Dental Radiograph Segmentation and Classification—A Comparative Study of Hu’s Moments and Histogram of Oriented Gradients

2019 ◽  
Vol 16 (8) ◽  
pp. 3612-3616
Author(s):  
Rameswari Poornima Janardanan ◽  
Rajasvaran Logeswaran
Keyword(s):  
Support Vector ◽  
Computer Assisted ◽  
X Rays ◽  
Histogram Of Oriented Gradients ◽  
Moment Invariant ◽  
Feature Descriptors ◽  
Assisted Diagnosis ◽  
Classification Of Images ◽  
Comparison Of The Results

This paper proposes a method to compare two feature descriptors to classify dental X-rays, using Hu’s Moments (HM) and the Histogram of Oriented Gradients (HOG). The dental radiographs are preprocessed, and the shape features of teeth are derived using HM and HOG. Support Vector Machine (SVM) is then used for tooth classification and recognition. Comparison of the results of using the two approaches as feature descriptors revealed that regardless of its orientation, size and position, moment invariant functions are very useful for object classification. The classification of images into molar and premolar has been done on manually cropped images. This method was validated on periapical radiographs. Results obtained show that using both HM and HOG to classify and recognize teeth shape description accuracy as better than, or at least comparable, to the state-of-the-art approaches. This work aids to improve the computer-assisted diagnosis and decision in dentistry. The forensic odonatological applications of this approach are wide and of immense benefits in both forensic and biometric identification.

Feature Extraction and Classification of Images Based on Corner Invariant Moments

2013 ◽  
Vol 475-476 ◽  
pp. 374-378
Author(s):  
Xue Ming Zhai ◽  
Dong Ya Zhang ◽  
Yu Jia Zhai ◽  
Ruo Chen Li ◽  
De Wen Wang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Image Feature ◽  
Support Vector ◽  
System Management ◽  
Image Feature Extraction ◽  
Invariant Moments ◽  
Harris Corner ◽  
Classification Of Images

Image feature extraction and classification is increasingly important in all sectors of the images system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates, so can reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of images, and the result shows that using this algorithm to extract invariant moments and classifying can achieve better classification accuracy.

scholarly journals Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

2019 ◽  
Vol 9 (11) ◽  
pp. 2357 ◽  
Author(s):  
Niccolò Dematteis ◽  
Daniele Giordan ◽  
Paolo Allasia
Keyword(s):  
Cross Correlation ◽  
Earth Science ◽  
Operative Procedure ◽  
Support Vector ◽  
Diffuse Illumination ◽  
Monitoring Applications ◽  
Correlation Processing ◽  
Classification Of Images ◽  
Active Processes

In Earth Science, image cross-correlation (ICC) can be used to identify the evolution of active processes. However, this technology can be ineffective, because it is sometimes difficult to visualize certain phenomena, and surface roughness can cause shadows. In such instances, manual image selection is required to select images that are suitably illuminated, and in which visibility is adequate. This impedes the development of an autonomous system applied to ICC in monitoring applications. In this paper, the uncertainty introduced by the presence of shadows is quantitatively analysed, and a method suitable for ICC applications is proposed: The method automatically selects images, and is based on a supervised classification of images using the support vector machine. According to visual and illumination conditions, the images are divided into three classes: (i) No visibility, (ii) direct illumination and (iii) diffuse illumination. Images belonging to the diffuse illumination class are used in cross-correlation processing. Finally, an operative procedure is presented for applying the automated ICC processing chain in geoscience monitoring applications.

Brazilian Coins Recognition Using Histogram of Oriented Gradients Features

2019 ◽  
Vol 16 (10) ◽  
pp. 4170-4178
Author(s):  
Sheifali Gupta ◽  
Gurleen Kaur ◽  
Deepali Gupta ◽  
Udit Jindal
Keyword(s):  
Region Of Interest ◽  
Initial Step ◽  
Support Vector ◽  
Histogram Of Oriented Gradients ◽  
Lighting Conditions ◽  
Roi Extraction ◽  
Feature Values ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.

scholarly journals Detection and classification of contrast-enhancing masses by a fully automatic computer-assisted diagnosis system for breast MRI

2012 ◽  
Vol 35 (5) ◽  
pp. 1077-1088 ◽  
Author(s):  
Diane M. Renz ◽  
Joachim Böttcher ◽  
Felix Diekmann ◽  
Alexander Poellinger ◽  
Martin H. Maurer ◽  
...  
Keyword(s):  
Breast Mri ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Automatic Computer ◽  
Computer Assisted Diagnosis ◽  
Fully Automatic ◽  
Assisted Diagnosis

Classification of Mice Head Orientation Using Support Vector Machine and Histogram of Oriented Gradients Features

2018 ◽  
Author(s):  
Richardson Santiago Teles de Menezes ◽  
Lucas de Azevedo Lima ◽  
Orivaldo Santana ◽  
Aron Miranda Henriques-Alves ◽  
Rossana Moreno Santa Cruz ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Head Orientation ◽  
Histogram Of Oriented Gradients

scholarly journals CLASSIFICATION OF PNEUMONIA USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 1 (1) ◽  
pp. 21-32
Author(s):  
Risha Ambar Wati ◽  
Hafiz Irsyad ◽  
Muhammad Ezar Al Rivan
Keyword(s):  
Support Vector Machine ◽  
Lung Disease ◽  
Extraction Method ◽  
Classification Method ◽  
Support Vector ◽  
Gray Level ◽  
X Rays ◽  
X Ray ◽  
Contrast Stretching

Pneumonia is a type of lung disease caused by bacteria, viruses, fungi, or parasites. One way to find out pneumonia is by x-ray. X-rays will be analyzed to determine whether there is pneumonia or not. This study aims to classify the x-ray results whether there is pneumonia or not on the x-ray results. The classification method used in this study were Support Vector Machine (SVM) and Gray Level Co-Occurrence (GLCM) for the extraction method. There are several stages before classification, namely cropping, resizing, contrast stretching, and thresholding then extracted using GLCM and classified using SVM. The results showed that the best accuracy of 62.66%.

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