A hybrid feature extraction method-based object recognition by neural network

2008 ◽  
Author(s):  
Amitabh Wahi ◽  
F. Mohamed Athiq ◽  
C. Palanisamy
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Object Recognition ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Hybrid Feature Extraction

scholarly journals Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256665
Author(s):  
Muhammad Rabani Mohd Romlay ◽  
Azhar Mohd Ibrahim ◽  
Siti Fauziah Toha ◽  
Philippe De Wilde ◽  
Ibrahim Venkat
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Object Recognition ◽  
Extraction Method ◽  
Point Cloud ◽  
Lidar Data ◽  
Low Density ◽  
Feature Extraction Method ◽  
Depth Measurement ◽  
Computing Capacity

Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.

A Hybrid Feature Extraction Method for Heart Disease Classification using ECG Signals

2021 ◽  
Author(s):  
Emir Akcin ◽  
Kemal Sami Isleyen ◽  
Enes Ozcan ◽  
Alaa Ali Hameed ◽  
Erdal Alimovski ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Extraction Method ◽  
Disease Classification ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Hybrid Feature Extraction

scholarly journals Deteksi Api Pada Video Menggunakan Metode Artificial Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 96-107
Author(s):  
Budiman Rabbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Validation Process ◽  
Feature Extraction Method ◽  
Time Value ◽  
Occurrence Matrix

Abstract The camera is one of the tools used to collect images. Cameras are often used for the safety of homes, highways and others. Then in this study camera captures are used to support fire objects because fire is one of the causes of safety that can be controlled. Therefore, by utilizing a capture camera will see the best model of backpropagation neural network by combining the local binary patern (LBP) feature extraction method and the Gray Level Co-occurrence Matrix (GLCM) to access the fire. Then to evaluate the performance of the model created using three parameters that contain accuracy, recall, precision. The data in this study consisted of videos with variations of fire and non-fire videos. Based on the final results of the study, accuracy, remember, the best precision obtained simultaneously 96%, 97%, 97%. Then the validation process was done using 30 videos with a variation of 15 fire videos and 15 non-fire videos and obtained an accuracy of 86.6% with an average time value of 6.029 minutes.

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