Edge detection and feature extraction by non-orthogonal image expansion for optimal discriminative SNR

2002 ◽  
Author(s):  
K.R. Rao ◽  
J. Ben-Arie
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Image Expansion

Snickometer Edge Detection by Feature Extraction in TF Plane and Wavelet Domain

2018 ◽  
Author(s):  
Avrajit Ghosh ◽  
Ayan Chatterjee ◽  
Arani Roy ◽  
Amitava Mukherjee ◽  
Mrinal Kanti Naskar
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Wavelet Domain

scholarly journals Feature Extraction of Gear Fault Signal Based on Sobel Operator and WHT

2013 ◽  
Vol 20 (3) ◽  
pp. 551-559 ◽  
Author(s):  
Jian-Hua Cai ◽  
Wei-Wen Hu
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Hough Transform ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Cross Term ◽  
Time Performance ◽  
Sobel Operator ◽  
Gear Fault ◽  
Influence Of Noise

Taking Wigner-Ville distribution of gear fault signal as a picture,Sobeloperator was applied for edge detection of picture and then Hough transform was used to extract signal feature. Some simulated and measured signals have been processed to demonstrate the effectiveness of new method, which was compared with traditional Wigner-Hough transform and SPWD-Hough transform. The results show that the proposed method can suppress cross term which is produced from using Wigner-Ville distribution to analyze multi-component signal, especially under the condition of low signal to noise ratio. The improved Wigner-Hough transform can effectively suppress the influence of noise and has a good real-time performance because its algorithm is fast. The proposed method provides an effective method to determine the state of gear accurately.

Ear biometric verification approach based on morphological and geometric invariants

2020 ◽  
Vol 20 (3) ◽  
pp. 1479
Author(s):  
Wurood A. Jbara
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Main Idea ◽  
High Accuracy ◽  
Geometric Invariants ◽  
Biometric Verification ◽  
Accuracy Level ◽  
Image Capturing ◽  
Verification Process ◽  
Biometric Model

<p>Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with different states. Experimental results using several trails verified that the proposed approach is achieved high accuracy level over a wide variety of ear images. Also, the verification process will be completed by matching query ear image with ear images that kept in database during real time.</p>

VISUAL TARGET TRACKING, EXTRACTION AND RECOGNITION FROM A SEQUENCE OF IMAGES USING THE LG GRAPH APPROACH

2002 ◽  
Vol 11 (04) ◽  
pp. 513-529 ◽  
Author(s):  
NIKOLAOS G. BOURBAKIS
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Target Tracking ◽  
Visual Target ◽  
Region Growing ◽  
Geometric Approach ◽  
Moving Target ◽  
Extraction Region ◽  
Fuzzy Like ◽  
Visual Target Tracking

This paper presents a methodology for visually tracking, extracting and recognizing targets from a sequence of images (video). The methodology is based on the local-global (LG) graph as a combination of algorithms, such as fuzzy-like segmentation, edge detection, thinning, region growing, fractals, feature extraction, region-graph with attributes, etc., appropriately used for tracking, extracting and recognizing targets under various conditions, such as moving target - still camera, still camera - moving target, moving target - moving camera. The main contribution of this paper is the real-time combination of algorithms that provides a human-like feedback geometric approach of processing low resolution information in a sequence of consecutive images. Simulated results of the metholodology are presented for synthetic and real images.

scholarly journals Automatic Brain Tumour Detection using New Structure Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 3402-3405
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Edge Detection ◽  
Clustering Algorithm ◽  
Developed Countries ◽  
The Body ◽  
Manual Segmentation ◽  
Tumour Detection ◽  
Structure Algorithm ◽  
Different Characteristics

Tumor is an abandoned development of tissues in any part of the body. Tumors have different treatment for different characteristics of tissues. Brain tumor is a very serious and dangerous, as we know. In developed countries most Research shows that due to the inaccurate detection of tumor many people have died. Normally, CT scan or MRI images will be used for the detection of tumor. In this research, we want to introduce a method which is very advanced and accurate for brain tumor detection based on a new structure algorithm. This technique focuses mainly on pre- processing, Edge detection, segmentation, Feature extraction. Pre-processing will be done first for filtering, after filtering edge detection is applied to the image, then after advanced fuzzy K- means (AFKM) clustering algorithm is used for the segmentation process. Finally thresholding will extract the tumor at a particular point in the image. This technique is very suitable for segmentation with exactness when we compare with the manual segmentation. In addition, it also shrinks the time for examination.

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