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.

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.

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.

scholarly journals Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Keranmu Xielifuguli ◽  
Akira Fujisawa ◽  
Yusuke Kusumoto ◽  
Kazuyuki Matsumoto ◽  
Kenji Kita
Keyword(s):  
Cross Correlation ◽  
Local Binary Patterns ◽  
Image Features ◽  
Entropy Method ◽  
Biological Information ◽  
Linear Regression Method ◽  
Similarity Retrieval ◽  
Classification Of Images ◽  
Biological Information Processing

People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.

scholarly journals Improving cloud type classification of ground-based images using region covariance descriptors

2021 ◽  
Vol 14 (1) ◽  
pp. 737-747
Author(s):  
Yuzhu Tang ◽  
Pinglv Yang ◽  
Zeming Zhou ◽  
Delu Pan ◽  
Jianyu Chen ◽  
...  
Keyword(s):  
Support Vector ◽  
Cloud Type ◽  
Training Samples ◽  
Multiclass Support Vector Machine ◽  
High Prediction ◽  
Classification Of Images ◽  
Region Covariance ◽  
The Relationship ◽  
Type Classification

Abstract. The distribution and frequency of occurrence of different cloud types affect the energy balance of the Earth. Automatic cloud type classification of images continuously observed by the ground-based imagers could help climate researchers find the relationship between cloud type variations and climate change. However, by far it is still a huge challenge to design a powerful discriminative classifier for cloud categorization. To tackle this difficulty, in this paper, we present an improved method with region covariance descriptors (RCovDs) and the Riemannian bag-of-feature (BoF) method. RCovDs model the correlations among different dimensional features, which allows for a more discriminative representation. BoF is extended from Euclidean space to Riemannian manifold by k-means clustering, in which Stein divergence is adopted as a similarity metric. The histogram feature is extracted by encoding RCovDs of the cloud image blocks with a BoF-based codebook. The multiclass support vector machine (SVM) is utilized for the recognition of cloud types. The experiments on the ground-based cloud image datasets show that a very high prediction accuracy (more than 98 % on two datasets) can be obtained with a small number of training samples, which validate the proposed method and exhibit the competitive performance against state-of-the-art methods.

scholarly journals Classification of Healthy and Rot Leaves of Apple using Gradient Boosting and Support Vector Classifier

2019 ◽  
Vol 8 (12) ◽  
pp. 2868-2872
Keyword(s):  
Neural Network ◽  
Confusion Matrix ◽  
Gradient Boosting ◽  
Support Vector ◽  
Diseased Leaf ◽  
Support Vector Classifier ◽  
Novel Approach ◽  
Good Scale ◽  
Classification Of Images

Conventional Techniques Such As Convolutional Neural Network (Cnn), Deep Neural Network Have Shown Its Own Footprints In The Field Of Image Classification With Promising Results. In The Past Decades, Classification Of Images Has Been Done With Varying Features Like Shape, Texture Etc. In This Paper, A Novel Approach Is Used To Classify The Leaf Images And Determine The Health And The Diseased Leaf. The Image Is Preprocessed By Extracting The Shape Feature And Classified The Leaves Of Apple As Healthy And Diseased (Rot Leaves) Using Two Novel Effective Approaches Gradient Boosting And Support Vector Classifier. We Have Collected 1813 Images Of Apple Leaves As Dataset And Out Of These, 70% Of The Data Is Used To Train And Remaining 30% Is Used To Test The Data. Our Algorithm Has Outperformed Other Traditional Techniques With Good Scale Of Accuracy(Gradient Boosting-87%, Support Vector Classifier91%). Strong Comparison Of Both Gradient Boosting And Support Vector Is Made And There Is Dominant Show Off Of The Confusion Matrix. Classification Of Healthy And Diseased Leaf Well In Advance Gives Nice Warning To The Producer Thereby Decreasing The Rate Of Diseased.

scholarly journals CLASSIFICATION OF MEDICAL IMAGES USING MACHINE LEARNING

10.6036/10117 ◽  
2022 ◽  
Vol 97 (1) ◽  
pp. 35-38
Author(s):  
EDUARDO PEREZ CARETA ◽  
RAFAEL GUZMÁN SEPÚLVEDA ◽  
JOSE MERCED LOZANO GARCIA ◽  
MIGUEL TORRES CISNEROS ◽  
RAFAEL GUZMAN CABRERA
Keyword(s):  
Neural Network ◽  
Medical Diagnosis ◽  
Cross Validation ◽  
Support Vector ◽  
Computational Tools ◽  
Standard Methods ◽  
Vector Machines ◽  
Test Sets ◽  
Classification Of Images

The popularity of the use of computational tools such as artificial intelligence has been increasing in recent years, and its importance in medicine is a fact. This field has benefited greatly thanks to the incorporation of more effective and faster methodologies in the medical diagnosis and registration processes. In the present work, the classification of images related to three diseases: Tuberculosis, Glaucoma and Parkinson's is carried out. We used deep learning and the RESNET50 convolutional neural network to extract classification characteristics, and then perform the classification based on standard methods, such as support vector machines, Naïve Bayes, and Centroid-based classifier, which are incorporated into two scenarios (cross validation; training and test sets). The classifier's performance is evaluated quantitatively using three evaluation metrics. The results obtained support the feasibility of the proposed methodology and its potential to improve medical diagnosis.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference
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