Abstract
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.
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Abbreviations
- 3D-DWT:
-
Three Dimensional wavelets transform
- GLCM:
-
Gray Level Co-occurrence Matrix
- EDMS:
-
Edge Direction Matrixes
- CVFH:
-
clustered viewpoint feature histogram
- GAK:
-
Global alignment kernels
- GGFM:
-
Global Geometric Feature Map
- HOG-TOP:
-
Histogram of Oriented Gradients from Three Orthogonal of Planes
- S3DRGFs:
-
spatial 3-D relational geometric features
- 3DCS-LBP-3D:
-
center-symmetric local binary patterns
- SLKP:
-
spatial local key point
- LDFA:
-
local deep feature alignment
- GOOD:
-
Global Orthographic Object Descriptor
- GASD:
-
Globally Aligned Spatial Distribution
- GFH:
-
Global Fourier Histogram
- VFH:
-
Viewpoint Feature Histogram
- OUR-CVFH:
-
Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram
- GSH:
-
Global Structure Histograms
- DA:
-
Discriminant Analysis
- SDVS:
-
Shape Distribution on Voxel Surfaces
- PCA:
-
Principal Component Analysis
- BPNN:
-
Backpropagation Neural Network
- GRNN:
-
General Regression Neural Network
- SSR:
-
Scale Retinex algorithm
- PCANet:
-
Palmprint recognition using unsupervised convolutional deep learning network
- HSA:
-
Hybrid simulated annealing
- WLPCA:
-
Widely linear PCA
- SVM:
-
support vector machine
- 3DDFA-d:
-
3D Dense Face Alignment
- CNN:
-
Convolution Neural Network
- Isomap-E-R:
-
Isomap-Eigenanalysis-Regression
- TLE:
-
Trimmed Likelihood Estimator
- HMC:
-
Hidden Markov Chains
- RMSEs:
-
root-mean-square errors
- RSEFNN:
-
recurrent self-evolving fuzzy neural network
- SVR:
-
Support vector regression
- SONFIN:
-
self-organizing neural fuzzy inference network
- NPCA-SVM:
-
Non-negative PCA-SVM
- KELM:
-
kernel-based extreme learning machine
- FWNN:
-
fuzzy wavelet neural network
- TRFN:
-
type recurrent fuzzy network
- RWENN:
-
Recurrent wavelet-based Elman neural network
- 3DHMM:
-
Three-dimensional hidden Markov model
- HMKM:
-
Hidden Markov Kernel Machine
- EM:
-
Expectation Maximization
- SeqViews2SeqLabels:
-
Sequential Views to Sequential Labels
- Bi-RNN:
-
Bidirectional Recurrent Neural Network
- CER:
-
Character error rate
- A3C:
-
asynchronous advantage actor-critic
- MLDP:
-
multilingual dependency parser
- HGM:
-
hierarchical graph method
- VNM:
-
virtual node method
- CKY:
-
Coke Kasami Younger
- CFG:
-
Context-Free Grammar
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Rani, S., Lakhwani, K. & Kumar, S. Three dimensional objects recognition & pattern recognition technique; related challenges: A review. Multimed Tools Appl 81, 17303–17346 (2022). https://doi.org/10.1007/s11042-022-12412-2
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DOI: https://doi.org/10.1007/s11042-022-12412-2