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

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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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Authors and Affiliations

  1. Research Scholar, Lovely Professional University, Phagwara, Punjab, India

    Shilpa Rani

  2. Assistant Professor, Department of CSE, Neil Gogte Institute of Technology, Hyderabad, Telangana, India

    Shilpa Rani

  3. Associate Professor, JECRC University, Jaipur, India

    Kamlesh Lakhwani

  4. Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, A.P., India

    Sandeep Kumar

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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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