Traditional pattern recognition technigues and latest image interpretation approaches

2009 ◽  
pp. 7-27
Author(s):  
Lidia Ogiela ◽  
Marek R. Ogiela
Keyword(s):  
Pattern Recognition ◽  
Image Interpretation ◽  
Traditional Pattern

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

2020 ◽  
Vol 24 (01) ◽  
pp. 38-49 ◽  
Author(s):  
Natalia Gorelik ◽  
Jaron Chong ◽  
Dana J. Lin
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Bone Tumors ◽  
Image Interpretation ◽  
Bone Age ◽  
Musculoskeletal Imaging ◽  
Radiology Workflow ◽  
Disease Response ◽  
Osseous Metastases ◽  
Applications Of Ai

AbstractArtificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

scholarly journals The Current Status of EMG Signals on Kinematics Needed for Precise Online Myoelectric Control and the Development Direction

2021 ◽  
Vol 271 ◽  
pp. 01029
Author(s):  
Tong Kang
Keyword(s):  
Pattern Recognition ◽  
Degrees Of Freedom ◽  
Quantum Control ◽  
Quantum Information Processing ◽  
Current Status ◽  
Prosthetic Control ◽  
Pattern Recognition Problem ◽  
Emg Signal ◽  
Traditional Pattern ◽  
Electric Signals

Widely accepted idea is the prosthetic control problem could be regarded as the pattern recognition problem. The prosthetic control means there are several differences such as distinguishable electric signals between different activation of muscle. However, this conventional method could not provide proper control of the artificial limbs. Kinematics behavior is continuous and needs the coordination of multiple physiological degrees of freedom (DOF) among various joints. Currently, a huge challenge is achieving precise, coherent and elegant coordination protheses which needs many DOFs to rehabilitation of patients with limb deficiency. This article analyzed the principles of control of bionic limbs from the aspect of EMG and traditional pattern recognition. According to the research results, the following conclusions can be given. Since the quantum amplitudes are complex numbers generally, different parameter should be included and analyzed together during the quantum information processing. Besides, the quantum control scheme could be combined with the classic one. What is more, other sensor modes should be applied for robust control instead of the EMG signal only.

scholarly journals Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3424 ◽  
Author(s):  
Ning Liu ◽  
Bo Fan ◽  
Xianyong Xiao ◽  
Xiaomei Yang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Power Cables ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Belief Network ◽  
Incipient Faults ◽  
Sparse Autoencoder ◽  
Traditional Pattern

Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method.

Pattern Recognition in Alphabets of Oriya Language using Kohonen Neural Network

1998 ◽  
Vol 12 (07) ◽  
pp. 1007-1015 ◽  
Author(s):  
S. Mohanty
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Dimensional Space ◽  
Average Distance ◽  
Weight Vector ◽  
Self Organizing Map ◽  
Learning Capacity ◽  
Kohonen Neural Network ◽  
Recognition Theory ◽  
Traditional Pattern

Here a computerized reading of alphabets of Oriya language is attempted using the Kohonen neural network and its unsupervized competitive learning capacity as self-organizing map or the Kohonen feature map. The proposed pattern recognition does not treat a pattern as an n-dimensional feature vector or a point in n-dimensional space as is done in the traditional pattern recognition theory. We have tried with all the Oriya alphabets and have presented the study with respect to five of them in this paper along with their average distance per pattern in each cycle till we reach the permissible average distance. In the output picture the variation of the weight vector with respect to the alphabets is clearly observed.

P-Box Theory and SVM Methods with Application in Pattern Recognition

2014 ◽  
Vol 651-653 ◽  
pp. 472-475
Author(s):  
Jia Man Ding ◽  
Yi Du ◽  
Qing Xin Wang ◽  
Ying Jiang ◽  
Lian Yin Jia
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Information Source ◽  
Recognition Rate ◽  
Extraction Process ◽  
Information Loss ◽  
New Method ◽  
Uncertainty Measures ◽  
Different Types ◽  
Traditional Pattern

In order to solve the problem of the information loss on the feature extraction process in the traditional pattern recognition, a new method based on probability boxes theory was proposed. Firstly, the skewness of the fault signal data were used as the information source to construct the tow p-boxes about X and direction. Then, to take advantage of the complementation of the information source, the tow p-boxes from different directions were fused. Finally, the SVM features database was established by extracting different types of cumulative uncertainty measures from p-boxes. The analysis result shows that the combination of p-box and SVM can achieve a high recognition rate, which makes a new way for pattern recognition.

The Principle of Homology Continuity and Geometrical Covering Learning for Pattern Recognition

2018 ◽  
Vol 32 (12) ◽  
pp. 1850042 ◽  
Author(s):  
Xin Ning ◽  
Weijun Li ◽  
Jiang Xu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Fundamental Property ◽  
Experimental Results ◽  
New Method ◽  
Recognition Problem ◽  
Pattern Learning ◽  
Learning Method ◽  
Pattern Recognition Problem ◽  
Traditional Pattern

Homology Continuity is a fundamental property of the nature, but few of the traditional pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to the Principle of Homology Continuity (PHC), and tries to mathematically redefine it. Then, we introduce a PHC-based pattern learning method — Geometrical Covering Learning (GCL), following the Hyper sausage neural network as an instance of GCL. Lastly, we propose a GCL solution to the “two-spirals” pattern recognition problem. The final experimental results show that the new method is feasible and efficient.

SUPPORT VECTOR IDENTIFICATION OF SEISMIC ELECTRIC SIGNALS

2003 ◽  
Vol 17 (04) ◽  
pp. 545-565 ◽  
Author(s):  
A. IFANTIS ◽  
S. PAPADIMITRIOU
Keyword(s):  
Pattern Recognition ◽  
A Priori ◽  
Support Vector ◽  
Mathematical Basis ◽  
Generalization Performance ◽  
Seismic Electric Signals ◽  
Feature Spaces ◽  
Intelligence Models ◽  
Traditional Pattern ◽  
Electric Signals

Traditional pattern recognition approaches usually generalize poorly on difficult tasks as the problem of identification of the Seismic Electric Signals (SES) electrotelluric precursors for earthquake prediction. This work demonstrates that the Support Vector Machine (SVM) can perform well on this application. The a priori knowledge consists of a set of VAN rules for SES signal detection. The SVM extracts implicitly these rules from properly preprocessed features and obtains generalization performance founded upon a robust mathematical basis. The potentiality of obtaining generalization potential even in feature spaces of high dimensionality bypasses the problems due to overtraining of the conventional machine learning architectures. The paper considers the optimization of the generalization performance of the SVM. The results indicate that the SVM outperforms many alternative computational intelligence models for the task of SES pattern recognition.

A Text Classification Algorithm Based on Improved Multidimensional–Multiresolution Topological Pattern Recognition

2019 ◽  
Vol 33 (10) ◽  
pp. 1950016 ◽  
Author(s):  
Zhichao Li ◽  
Jilin Huang
Keyword(s):  
Pattern Recognition ◽  
Text Classification ◽  
Dimensional Space ◽  
Set Cover ◽  
High Dimensional ◽  
High Dimensional Space ◽  
Sample Number ◽  
Training Time ◽  
Topological Pattern ◽  
Traditional Pattern

Traditional pattern recognition is based on “optimal partition” and the goal is to find an optimal classification interface based on the distribution of each category in high-dimensional space, thus has its inherent shortcomings and deficiencies. While topology pattern recognition can effectively compensate for the shortcomings of traditional pattern recognition, topological pattern recognition is based on “cognition” and the goal is to find the appropriate cover according to the “complex set cover” in high-dimensional space to achieve cognitive effect. Topological pattern recognition can effectively consummate the characteristics of high error rate, low recognition rate and repetitive training in the existing recognition system with low training sample number. At present, topology pattern recognition has been applied in many areas of social life. However, one problem that can’t be ignored is that topological pattern recognition requires a long training time and low fault tolerance rate. Therefore, this paper proposes an improved multidimensional–multiresolution topological pattern recognition, and applies it to text classification and recognition. The results show that the improved multidimensional–multiresolution topological pattern recognition method can effectively reduce the training time of text classification and improve the classification efficiency.

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