A New Approach to the Analysis of Water Treeing Using Feature Extraction of Vented Type Water Tree Images

2021 ◽  
Author(s):  
Mustafa Karhan ◽  
Musa Faruk Çakır ◽  
Mukden Uğur
Keyword(s):  
Feature Extraction ◽  
New Approach ◽  
Water Tree ◽  
Type Water

A new approach for the estimation of non-cooperative satellites based on circular feature extraction

2020 ◽  
Vol 129 ◽  
pp. 103532
Author(s):  
Yang Liu ◽  
Zongwu Xie ◽  
Qi Zhang ◽  
Xiaoyu Zhao ◽  
Hong Liu
Keyword(s):  
Feature Extraction ◽  
New Approach

SYMMETRY AXIS BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING

2009 ◽  
Vol 19 (01) ◽  
pp. 25-42 ◽  
Author(s):  
MASHUD HYDER ◽  
MD. MONIRUL ISLAM ◽  
M. A. H. AKHAND ◽  
KAZUYUKI MURASE
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Symmetry Axis ◽  
Recognition Performance ◽  
Recognition System ◽  
New Approach ◽  
Invariant Object Recognition ◽  
Different Shapes ◽  
Axis Of Symmetry ◽  
Concentric Circles

This paper presents a new approach, known as symmetry axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the axis of symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.

A new approach for EEG feature extraction in P300-based lie detection

2009 ◽  
Vol 94 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Vahid Abootalebi ◽  
Mohammad Hassan Moradi ◽  
Mohammad Ali Khalilzadeh
Keyword(s):  
Feature Extraction ◽  
Lie Detection ◽  
New Approach

scholarly journals A NEW APPROACH FOR URBAN ROADS DETECTION USING LASER DATA AND AERIAL DIGITAL IMAGES

2016 ◽  
Vol 10 (1) ◽  
pp. 83-97 ◽  
Author(s):  
Francisco Assis Da Silva ◽  
Anderson Akio Gohara ◽  
Mário Augusto Pazoti ◽  
Danillo Roberto Pereira ◽  
Almir Olivette Artero ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Aerial Images ◽  
Automatic Extraction ◽  
New Approach ◽  
Additional Information ◽  
Automatic Feature Extraction ◽  
Laser Data ◽  
Sensitivity Rate ◽  
Road Segments

The automatic feature extraction from digital aerial images is not a trivial task mainly due to occlusion problems, shadows and different viewpoints. To obtain an improved feature extraction we used laser data, which have additional information such as height and material type of the surface. In this paper we performed the combination of digital image and laser data in order to improve the results of automatic extraction of urban roads. Initially, the urban roads were detected from the response of laser information; in the sequence we applied two different approaches to connect the disconnected road segments. The results were very promising, with sensitivity rate of 92%.

scholarly journals Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Methods ◽  
Classification Performance ◽  
Model Complexity ◽  
Program Structure ◽  
New Approach ◽  
Classification Tasks

IEEE Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming with a flexible representation can find the best solution without the use of domain knowledge. This paper proposes a new genetic programming-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualisation of the learned features provide deep insights on the proposed approach.

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