SVM-based automatic scanned image classification with quick decision capability

被引:2
作者
Lu, Cheng [1 ]
Wagner, Jerry [2 ]
Pitta, Brandi [2 ]
Larson, David [2 ]
Allebach, Jan [1 ,2 ]
机构
[1] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
[2] Hewlett Packard Corp, Boise, ID 83706 USA
来源
COLOR IMAGING XIX: DISPLAYING, PROCESSING, HARDCOPY, AND APPLICATIONS | 2014年 / 9015卷
关键词
Digital copier; classification; support vector machine;
D O I
10.1117/12.2047335
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Digital copiers are now widely used. One major issue for a digital copier is copy quality. In order to achieve as high quality as possible for every input document, multiple processing pipelines are included in a digital copier. Every processing pipeline is designed specifically for a certain class of document, which may be text, picture, or a mixture of both as is illustrated by the three examples shown in Fig. 1. In this paper, we describe an algorithm that can effectively classify an input image into its corresponding category.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] Automatic classification approach to weld defects based on PCA and SVM
    Mu, Weilei
    Gao, Jianmin
    Jiang, Hongquan
    Wang, Zhao
    Chen, Fumin
    Dang, Changying
    INSIGHT, 2013, 55 (10) : 535 - 539
  • [42] SVM-based decision support system for clinic aided tracheal intubation predication with multiple features
    Yan, Qing
    Yan, Hongmei
    Han, Fei
    Wei, Xinchuan
    Zhu, Tao
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) : 6588 - 6592
  • [43] SVM-BASED FEATURE EXTRACTION AND CLASSIFICATION OF AFLATOXIN CONTAMINATED CORN USING FLUORESCENCE HYPERSPECTRAL DATA
    Yao, Haibo
    Hruska, Zuzana
    Kincaid, Russell
    Brown, Robert L.
    Bhatnagar, Deepak
    Cleveland, Thomas E.
    2012 4TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING (WHISPERS), 2012,
  • [44] Hyperspectral Remote Sensing Image Classification Based on SVM Optimized by Clonal Selection
    Liu Qing-jie
    Jing Lin-hai
    Wang Meng-fei
    Lin Qi-zhong
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33 (03) : 746 - 751
  • [45] Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification
    Qiu, Ji
    Shi, Hongmei
    Hu, Yuhen
    Yu, Zujun
    APPLIED SCIENCES-BASEL, 2023, 13 (23):
  • [46] SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training
    Cai, Siqi
    Chen, Yan
    Huang, Shuangyuan
    Wu, Yan
    Zheng, Haiqing
    Li, Xin
    Xie, Longhan
    FRONTIERS IN NEUROROBOTICS, 2019, 13
  • [47] An SVM-Based Nested Sliding Window Approach for Spectral-Spatial Classification of Hyperspectral Images
    Ren, Jiansi
    Wang, Ruoxiang
    Liu, Gang
    Wang, Yuanni
    Wu, Wei
    REMOTE SENSING, 2021, 13 (01) : 1 - 26
  • [48] MFL-Based Local Damage Diagnosis and SVM-Based Damage Type Classification for Wire Rope NDE
    Kim, Ju-Won
    Tola, Kassahun Demissie
    Dai Quoc Tran
    Park, Seunghee
    MATERIALS, 2019, 12 (18)
  • [49] Application of Adaboost Based Ensemble SVM on IKONOS Image Classification
    Liu, Chengming
    Li, Manchun
    Liu, Yongxue
    Chen, Jieli
    Shen, Chenglei
    2010 18TH INTERNATIONAL CONFERENCE ON GEOINFORMATICS, 2010,
  • [50] Efficient hybrid image denoising scheme based on SVM classification
    Routray, Sidheswar
    Ray, Arun Kumar
    Mishra, Chandrabhanu
    Palai, G.
    OPTIK, 2018, 157 : 503 - 511