Three dimensional objects recognition & pattern recognition technique; related challenges: A review

被引:17
|
作者
Rani, Shilpa [1 ,2 ]
Lakhwani, Kamlesh [3 ]
Kumar, Sandeep [4 ]
机构
[1] Lovely Profess Univ, Phagwara, Punjab, India
[2] Neil Gogte Inst Technol, Dept CSE, Hyderabad, Telangana, India
[3] JECRC Univ, Jaipur, Rajasthan, India
[4] Koneru Lakshmaiah Educ Fdn, Dept CSE, Hyderabad, Andhra Pradesh, India
关键词
Local feature; Global feature; 3D object recognition; Pattern recognition; PRINCIPAL COMPONENT ANALYSIS; HIDDEN MARKOV-MODELS; FACE-RECOGNITION; RANGE IMAGES; NEURAL-NETWORKS; 3D; MACHINE; CLASSIFICATION; REPRESENTATION; PCA;
D O I
10.1007/s11042-022-12412-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
3D object recognition and pattern recognition are active and fast-growing research areas in the field of computer vision. It is mandatory to define the pattern class, feature extraction, design classifiers, clustering, and selection of test datasets and evaluate performance for any pattern recognition system. The pattern recognition system recognizes the object, so it is required to extract the features in such a way that it will be suitable for a particular recognition method. Features can be retrieved either locally or globally. The object recognition technique is divided into two parts: the local feature extraction method and the global feature extraction method. Many researchers have done admirable work in the field of local and global feature extraction. Local feature-based techniques are more suitable for the real-world scene. The Global feature-based methods are more suitable for retrieving the 3D model & identifying the object's shape when the object's geometric structure is fragile. A lot of research has been done on pattern recognition in the last 50 years. Still, no single technique can be used for all types of applications, such as bioinformatics, data mining, speech recognition, remote sensing, multimedia applications, text detection, localization, etc. The main agenda of this paper is to summarize the 3D object recognition methodologies. This paper provides a complete study of 3D object recognition based on local and global feature-based methods and different techniques of pattern recognition. We have tried to summarize the results of different technologies and the future scope of this paper's particular technique. We enlisted the accessible online 3D database and their attributes, evaluation parameters of the 3D datasets. This paper will immensely help the researchers to Identify the research gap and limitations in pattern recognition and object recognition so that the researchers will be motivated to do something new in this field.
引用
收藏
页码:17303 / 17346
页数:44
相关论文
共 50 条
  • [21] Review of Electromyographic Control Systems Based on Pattern Recognition
    Ahmad, S. A.
    Ishak, A. J.
    Ali, S. H.
    5TH KUALA LUMPUR INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING 2011 (BIOMED 2011), 2011, 35 : 556 - +
  • [22] Syntactic Pattern Recognition in Computer Vision: A Systematic Review
    Astolfi, Gilberto
    Cesar Rezende, Fabio Prestes
    De Andrade Porto, Joao Vitor
    Matsubara, Edson Takashi
    Pistori, Hemerson
    ACM COMPUTING SURVEYS, 2022, 54 (03)
  • [23] A review on face recognition systems: recent approaches and challenges
    Oloyede, Muhtahir O.
    Hancke, Gerhard P.
    Myburgh, Hermanus C.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (37-38) : 27891 - 27922
  • [24] A Comprehensive Review of Face Recognition Techniques, Trends, and Challenges
    Gururaj, H. L.
    Soundarya, B. C.
    Priya, S.
    Shreyas, J.
    Flammini, Francesco
    IEEE ACCESS, 2024, 12 : 107903 - 107926
  • [25] Review on Reliable Pattern Recognition with Machine Learning Techniques
    Bhamare, Devyani
    Suryawanshi, Poonam
    FUZZY INFORMATION AND ENGINEERING, 2018, 10 (03) : 362 - 377
  • [26] Three decades of statistical pattern recognition paradigm for SHM of bridges
    Figueiredo, Eloi
    Brownjohn, James
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2022, 21 (06): : 3018 - 3054
  • [27] Formal methods in pattern recognition: A review
    Nieddu, L
    Patrizi, G
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2000, 120 (03) : 459 - 495
  • [28] Three-dimensional connectivity index for texture recognition
    Florindo, Joao B.
    Landini, Gabriel
    Bruno, Odemir M.
    PATTERN RECOGNITION LETTERS, 2016, 84 : 239 - 244
  • [29] Optical technique for classification, recognition and identification of obscured objects
    Beiderman, Yevgeny
    Teicher, Mina
    Garcia, Javier
    Mico, Vicente
    Zalevsky, Zeev
    OPTICS COMMUNICATIONS, 2010, 283 (21) : 4274 - 4282
  • [30] Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges
    Lim, Jia Zheng
    Mountstephens, James
    Teo, Jason
    SENSORS, 2020, 20 (08)