Pub Date : 1900-01-01DOI: 10.6025/jmpt/2020/11/4/124-129
S. Bozinovski, Adrijan Božinovski
{"title":"Brain-Computer Interface for the study of Brain Rhythms","authors":"S. Bozinovski, Adrijan Božinovski","doi":"10.6025/jmpt/2020/11/4/124-129","DOIUrl":"https://doi.org/10.6025/jmpt/2020/11/4/124-129","url":null,"abstract":"","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122518972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.6025/jmpt/2021/12/1/1-8
N. Christoff
1 ABSTRACT: Recently the feature selection algorithms are extensively studied. Using 3D data, the features are drawn for automatic classification and identify craters. This will also help to text the performance of the classifiers. Our intention in this work is to observe the discriminative power of the original values, hereafter called “pure” values, of a minimal curvature by only converting them in the range of grey scale. We have tested the system and found that the five different classifiers show that better accuracy results are obtained over the features selected from the grey scale image. We also found that the method from computer vision is applied for the crater detection.
{"title":"Feature Extraction Algorithms for Automatic Craters Identification","authors":"N. Christoff","doi":"10.6025/jmpt/2021/12/1/1-8","DOIUrl":"https://doi.org/10.6025/jmpt/2021/12/1/1-8","url":null,"abstract":"1 ABSTRACT: Recently the feature selection algorithms are extensively studied. Using 3D data, the features are drawn for automatic classification and identify craters. This will also help to text the performance of the classifiers. Our intention in this work is to observe the discriminative power of the original values, hereafter called “pure” values, of a minimal curvature by only converting them in the range of grey scale. We have tested the system and found that the five different classifiers show that better accuracy results are obtained over the features selected from the grey scale image. We also found that the method from computer vision is applied for the crater detection.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116737397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.6025/jmpt/2021/12/1/18-25
R. Mironov, Stoyan Kushlev
In this work we have presented using a complex hadamard transform an algorithm for digital watermarking of health system images. We are able to detection the unauthorized access and attacks in the watermarking with the help of the newly introduced algorithms. The experimental results of the some attacks over the test medical images are drawn made on the base of mean-squared error and signal to noise ratio of the reconstructed images.
{"title":"Algorithms for Digital Watermarking of the Health System Images with Hadamard Transform","authors":"R. Mironov, Stoyan Kushlev","doi":"10.6025/jmpt/2021/12/1/18-25","DOIUrl":"https://doi.org/10.6025/jmpt/2021/12/1/18-25","url":null,"abstract":"In this work we have presented using a complex hadamard transform an algorithm for digital watermarking of health system images. We are able to detection the unauthorized access and attacks in the watermarking with the help of the newly introduced algorithms. The experimental results of the some attacks over the test medical images are drawn made on the base of mean-squared error and signal to noise ratio of the reconstructed images.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127190978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.6025/jmpt/2018/9/2/45-58
Fengfeng Duan
Feature extraction is the key and foundation of content-based retrieval of video and image. In order to realize the content-based index and retrieval of binocular stereo vision resources efficiently, the method of feature extraction based on Principal Component Analysis-Histogram of Oriented Depth Gradient (PCA-HODG) and Main Color Histograms (MCH) is proposed. In the method, on the one hand, for the depth map obtained from matching of right image and left image, the PCAHODG algorithm is proposed to extract shape features. In the algorithm, edge detection and gradient calculation in depth map windows are performed to obtain the regional shape histogram features. Moreover, sliding window detection over a depth map is performed to extract the full features. At the same time, in feature extraction of depth map windows and full depth map, principal component analysis is used to realize dimensional reduction respectively. On the other hand, for the left image of binocular stereo vision, the improved MCH algorithm is used to extract color features. Then the shape and color descriptors can be obtained as 2-dimensional factors for similarity calculation. The experimental results show that the proposed method can detect and extract the features of binocular stereo vision image more effectively and achieve similar classification more accurately compared with the existing HOD, RSDF and GIF algorithms. Moreover, the proposed method also has better robustness.
{"title":"A Feature Extraction Method Combining Color-Shape for Binocular Stereo Vision Image","authors":"Fengfeng Duan","doi":"10.6025/jmpt/2018/9/2/45-58","DOIUrl":"https://doi.org/10.6025/jmpt/2018/9/2/45-58","url":null,"abstract":"Feature extraction is the key and foundation of content-based retrieval of video and image. In order to realize the content-based index and retrieval of binocular stereo vision resources efficiently, the method of feature extraction based on Principal Component Analysis-Histogram of Oriented Depth Gradient (PCA-HODG) and Main Color Histograms (MCH) is proposed. In the method, on the one hand, for the depth map obtained from matching of right image and left image, the PCAHODG algorithm is proposed to extract shape features. In the algorithm, edge detection and gradient calculation in depth map windows are performed to obtain the regional shape histogram features. Moreover, sliding window detection over a depth map is performed to extract the full features. At the same time, in feature extraction of depth map windows and full depth map, principal component analysis is used to realize dimensional reduction respectively. On the other hand, for the left image of binocular stereo vision, the improved MCH algorithm is used to extract color features. Then the shape and color descriptors can be obtained as 2-dimensional factors for similarity calculation. The experimental results show that the proposed method can detect and extract the features of binocular stereo vision image more effectively and achieve similar classification more accurately compared with the existing HOD, RSDF and GIF algorithms. Moreover, the proposed method also has better robustness.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127976644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.6025/jmpt/2021/12/1/9-17
M. Kostov, Elena Kotevska, M. Atanasovski, Gordana Janevska
In this paper, we propose a CBIR method that uses wavelet transformation. The property of wavelets to localize both time and frequency makes them very suitable for analysis of nonstationary signals [1]. They are an excellent tool for feature extraction, signal and image compression, edge detection and compression. The reason of using the wavelet transform is that the basis functions used in wavelet transforms are locally supported; they are nonzero only over part of the domain represented. Hence, adequately chosen wavelet basis groups the coefficients in two groups – one with a few coefficients with high SNR, and other with a lot of coefficients with low SNR. Using the wavelet coefficients of images we compute a pseudo-hash information that is later used for fast querying the database. This approach for searching an image database in which a query is expressed as a low-resolution image is known as query by content [2]-[5].
{"title":"Image Decomposing by Discrete Wavelet Transform in the Image Retrieval Systems","authors":"M. Kostov, Elena Kotevska, M. Atanasovski, Gordana Janevska","doi":"10.6025/jmpt/2021/12/1/9-17","DOIUrl":"https://doi.org/10.6025/jmpt/2021/12/1/9-17","url":null,"abstract":"In this paper, we propose a CBIR method that uses wavelet transformation. The property of wavelets to localize both time and frequency makes them very suitable for analysis of nonstationary signals [1]. They are an excellent tool for feature extraction, signal and image compression, edge detection and compression. The reason of using the wavelet transform is that the basis functions used in wavelet transforms are locally supported; they are nonzero only over part of the domain represented. Hence, adequately chosen wavelet basis groups the coefficients in two groups – one with a few coefficients with high SNR, and other with a lot of coefficients with low SNR. Using the wavelet coefficients of images we compute a pseudo-hash information that is later used for fast querying the database. This approach for searching an image database in which a query is expressed as a low-resolution image is known as query by content [2]-[5].","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130408186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.6025/jmpt/2020/11/4/117-123
Ivana P. Markovió, Jovica M. Stankovió, Jelena Z. Stankovió, Milos B. Stojanovió
{"title":"Clustering Algorithms for Risk Management","authors":"Ivana P. Markovió, Jovica M. Stankovió, Jelena Z. Stankovió, Milos B. Stojanovió","doi":"10.6025/jmpt/2020/11/4/117-123","DOIUrl":"https://doi.org/10.6025/jmpt/2020/11/4/117-123","url":null,"abstract":"","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124178525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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