Optimizing feature-vector extraction algorithm from grayscale images for robust medical radiograph analysis

2003 ◽  
Author(s):  
M. Yagi ◽  
T. Shibata ◽  
K. Takada
Keyword(s):  
Feature Vector ◽  
Extraction Algorithm ◽  
Grayscale Images

scholarly journals Dihedral Group D4—A New Feature Extraction Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 548
Author(s):  
Puneet Sharma
Keyword(s):  
Dihedral Group ◽  
Feature Vector ◽  
Feature Descriptor ◽  
Image Region ◽  
Histograms Of Oriented Gradients ◽  
Wide Range ◽  
Extraction Algorithm ◽  
Complete Set ◽  
New Feature ◽  
Group D

In this paper, we propose a new feature descriptor for images that is based on the dihedral group D 4 , the symmetry group of the square. The group action of the D 4 elements on a square image region is used to create a vector space that forms the basis for the feature vector. For the evaluation, we employed the Error-Correcting Output Coding (ECOC) algorithm and tested our model with four diverse datasets. The results from the four databases used in this paper indicate that the feature vectors obtained from our proposed D 4 algorithm are comparable in performance to that of Histograms of Oriented Gradients (HOG) model. Furthermore, as the D 4 model encapsulates a complete set of orientations pertaining to the D 4 group, it enables its generalization to a wide range of image classification applications.

scholarly journals Feature Vector Extraction Algorithm Based on Big Data in Engineering Quality

2021 ◽  
Vol 257 ◽  
pp. 02029
Author(s):  
Fan Zhang ◽  
Yuhua Yang
Keyword(s):  
Quality Control ◽  
Big Data ◽  
Evaluation System ◽  
New Technologies ◽  
Feature Vector ◽  
Risk Indicators ◽  
Vector Model ◽  
Construction Engineering ◽  
Extraction Algorithm ◽  
Quality Risk

With the advent of the information age, the network has played a role in promoting the development of various industries. As a construction enterprise, it is necessary to integrate new technologies to achieve scientific management and construction. Engineering quality control management is the lifeblood of determining the merits of a project, which is the life of construction engineering and the key to winning users, developing enterprises and occupying the market. Based on the current problems encountered in the construction quality control of China’s construction industry, a comprehensive evaluation system based on network big data in the paper is proposed, and the data of method in the engineering quality risk eigenvector model are extracted, processed and analyzed. In the paper, the engineering quality risk feature vector model is designed. The genetic algorithm is used to solve the function as a nonlinear optimization problem. The vector feature extraction algorithm is optimized. The data projection vector of the feature vector data processing is used to define the quality influencing factor evaluation value. The quality of the project is analyzed. After testing and analyzing the model, it proves that the data based on big data extraction is more objective and reasonable from engineering quality risk analysis, risk generation mechanism and optimization risk indicators, which provides reference for China’s construction engineering enterprises.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

Spatio-Temporal LED Driving for Subjective Super-Resolution of Grayscale Images

2019 ◽  
pp. 1243
Author(s):  
Kojiro Matsushita ◽  
Toyotaro Tokimoto ◽  
Kengo Fujii ◽  
Hirotsugu Yamamoto
Keyword(s):  
Super Resolution ◽  
Spatio Temporal ◽  
Grayscale Images

An Answer Extraction Algorithm Based on Syntax Structure Feature Parsing and Classification

2009 ◽  
Vol 31 (4) ◽  
pp. 662-676 ◽  
Author(s):  
Bao-Shun HU ◽  
Da-Ling WANG ◽  
Ge YU ◽  
Ting MA
Keyword(s):  
Structure Feature ◽  
Answer Extraction ◽  
Extraction Algorithm
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