2004.4.30_3.

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37

Bezruk, V. M. "Recognition Methods Based on Autoregression Model of Signals." Telecommunications and Radio Engineering 58, no. 3-4 (2002): 8. http://dx.doi.org/10.1615/telecomradeng.v58.i3-4.20.

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38

KumarChoudhury, Manas, and Y. Srinivas Y.Srinivas. "A Model based Approach for Multimodal Biometric Recognition." International Journal of Computer Applications 104, no. 11 (October 18, 2014): 35–38. http://dx.doi.org/10.5120/18250-9338.

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39

XiuJie Yang. "Infrared Gait Recognition Based on Hidden Markov Model." International Journal of Advancements in Computing Technology 4, no. 23 (December 31, 2012): 145–52. http://dx.doi.org/10.4156/ijact.vol4.issue23.17.

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40

Al-Taei, Ali. "A Smartphone -Based Model for Human Activity Recognition." Ibn AL- Haitham Journal For Pure and Applied Science 30, no. 3 (December 29, 2017): 243. http://dx.doi.org/10.30526/30.3.1628.

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Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classifier overperforms other classifiers that were examined in this research paper.

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41

Elmir, Youssef, and Mohmmed Soumer. "Model–View–Controller based Online Face Recognition System." International Journal of Web Applications 11, no. 2 (June 1, 2019): 49. http://dx.doi.org/10.6025/ijwa/2019/11/2/49-57.

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42

Cui, Yu Quan, Le Jun Shi, and Yu Wei Fang. "Acoustic Signal Recognition Based on Time Series Model." Advanced Materials Research 189-193 (February 2011): 3243–48. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.3243.

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Using time series model, isometric transformation time series model and ARTAFIT model, we deal with acoustic signal, obtaining different sets of parameters according to different acoustic signals. We use support vector machine (SVM) to recognize different acoustic signals by analyzing different sets of parameters. When the parameter set is too large, we should first reduce order making use of principal component analysis (PCA), then we can recognize them using support vector machine. In the end, we give a case study, which indicate the results of applying our models are satisfactory.

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43

Ahmad, Nafees, Lansheng Han, Khalid Iqbal, Rashid Ahmad, Muhammad Adil Abid, and Naeem Iqbal. "SARM: Salah Activities Recognition Model Based on Smartphone." Electronics 8, no. 8 (August 8, 2019): 881. http://dx.doi.org/10.3390/electronics8080881.

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Alzheimer’s is a chronic neurodegenerative disease that frequently occurs in many people today. It has a major effect on the routine activities of affected people. Previous advancement in smartphone sensors technology enables us to help people suffering from Alzheimer’s. For people in the Muslim community, where it is mandatory to offer prayers five times a day, it may mean that they are struggling in their daily life prayers due to Alzheimer’s or lack of concentration. To deal with such a problem, automated mobile sensor-based activity recognition applications can be supportive to design accurate and precise solutions with an objective to direct the Namazi (worshipper). In this paper, a Salah activities recognition model (SARM) using a mobile sensor is proposed with the aim to recognize specific activities, such as Al-Qayam (standing), Ruku (standing to bowing), and Sujud (standing to prostration). This model entails the collection of data, selection and placement of sensor, data preprocessing, segmentation, feature extraction, and classification. The proposed model will provide a stepping edge to develop an application for observing prayer. For these activities’ recognition, data sets were collected from ten subjects, and six different features sets were used to get improved results. Extensive experiments were performed to test and validate the model features to train random forest (RF), K-nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT). The predicted average accuracy of RF, KNN, NB, and DT was 97%, 94%, 71.6%, and 95% respectively.

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44

Shin, Beomju, Chulki Kim, Jae Hun Kim, Seok Lee, Changdon kee, and Taikjin Lee. "Hybrid Model–Based Motion Recognition for Smartphone Users." ETRI Journal 36, no. 6 (December 1, 2014): 1016–22. http://dx.doi.org/10.4218/etrij.14.0113.1159.

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45

Fachinger, Uwe, and Birte Schöpke. "Business model for sensor-based fall recognition systems." Informatics for Health and Social Care 39, no. 3-4 (August 22, 2014): 305–18. http://dx.doi.org/10.3109/17538157.2014.931855.

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46

Zhong, Lisha, Jiangzhong Wan, Zhiwei Huang, Gaofei Cao, and Bo Xiao. "Heart Murmur Recognition Based on Hidden Markov Model." Journal of Signal and Information Processing 04, no. 02 (2013): 140–44. http://dx.doi.org/10.4236/jsip.2013.42020.

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47

Guiheux, H., C. Mardapittas, D. R. Wilson, and S. C. Winter. "A model based recognition system for tactile data." Microprocessing and Microprogramming 24, no. 1-5 (August 1988): 425–31. http://dx.doi.org/10.1016/0165-6074(88)90090-7.

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48

Fan, Dongjin, Peng Yu, Peng Du, Wenda Li, and Xiaofei Cao. "A Novel Probabilistic Model Based Fingerprint Recognition Algorithm." Procedia Engineering 29 (2012): 201–6. http://dx.doi.org/10.1016/j.proeng.2011.12.695.

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49

Zhou, Pan, Chao Zhang, and Zhouchen Lin. "Bilevel Model-Based Discriminative Dictionary Learning for Recognition." IEEE Transactions on Image Processing 26, no. 3 (March 2017): 1173–87. http://dx.doi.org/10.1109/tip.2016.2623487.

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50

Riedel, D. E., S. Venkatesh, and W. Liu. "A chemotactic-based model for spatial activity recognition." International Journal of Systems Science 37, no. 13 (October 20, 2006): 949–59. http://dx.doi.org/10.1080/00207720600891513.

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